research paper reading difficulties

Psychology: Research and Review

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• Published: 20 March 2021

Identification of struggling readers or at risk of reading difficulties with one-minute fluency measures

• Maíra Anelli Martins   ORCID: orcid.org/0000-0001-6946-6755 1 , 2 , 3 &
• Simone Aparecida Capellini 2 , 3 , 4  

Psicologia: Reflexão e Crítica volume  34 , Article number:  10 ( 2021 ) Cite this article

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To identify readers who are struggling or at risk of reading difficulties, reference standards in oral reading fluency (ORF) are used to conduct an assessment that is based on a widely reported method known as curriculum-based measurement (CBM), which itself is based on 1-min fluency measures. The purpose of this study was to evaluate students’ ORF (with a 1-min fluency measure) to characterize their fluency and to determine references of appropriate development in reading at the 50th percentile.

For this study, a database of readings made available by the Learning Studies Research Laboratory was used. This database consisted of 365 readings by elementary-school students from the third to fifth grades in two cities in the interior of the state of São Paulo from two different public school systems that use the same teaching methodology. The data consisted of digital audio recordings of the passage “The Umbrella” (text suitable for schooling levels) of the Protocol for Assessment of Reading Comprehension procedure. For this procedure, three steps were performed: step 1—listening to the 365 readings and assessing the scores for the number of words read correctly per minute; step 2—the calculation of the mean and percentiles for each grade; and step 3—the adaptation of the reference table to indicate students eligible to receive reading fluency intervention.

Third-year students who correctly read 86 or more words per minute, fourth-year students who correctly read 104 or more words per minute, and fifth-year students who correctly read 117 or more words per minute were considered students who had made adequate progress in reading.

It was possible to classify students based on the 1-min fluency measures, with reference intervals of words read correctly per minute per school year (for the third, fourth, and fifth years) for those who were making adequate progress in reading and reference intervals for those who were considered readers who were struggling or at risk of reading difficulties.

Little research has been conducted in Brazil on measures to assess reading fluency (Gentilini et al, 2020 ; Andrade, Celeste, & Alves, 2019 ; Moutinho, 2016 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ), and a search for research on reading fluency in official documents of the Brazilian Ministry of Education (Martins, 2018 ) also reveals that such measures are not a type of assessment that is widely known or applied by teachers within the classroom. Nonetheless, research has continually indicated the importance of developing oral reading fluency (ORF; reading with appropriate rate, accuracy, and prosody) as a vital and necessary skill for the overall development of proficient reading (Machado, Santos, & Cruz, 2019 ; Rasinski & Young, 2017 ).

In addition to the lack of Brazilian research widely exploring this theme, the low performance data of Brazilian students in reading indicates that these students also face difficulties in learning this highly complex activity, including the many who do not become proficient, effective readers. It is noted that this is a recurring problem that affects students and, consequently, concerns educators. As is clear from the evaluations conducted throughout the national territory (large-scale evaluations), the problem has continued throughout the years and affects even the regions with the best educational indexes or socioeconomic status.

Measures assessment of reading oral fluency

The method widely publicized as curriculum-based measurement (CBM) is a curriculum-based progress-monitoring method for measuring growth in specific areas of basic knowledge and skills and assessing the effects of instructional programs (response to intervention). Curriculum-based assessment, as a longstanding assessment practice asserting that learning assessments should be based on what has been taught, has become popular in the field of special education. Thus, the CBM method is described as curriculum-based, as it is used within the context of the school curriculum (Deno, 1985 ).

The CBM method proposes simple measures for the assessment of academic competence that can be applied quickly by teachers. These measures help provide an overview of each student’s academic development; furthermore, when these simple measures are applied systematically over time, they can be used to track a student’s potential difficulties (Fuchs, 2017 ).

For example, to identify struggling readers, reference standards for ORF are used, which, based on the CBM assessment method initially proposed by Deno ( 1985 ), enable reading analysis in just 1 min (e.g., the number of words read correctly per minute–WCPM). The most widely used assessment of ORF, which focuses on two of the three components of fluency (rate and accuracy), simply requires the student to read a grade-appropriate passage, which they have not seen previously, for 1 min. At the end of 1 min, errors are subtracted from the total words read, and then the WCPM score is calculated (Hasbrouck & Tindal, 2006 ).

Thus, the method was developed to create procedures for measuring progressive development in a simple, reliable, and valid way. These procedures enable teachers to frequently and repeatedly measure students’ progress in basic reading, spelling, writing, and expression skills (Rasinski, 2004 ).

Regarding reading fluency assessment, it is recommended that the scoring of the number of words read correctly per minute (WCPM) and the number of words read incorrectly per minute (WIPM) be performed with three passages of the same difficulty level to then calculate the mean score. Thus, the WCPM measure can serve to screen for academically at-risk students, assign placement in remedial and special education programs, monitor student progress, improve teaching programs, and predict performance in high-risk assessments (Hasbrouck & Tindal, 2006 ; Rasinski, 2004 ).

A series of discussions began in the last decade in Brazil on the question of the “wait to fail to act” model, which highlighted the importance of the early identification of learning difficulties. There are also discussions about the broadening of knowledge about the advantages of early identification and scientific evidence-based assessment and screening methods (Almeida, Piza, Toledo, Cardoso, & Miranda, 2016 ; Batista & Pestun, 2019 ; Brito, Seabra, & Macedo, 2018 ; Justi & Cunha, 2016 ; Mayeda, Navatta, & Miotto, 2018 ; Nicolau & Navas, 2015 ; Palles da Silva & Guaresi, 2019 ; Rodrigues & Ciasca, 2016 ; Silva & Capellini, 2017 ; Silva & Capellini, 2019a ; Silva & Crenitte, 2016 ).

According to Elliott, Huai and Roach ( 2007 ), several factors contribute to the prevalence of the “wait to fail to act” model, such as the fact that educators understand that there is a certain heterogeneity of development and learning among students and seek to allow appropriate time for this development. By doing so, they are also allowing students a fair chance of progressing without early determination of the problem. Another factor for the prevalence of this action model is the fact that few large-scale screening instruments are time efficient and technically simple for teachers to apply.

In the Brazilian literature, early screening instruments are recent and focus primarily on metalinguistic skills, such as the “Early Identification and Reading Problems Protocol” (Capellini, César, & Germano, 2017 ), the “Evaluation of Cognitive-Language Skills Protocol: Professional and Teacher’s Book” (Capellini, Smythe, Silva, 2017 ) and the “Protocol for Cognitive-Language Skills Assessment of Students in Early Literacy” (Silva & Capellini, 2019b ). These instruments assess skills considered predictive of literacy, such as reading and writing skills; arithmetic; auditory and visual processing; metalinguistic skills; and processing speed with the rapid automatic naming test. Some tests evaluate mathematical logical reasoning, for example, the “Cognitive-Language Skills Assessment Protocol.”

Likewise, there has been a movement in Brazilian research in recent years to describe the importance of reading fluency measures, especially those related to using a chronometer for timing as measures for screening difficulties, in addition to the development of instruments to assist in this assessment. Alves et al. ( 2019 ) described such issues in the most recent publication of the LEPIC® software, which proposes a semiautomatic and instantaneous reading fluency analysis to assess and assist in diagnostics or to monitor reading skills. This analysis focuses on the importance of evaluating parameter fluency, which may include indicators of reading problems such as dyslexia. Another instrument recently developed by Brazilian researchers is a collection of passages in sequential order according to difficulty level and suitable for elementary-school students from the first through fourth grades, called the “Reading Fluency Performance Assessment” (Martins & Capellini, 2018 ).

Additionally, on 22 February 2018, the More Literacy Program (PMAlfa) was created via MEC Ordinance No. 142, a strategy by the Ministry of Education that aims to strengthen and support school units in the process of increasing the literacy of elementary-school students enrolled in the first and second grades; the program fulfills the criteria established in the Common National Curriculum Base (CNCB). The objective of the program is to perform reading, writing, and math evaluations. For the first time, a formal program of the Brazilian government will evaluate the fluency and accuracy in the reading ability of students in the second grade of elementary school. The assessment is performed individually and uses a proprietary application suitable for smartphones or tablets.

However, despite efforts to create adequate assessment procedures for ORF, research into the characterization of ORF in this population is still incipient. Pacheco and Santos ( 2017 ), for example, evaluated three groups of readers in relation to reading fluency who were classified into three groups: group I–second-grade readers with little reading experience and expectation of low reading fluency; group II–second-year high school readers with the expectation of having slightly more reading experience and moderate fluency; and group III–readers with a higher education level. However, the relatively small sample consisted of 12 participants (four participants in each group), and the reading rate was evaluated by using the number of words read compared to the total reading time measured in seconds, considering a total reading time of 180 s (3 min).

In another study (Moutinho, 2016 ), 46 sixth-grade students from public and private schools were evaluated by measuring the WCPM in 1 min from three different passages. However, the article focused on describing the accuracy errors, i.e., the number and type of WIPM, while data for the WCPM are not presented. Other researchers evaluated 55 students from the third to the seventh grades with the number of words per minute, reading four different types of passages, and analyzing student performance in each (Dellisa & Navas, 2013 ).

Some researchers have also conducted reading fluency assessment with elementary students, as in a study that evaluated 32 students in ninth grade and calculated the speed of words read per minute (using the formula of total number of words from the passage, divided by the time in seconds spent to complete the reading, and multiplied by 60) (Komeno, Ávila, Cintra, & Schoen, 2015 ). Furthermore, in another recent study, researchers characterized the ORF by 232 middle-grade students from the sixth to the ninth grades from public and private education. The study provided an estimate of the expected values for each grade surveyed by reading an easy passage based on the 1-min oral fluency assessment, with scores for words read per minute and WCPM (Andrade et al., 2019 ).

While only a small number of studies for elementary and middle students exist, even fewer studies evaluate reading fluency in high school students or adults. One research study evaluated 88 students in the second grade of high school. The CBM method was followed by selecting a passage compatible with students’ age and grade and comprising subjects corresponding to the basic curriculum studied in the classroom. Students read three different passages, lasting 1 min each, for the subsequent calculation of the number of WCPM (Oliveira, Amaral, & Picanço, 2013 ). Only one study evaluating reading fluency in adults was found, in which the sample consisted of 30 adolescents and adults who were evaluated by measuring the number of words per minute (Peres & Mousinho, 2017 ).

The assessment of ORF conducted through WCPM scores presents 30 years of validation research indicating that this is a valid and reliable measure that reflects a student's overall performance in reading development during the first years after literacy (Morris et al., 2017a , b ; Tindal, 2017 ; Valencia et al., 2010 ). Reading fluency benchmarks have been used both for screening and for monitoring reading development, and research in these fields seeks to answer questions such as “How is student performance compared to their peers?” and “Who are the students struggling with reading?” This practice of frequent assessment enables early intervention and the planning of activities that focus on the skills already acquired and those that still require further attention.

Benchmarks in ORF have been established by American researchers and collected from a range of students, from those identified as talented or otherwise exceptionally skilled to those diagnosed with reading disabilities, such as dyslexia. The largest sample of the ORF benchmark was collected from schools and districts in 23 states in the USA for over 4 years. Based on their vast experience in interpreting ORF data, it was established that a score of 10 words above or below the 50th percentile should be interpreted as an expected score, meaning that students are making satisfactory reading progress (Hasbrouck & Tindal, 2006 ).

Given the implications that ORF benchmarks would have for Brazilian education, a study to determine a fluency reference through appropriate assessment material would be of great relevance. This benchmarking considers the indication of a median score (50th percentile), with scores of 10 words above or below this median indicating students who have made appropriate reading progress, to assist in assessment and to create parameters for selecting students for interventional programs who are struggling readers or at risk for developing difficulties in reading proficiency later.

The purpose of this study was to evaluate the ORF of students from the third to the fifth grades (with a 1-min fluency measure) to characterize their fluency and determine references of appropriate development in reading at the 50th percentile and those below this reference.

This is a quantitative, descriptive-explanatory study. The dependent variable is a 1-min fluency measure. The independent variable is student grade.

General procedures and database

This study was approved by the Ethics Committee of the Faculdade de Filosofia e Ciências of Sao Paulo State University–UNESP-Campus de Marília-SP under protocol 2.550.190–CAAE 50201915.9.0000.5406.

For this study, a database of readings made available by the Investigation Learning Disabilities Laboratory (in Portuguese: Laboratório de Investigação dos Desvios da Aprendizagem–LIDA), registered by a research group of the National Counsel of Technological and Scientific Development (CNPq), called “Language, Learning, Education,” was used. All information related to the sample of students comprising our database was made available by the members of this group.

The readings database made available consists of 365 readings from elementary-school students from the third to the fifth grades in two cities in the interior of the state of São Paulo (in a medium- and a small-sized Brazilian city, Southeast Region of Brazil) from two different public school systems with the same teaching methodology. In the city of Marília-SP, there are 51 schools with regular elementary education in urban locations, in basic education, with 2221 students enrolled in the third year, 2119 students enrolled in the fourth year and 2033 students enrolled in the fifth year according to the School Census/(Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP, 2018 ).

In the city of Garça-SP, there are 14 schools with regular elementary education in urban locations, in basic education, with 478 students enrolled in the third year, 436 students enrolled in the fourth year and 401 students enrolled in the fifth year according to the School Census/(Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira – INEP, 2018 ). The schools were selected through convenience sampling (simple convenience sample). The students participating in the studies did not have a history of repeating grades; they were monolinguals and native speakers of Brazilian Portuguese. The data were digital recordings of participants reading the passage “The Umbrella” (text suitable for schooling levels) from the procedure “Protocol for Assessment of Reading Comprehension” (Cunha & Capellini, 2014 ).

Of the 365 readings, 98 were third-grade students (48.9% female), 130 were fourth-grade students (49.2% female), and 137 were fifth-grade students (51.8% female) (participants were elementary-school students ranging from 7 to 11 years old).

According to the latest results published (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, 2015-2017 ) by the Socioeconomic Level Indicator (Inse) of basic education schools in Brazil, developed by the National Institute of Educational Studies and Research Anísio Teixeira (Inep), in the Basic Education Assessment Directorate (Daeb), the schools from which the analyzed data were obtained have an average Inse (absolute value 58.46 and 57.47), with an average rating (group 5).

The inclusion and exclusion criteria used by the laboratory researchers in the data collection of the reading audio bank are described. The inclusion criteria for the sample selection were as follows: informed consent form signed by the parents or guardians for the students; students with no history of neurological or psychiatric illnesses, uncorrected auditory and visual impairments, and cognitive performance within normal, according to the description at the school records and teachers’ reports. The exclusion criteria for the sample selection were the presence of genetic or neurological syndromes in the students, students who did not present a satisfactory reading domain level for the observation of the variable proposed in the study, and students who presented recording errors in their respective audio files.

Specific instruments and procedures

The passage used was “The Umbrella” (history appropriate for the educational level) from the procedure “Reading Comprehension Assessment Protocol” (Cunha & Capellini, 2014 ). The choice for using this protocol occurred due to its careful assessment and development, since its issues were built from the rules for the psychometric tool development described by The Federal Council of Psychology. The Council is an official body that studies and establishes criteria and rules in Brazil for the construction of evaluation tools that ensures their accuracy and validity, and defines, as reliable procedures, those whose accuracy is understood as their level of consistency and their ability to reach the objectives for which they were built as their validity.

The protocol consists of four passages, two narratives, and two expository narratives. A medium-length (297 words) narrative passage was chosen. The choice of a passage with a narrative gender protocol occurred because the students had been more commonly exposed to such passages since childhood and throughout the education process, which would simplify the fluency evaluation and avoid the interference of any cultural issues of the passage in the reading results of the students of different schooling levels.

The choice of protocol also occurred because it presents passages that were selected to reach students from the third, fourth and fifth grades at representatively similar levels of difficulty for all school years, making it possible to apply a single passage in all school years.

Although the procedure is an instrument for assessing reading comprehension, due to the objectives of this study, only the reading recordings were used to assess fluency, while the multiple-choice questions were not applied.

The equipment used in the recordings was a Karsect microphone headset, which was unidirectional since the microphone picks up sounds with greater intensity and orients towards where it is directed, reducing the intensity of the external noise. The microphone was connected to an HP notebook with an Intel Pentium processor, 3 GB memory, and a 32-bit operating system. Recordings were made with an original HP software application and were saved as .wav files.

The collections were carried out by the researchers of the mentioned research group, following the guidelines for individual application. Each reading of the entire passage was recorded, taking an average of 5 min total for each individual recording session in spaces reserved for the researchers in the schools during class hours.

To analyze the readings on digital media, the following steps were planned and performed:

Step 1 : The rate was scored by listening to 365 digital recordings and assessing the WCPM scores, which was performed according to the reading error classification used by Begeny, Capellini, and Martins ( 2018 ) and by other researchers (Valencia et al., 2010 ). In this approach, the types of errors that are marked as WIPM are mispronounced words, words substituted with others, words omitted, words read out of order, addition or omission of word endings, and hesitation (words on which the student paused more than 3 s, after which he or she is told the word, and it is marked as incorrect. If necessary, the student is told to continue with the next word).

The following items indicate all situations that are marked as WCPM: words pronounced correctly, self-corrections, words decoded slowly but ultimately read correctly, repeated words, words mispronounced due to dialect or regional differences, and words inserted. To quantify errors, scoring rules are also proposed for certain situations: lines or multiple words omitted; when one or more lines are not read (four or more omitted words in sequence), they are not considered errors, although those words are excluded from the WCPM (such that this rule is applied whenever a student skips four or more words within a sentence). If the student skips one, two, or three consecutive words, each word should be counted as an error (WIPM). Regarding hyphenated words that can exist independently, each morpheme separated by a hyphen counts as an individual word if the two parts exist independently when the hyphen is removed, such as “Guarda-chuva ” [Umbrella in Portuguese] (counts as two words but is only marked incorrect when the student misreads), as opposed to the word “ anglo-China ” (considered as one word, regardless of which or both are misread).

Step 2 : The data thus obtained were tabulated and processed with Microsoft Excel® 2010. Data were analyzed through descriptive statistics (mean, standard deviation, and percentiles). Percentiles 5, 10, 25, 50, 75, 90, and 95 were calculated for each grade. Stratifying these percentiles helps to understand the different levels of difficulty that students may present.

Step 3 : The reference table was adjusted for the selection of students eligible to receive reading fluency interventions or programs. For this, the minimum reference threshold was the 25th percentile, and the maximum reference limit was the 50th percentile. The reference to the 25th percentile represents an approximate limit on the minimum level of ORF that a student should present to benefit from a fluency program. This reference was developed through years of research and related interventions (Begeny et al., 2018 ; Field, Begeny, & Kim, 2019 ).

Thus, it was determined that in the present research, WCPM intervals (maximum and minimum limits) would be established to select students who were not making adequate reading progress based on the ORF standard published by Hasbrouck and Tindal ( 2006 ).

The results regarding the reading fluency assessment measure as a procedure for selecting struggling readers or at risk of developing reading difficulties (grades 3 to 5) are summarized in Tables 1 and 2 .

From the data presented in Table 1 , students in the third year who read 86 or more WCPM, in the fourth year who read 104 or more WCPM, and in the fifth year who read 117 or more WCPM are considered students who are making adequate progress in reading. As shown in Table 1 , the lower the student scored beneath the 25th percentile, the more difficulties with reading the student will present, and the higher the student scored above the 50th percentile, the better the student’s performance.

Considering the standards proposed by Hasbrouck and Tindal ( 2006 , p. 639), in which students who read more than 10 WCPM above the 50th percentile present appropriate reading progress (unless there are other indicators for concern), the WCPM was established for Brazilian students (Table 2 ).

The reference intervals were calculated from the readings by the 365 students, considering that those who presented a WCPM score between the 25th and 50th percentiles did not make satisfactory progress in their reading fluency and taking the 25th percentile as the minimum reference limit and the 50th percentile as the maximum reference limit (Table 2 ). Students with WCPM scores at the 25th percentile or below are unlikely to benefit from a fluency-based intervention because they likely need assistance with decoding, phonics, and/or phonemic awareness.

Measures such as the number of WCPM offer numerous advantages for use in the context of ORF assessment. This measure has already been proven to be valid and is a quick and simple measure; it can be easily implemented in educators’ routines, either within the school routine or with professionals in their clinics. The reliability coefficient of this study could not be used if the test used because a single item test was used (number of words read correctly). If used as a screening measure for students at risk of reading difficulties, it should be performed by teachers from the third grade, since it is from this series that all students are expected to have passed the literacy phase and to move from the phase of learning to read to the phase of reading to learn. Consequently, within just a few hours, a teacher can evaluate their entire class because the assessment is performed quickly, which would also enable frequent assessments, which would, in turn, enable the monitoring of students’ progress in their fluency (Hasbrouck & Tindal, 2006 ; Rasinski, 2004 ; Rasinski & Young, 2017 ).

For reference values, the data obtained in this study served to identify students who were making adequate reading progress and those who could benefit from a fluency program. Among the academic skills considered central to reading success, fluency reveals not only its importance in assessing and screening key components but also in intervention response strategies and models for absorbing the demand encountered after the screening and early identification of reading difficulties (Kostewicz et al., 2016 ).

Considering the Brazilian studies on the characterization of ORF, we note that despite their small number (Andrade et al., 2019 ; Dellisa & Navas, 2013 ; Komeno et al., 2015 ; Moutinho, 2016 ; Oliveira et al., 2013 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ), the results help to predict and compare student performance. It is necessary to advance the description of the results to create fluency references so that they can be used to screen for students with general reading difficulties, according to each region of the country. It is emphasized that due to the continental dimensions of the Brazilian national territory, there are considerable cultural and educational differences among regions.

Therefore, the method of assessing a measure of ORF in given passages can be used to assess student progress in reading fluency competence; to predict and compare students’ performance with peers or benchmarks (since their performance is compared over time) as well as conduct individual assessments; set annual goals; assess the effectiveness of intervention programs; develop standards for the class, school, and/or region; identify students at risk of dyslexia or in need of further intervention; and serve as the initial source of data collection in the response-to-intervention model (Mendonça & Martins, 2014 ).

Implications

There are public policy problems that involve this issue of early identification in Brazil, as there are no projects or actions directed at absorbing the demand of learning disabilities within the school itself. This difficulty makes the implementation of a screening process for early identification more difficult, since once these students with difficulties have been identified, there is a corresponding need for interventions, such as intervention response models together with the need for a complete structural and practical change within the classroom to modify the deeply rooted tradition of “waiting to fail to take action” (Elliott et al., 2007 ). However, as observed in a recent program created by the Ministry of Education (More Literacy Program–PMAlfa), new ways of implementing the screening of reading difficulties and continuing teacher education to ensure that they master the methodologies for progress monitoring and evaluation of student performance are beginning to appear.

It is also important to underscore that recent research has focused on the development of instruments and materials suitable for this type of evaluation and progress-monitoring, such as passages that are appropriate for the grade level and classified according to their difficulty, that not only allow the modification of the “waiting to fail to act” tradition but also allow suitable fluency assessment applications with materials that not only accelerate but also facilitate evaluation (such as software and applications) (Alves et al., 2019 ). This approach also means that three passages of the same level of difficulty can be offered (as a collection of sequential passages) to the students for assessment (Martins & Capellini, 2018 ), with sets of three passages to be applied throughout the school year to facilitate the monitoring of student progress.

Despite its limitations, this study extended the literature (Andrade et al., 2019 ; Dellisa & Navas, 2013 ; Komeno et al., 2015 ; Moutinho, 2016 ; Oliveira et al., 2013 ; Pacheco & Santos, 2017 ; Peres & Mousinho, 2017 ) as part of the research movement to obtain ORF subsidiary reference data for professionals in the health-education interface. However, it is necessary to note that one limitation of this study is the number of samples used. To complement this study and other Brazilian research in this context, new research is needed that increases the number and the representativeness of the sample of Brazilian readers who struggle.

From this study, it was possible to evaluate and characterize the reading fluency of Brazilian students. It was also possible to establish reference intervals for the assessment of ORF, which can be used to screen struggling readers or students at risk who present or may develop reading difficulties.

Therefore, similar research should be carried out and expanded to create measurement parameters related to ORF, which will help teachers make decisions about which paths need to be constructed or improved to assist those students who are presenting difficulty in this learning process.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Curriculum-based measurement

• Oral reading fluency

Words read correctly per minute

Words read incorrectly per minute

More Literacy Program

Common National Curriculum Base

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Martins, M.A., Capellini, S.A. Identification of struggling readers or at risk of reading difficulties with one-minute fluency measures. Psicol. Refl. Crít. 34 , 10 (2021). https://doi.org/10.1186/s41155-021-00174-z

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Preventing Reading Difficulties in Young Children (1998)

Chapter: part i: introduction to reading, part i introduction to reading.

Reading is a complex developmental challenge that we know to be intertwined with many other developmental accomplishments: attention, memory, language, and motivation, for example. Reading is not only a cognitive psycholinguistic activity but also a social activity.

Being a good reader in English means that a child has gained a functional knowledge of the principles of the English alphabetic writing system. Young children gain functional knowledge of the parts, products, and uses of the writing system from their ability to attend to and analyze the external sound structure of spoken words. Understanding the basic alphabetic principle requires an awareness that spoken language can be analyzed into strings of separable words, and words, in turn, into sequences of syllables and phonemes within syllables.

Beyond knowledge about how the English writing system works, though, there is a point in a child's growth when we expect "real reading" to start. Children are expected, without help, to read some unfamiliar texts, relying on the print and drawing meaning from it. There are many reasons why children have difficulty learning to read. These issues and problems led to the initiation of this study.

Even though quite accurate estimates can be made on the basis of known risk factors, it is still difficult to predict precisely which young children will have difficulty learning to read. We therefore propose that prevention efforts must reach all children. To wait to initiate treatment until the child has been diagnosed with a specific disability is too late. However, we can begin treatment of conditions associated with reading problems, for example, hearing impairments.

Ensuring success in reading requires different levels of effort for different segments of the population. The prevention and intervention efforts described in this report can be thought of in terms of three levels (Caplan and Grunebaum, 1967, cited in Simeonsson, 1994; Pianta, 1990; and Needlman, 1997).  Primary prevention is concerned with reducing the number of new cases (incidence) of an identified condition or problem in the population, such as ensuring that all children attend schools in which instruction is coherent and competent.

Secondary prevention is concerned with reducing the number of existing cases (prevalence) of an identified condition or problem in the population. Secondary prevention likewise involves the promotion of compensatory skills and behaviors. Children who are growing up in poverty, for example, may need excellent, enriched preschool environments or schools that address their particular learning needs with highly effective and focused instruction. The extra effort is focused on children at higher risk of developing reading difficulties but before any serious, long-term deficit has emerged.

Tertiary prevention is concerned with reducing the complications associated with identified problem, or conditions. Programs, strategies, and interventions at this level have an explicit remedial or rehabilitative focus. If children demonstrate inadequate progress under secondary prevention conditions, they may need instruction that is specially designed and supplemental—special education, tutoring from a reading specialist—to their current instruction.

While most children learn to read fairly well, there remain many young Americans whose futures are imperiled because they do not read well enough to meet the demands of our competitive, technology-driven society. This book explores the problem within the context of social, historical, cultural, and biological factors.

Recommendations address the identification of groups of children at risk, effective instruction for the preschool and early grades, effective approaches to dialects and bilingualism, the importance of these findings for the professional development of teachers, and gaps that remain in our understanding of how children learn to read. Implications for parents, teachers, schools, communities, the media, and government at all levels are discussed.

The book examines the epidemiology of reading problems and introduces the concepts used by experts in the field. In a clear and readable narrative, word identification, comprehension, and other processes in normal reading development are discussed.

Against the background of normal progress, Preventing Reading Difficulties in Young Children examines factors that put children at risk of poor reading. It explores in detail how literacy can be fostered from birth through kindergarten and the primary grades, including evaluation of philosophies, systems, and materials commonly used to teach reading.

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The Comprehension Problems of Children with Poor Reading Comprehension despite Adequate Decoding: A Meta-Analysis

The purpose of this meta-analysis was to examine the comprehension problems of children who have a specific reading comprehension deficit (SCD), which is characterized by poor reading comprehension despite adequate decoding. The meta-analysis included 86 studies of children with SCD who were assessed in reading comprehension and oral language (vocabulary, listening comprehension, storytelling ability, and semantic and syntactic knowledge). Results indicated that children with SCD had deficits in oral language ( d = −0.78, 95% CI [−0.89, −0.68], but these deficits were not as severe as their deficit in reading comprehension ( d = −2.78, 95% CI [−3.01, −2.54]). When compared to reading comprehension age-matched normal readers, the oral language skills of the two groups were comparable ( d = 0.32, 95% CI [−0.49, 1.14]), which suggests that the oral language weaknesses of children with SCD represent a developmental delay rather than developmental deviance. Theoretical and practical implications of these findings are discussed.

Reading comprehension, or the process of engaging text for the purpose of extracting and constructing meaning ( Snow, 2002 ), has paramount importance to academic success and future life outcomes ( National Institute of Child Health and Human Development [NICHD], 2000 ; Snow, 2002 ). Yet only about 36% of fourth graders and 34% eighth graders in the United States have reading comprehension scores at or above proficiency by the end of the academic year ( U.S. Department of Education, 2015 ). Furthermore, nearly 31% of fourth graders and nearly 24% of eighth graders continue to attain reading comprehension scores that are below even the basic level. This indicates that a substantial proportion of fourth and eighth graders would have problems with more complex activities that extend beyond the text itself (e.g., comparing and contrasting ideas or making inferences beyond the text). This is particularly troubling given the importance of comprehension skills for success in school, in the workplace, and in daily life (e.g., understanding newspapers and forms and contracts to be signed).

Given the importance of decoding to reading comprehension it is not surprising that decoding deficits often result in comprehension difficulties ( Perfetti, 1985 ; Perfetti & Hart, 2001 ; Perfetti & Hogaboam, 1975 ; Perfetti, Landi & Oakhill, 2005 ; Shankweiler et al., 1999 ; Snow, Burns, & Griffin, 1998 ). However, it is estimated that between 10 and 15% of 7- to 8-year-old children have normal performance on decoding measures yet still experience deficits in reading comprehension ( Nation & Snowling, 1997 ; Stothard & Hulme, 1995 ; Yuill & Oakhill, 1991 ); that is, these children are characterized as having a specific reading comprehension deficit (SCD). Although this estimate varies depending on the criteria used to identify children with SCD (see Rønberg & Petersen, 2015 ), large-scale identification studies have shown that the prevalence of SCDis most likely around 8% for children between the ages of 9 and 14 years ( Keenan et al., 2014 ). Even an 8% prevalence rate would mean an average of two students in a classroom could meet the criteria for SCD.

Reading comprehension is a complex process, involving a variety of cognitive and linguistic skills. As a result, deficits in any cognitive ability important to the comprehension process can potentially lead to deficits in reading comprehension performance. Perfetti and colleagues ( Perfetti et al., 2005 ; Perfetti & Stafura, 2014 ) provide a comprehensive framework for understanding the processes and skills involved in reading comprehension; deficits in comprehension could result from a variety of sources beyond decoding, including differences in sensitivity to story structure, inference making, comprehension monitoring, syntactic processing, verbal working memory, and oral language skills ( Cain & Oakhill, 1996 , 1999 ; Cain, Oakhill, Barnes, & Bryant, 2001 ; Nation, Adams, Bowyer-Crane, & Snowling, 1999 ; Nation & Snowling, 1998b , 1999 ; Oakhill, Hartt, & Samols, 2005 ; Pimperton & Nation, 2010a ; Snowling & Hulme, 2012 ).

Existing studies of children with SCD show that they perform poorly on a range of oral language assessments ( Cain, 2003 ; Cain, 2006 ; Cain et al., 2005 ; Cain & Oakhill, 1996 ; Carretti et al., 2014 ; Nation & Snowling, 2000 ; Oakhill et al., 1986 ; Stothard & Hulme, 1996 ; Tong, Deacon, & Cain, 2014 ; Tong, Deacon, Kirby, Cain, & Parrila, 2011 ; Yuill & Oakhill, 1991 ). However, relatively little is known about whether the comprehension problems of children with SCD are the result of their oral language deficits. Although it is possible that the documented deficits in oral language account for the observed deficits in reading comprehension, they may only be a contributing factor. A better understanding of the comprehension problems for children with SCD may be a first step towards better identification and remediation.

We briefly describe relevant theories of reading comprehension because existing theories may inform our understanding of the comprehension problems of children with SCD and understanding the comprehension problems of children with SCD in turn may inform theories of comprehension.

Several theories of reading comprehension have emerged over the years. These include the bottom-up view, the top-down view, the interactive view, the metacognitive view, and the simple view of reading comprehension. Each of these theories are relevant within the present context. Thus, we briefly discuss each theory below.

According to the bottom-up view of reading comprehension, readers move from an understanding of parts of language (e.g., letters, words) to an understanding of meaning or the whole (e.g., phrases, passages; Gough, 1972 ; Holmes, 2009 ; LaBerge & Samuels, 1974 ). Comprehension is thought to be a product of the acquisition of hierarchically arranged subskills ( Dole et al., 1991 ). Thus, lower-level word recognition skills precede the development of more complex skills that lead to an eventual understanding of phrases, sentences, and paragraphs. Automaticity in processing and understanding written text is also thought to affect text comprehension ( LaBerge & Samuels, 1974 ). Automaticity refers to the fact that proficient readers can read text automatically and that they do not need to focus consciously on lower-level word recognition. Thus, children with decoding problems allot greater cognitive resources to word recognition – and less to comprehension – whereas proficient readers are able to devote greater cognitive resources to higher-level cognitive processes (e.g., working memory; Daneman & Carpenter, 1980; Perfetti, 1985 ; Perfetti & Hogaboam, 1975 ).

Based on the top-down (i.e., conceptually-driven) view of reading comprehension, readers are moving from meaning down to the component parts of words as they engage with text ( Rumelhart, 1980 ; Shank & Abelson, 1977 ). According to this view, a reader's mental frameworks or schemas are the driving force behind successful reading comprehension ( Rumelhart, 1980 ). Readers are actively integrating new information that is encountered in the text with information that they have already stored within their previously established mental representations (i.e., background knowledge).

Top-down and bottom-up aspects are combined in the interactive view of reading comprehension. Based on this view, reading comprehension requires the reader to devote attentional resources to the more basic features of the text (e.g., letters, words) while simultaneously focusing on the more general aspects (e.g., syntax, semantics) and actively interpreting what is being read ( Perfetti et al., 2005 ). Proficient readers are those who successfully engage with multiple sources of information provided within the text and information that is not readily available from the text (Kintsch, 1998; Perfetti & Stafura, 2014 ; van Dijk & Kintsche, 1983 ). Good readers are are able to recognize and interact with key features of the text, such as lexical characteristics, at the same time that they are more broadly identifying the purpose of a passage or a paragraph ( Rayner, 1986 ; Rayner et al., 2001 ).

The simple view of reading asserts that reading comprehension is the product of decoding ability and language comprehension ( Gough & Tunmer, 1986 ; Hoover & Gough, 1990 ). The simple view also has substantial empirical validation. For example, decoding has emerged as a reliable predictor of reading comprehension ability in a variety of instances (e.g., Kendeou, van den Broek, White, & Lynch, 2009 ; Shankweiler et al., 1999 ). In fact, poor decoding skills are associated with reading comprehension problems ( Perfetti, 1985 ). Additionally, oral language skills remain a robust and unique predictor of reading comprehension over and above word reading skills ( Nation & Snowling, 2004 ).

Oral language is defined as the ability to comprehend spoken language ( National Early Literacy Panel, 2008 ) and includes a wide variety of skills, such as expressive and receptive vocabulary knowledge, grammar, morphology, syntactic knowledge, conceptual knowledge, and knowledge about narrative structure ( Beck, Perfetti, & McKeown, 1982 ; Bishop & Adams, 1990 ; Bowey, 1986 ; Perfetti, 1985 ; Roth, Speece, & Cooper, 2002 ). Oral language skills impact reading comprehension directly, such as through the understanding of the words presented in a text, as well as indirectly via other literacy-related skills (e.g., phonological awareness; NICHD, 2000 ; Wagner & Torgesen, 1987 ). Furthermore, the unique contribution of oral language to reading comprehension remains even after accounting for word recognition ( Oullette, 2006 ).

The simple view provides a potential explanation for the reading comprehension problems of children with SCD that is consistent with their observed oral language deficits: Reading comprehension requires both adequate decoding and adequate oral language comprehension. This would explain the observation that children with SCD have adequate decoding but not adequate oral language comprehension. Catts, Adolf, and Weismer (2006) and Nation and Norbury (2005) applied this simple view of reading framework to identify different types of reading problems in eighth graders and 8-year-old children, respectively. According to this classification system, children with good decoding and good comprehension are adequate readers; children with poor decoding and poor comprehension are garden-variety poor readers; children with good comprehension and poor decoding meet criteria for dyslexia; and children with good decoding and poor comprehension have SCD. Thus, a mastery of both decoding and language comprehension is necessary for reading proficiency.

Developmental Delay or Developmental Deficit?

Developmental delay and developmental deficit are two hypotheses that are often discussed in relation to the nature of reading disability (e.g., dyslexia; see Francis, Shaywitz, Stuebing, Shaywitz, & Fletcher, 1996 ). The developmental delay hypothesis asserts that poor reading performance results from a delayed acquisition of reading-related skills ( Francis et al., 1996 ). However, these children follow the same developmental trajectory as typical readers ( Francis et al., 1996 ). The developmental deficit hypothesis, on the other hand, states that the underlying skill shows a different or deviant developmental trajectory ( Francis et al., 1996 ). For the case of reading disability, the underlying skill examined was phonological processing. We are interested in determining whether an oral language weakness represents a developmental delay or deficit for children with SCD. This hypothesis could be tested within studies that matched children with SCD to a younger group of typically-developing children (comprehension-age matching; see Cain, Oakhill, & Bryant, 2000 ). If children with SCD demonstrated similar performance to the comprehension-age matched group this would support developmental delay. If children with SCD had worse performance than the comprehension-age matched group, this outcome would support developmental deviance.

The importance of the distinction between developmental delay and developmental deficit is that a skill that is characterized as a developmentally deficit is more likely to be a contributing factor in the development of the reading problem. Developmental delay implies that the skill is consistent with the observed delay in reading and is therefore less likely to be a contributing factor. To our knowledge, an empirical examination of these two hypotheses has not yet been conducted for the observed oral language deficits in children with SCD.

Below, we describe a study conducted by Cain and Oakhill (2006) that has several characteristics that are typical of studies involving children with SCD. In this investigation, the authors were interested in the cognitive profiles of 7- to 8-year-old children with SCD; this age range is very common for investigations of children with SCD (e.g., Cain, 2003 ; Cain & Oakhill, 1996 , 2007; Jerman, 2007; Oakhill, 1982 ). Children were selected based on their performance on measures of reading comprehension and word reading accuracy and were followed longitudinally. In this case, the Neale Analysis of Word Reading Ability was used to categorize children into groups of good and poor comprehenders. Age-appropriate word reading accuracy was defined as being between 6 (lower limit) and 12 months (upper limit) of their chronological age (e.g., Clarke, 2009 ). Poor reading comprehension was defined as a 12-month discrepancy between comprehension age and chronological age and their reading accuracy age and comprehension age (e.g., Nation & Snowling, 1999 , 2000 ; Weekes, Hamilton, Oakhill, & Holliday, 2008 ). Typical readers are defined as attaining reading comprehension scores that are at or above word reading accuracy performance. Due to one-to-one matching and the low proportion of SCD in the population, final groups were small (23 children per group); this is typical of many studies involving children with SCD (e.g., Ehrlich & Remond, 1997 ; Geva & Massey-Garrison, 2012 ; Nation & Snowling, 1998a , 1998b ). In this study, children were given a battery of assessments that included a combination of standardized and experimenter-created measures (e.g., Nation et al., 1999 ; Nation & Snowling, 2000 ). A unique aspect of this investigation is that children were followed longitudinally; many studies involving children with SCD are single time point studies (e.g., Cain & Oakhill, 1999 ; Oakhill, 1983 ).

SCD has been defined in a variety of ways across different studies. Although researchers tend to agree on the need for a discrepancy between an individual's decoding ability and their reading comprehension skills, individuals with SCD (also referred to as poor comprehenders or less-skilled comprehenders in the literature) have been identified using one of four criteria:

• A discrepancy between reading comprehension and decoding (e.g., Isakson & Miller, 1976 ; Nation & Snowling, 1998a ; Oakhill, Yuill, & Parkin, 1986 ; Pimperton & Nation, 2010a );
• A discrepancy between reading comprehension and decoding with an additional requirement that decoding skills are within the normal range (e.g., Cain et al., 2001 ; Cataldo & Oakhill, 2000 ; Cragg & Nation, 2006 ; Torppa et al., 2009);
• Discrepancies between reading comprehension, decoding, and chronological age with an additional requirement that decoding skills are within the normal range ( Cain, 2003 ; Cain, 2006 ; Cain et al., 2000 ; Cain & Oakhill, 2006 , 2011 ; Cain, Oakhill, & Elbro, 2003 ; Cain, Oakhill, & Lemmon, 2004 ; Cain & Towse, 2008 ; Clarke, 2009 ; Marshall & Nation, 2003 ; Nation & Snowling, 1997 , 2000 ; Nation et al., 2001 ; Oakhill et al., 2005 ; Spooner, Gathercole, & Baddeley 2006 ; Stothard & Hulme, 1995 ; Yuill, 2009 ; Yuill & Oakhill, 1991 );
• A discrepancy between reading comprehension and word-level decoding with additional requirements that decoding skills are within the normal range and that comprehension scores fall below a given percentile or cut point ( Cain & Towse, 2008 ; Carretti, Motta, & Re, 2014 ; Catts et al., 2006 ; Henderson, Snowling, & Clarke, 2013 ; Kasperski & Katzir, 2012; Megherbi, Seigneuric, & Ehrlich, 2006 ; Nation, Clarke, Marshall, & Durand, 2004 ; Nation, Snowling & Clark, 2007 ; Nesi, Levorato, Roch & Cacciari, 2006 ; Pelegrina, Capodieci, Carretti, & Cornoldi, 2014 ; Pimperton & Nation, 2014 ; Ricketts, Nation, & Bishop, 2007 ; Shankweiler et al., 1999 ; Tong et al., 2011 ; Tong et al., 2014 ).

Despite the fact that differences in identification criteria influence the percentage of children identified as having SCD (see Rønberg & Petersen, 2015 ), children with SCD likely represent a small but significant proportion of struggling readers. Moreover, across studies included within the present review, SCD was identified using all of these different criteria. Therefore, our findings provide an overall estimate of the nature of children's comprehension problems regardless of identification method.

The purpose of the present meta-analysis is to better understand the comprehension deficits of children who have SCD. The framework for the present meta-analysis grew out of a recent investigation that tested three hypotheses regarding the nature of the comprehension problem in a large sample of over 425,000 first-, second-, and third graders with SCD ( Spencer, Quinn, & Wagner, 2014 ). The three hypotheses tested whether comprehension problems for these children were largely specific to reading, general to oral language, or both (i.e., a mixture). Children were obtained from a statewide database, and prevalence of SCD was calculated based on percentile cutoffs. The results indicated that over 99 percent of children in each grade who had SCD also had deficits in vocabulary knowledge. This finding indicates that children's comprehension deficits were general to reading and at least one important aspect of oral language.

Although these results provide compelling evidence that comprehension problems are general to at least one aspect of oral language (i.e., vocabulary knowledge), three limitations of the study need to be noted. First, participants included mostly children attending Reading First schools, a Federal program for improving reading performance for students from low socioeconomic backgrounds. Because poverty is a risk factor for delayed development of oral language, the results may not generalize to students not living in poverty. Second, the assessments were brief and receptive vocabulary knowledge served as the only measure of oral language comprehension, when in fact, oral language is potentially comprised of a variety of different skills that might affect reading comprehension. Third, the study did not compare the relative magnitudes of the deficits observed in reading comprehension and vocabulary, a potentially important new source of data that could be used to compare alternative hypotheses about the nature of the comprehension problems of children with SCD.

These limitations suggest the need for a comprehensive review of the literature on the nature of the comprehension problems of children who have SCD. Such a review could incorporate results from studies with more representative samples and using a variety of measures. By examining magnitudes as well as the existence of deficits in reading versus oral-language comprehension, it would be possible to test a previously neglected hypothesis in Spencer et al. (2014) , namely that children with SCD could have deficits in oral language that are not as severe as their deficits in reading comprehension.

Thus, in addition to testing two hypotheses from Spencer et al. (2014) – (a) Children with SCD have comprehension deficits are specific to reading, such that they demonstrate impaired reading comprehension but no impairments in oral language and (b) children with SCD have comprehension deficits are general to reading and oral language, such that they demonstrate equal impairment in reading comprehension and oral language – we also test a third hypothesis in the present meta-analysis, (c) children with SCD have comprehension deficits that extend beyond reading to oral language, but they demonstrate greater impairment in reading comprehension than in oral language.

Hypothesis one: children with SCD have comprehension problems that are specific to reading

Theoretical support for this hypothesis comes from the bottom-up view of reading comprehension and from the automaticity of reading ( Gough, 1972 ; Holmes, 2009 ; LaBerge & Samuels, 1974 ). It is possible that children might have adequate decoding but their adequate decoding requires processing resources that are then not available for comprehension while reading. If this were the case, their comprehension would be impaired for reading comprehension because decoding is required but not impaired for oral language.

Empirical support for this hypothesis comes from studies that demonstrate the existence of individuals who have been identified as having SCD in the presence of intact or relatively intact vocabulary knowledge ( Cain, 2006 ; Nation, et al., 2010 ). Moreover, some studies that compared children with and without SCD matched them on vocabulary performance (e.g., Cain, 2003 , Cain, 2006 ; Spooner et al., 2006 ; Tong et al., 2014 ). That it was possible to do this match supports the possibility that comprehension problems are specific to the domain of reading.

Hypothesis two: children with SCD have comprehension problems that are general to reading and oral language

Several theoretical perspectives provide a rationale for this hypothesis, including the simple view, top-down view, and interactive views of reading comprehension. The simple view ( Gough & Tunmer, 1986 ; Hoover & Gough, 1990 ) provides support for this hypothesis because it explains SCD as resulting from a deficit in oral language comprehension ( Catts et al., 2006 ; Nation et al., 2004 ). The top-down and interactive views are in line with this hypothesis because both frameworks emphasize the readers' mental frameworks ( Rumelhart, 1980 ; Shank & Abelson, 1977 ). The top down processing highlighted in both frameworks would affect comprehension regardless of whether the context is written or oral language.

Empirical support for this hypothesis comes from studies showing that oral language ability is a predictor of future reading comprehension success and failure ( Nation & Snowling, 2004 ; Snow et al., 1998 ); children with reading comprehension problems tend to have deficits in oral language ( Catts, Fey, Tomblin, & Zhang, 2002 ). For example, Catts, Fey, Zhang, and Tomblin (1999) investigated relations between oral language and reading comprehension skills in second graders. Results indicated that children with reading comprehension deficits were significantly more likely to have had oral language weaknesses in kindergarten compared to students with more typical comprehension development (see also Elwer, Keenan, Olson, Byrne, & Samuelsson, 2013 ).

The view that comprehension problems are general to oral language and reading is supported by multiple investigations. Children with SCD have demonstrated weaknesses related to a variety of oral language domains, such as semantic processing, listening comprehension, and syntactic ability ( Carretti, Motta, & Re, 2014 ; Nation & Snowling, 2000 ; see Cain & Oakhill, 2011 and Justice, Mashburn, & Petscher, 2013 for longitudinal evidence). When compared to typical readers, these children also tend to perform significantly poorer on measures tapping verbal working memory skills (see Carretti, Borella, Cornoldi, & De Beni, 2009 ). Differences between typically-developing readers and individuals with SCD have also been reported using a wide variety of behavioral and EEG/ERP measures (e.g., Landi & Perfetti, 2007 ).

Hypothesis three: children with SCD have comprehension problems that extend to oral language but are less severe for oral language than for reading

Theoretical support for this hypothesis is provided by a combination of theoretical rationales discussed for the previous two hypotheses. Specifically, a deficit that is general to oral language as well as reading comprehension is assumed, combined with additional deficits that are specific to reading. For example, a deficit in vocabulary would impair performance in reading comprehension and oral language. Simultaneously, decoding and orthographic processing could require attention and cognitive resources that are not required by listening, such as visual processing. The combined result would be impairments in both oral language and reading comprehension, but the impairment would be greater for reading comprehension.

Empirical support for this hypothesis comes from studies showing that these children demonstrate differential performance across various oral language tasks ( Cain, 2003 ; Cain, 2006 ; Cain, Oakhill, & Lemmon, 2005 ; Stothard & Hulme, 1992 ; Tong et al., 2014 ). For example, Cain (2003) examined language and literacy skills in children with SCD who were matched to typical readers based on vocabulary; however, these same children exhibited significantly poorer performance on other oral language tasks, such as listening comprehension and a story structure task. Similarly, Tong et al. (2014) included children with SCD who were vocabulary-matched to typical readers. Yet, children with SCD exhibited poor performance on a morphological awareness task. Therefore, it may be that the comprehension problems of children with SCD affects some but not all aspects of oral language.

Additionally, we were interested in examining the effect of several potential moderators of effect size outcomes, specifically the effects of (a) publication type, (b) participant age, and (c) type of oral language measure. The rationale for these moderators are as follows: First, if publication type (e.g., published journal article versus unpublished dissertation) significantly predicts effect size outcomes, we would attribute this, at least partially, to publication bias. Thus, we wanted to include this variable within each meta-analysis. Second, we were interested in participant age as a moderator of effect sizes ( Catts et al., 2006 ; Elwer et al., 2013 ; Nation, Cocksey, Taylor, & Bishop, 2010 ). Previous research has also indicated that younger children with SCD tend to have weaker reading comprehension skills compared to older children ( Authors, 2017 ). We sought to investigate whether this finding would be replicated within a different sample and also whether these differences transfer to oral language skills as well. Finally, type of oral language measure was included as a potential moderator due to the fact that oral language measures vary greatly in the skills that they assess ( Cain & Oakhill, 1999 ; Nation et al., 2004 , 2010 ; Tong et al., 2011 ). For instance, a receptive vocabulary assessment is likely to be much less difficult for a child with SCD compared with a syntactic or morphological task. Therefore, examining the potential effects of type of oral language measure may provide additional insight into which tasks may be best to use for identifying children with SCD.

Across four decades, multiple systematic reviews of reading comprehension have been conducted. These reviews have examined a variety of topics, including an examination the component skills of reading comprehension and intervention research for struggling readers (e.g., Bus & van Ijzendoorn, 1999 ; Ehri, Nunes, Stahl, & Willows, 2001 ; Swanson, Tranin, Necoechea, & Hammill, 2003 ). In more recent years, there have been several narrative reviews focusing specifically on children with SCD ( Hulme & Snowling, 2011 ; Nation & Norbury, 2005 ; Oakhill, 1993 ), but only one known meta-analysis to date has investigated the cognitive skills of these individuals ( Carretti et al., 2009 ). However, Carretti et al. (2009) focused exclusively on working memory skills whereas the present investigation examines performance of children with and without SCD on a wide array of oral language tasks in addition to verbal working memory.

In the present review, we examine studies using five methods. First, we conducted between-group meta-analyses comparing the reading comprehension performance of children with SCD with the reading comprehension performance of typically-developing readers. Second, we conducted between-group analyses comparing the oral language performance (as indexed by measures of vocabulary, listening comprehension, storytelling ability, morphological awareness, and semantic and syntactic knowledge) of children with SCD with the oral language performance of typically-developing readers. Third and fourth, we conducted the same meta-analyses for reading comprehension and oral language performance for studies that included a comprehension-age matched group (see Cain et al., 2000 ). The existence of such studies makes it possible to determine whether impaired oral language performance represents developmental delay (i.e., performance similar to younger normal comprehenders) or a developmental difference (i.e., performance different than that of younger normal comprehenders; Francis et al., 1996 ). Finally, we conducted a separate meta-analysis for studies reporting performance on standardized reading comprehension and oral language measures for the same participants (i.e., a within-child comparison of reading comprehension and oral language) because we were interested in the comparability of oral language skills to reading comprehension within children who have SCD.

Study Collection

The current meta-analysis includes studies published in English from January 1, 1970 to February 20, 2016. Several electronic databases and keywords were used to locate relevant studies. These databases included PsycINFO, ERIC, Medline, and ProQuest Dissertations. In an effort to reduce the likelihood of publication bias within the present review, we also searched several gray literature databases (i.e., SIGLE, ESRC, and Web of Science ). We used title-based keywords related to reading comprehension and reading disabilities ( specific comprehension deficit*, poor comprehender*, comprehension difficult*, less-skilled comprehen*, comprehension failure, reading difficult*, difficulty comprehending, poor comprehension, struggling reader*, specific reading comprehension difficult*, specific reading comprehension disabilit*, low comprehender*, weak reading comprehen*, reading comprehension disab*, poor reading comprehension ) in combination with other reading-related keywords ( reader*, reading, subtype*, subgroup ). Our search spanned peer-reviewed and non-peer-reviewed journal articles, dissertations and theses, book chapters, reports, and conference proceedings. The references of relevant articles were also hand searched, and we contacted researchers who had at least three relevant publications (first authored or not) as a way of including unpublished data within the present review. We conducted additional searches for these same researchers using author- and abstract-based keyword searches [au( author ) AND ab( comprehen* )].

Inclusionary criteria

Several inclusionary criteria were used to select studies to be included within the present synthesis. Studies were required to: (a) report original data (i.e., sample means, standard deviations, correlations, sample sizes, t -tests, and/or F -tests); (b) include native speakers of a language; (c) assess children between the ages of 4 and 12 years; (d) contain at least one measure of reading comprehension, decoding ability, and oral language; (e) include a sample of children with SCD based on their performance on measures of reading comprehension and decoding ability; and (f) include a typically-developing group of readers for comparisons 2 .

We applied the language-based criterion because we wanted to be able to investigate the relation between poor reading comprehension and oral language skills separate from language status because language status is known to affect reading comprehension (e.g., Kieffer, 2008 ). However, studies could include monolingual samples that spoke a language other than English (e.g., Italian) provided that the study was reported in English. Acceptable measures of reading comprehension included assessments that measured individuals' comprehension of the text beyond word reading ability; acceptable measures of decoding ability included assessments that measured real word decoding, nonword decoding, and/or reading accuracy; and acceptable measures of oral language included tasks that assessed vocabulary knowledge, syntactic and semantic processing, listening comprehension, and/or storytelling ability.

Exclusionary criteria

Three exclusionary criteria were applied for studies included in the current meta-analysis: (a) teacher and parent ratings were not acceptable methods for identifying children with SCD, (b) samples of non-native speakers, and (c) samples could not also contain children characterized as having intellectual disability, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), aphasia, hydrocephalus, or hearing or vision impairments.

Final study selection

The initial search yielded approximately 3,050 results. After eliminating duplicates, studies that did not adhere to our inclusion/exclusion criteria, and studies reporting results from identical participants, a total of 86 studies remained.

A random sample of 10% of the studies was coded twice by the first author and a graduate student in order to establish inter-coder reliability; studies were coded based on study features (i.e., study type, sample size, operational definition of SCD, matching variables, language spoken, and sample age) and reading comprehension- and oral language-related constructs (i.e., reported reliabilities, correlations with oral language measures, means and standard deviations for each assessment, and reported t values or F ratios). We additionally coded participant age, type of oral language measure (i.e., vocabulary knowledge, narrative, listening comprehension, syntactic/grammar, semantic knowledge, and figurative language), and type of publication (i.e., journal article, book chapter, theses/dissertations, and unpublished data). Cohen's kappa was used to measure inter-coder reliability (96% for study features; 98% for reading comprehension-related constructs; 94% for the oral language-related constructs). The overall reliability exceeded acceptability of kappa ≥ .70 (kappa = 96%). Discrepancies were resolved through discussion or by referring to the article.

The final sample included 84 studies for between ( k brc = 152 effect sizes for reading comprehension ; k bol = 309 effect sizes for oral language) and within-group analyses ( k wrc = 97 effect sizes). The between-group analyses were twofold. One was a comparison of children with SCD to typical readers and another was a comparison of children with SCD to a comprehension-age matched group of children. Between-group comparisons of children with SCD to typical readers allowed for a test of the three hypotheses outlined previously: (a) children with SCD have comprehension problems that are specific to reading; (b) children with SCD have comprehension problems that are general to reading and oral language; or (c) children with SCD have comprehension problems that extend to oral language but are less severe for oral language than for reading. Between-group comparisons of children with SCD to a comprehension-age matched group allowed for a test of the delay versus deficit hypotheses for the anticipated oral language difficulties. A subsample of the original study sample ( n = 4) included comprehension-age matched groups for additional analyses ( k brc = 4 effect sizes for reading comprehension ; k bol = 30 effect sizes for oral language).

Within-child analyses require that both measures within a single study use the same scale. Thus, in order to be included within the within-child analysis, studies had to include standardized measures of reading comprehension and oral language and report standard scores, scaled scores, z -scores, or t -scores. Our within-child analyses allowed us to test the robustness of the pattern of results observed in the between-group comparison. That is, we were able to compare the reading comprehension and oral language skills within children who had SCD.

Meta-Analytic Methods

All analyses were conducted using Microsoft Excel (Version 14.0), and Metafor ( Viechtbauer, 2010 ) and Robumeta packages in R ( Fisher & Tipton, 2015 ). Effect sizes were calculated using Hedge's g (Hedges, 1981), which is Cohen's d ( Cohen, 1977 ) after incorporating a correction for small sample sizes. Negative effect size values indicate that children with SCD had a lower group mean than typically developing readers. In several instances, groups were vocabulary-matched (i.e., children with SCD were selected on the basis of having average vocabulary performance compared to a group of typical readers). 3

Average weighted effect sizes for each meta-analysis were calculated using random-effects models, which assume all parameters to be random as opposed to fixed ( Shadish & Haddock, 2013 ). We used random-effects models in the present investigation because Q (i.e., homogeneity of effect size; Hedges & Olkin, 1985 ) was rejected across most comparisons. For one comparison, Q was not rejected; for this meta-analysis, we used a fixed-effects model. We also estimated I 2 , which calculates the percentage of variance due to heterogeneity. We used random-effects models to calculate a 95% confidence interval (CI) in order to determine whether each calculated average weighted effect size was statistically significant (i.e., different from zero). A CI within random-effects models assumes systematic study variability (i.e., that differences across studies do not result from random sampling error; Shadish & Haddock, 2013 ). We additionally conducted an Egger test for funnel plot asymmetry within each meta-analysis as a means of testing whether publication bias was present (significant plot asymmetry) or absent (non-significant plot asymmetry; Egger, Smith, Schneider, & Minder, 1997 ).

Across meta-analyses, there were several instances in which a single study resulted in multiple effect size estimates. We used robust variance estimation with the small sample size correction to handle dependent effect sizes ( Hedges, Tipton, & Johnson, 2010 ; Tipton, 2015 ). This relatively recent approach has advantages over alternative approaches to handling dependent effect sizes such as including only one effect size per study, creating an average effect size, or using multivariate approaches to model the dependency. Robust variance estimation allows one to use all effect sizes including multiple ones from the same sample in the meta-analysis for estimating average weighted effect sizes and for testing possible moderators, then corrects for the effects of the dependencies in the significance testing. Although robust variance estimation can be implemented in macros to common statistical packages such as SPSS, an efficient way of doing so is by using the Robumeta package available in R ( Fisher & Tipton, 2015 ). We carried out meta-regressions analyses of potential moderators using Robumeta when there were dependent effect sizes. For meta-analyses that did not demonstrate dependency among effect size estimates (i.e., between group comparison of reading comprehension for children with SCD and comprehension-age matched children), we calculated the average weighted effect size estimate using traditional methods in Metafor.

A total of 86 independent studies were included within the analyses. Effect sizes for each comparison are reported in Table 1 (see also Appendices A, B, and C). A substantial portion of studies included English-speaking samples (Study n = 72). Fourteen studies included children who spoke Italian ( n = 5), French ( n = 3), Finnish ( n = 1), Hebrew ( n = 1), Chinese ( n = 2), Portuguese ( n = 1), and Spanish ( n = 1). Across studies, children were between the ages of 4 and 12 years.

Note. k = Number of effect sizes; d = Average-weighted effect size estimate; CI = Confidence interval; SCD = Children with specific reading comprehension deficits;

Effect Size Analyses

Comparisons of children with scd to typical readers.

We compared children with SCD to typical readers on measures of reading comprehension and oral language. These analyses served as a means to test whether: (a) children with SCD have comprehension problems that are specific to reading; (b) children with SCD have comprehension problems that are general to reading and oral language; or (c) children with SCD have comprehension problems that extend to oral language but are less severe for oral language than for reading.

Reading comprehension

One hundred and fifty-two comparisons were made for the reading comprehension of children with SCD and typically-developing readers (Study n = 84). Across studies, there were 17,600 children with SCD ( M = 209.53; SD = 703.14; range: 7-3,236) who were compared with 155,874 typically developing children ( M = 1,855.64; SD = 6,737.96; range: 8-29,676). The average weighted effect size was negative, large, and statistically significant (random-effects robust variance estimation: d = −2.78, 95%CI [−3.01, −2.54]). Because the CI does not include zero, this indicates that the effect size estimate is significantly different from zero. This suggests that children with SCD performed substantially poorer on measures of reading comprehension compared to their typically developing peers, which was expected. Study-specific effect sizes for reading comprehension, participant ages, and sample sizes for these comparisons are reported in Appendix A ; effect sizes are reported in descending order. There was a large variability in effect size estimates across studies due to heterogeneity, I 2 = 94.39 (see Table 1 ). Sensitivity analyses indicated that varying values of rho (ρ) from 0 to 1 in .20 increments did not affect tau squared (τ 2 ), the subsequent weights, and the average weighted effect size estimate. This outcome suggests that the observed effect size is fairly robust. An Egger test of funnel plot asymmetry was significant, z = −7.09, p < .0001 (see Figure 1 ), indicating asymmetry in effect size estimates across studies.

Funnel plots for between- and within-group comparisons. Note . RC = Reading comprehension; OL = Oral language; WM = Working memory; CAM = Reading-comprehension age-match.

Oral language

Three hundred and nine comparisons were made for the oral language skills of children with SCD and typically-developing children, (Study n = 76). There were 16,494 children with SCD ( M = 219.93; SD = 706.39; range: 7-3,016) who were compared with 144,857 typically developing children ( M = 1,931.43; SD = 6,676.47; range: 8-28,970). The average weighted effect size was also negative, large, and statistically significant (random-effects robust variance estimation: d = −0.78, 95% CI [−0.89, −0.68]). Thus, when compared to children without comprehension problems, children with SCD additionally exhibit difficulty completing oral language tasks; however this deficit was not as severe as for reading comprehension. Study-specific effect sizes for oral language, participant ages, and sample sizes for these comparisons are reported in Appendix A ; effect sizes are reported in descending order. Variability due to heterogeneity was large across studies, I 2 = 85.55 (see Table 1 ). Sensitivity analyses indicated that the observed effect size is fairly robust; varying values of ρ resulted in no differences. An Egger test of funnel plot asymmetry was significant, z = −2.11, p < .05 (see Figure 1 ), suggesting some asymmetry in estimates. Additionally, we also examined verbal working memory for studies that were already included in the analysis, which added 91 additional comparisons to the analysis. The average weighted effect size remained negative, large, and statistically significant (random-effects robust variance estimation: d = −0.77, 95% CI [−0.87, −0.67]; I 2 = 85.12; see Table 1 ).

It is important to note that across comparisons of reading comprehension and oral language, different studies were available for analyses; however, when we analyzed only overlapping studies (Study n = 74), the effects for reading comprehension (random-effects robust variance estimation: d = −2.80, 95% CI [−3.05, −2.55]; I 2 = 94.68) and oral language were nearly identical (random effects robust variance estimation: d = −0.79, 95% CI [−0.90, −0.68]; I 2 = 85.50).

Comparisons of children with SCD to comprehension-age matched readers

Given that we found evidence that children with SCD do exhibit deficits in oral language, we were additionally interested in how such deficits were best characterized. Thus, we conducted a between-groups meta-analysis that compared the performance of children with SCD to younger comprehension-age matched readers. Children in the comprehension-age matched group were selected on the basis of having performance equivalent to children with SCD (see Cain et al., 2000 ). 4 Across studies, children within the comprehension-age matched group were approximately two years younger than children with SCD.

Four comparisons were made for the reading comprehension skills of children with SCD and comprehension-age matched control children (Study n = 4). There were 73 children with SCD ( M = 18.25; SD = 7.23; range: 14-29) compared with 68 typically-developing children across studies ( M = 17.00; SD = 6.78; range: 14-27). Study-specific effect sizes for reading comprehension, participant ages, and sample sizes for these comparisons are reported in Appendix B ; effect sizes are reported in descending order. The average weighted effect size was moderate and negative, but it was not statistically significant (fixed-effects: d = −0.31, 95% CI [−0.31, 0.02)]; Q (3) = .38, p = .94, I 2 = <1%; see Table 1 ). This outcome was expected given that the two groups were matched for reading comprehension performance. An Egger test of funnel plot asymmetry was non-significant, z = −.13, p = .90 (see Figure 1 ).

Thirty comparisons were made for the oral language skills of children with SCD and children within comprehension-age matched groups (Study n = 4). There were 73 children with SCD ( M = 18.25; SD = 7.23; range: 14-29) and 68 typically-developing children across studies ( M = 17.00; SD = 6.78; range: 14-27). The average weighted effect size was moderate and in favor of comprehension age-matched readers, but it was not statistically significant (random-effects robust variance estimation: d = 0.32, 95% CI [−0.49, 1.14]). These findings suggest that the oral language performance of children with SCD is similar to the performance of younger typical readers. In other words, there is a developmental delay in the oral language skills of children with SCD. Study-specific effect sizes for oral language, participant ages, and sample sizes for these comparisons are reported in Appendix B ; effect sizes are reported in descending order. Across studies, the variability due to heterogeneity was relatively high, I 2 = 77.13 (see Table 1 ). Sensitivity analyses indicated that the observed effect size was quite robust; varying values of ρ resulted in a .02 difference (τ 2 =.402 when ρ = 0; .423 when ρ = 1), which was minimal. However, because the degrees of freedom for these analyses were less than four, it is important to interpret these results cautiously ( Fisher & Tipton, 2015 ). An Egger test of funnel plot asymmetry was non-significant, z = −0.71, p = .48 (see Figure 1 ).

Within-child comparisons of reading comprehension and oral language for children with SCD

In addition to comparing the language and literacy skills of children with SCD to typically-developing readers and comprehension age-matched readers, we also compared the oral language skills to reading comprehension within children who have SCD. The aim of this meta-analysis was so test to robustness of the results (i.e., would the same pattern of findings emerge if comparisons were made for the same group of children [within-group] as opposed to comparisons across different groups [between-group]). Thus, we additionally conducted analyses that examined the reading comprehension and oral language skills within individuals.

Ninety-seven comparisons were included within the analysis (Study n = 32). There were 12,711 children with SCD ( M = 397.22; SD = 822.21; range:7-2,830). Because these analyses included children with SCD, we corrected correlations for range restriction using Thorndike's (1949) correction equation. 5 The average weighted effect size was moderate, negative, and statistically significant (random-effects robust variance estimation: d = −0.84, 95% CI [−1.06, −0.62]), which indicates that the reading comprehension skills of children with SCD are significantly weaker than their oral language skills. These results can be found in Table 1 . Study-specific effect sizes, participant ages, and sample sizes for these comparisons are reported in Appendix C ; effect sizes are reported in descending order. Across studies, the variability due to heterogeneity was substantial, I 2 = 96.06. However, sensitivity analyses indicated that the observed effect size was fairly robust; varying values of ρ resulted in no difference in estimates of τ 2 . An Egger test of funnel plot asymmetry was non-significant for these comparisons, z = 1.33, p = .18 (see Figure 1 ).

It is important to note that different sets of studies were included within our analyses of between-group and within-child comparisons. This may explain why the difference between reading comprehension and oral language performance within children ( d = −0.84) was not equivalent to the differences found between groups for reading comprehension and oral language (effect size difference between −2.78 and −0.78 was −2.00). We empirically tested this by analyzing only those studies that were included within the between-group reading comprehension (random-effects robust variance estimation: d = −2.73, 95% CI [−3.05, −2.42]; I 2 = 96.82) and oral language comparisons (random-effects robust variance estimation: d = −0.95, 95% [CI −1.06, −0.83]; I 2 = 91.00) and the within-child comparisons. Applying this method, we achieved anoticeable reduction in the effect size differences across comparisons (effect size difference between −2.73 and −0.95 was 1.78). This outcome may be a partially due to the absence of publication bias within the within-group comparisons relative to the potential presence of publication bias within the reading comprehension and oral language comparisons.

Moderator Analyses

Metaregressions of study type, age, and oral language measures for comparisons of children with scd to typical readers.

Due to the substantial amount of heterogeneity across studies, we were interested in examining three possible moderators – age, type of oral language measure, and study type (i.e., published journal article, book chapter, thesis/dissertation, unpublished data) – that may explain effect size differences among various studies (see Table 1 and Appendices D and E ). Due to the dependency of effect sizes across studies, we used robust variance estimation to conduct moderator analyses for the present comparisons.

Study type, β = .14, p > .05, t (11.8) = 1.05, was not a significant moderator of differences in effect size estimates for reading comprehension for comparisons of children with SCD to typical readers. However, age, β = −.47, p < .05, t (23.9) = −2.53, was a significant moderator of effect size differences. Next, we examined moderators for comparisons of oral language. Neither study type nor age were significant moderators of differences in effect size outcomes for oral language, β = −.04, p > .05, t (17) = −0.77 for study; β = −.06, p > .05, t (20.1) = −0.85 for age. Because oral language was assessed using different measures across studies, we also conducted a metaregression to examine the potential for differences in oral language measures to be a moderator of effect size outcomes. Because oral language varied both within and across studies, it is important to include both the mean (i.e., between-study covariate) and mean-centered predictors (i.e., within-study covariate) within the moderator analyses to account for the potentially hierarchical structure of the effect size dependencies ( Fisher & Tipton, 2015 ). Using this method, type of oral language measure was not a significant moderator of effect size across studies, β m = −.05, p > .05, t (16.5) = −0.91; β mc = .00, p > .05, t (16.9) = 0.02.

Metaregressions of study type, age, and oral language measures for comparisons of children with SCD to comprehension-age matched readers

We also examined potential moderators within our reading comprehension age-matched comparisons (see Table 2 ). Similar to our between group comparisons, the type of oral language measure, β m = −.10, p > .05, t (1.08) = −0.18; β mc = −.23, p > .05, t (1.20) = −1.05, was not a significant moderator of effect size for the oral language comparisons. 6 However, because the degrees of freedom were less than four, this finding should be interpreted cautiously. Study type and the age range of participants was constant across studies, thus negating the need to conduct moderator analyses for these constructs for the reading comprehension and oral language comparisons.

Note . SCD = Children with specific reading comprehension deficits;

Metaregressions of study type, age, and oral language measures for within-child comparisons

We examined the moderators of study type, age, and oral language measure within our within-group comparisons as well, which are summarized in Table 2 . Study type was a significant predictor of differences in effect size, β = −.24, p < .01, t (15.3) = −2.77. Similarly, type of oral language measure was a significant predictor at the mean, β m = .20, p < .01, t (15.40) = 2.35; β mc = −.03, p > .05, t (8.30) = −0.85. Age, however, was a non-significant predictor in the model, β = −.00, p > .05, t (12.9) = −0.02.

The aim of the present meta-analysis was to determine the nature of the comprehension problems for children with SCD. This investigation was guided by three competing hypotheses: (a) children with SCD have comprehension deficits that are specific to reading; (b) children with SCD have comprehension deficits that are general to reading and oral language; or (c) children with SCD have comprehension problems that extend beyond reading but are more severe for reading than for oral language. The findings of the present meta-analysis support the third hypothesis. Children's weakness in oral language was substantial ( d = −0.78), but not as severe as their deficit in reading comprehension ( d = −2.78). The effects size estimates for oral language were comparable regardless of whether verbal working memory was included in the analysis ( d = −0.77). Within-child comparisons also indicated that performance in reading comprehension was worse than for oral language ( d = −0.84). The pattern of poorer performance in reading comprehension compared to oral language was consistent across all analyses.

When compared to comprehension age-matched readers, children with SCD tended to have comparable oral language ( d = 0.32, ns ) and reading comprehension skills ( d = −0.31, ns ). The fact that older children with SCD did not differ from younger normal readers on reading comprehension was expected rather than informative because the groups were matched on reading comprehension. However, the fact that they did not differ in oral language is informative. It supports the idea that the oral language weaknesses for children with SCD are best characterized as arising from a developmental delay as opposed to a developmental deviance ( Francis et al., 1996 ). A developmental deviance would have been supported had the oral language performance of the older children with SCD been worse than that of the younger comprehension-age matched normal readers.

Overall, our results are consistent with previous investigations. Children with SCD perform poorly on a range of oral language assessments including receptive and expressive vocabulary knowledge, listening comprehension, story structure, knowledge of idioms, awareness of syntactic structure, and morphological awareness among others ( Cain, 2003 ; Cain, 2006 ; Cain & Oakhill, 1996 ; Cain et al., 2005 ; Carretti et al., 2014 ; Nation & Snowling, 2000 ; Oakhill et al., 1986 ; Stothard & Hulme, 1996 ; Tong et al., 2011 , 2014 ; Yuill & Oakhill, 1991 ). These weaknesses emerged despite children's adequate decoding and seemingly intact phonological processing abilities ( Nation & Snowling, 2000 ; Nation et al., 2007 ; Stothard & Hulme, 1992 ). Yet, this pattern makes sense given that phonological processing appears to underlie decoding ability ( Nation et al., 2007 ; Shankweiler et al., 1999 ; Stothard & Hulme, 1996 ).

Explanations for Greater Deficits in Reading Comprehension than in Oral Language

A number of possible explanations for the observed discrepancies between reading comprehension and oral language exist. Although it is not possible to test alternative explanations in the context of the present meta-analysis, they could be tested in future studies.

A latent decoding deficit

At first glance, it seems counterintuitive that a decoding deficit would explain comprehension differences in children with SCD. However, in several studies, only decoding accuracy was used to categorize children (e.g., Cain & Oakhill, 2006 ). It is possible to be adequate in decoding accuracy yet inadequate in decoding fluency. In fact, this is a common outcome of intervention studies (e.g., de Jong & van der Leij, 2003; Torgesen & Hudson, 2006 ). The effortful application of phonics rules or other decoding strategies can result in accurate but slow decoding. This could impair reading comprehension because children's reading would be less automatic ( LaBerge & Samuels, 1974 ) and/or because fewer cognitive resources would be available for comprehension (e.g., Perfetti, 1985 ). This possible explanation could be tested in future studies by using measures of decoding fluency as well as accuracy. A dual-task paradigm could also be used to determine whether the cognitive resources required by decoding were comparable for children with and without SCD.

Differences between written and oral language

Written language differs from oral language in important ways ( Perfetti et al., 2005 ). Written language oftentimes contains more complex sentence structures and more difficult vocabulary than spoken language ( Akinnaso, 1982 ; Halliday, 1989 ). Thus, if children are having difficulty completing tasks that require the use of syntactic knowledge, for instance, they will most likely have difficulty reading grammatically complex texts. Fundamental differences between written and spoken text may also extend to increased demands on background knowledge (e.g., Wolfe & Woodwyck, 2010 ). Background knowledge has been identified as a critical component within several models of reading comprehension ( Kintsch, 1988 ; Kintsch, & van Dijk, 1983 ; Rumelhart, 1980 ). For instance, Kintsche and van Dijk's (1983) situation model describes the comprehension process as arising from an interaction of three mental models: the reader's text representation, semantic or meaning-based representation, and situational representation (i.e., prior knowledge, experiences, and interest).

There is also empirical evidence for the importance of background knowledge in reading comprehension (e.g., Stahl, Hare, Sinatra, & Gregory, 1991 ). This may explain why children with SCD also have problems with elaborative inference making and comprehension monitoring ( Cain et al., 2001 ; Oakhill, 1984 , 1993 ; Oakhill & Yuill, 1996 ). Further, differences in the amount of background knowledge required across oral language and reading comprehension tasks may explain the present pattern of skill deficits. This explanation could be tested in future studies by having children perform reading comprehension and listening comprehension tasks on identical passages and have the tasks counterbalanced across two groups. However, deficits in background knowledge may not sufficiently explain why children have SCD. In some instances, children with SCD continue to perform below expectations even after background knowledge is controlled (e.g., Cain & Oakhill, 1999 ; Cain et al., 2001 ).

Regression to the mean

Another potential explanation for the discrepancy between the reading comprehension and oral language skills of children with SCD is regression to the mean. Across studies, children were selected on the basis of poor reading comprehension. This design can lead to an over-representation of children whose observed reading comprehension score is below their true score. Consequently, they will regress to their true score on almost any subsequent measure that is correlated with the original measure. In the present context, children who were selected on the basis of poor reading comprehension may perform less poorly on oral language due to regression to the mean. Future studies could test this hypothesis by administering a second reading comprehension measure and then comparing performance on this measure to oral language. Using another design that does not involve selection based on poor reading comprehension performance would also be helpful to rule out this explanation.

Theoretical and Practical Implications of the Findings

We began this article with a review of theories of reading comprehension. We now consider the implications of our results for the theories that we reviewed. We first consider our results within the simple view of reading framework. ( Gough & Tunmer, 1986 ; Hoover & Gough, 1990 ). Based on this framework, the view is that reading comprehension is the product of decoding and oral language comprehension. Our results are not consistent with the common version of the simple view in which reading comprehension is predicted by additive effects (i.e., main effects) of decoding and oral language comprehension. If the simple view is operationalized as the interaction (i.e., multiplicative effects) between decoding and oral language comprehension, however, the results could be considered consistent with this framework. Essentially, the oral language deficit of children with SCD interacts with their decoding to produce reading comprehension that is more impaired than would be accounted for by the simple main effects. This same logic would apply to interactive activation models of reading to the extent that the interactive activation is truly interactive.

As is emphasized by the simple view and interactive models of reading comprehension, oral language is a critical component of reading comprehension. This assertion is supported by the current findings and previous studies ( Kendeou et al., 2009 ; Roth et al., 2002 ). For instance, two studies included within the present meta-analysis, Catts et al. (2006) and Nation et al. (2004) , found that a substantial portion of children who are identified as having specific language impairment (SLI) also have coexisting reading comprehension difficulties. In both investigations, 30% or one-third of children with SCD were eligible for SLI identification. Even children who were not identified as having SLI were identified as having subclinical levels of poor language comprehension (Catts et al.). Children with SCD had very poor performance on the vocabulary measure and grammatical understanding task. Catts et al. and Nation et al. referred to this subclinical poor language comprehension as hidden language impairment because these children are not typically classified as having SLI. Yet, these impairments could still potentially lead to the comprehension problems observed in these children.

If we allow for the possibility of a latent decoding problem, then nearly all of the theories of reading comprehension could account for the pattern of results that were obtained. Similarly, if we allow for the possibility of differences between written and oral language, the results would be consistent with multiple theories of reading. It will be important to carry out research to determine the best explanation for the pattern of a greater deficit in reading comprehension than in oral language. The outcome of this research will potentially affect implications for theories of reading. For example, if the pattern of a greater deficit in reading comprehension than in oral language is found when (a) groups are matched on decoding fluency as well as accuracy, (b) the reading and oral language tasks are for equivalent material, and (c) the study design eliminates the possible confound of regression to the mean, the results would only be consistent with a theory of reading that had an interactive component in addition to whatever main effects might be represented.

The implications for practice are threefold. First, the results suggest that early oral language measures may serve as a means of identifying children who are at risk for later reading comprehension problems ( Cain & Oakhill, 2011 ; Justice et al., 2013 ; Kendeou et al., 2009 ; Nation & Snowling, 2004 ; Nation et al., 2010 ; Roth et al. 2002 ). Oral language weaknesses for children with SCD are evident fairly early on, are relatively stable over time, and are predictive of future reading comprehension performance (e.g., Cain & Oakhill, 2011 ; Justice et al., 2013 ; Nation et al., 2010 ). Thus, oral language measures can potentially serve as a screening method to identify which children have weaknesses in language skills. However, this must be approached cautiously because not all oral language measures are equally predictive of a child's future reading comprehension status. For instance, Tong et al. (2011) gave children with SCD morphological tasks that assessed derivational morphological awareness. Performance of readers with SCD in Grade 3 did not significantly differentiate children with SCD from those with normal reading comprehension in Grade 5. Yet, performance on this morphological task in Grade 5 did result in significant differences between the two groups. This suggests that measures of derivational morphological awareness, for instance, may not be ideal for assessing early oral language skills (see Nippold & Sun, 2008 ). Consequently, it is important to consider this when selecting potential screening measures.

Second, the findings suggest that children with deficits in critical oral language skills should receive targeted oral language instruction and intervention. Intervention studies focusing specifically on children with SCD have indicated that interventions containing an oral language component are more effective. For example, Clarke, Snowling, Truelove, and Hulme (2010) randomly assigned three groups of 8- and 9-year-olds with SCD to receive three different types of interventions: text comprehension training, oral language training (without reading or writing), and a combined text comprehension-oral language training format. All three groups showed reliable and statistically significant gains in reading comprehension compared to the control group; however, the group that received the oral language training maintained the greatest gains after an 11-month follow up (for a review, see Snowling & Hulme, 2012 ). These outcomes are also aligned with the findings of the present review. Thus, classroom instruction and intervention that incorporate elements that encourage comprehension proficiency, such as reading fluency ( NICHD, 2000 ) and oral language ( Snow et al., 1998 ), will likely be more effective at remediating reading comprehension difficulties.

Third, the current investigation highlights the need to develop a consistent operational definition of SCD (see Rønberg & Petersen, 2015 ). For studies included in the present investigation, there were multiple ways in which children with SCD were identified. Differences in identification criteria are potentially problematic because it can lead to over- or under-identification. Such differences can also potentially lead to different groups of children being identified as having SCD over time. Yet, variability in identification criteria is not exclusive to the present population of poor readers. There remains much discourse about this issue more broadly within the field of learning disabilities ( Mellard, Deshler, & Barth, 2004 ).

Limitations and Future Directions

There are several limitations of the present meta-analysis that must be addressed. First, the present review focused specifically on monolingual school-age children. Consequently, the results may not apply to second-language learner or adult populations. Second, several studies included in the present review used the Neale Analysis of Reading to assess reading comprehension and decoding ability without incorporating an additional measure of either skill. This is potentially problematic because both decoding and comprehension scores are obtained simultaneously as children read passages. Decoding problems could therefore affect comprehension scores (see Spooner et al., 2004 ). Third, we did not examine the effect of IQ on the obtained effect size estimates. It may be the case that variability in IQ may affect effect size outcomes. Fourth, it is important to acknowledge the potential presence of some publication bias for the between-group comparisons of reading comprehension and oral language. This may contribute to the larger deficits seen between these skills.

Another limitation of this meta-analysis is that it does not address possible causal relations between the deficits in oral language and reading comprehension. It is certainly possible that poor oral language skills may contribute to the deficits in reading comprehension; children must know a substantial portion of the words in a text in order to comprehend it ( Hu & Nation, 2000 ; Kendeou et al., 2009 ). However, it is also possible that poor reading comprehension constrains future vocabulary growth because text reading provides a basis for incidental word learning ( Cain et al., 2004 ). These relations may also be reciprocal (e.g., Wagner, Muse, & Tannenbaum, 2007 ). Additionally, the general absence of longitudinal data did not allow for a more comprehensive examination of the developmental delay versus deficit hypotheses. A final limitation of the present study is that it was limited to children who were monolingual speakers of their native language. It is increasingly common for children to know more than one language. Would the results of the present meta-analysis generalize to children who were second-language learners? We decided to answer this question by carrying out a similar meta-analysis of children with poor reading comprehension yet adequate decoding, but for children who were second-language learners ( Authors, 2017 ). Sixteen studies were identified that met inclusionary and exclusionary criteria. Hedge's g was used as the effect-size measure, random-effects models were used, and robust variance estimation was used to correct significance testing for dependent effect sizes. The results were remarkably consistent with those of the present meta-analysis. A deficit in oral language was replicated with an average weighted effect size of -0.80. The pattern of the deficit in oral language being only about a third as large as the deficit in reading comprehension was also replicated, with an average weighed effect size of -2.47. In summary, the pattern of results found in the present meta-analysis of studies whose participants were monolingual children generalize to children who are second language learners.

In conclusion, children who have SCD are typically impaired in oral language, but not to the degree they are impaired in reading comprehension. Consequently, the oral language impairment is not sufficient to explain the impairment in reading comprehension. Possible explanations for this pattern of results were considered, including a latent decoding deficit, differences between written and oral language, regression to the mean, and interactive effects. Testing these alternative explanations and others that might be considered represents a critical next step to advance our understanding of an important problem in reading.

Acknowledgments

This research was supported by Grant Numbers P50 HD52120 and 1F31HD087054-01 from the National Institute of Child Health and Human Development, Grant Numbers R305F100005 and R305F100027 from the Institute for Education Sciences, and a Predoctoral Interdisciplinary Training Grant Number R305B090021 from the Institute for Education Sciences.

Appendix A. 

Study descriptions and effect size estimates for children with specific reading comprehension deficits and typical readers (Study n = 86).

Note. RC = Reading comprehension; OL = Oral language; SCD = Children with specific reading comprehension deficits; TR = Typical readers.

Appendix B. 

Study descriptions and effect sizes for children with specific reading comprehension deficits compared with comprehension-age matched readers (Study n = 4).

Appendix C. 

Study descriptions and effect size estimates for within-child comparisons (Study n = 32).

Appendix D. 

Coding scheme for study type, participant age, and type of oral language measure.

Appendix E. 

Types of oral language skills assessed across studies (Study n = 86).

Note. For some studies, oral language was assessed but not explicitly reported.

2 For some comparisons, this comparison included skilled comprehenders.

3 Although groups were matched, correlations for the same measure between the two groups were not reported in most instances; thus, independent effect sizes were calculated.

4 Although groups were matched, correlations for the same measure between the two groups were not reported in most instances; thus, independent effect sizes were calculated.

5 In several instances, studies did not report correlations. For these studies, an estimated correlation was substituted.

6 We also conducted moderator analyses for type of oral language measure without accounting for hierarchical structure and the results remained the same [β #x0003D; −0.31, p > .05, t(1.40) = −0.98].

References marked with an asterisk indicate studies included in the meta-analysis. The in-test citations to studies selected for meta-analysis are not preceded by asterisks.

ORIGINAL RESEARCH article

The overlap of poor reading comprehension in english and french.

• 1 Ontario Ministry of Education, Toronto, ON, Canada
• 2 Department of Applied Psychology & Human Development, Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada

This study examined overlap and correlates of poor reading comprehension in English and French for children in early French immersion. Poor comprehenders were identified in grade 3 in English and French using a regression method to predict reading comprehension scores from age, non-verbal reasoning, word reading accuracy, and word reading fluency. Three groups of poor comprehenders were identified: 10 poor comprehenders in English and French, 11 poor comprehenders in English, and 10 poor comprehenders in French, and compared to 10 controls with good reading comprehension in both English and French. There was a moderate degree of overlap in comprehension difficulties in English and French among poor comprehenders with equivalent amounts of exposure to French, with a prevalence rate of 41.7% in our sample. Children who were poor comprehenders in both English and French consistently scored the lowest on English vocabulary in grade 1 and grade 3 and in French vocabulary in grade 3 suggesting that poor comprehenders’ vocabulary weaknesses in English as a primary language may contribute to comprehension difficulties in English and French.

Introduction

There is considerable evidence to suggest that children who are at risk for reading difficulties in a second language (L2) can be identified through early assessment of word reading and cognitive skills in their first language (L1), before their oral language proficiency is fully developed in L2 ( Geva and Clifton, 1994 ; Da Fontoura and Siegel, 1995 ; MacCoubrey et al., 2004 ). Much of this previous research is based on the premise that certain cognitive and linguistic skills, such as phonological processing, transfer across languages (e.g., Comeau et al., 1999 ; August and Shanahan, 2006 ). More recently, studies have investigated children’s reading comprehension difficulties that occur despite age-appropriate decoding skills (e.g., Nation et al., 2010 ; Tong et al., 2011 ). Relatively little is known about the identification of poor reading comprehension in the absence of poor decoding, and even less is known about whether reading comprehension difficulties manifest in a similar manner in L1 and L2 for children learning in a bilingual context. The present study aims to investigate overlap and early contributors of poor reading comprehension for children in early French immersion programs in Canada who receive school instruction in French, an additional language, while being exposed to English, their primary language of the community.

Reading comprehension is a complex process that involves the integration and coordination of various skills, including word decoding, the ability to decipher or recognize printed words, and oral language or listening comprehension, the ability to understand what is decoded in spoken form (Simple View of Reading; Gough and Tunmer, 1986 ). Most research into reading comprehension difficulties has focused on children with poor decoding whose weaknesses manifest early in reading development as phonological awareness and word reading deficits (e.g., Snowling, 2000 ). In contrast to poor decoders, poor comprehenders’ difficulties appear to emerge later, when decoding becomes automatized and more variance in reading comprehension is accounted for by oral language skills ( Catts et al., 2012 ). Oral language difficulties tend to be masked by poor comprehenders’ age-appropriate decoding skills, and as a result, early indicators of later reading comprehension difficulties are often overlooked.

Existing longitudinal studies have used a retrospective approach to examine poor comprehenders’ deficits across previous grades and suggest that oral language weaknesses are prevalent in poor comprehenders before their reading comprehension difficulties become apparent ( Catts et al., 2006 ; Nation et al., 2010 ; Tong et al., 2011 ). For example, Nation et al. (2010) identified poor comprehenders based on reading achievement at age 8 and retrospectively examined their reading and language skills beginning at age 5. While poor comprehenders’ phonological processing and word reading skills progressed over time, their oral language skills remained persistently weak, suggesting that early weaknesses in understanding and producing spoken language contributed to poor comprehenders’ comprehension difficulties.

The linguistic interdependence hypothesis suggests that L1 and L2 reading skills are interdependent, and that language and literacy skills acquired in one language facilitate reading development in the L2 ( Cummins, 1984 ). Thus, it seems probable that the same cognitive and linguistic skills needed for successful reading comprehension in L1 contribute to reading development in L2 (e.g., Gottardo and Mueller, 2009 ; Mancilla-Martinez and Lesaux, 2010 ). Indeed, previous research suggests that it is possible to identify children at-risk for L2 reading difficulties based on their performance in L1 ( Geva and Clifton, 1994 ; Da Fontoura and Siegel, 1995 ). However, few studies have investigated poor comprehenders in a bilingual context largely due to the complexity of understanding reading comprehension processes in L1 and L2. Children learning in an L2 are in the process of acquiring the language of instruction and it may be difficult to determine whether weaknesses in L2 reading comprehension reflect limited language learning experiences or are indicative of a language or reading impairment ( Paradis et al., 2010 ; Li and Kirby, 2014 ; D’Angelo and Chen, 2017 ).

Li and Kirby (2014) examined the reading comprehension profiles of grade 8 emerging Chinese-English bilinguals in an English immersion program in China. Poor comprehenders were distinguished from average comprehenders based on their performance on English L2 vocabulary measures. The authors concluded that because the groups did not differ on Chinese L1 word reading and reading comprehension, poor comprehenders’ reading comprehension difficulties were due to limited English L2 proficiency. However, the comprehender groups in this study were selected using English L2 assessments only and therefore, children with an underlying oral language impairment across the two languages could not be identified. Since Chinese and English and are not closely related languages, vocabulary and reading comprehension may not have the same underlying mechanisms in each language.

A few studies have identified poor comprehenders based on English L1 reading performance in a French immersion context and suggest that poor comprehenders demonstrate relatively poor oral language skills in both English L1 and French L2 (e.g., D’Angelo et al., 2014 ; D’Angelo and Chen, 2017 ). D’Angelo et al. (2014) retrospectively investigated the reading and language abilities of a small sample of English L1 children in French immersion who were identified as poor and average comprehenders based on their English L1 reading performance in grade 3. They found that poor comprehenders scored relatively lower on English and French vocabulary across grades 1 to 3, despite average phonological awareness and word reading skills in both languages. Such findings suggest that poor comprehenders may indeed have an underlying problem in oral language. The current study extends the existing research to a larger, more representative sample of children in French immersion to facilitate comparison. The purpose is to determine the extent to which those identified as having poor reading comprehension in English, the societal language, also demonstrate poor reading comprehension in French, an additional language and the language of instruction.

Studies that have examined the co-occurrence of reading difficulties between an L1 and L2 have primarily focused on poor readers and suggest that there is some overlap of reading difficulty in L1 and L2 ( Manis and Lindsey, 2010 ; McBride-Chang et al., 2013 ; Tong et al., 2015 ; Shum et al., 2016 ). For example, Manis and Lindsey (2010) found that 55% of grade 5 children who met the criteria for reading difficulties in English L2 (decoding scores at or below the 25 th percentile) were also identified with reading difficulties in Spanish L1. Similarly, McBride-Chang et al. (2013) tested the overlap of poor readers in Chinese L1 and English L2 (defined as those at or below the 25 th percentile on Chinese and English word reading tests) among 8-year-old children in Beijing and found that 40% of poor readers in Chinese L1 were also poor readers in English L2. In each study, children who were identified as poor readers in both languages scored lower on cognitive and linguistic tasks than children who were poor readers in only one language. On the other hand, children with poor reading in one language did not necessarily have difficulties in the other. It appears that the degree of overlap between poor reading is increased when the two languages are more closely related. However, these studies focused on the overlap status of poor readers based on poor decoding. We were interested in whether such overlap occurs for poor comprehenders who show discrepancies between their reading comprehension and decoding skills.

Only one known study at this time has explored the overlap between L1 and L2 reading comprehension difficulties. Tong et al. (2017) examined the co-occurrence of reading comprehension difficulties and associated longitudinal correlates in 10-year-old children with poor reading comprehension (defined as those at or below the 25 th percentile on reading comprehension tasks) in Chinese L1 and English L2. The authors found that approximately half (53%) of children with poor reading comprehension in Chinese L1 also experienced poor reading comprehension in English L2. Results indicated that word reading and language skills were longitudinal correlates of poor reading comprehension in Chinese and English. This study was among the first to investigate overlap of reading comprehension difficulties in L1 and L2 and to retrospectively examine sources of poor reading comprehension. However, the selection method used in this study identified poor comprehenders based on reading comprehension scores only and did not distinguish between children with poor oral language skills from those with poor decoding skills. In the present study, we aimed to understand the overlap of poor reading comprehension in English and French in the absence of decoding problems.

Given the challenges associated with defining poor reading comprehension in an additional language, the goal of the present study was to extend previous research on reading comprehension difficulties to English–French bilinguals to answer two specific research questions.

First, we asked whether children identified as poor comprehenders in English are also identified as poor comprehenders in French. Whereas most previous studies have examined overlap with word reading and reading comprehension scores at or below an arbitrary cut-off score, we utilized a regression technique to identify poor comprehenders in English and French by examining associations between reading comprehension scores, age, non-verbal reasoning, word reading accuracy, and word reading fluency. This approach defines groups more precisely than the cut-off score method because it examines relative discrepancies between various skills related to reading comprehension by distinguishing poor comprehenders from average and good comprehenders (e.g., Tong et al., 2011 , 2014 ; Li and Kirby, 2014 ; D’Angelo and Chen, 2017 ).

Second, we asked what reading and language skills distinguish between poor comprehenders in English and French, poor comprehenders in English, and poor comprehenders in French. We anticipated that children identified as poor comprehenders in both English and French would show early and persistent oral language difficulties in both languages. English and French share many similarities in vocabulary, morphology, and syntax (e.g., LeBlanc and Seguin, 1996 ; Roy and Labelle, 2007 ; D’Angelo and Chen, 2017 ; D’Angelo et al., 2017 ). Both are represented by the Roman alphabet and an opaque writing system ( Seymour et al., 2003 ). These shared structural properties are thought to facilitate cross-language associations between two languages ( Koda, 2008 ). Therefore, we expected to see similar characteristics of reading comprehension difficulties between the two languages.

The socio-linguistic and educational context of the current study makes it possible to assess and compare English and French reading outcomes among children acquiring both languages. In Canada, French immersion is an additive dual language program that promotes oral and written language proficiency in both English and French, the official languages. Children in early French immersion programs are non-francophones who receive integrated language and content instruction primarily in French beginning in kindergarten or grade 1. However, these children often live in predominantly English-speaking environments with limited opportunity to hear and speak French outside of the classroom. Thus, French immersion classrooms are comprised of English-speaking children for whom French is the L2 and minority language children for whom English is the L2 and French the L3. English language arts instruction is generally introduced in grade 4.

Since the children in this study had similar and limited levels of French proficiency upon school entry, any differences in French reading and language abilities between children would be unlikely a result of differences in the amount of exposure the children had to French. Specifically, for children with poor reading comprehension in both English and French, we could be confident that weaknesses in oral language reflect a pervasive language impairment rather than a less developed French proficiency.

Materials and Methods

Participants.

Participants were 180 children consisting of 83 males and 97 females who were recruited from early French immersion schools in a large Canadian city and tested in English and French in the spring of grade 1 ( M age = 80.36 months, SD = 4.18) and grade 3 ( M age = 104.66 months, SD = 4.06). As part of the inclusion criteria, children selected for this study were non-native speakers of French receiving school instruction entirely in French since school entry. Out of the 180 children, 135 (75%) spoke English as a primary language. Forty-five children (25%) were exposed to additional languages at home.

The data in this study are from longitudinal research, in which several reading-related tasks were administered to participants between grades 1 and 3. Trained research assistants, who were fluent in the respective test language, administered tasks to participants at school. English and French instructions were used for French measures to ensure comprehension of the task. The order of the sessions was counterbalanced across participants and within each session the order of the task administration was randomized. Due to limited testing time, not all the same tasks were administered in each year of the study.

Non-verbal Reasoning

Children were administered the reasoning by analogy subtest of the Matrix Analogies Test in English to assess non-verbal reasoning in grade 1 (expanded form; Naglieri, 1985 ). For each item, children were asked to complete a figural matrix by choosing the missing piece from 5 to 6 possible choices. There were 16 items and testing was discontinued after four consecutive errors.

Phonological Awareness

This task was measured in grade 1 using the elision subtest of the Comprehensive Test of Phonological Processing (CTOPP; Wagner et al., 1999 , 2013 ). The examiner read individual words aloud and children were asked to delete a syllable or phoneme from each word (e.g., “say time without saying/ m /”). There were 34 test items presented in order of increasing difficulty. Testing was discontinued after three consecutive errors.

A parallel measure was created to assess phonological awareness in French. Twenty-six items were selected to match characteristics of the English task (i.e., syllable and phoneme deletion) and presented in order of increasing difficulty. The administration of the test was discontinued if the children made six consecutive errors.

The Peabody Picture Vocabulary was used to measure English receptive vocabulary (PPVT-IV Form A; Dunn and Dunn, 2007 ) in grades 1 and 3. Each time a tester orally presented a target word, the child was required to point to one of four pictures that best corresponded to that word. Testing was discontinued when the child made eight or more errors in a set of 12.

The Échelle de Vocabulaire en Images Peabody (EVIP Form A; Dunn et al., 1993 ) was used to assess French receptive vocabulary in both grades. The examiner read a target word and the child was asked to identify the picture that best represented the word from a set of four pictures. Testing was discontinued after six errors were made on the previous eight consecutive items.

Word Reading Accuracy

Word reading accuracy in English was assessed in grades 1 and 3 with the Letter-Word Identification subtest from the Test of Achievement, Woodcock Johnson-III (WJ-III; Woodcock et al., 2001 ). Children were asked to read a series of 76 letters and words that were presented in order of increasing difficulty. Testing was discontinued after participants misread the six consecutive highest-numbered items on a given page.

French word reading accuracy was assessed using an experimental task ( Au-Yeung et al., 2015 ). The test consists of 120 items arranged in 15 sets of eight words each. The children were asked to read the words accurately and fluently. Testing was discontinued when the children misread five or more words within a set of eight words. The total score represents the number of words read correctly.

Word Reading Fluency

Children’s word reading fluency in English was measured by the Sight Word Efficiency subtest of the Test of Word Reading Efficiency (TOWRE Form A; Torgesen et al., 1999 ) in grade 3. Children were provided with 45 s to quickly and accurately identify as many words as they could from a vertical list of 104 items. A parallel experimental measure was created to assess word reading fluency in French.

Reading Comprehension

The comprehension subtest (Level 3 Form S) of the Gates-MacGinitie Reading Tests (GMRT; MacGinitie et al., 2000 ) was used to assess children’s English reading comprehension in grade 3. Children were asked to read short passages and answer 48 corresponding multiple-choice questions. The score was the total number of correct answers. Level C Form 4 of the Gates-MacGinitie Reading Tests – Second Canadian Edition ( MacGinitie and MacGinitie, 1992 ) was translated into French and administered in the same way as the English task.

To prepare the data for analyses, we first examined whether there was statistical support for merging the samples of children who spoke English as a primary language at home and those who were exposed to additional home languages into one sample. A Box’s M test using the grades 1 and 3 measures, indicated no significant difference in variance-covariance patterns between the two language groups on English, Box’s M = 40.88, p = 0.09, and French, Box’s M = 7.74, p = 0.99, reading and language measures. Based on these results, the two groups were combined to create one sample. Table 1 presents the mean raw scores, standard scores for standardized measures, standard deviations and reliability estimates for the entire sample on all English and French measures in grade 1 and grade 3.

Table 1. Means, standard deviations, and reliabilities for the total sample ( N = 180) on English and French measures in grade 1 and grade 3.

We selected groups of comprehenders in grade 3 using separate regression techniques for English and French measures to predict children’s reading comprehension scores from age, non-verbal reasoning, word reading accuracy, and word reading fluency. These variables are correlated with reading comprehension (e.g., Deacon and Kirby, 2004 ; Lesaux et al., 2006 ) and have been widely used for identifying comprehender subgroups ( Li and Kirby, 2014 ; Tong et al., 2014 ; D’Angelo and Chen, 2017 ). Together, the predictors explained a total of 43% of the variance in English reading comprehension and 37% of the variance in French reading comprehension. The observed reading comprehension scores were plotted against the standardized predicted scores. Children below the lower 65% confidence interval of the regression line were identified as poor comprehenders and those above the upper 65% confidence interval were identified as good comprehenders. Those children who scored within the 15% confidence interval were identified as average comprehenders. Children with very poor or good word reading skills (predicted value 1 SD above or below the mean) were not selected and excluded from analyses.

Through this regression method, we identified three groups of comprehenders in English (24 poor, 24 average, and 24 good) and three groups of comprehenders in French (24 poor, 24 average, and 24 good). Sixteen children out of the 24 poor comprehenders of English and 18 children out of the 24 poor comprehenders of French identified as English-speaking. 1 The remaining children came from diverse linguistic backgrounds and were exposed to additional languages at home, including Russian, Hebrew, and Mandarin. A chi-square test of independence indicated a non-significant relationship between the children who spoke English as a primary language at home and those who were exposed to additional languages at home within the comprehender groups identified in English, χ 2 (1, N = 72) = 3.11, p = 0.21, and in French, χ 2 (1, N = 72) = 1.01, p = 0.61. Based on these results, and given that the children exposed to additional languages met the inclusion criteria (non-native speakers of French), they were retained in the sample.

We conducted multivariate analyses of variance (MANOVAs) to confirm the reading comprehension profiles of the English comprehender groups and to determine whether poor comprehenders differed from average and good comprehenders on English and French reading-related measures in grade 1 and grade 3. As illustrated in Table 2 , there were no significant differences between the three groups on age, non-verbal reasoning, English and French word reading accuracy, and English and French elision in grade 1 and English and French word reading accuracy and fluency in grade 3 (all p s > 0.08). However, as expected, poor comprehenders differed significantly from average ( p < 0.001) and good comprehenders ( p < 0.001) on English and French reading comprehension in grade 3. Poor comprehenders also differed from average ( p < 0.001) and good comprehenders ( p < 0.001) on English vocabulary in grade 1 and grade 3. Similarly, French vocabulary distinguished poor comprehenders from average comprehenders in grade 1 ( p < 0.05) and grade 3 ( p < 0.01).

Table 2. Means (standard deviations) of poor, average, and good comprehenders selected with English measures on English and French reading and language variables in grade 1 and grade 3.

For the comprehender groups identified using French measures, there were no significant differences between poor, average, and good comprehenders on age, non-verbal reasoning, and English and French phonological awareness in grade 1. Poor comprehenders differed significantly from average and good comprehenders on grade 1 measures of English ( p < 0.01) and French vocabulary ( p < 0.01) and English ( p < 0.001) and French word reading accuracy ( p < 0.001). In grade 3, English ( p < 0.05) and French vocabulary ( p < 0.001), English word reading accuracy ( p < 0.001), English ( p < 0.001) and French word reading fluency ( p < 0.001), and English ( p < 0.001) and French reading comprehension ( p < 0.001) distinguished poor comprehenders from average and good comprehenders ( Table 3 ).

Table 3. Means (standard deviations) of poor, average, and good comprehenders selected with French measures on English and French reading and language variables in grade 1 and grade 3.

Table 4 presents the prevalence rates of the overlap between comprehender groups in English and French. Of particular interest to this study was the number of children who were identified through the regression technique as poor comprehenders for both English and French relative to the entire sample. Three subgroups of reading comprehension difficulties in the two languages were considered: 10 children who were poor comprehenders in both English and French (PCB), 11 children who were poor comprehenders in English only (PCE), and 10 children who were poor comprehenders in French only (PCF). We selected an additional 10 children from among the good comprehenders in both English and French, matched on age and gender, to serve as the control group. In this way, we could compare the three groups of comprehenders to children who had average English and French word reading skills, but good comprehension in both English and French. There were no significant differences between the four groups on age (PCB: M = 104.26, SD = 3.97; PCE: M = 105.01, SD = 4.98; PCF: M = 104.01, SD = 4.40; Control: M = 105.02, SD = 3.46) and non-verbal reasoning (PCB: M = 3.80, SD = 3.01; PCE: M = 2.82, SD = 2.40; PCF: M = 3.80, SD = 2.25; Control: M = 5.00, SD = 4.14). Chi-square results demonstrated that the chance of poor comprehenders in English also being poor comprehenders in French was significantly above the baseline level, χ 2 (1, N = 180) = 14.02, p < 0.001.

Table 4. The overlap and distribution of poor reading comprehension in English and French.

It should be noted that children identified as poor comprehenders in English only had not been selected for a comprehender status in French. Similarly, those identified as poor comprehenders in French only did not fit a comprehender group in English. Of the remaining children who were poor comprehenders identified in English, two were average comprehenders in French and one was a good comprehender in French. Of the remaining poor comprehenders identified in French, two were average comprehenders in English and two were good comprehenders English.

The next step in our analyses was to retrospectively examine the correlates of English and French reading comprehension difficulties for each of the three subgroups of poor comprehenders and the control group. We conducted separate MANOVAs, controlling for gender, for the English and French reading and language measures in each grade. Univariate analyses were computed for tasks tested at one time point only (i.e., English and French phonological awareness, English and French word reading fluency, and English and French reading comprehension). Table 5 shows the mean raw scores and standard deviations of the English and French reading and language measures for each group in grade 1 and grade 3, as well as comparisons across groups.

Table 5. Means (standard deviations) and comparisons of poor comprehenders in English and French, poor comprehenders in English only, poor comprehenders in French only, and controls on English and French measures in grade 1 and grade 3.

As expected, there were no significant differences between the four groups on the word reading measures used to select comprehender groups, word reading accuracy and fluency, for both English and French in grade 3, and consistent findings were revealed retrospectively for English and French word reading accuracy in grade 1. Similarly, the groups did not differ significantly on English and French phonological awareness in grades 1 and 3.

Results of univariate analyses showed that there was a significant overall group effect for English reading comprehension, F (3,41) = 38.83, p < 0.001, η p 2 = 0.76 and French reading comprehension, F (3,41) = 37.84, p < 0.001, η p 2 = 0.76. Tukey’s HSD post hoc comparisons showed that the PCB, PCE, and PCF groups performed worse than the control group on English reading comprehension in grade 3. The PCB group also scored significantly lower than the PCF group on English reading comprehension. For French reading comprehension in grade 3, all three poor comprehender groups (PCB, PCE, and PCF) scored significantly lower than the control group, with the PCF group also scoring lower than PCE group.

There was a significant overall group effect for English vocabulary, Wilks’ Λ = 0.41, F (6,70) = 6.64, p < 0.001, η p 2 = 0.36, and French vocabulary, Wilks’ Λ = 0.29, F (6,72) = 3.60, p < 0.05, η p 2 = 0.22. Univariate tests revealed that the four groups differed significantly in English vocabulary in grade 1, F (3,41) = 9.05, p < 0.001, η p 2 = 0.43, and in grade 3, F (3,41) = 13.47, p < 0.001, η p 2 = 0.54. Tukey’s HSD post hoc comparisons showed that children in the PCB and PCE groups scored significantly lower than the control group on English vocabulary in grades 1 and 3. However, in grade 3, the PCB group also scored lower than the PCF group on English vocabulary. The univariate tests for French vocabulary found no significant difference between groups on grade 1 French vocabulary, but there were significant group differences on French vocabulary in grade 3, F (3,41) = 3.04, p < 0.05, η p 2 = 0.20. The post hoc test for French vocabulary showed that the PCB and PCF groups had significantly lower scores than control groups on French vocabulary in grade 3. The PCB group also had lower French vocabulary scores than the PCE group in grade 3. 2

The aim of the present study was to investigate correlates and overlap of reading comprehension difficulties for bilingual poor comprehenders who are exposed to English, the societal language, and French, the language of classroom instruction. By identifying poor comprehenders of both English and French, we were able to determine to what extent poor comprehenders in English, a primary language, are also poor comprehenders in French, an additional language.

We found that there is a moderate degree of overlap in comprehension difficulties in English and French among poor comprehenders with equivalent amounts of exposure to French, with a prevalence rate of 41.7% in our sample. However, our findings also indicate that children who have reading comprehension difficulties in one language do not necessarily have difficulties in another. In addition, we found that English and French vocabulary was a strong and persistent indicator of reading comprehension difficulties in the same language for poor comprehenders of English, French, and both English and French.

Consistent with previous studies, results demonstrate that deficits in oral language are characteristic of children with poor reading comprehension (e.g., Nation et al., 2004 , 2010 ; Catts et al., 2006 ). Building on previous work ( D’Angelo et al., 2014 ), we found that poor comprehenders of English who received classroom instruction in French demonstrated concurrent vocabulary weaknesses in English and French relative to average and good comprehenders, despite comparable word decoding skills. Lower English vocabulary scores distinguished poor comprehenders from average and good comprehenders, whereas lower French vocabulary scores distinguished poor comprehenders from good comprehenders but not from average comprehenders. Similarly, for children identified in French, poor comprehenders differed from average and good comprehenders on English vocabulary, and from good comprehenders, but not average comprehenders on French vocabulary. These findings suggest that the average comprehenders in this study may have not yet reached a level of French proficiency needed to move beyond the performance of the poor comprehenders on French vocabulary. Vocabulary acquisition in French, an additional language, may be more challenging for immersion children because of their limited exposure to French outside of the classroom. Future research should include measures of cognitive abilities, such as phonological short-term memory that may be better at distinguishing group differences in the early grades ( Farnia and Geva, 2011 ).

Regardless of English or French identification, the retrospective analyses indicated that differences between the three comprehender groups in English and French vocabulary were apparent in grades 1 and 3, with no group differences on English and French phonological awareness in grade 1. These findings clearly demonstrate that poor comprehenders’ oral language weaknesses are evident in the early stages of learning to read in both English and French. Although our study examines poor comprehenders in a bilingual context, these results are strikingly similar to findings reported by Catts et al. (2006) and Nation et al. (2010) and confirm that vocabulary weaknesses are apparent before poor comprehenders’ reading comprehension difficulties emerge. However, our study also found that there were differences between poor and average and good comprehenders identified in French on word reading measures in grade 1 and grade 3, indicating that different skills may lead to poor reading comprehension in English and French, and French reading comprehension may be more dependent on word level skills.

This study is the first to demonstrate that children with poor reading comprehension may experience difficulties with comprehension in English, in French, or in both English and French. Of these groups, children who were poor comprehenders in both English and French consistently scored the lowest on English vocabulary in grade 1 and grade 3 and in French vocabulary in grade 3 suggesting that severe English vocabulary weaknesses in poor comprehenders may contribute to comprehension difficulties in English and French. While there were no significant group differences found on phonological awareness, word reading and word fluency tasks, it is interesting to note that the poor comprehenders of both English and French, who were the poorest on English and French reading comprehension, also scored the lowest on all English and French reading and language measures in both grades 1 and 3. Results provide support for the linguistic interdependence hypothesis and suggest that children with poor reading comprehension in L1 may be at risk for being a poor comprehender in L2.

We found that 41.7% of children classified as poor comprehenders in grade 3 were poor comprehenders of both English and French. As expected, this overlap is less than reported in previous studies (e.g., Tong et al., 2017 ) in part due to differences in the approach to defining poor comprehender groups. More specifically, whereas most previous studies have defined poor comprehender groups based on a cut-off score on word reading, reading comprehension, or both, the present study utilized a regression method to identify poor comprehenders based on the relative discrepancy between wording reading, word reading fluency, and reading comprehension, while controlling for age and non-verbal reasoning, therefore, avoiding overidentification and narrowing the sample of children who qualify for poor comprehender status.

However, it could be argued that the overlap between English and French poor comprehender status should be greater given that English and French are alphabetic orthographies and share many linguistic features. It is worth noting that children in this study had been receiving classroom instruction in French for approximately 3 years at the time of comprehender classification. It is possible that children’s poor comprehension in French would have been more apparent had they been exposed to French for a longer period of time. This explanation is consistent with that of previous research, which has demonstrated that relative to poor decoders, poor comprehenders’ difficulties with reading comprehension emerge around the age 10, when performance in reading comprehension is equally accounted for by oral language and decoding skills (e.g., Elwér et al., 2013 ). Therefore, it seems plausible that there would be a greater overlap of poor comprehender status with more exposure to the French language in spoken and written form. Further research is needed to investigate the overlap of English and French reading comprehension difficulties in the later elementary grades, as decoding becomes more automatized and greater variance is accounted for by oral language skills.

The current study examined the learning needs of poor comprehenders in immersion education and has important implications for the assessment and remediation of reading comprehension difficulties in emerging bilingual learners. Our findings demonstrate that poor comprehenders exhibit pervasive oral language difficulties from the onset of reading that manifest similarly in English, their primary language, and French, the language of instruction. Furthermore, the results suggest that it is possible for children to experience poor reading comprehension in one language but be relatively good at comprehension in another language. Since many children begin French immersion with limited levels of French language proficiency, it is beneficial to gather information on children’s reading and language abilities with parallel measures in English and French. Limiting assessment to French, an additional language, may underestimate children’s reading and language ability or misattribute reading difficulties to a lack of French proficiency ( Geva and Herbert, 2012 ).

This research also suggests that intervention strategies should be targeted at poor comprehenders’ underlying language difficulties regardless of language of instruction. While there have been relatively few intervention studies with poor comprehenders, existing studies have shown that intervention practices that promote oral language skills and text comprehension strategies are effective supports for monolingual children with poor reading comprehension ( Snowling and Hulme, 2012 ). Evidently, there is a need for future intervention research that fosters the development of children’s oral language skills in immersion programs.

There are some limitations of the current study that should be noted. First, the sample of poor comprehenders identified within the three subgroups (i.e., PCB, PCE, PCF) was small, which limits the generalizability of our findings. However, obtaining a large sample of poor comprehenders is particularly challenging in a bilingual educational context. Our study is among the few longitudinal studies that have examined bilingual poor comprehenders’ reading and language skills in both languages over time. Given the attrition of students in French immersion (e.g., Chen et al., 2019 ) and the prevalence rate of poor comprehenders in middle elementary years at approximately 10% (e.g., Nation and Snowling, 1998 ; Clarke et al., 2010 ), our sample size may be considered representative of poor comprehenders in a bilingual context. Nevertheless, larger sample sizes for the subgroups of poor comprehenders would benefit future work.

Reading comprehension is a complex process that involves the coordination of various skills that are assessed differently across measures of reading comprehension. In the present study, we used a single standardized measure of reading comprehension. Although the use of this standardized test makes our sample of poor comprehenders comparable to those in the existing monolingual literature (e.g., Tong et al., 2014 ), results reported in this study need to be replicated with more varied reading comprehension measures to disentangle whether poor comprehenders score low on reading comprehension because they do not understand the text or because they are unable to read the question. Similarly, the use of a single measure of vocabulary knowledge may not fully capture the influence of other language skills on reading comprehension, such as vocabulary depth, listening comprehension, morphological awareness, and inference ( Nation and Cocksey, 2009 ; D’Angelo and Chen, 2017 ).

Another limitation is that approximately 25% of the children identified as poor comprehenders in either English, French, or both were exposed to another language at home in addition to English. While this sample is representative of students enrolled in French immersion programs in Canada, there is a need for further research to explore whether significant differences exist between children identified as poor comprehenders from English monolingual backgrounds and those who speak additional languages.

Finally, there is some difficulty in interpreting poor comprehender status in French only, particularly for children in this study who grew up in an English-speaking community. Poor reading comprehension in French may not be attributed to a language impairment or limited proficiency in French but associated with children’s lack of motivation to learn in an L2. Evidently, there is a need for further research to explore the role of motivation in L1 and L2 reading comprehension for children enrolled in immersion programs.

Taken together, the present study demonstrates that poor comprehenders experience similar and persistent difficulties with components of language in both English, a primary language, and French, an additional language, that are present in the early stages of reading development, and therefore, likely indicators of later reading comprehension difficulties in both languages. These results also show while there is a moderate degree of overlap in English and French reading comprehension difficulties, not all poor comprehenders of English are poor comprehenders of French, suggesting that somewhat different skills may be involved in comprehending text in English and French.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by University of Toronto Research Ethics Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

ND’A and XC contributed to the conception and design of the study. ND’A and KK organized data collection and managed the database and performed the statistical analyses. ND’A wrote the first draft of the manuscript. KK and XC wrote sections of the manuscript. All authors contributed to manuscript revisions and read and approved the submitted version.

This research was funded by the Social Sciences and Humanities Research Council (SSHRC) (Grant Number: 435-2013-1745) (Title: Ensuring reading success for all students in early French immersion).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors are grateful to the parents, educators, and students in participating school boards.

• ^ For children to be classified as English-speaking, parents had to indicate that English was spoken in the home environment 50% of the time or more.
• ^ Due to the small group sizes, equivalent non-parametric tests were calculated for each analysis. The Kruskal–Wallis test, used for comparing two or more independent samples, confirmed our parametric results.

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Keywords : poor comprehenders, reading comprehension, French immersion, oral language skills, vocabulary, comprehension difficulties, bilingual learners

Citation: D’Angelo N, Krenca K and Chen X (2020) The Overlap of Poor Reading Comprehension in English and French. Front. Psychol. 11:120. doi: 10.3389/fpsyg.2020.00120

Received: 11 July 2019; Accepted: 16 January 2020; Published: 05 February 2020.

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*Correspondence: Nadia D’Angelo, [email protected]

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What can writing-process data add to the assessment of spelling difficulties?

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• Published: 22 April 2024

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• Åsa Wengelin   ORCID: orcid.org/0000-0002-6913-5128 1 ,
• Sanna Kraft   ORCID: orcid.org/0000-0003-3324-7328 2 ,
• Fredrik Thurfjell 3 &
• John Rack   ORCID: orcid.org/0000-0001-7525-6180 4  

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Spelling difficulties are commonly associated primarily with spelling errors. However, it is not uncommon for spelling challenges to transform the whole writing process into a formidable struggle. This paper delves into the exploration of whether and to what extent analyses of children’s writing processes can enhance our understanding of their difficulties, potentially contributing to the assessment of spelling challenges. We focused particularly on the degree of hesitation within words and the ability to detect and correct spelling errors among children with and without reading and spelling difficulties, as well as how these processes impact the quality and lexical diversity of their texts. Additionally, we sought to contribute to disentangling the influence of spelling and decoding abilities on these processes. A cohort of 47 children, aged 10–13, participated in the study, comprising 16 typically developing children, 16 with predominantly spelling difficulties, and 15 with both reading and spelling difficulties. Our analysis encompassed their spelling performance in both standardized tests and task-oriented writing samples, as well as an examination of their pausing and revision behaviour. As expected, we found robust correlations between the children’s spelling test scores and the proportions of spelling errors in their texts. Furthermore, our findings indicated that children encountering spelling difficulties exhibited a reduced ability to detect and correct errors compared to their peers without such challenges. Additionally, they displayed a slightly higher tendency to experience word-internal interruptions, aligning with prior research. The children who also had reading difficulties produced fewer words and processed words more slowly compared to children in both the other groups. Intriguingly, process data did not reliably predict text characteristics, suggesting that dysfluent writing may not significantly detriment the overall quality of the text, contrary to our initial expectations based on prevailing writing development models. Nevertheless, the study revealed considerable individual variation, with some participants demonstrating a high degree of struggling and dysfluency, resulting in poorer text outcomes, but also others whose struggling processes led to better outcomes. We posit that the crucial aspect lies in identifying these individuals within a classroom context and gaining insights into their processes to provide them with appropriate, formative feedback and adequate writing tools to facilitate their writing.

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Introduction

Individuals with reading and writing difficulties often identify spelling as their most significant (e.g., Hatcher et al., 2002 ; Sumner & Connelly, 2020 ) and enduring challenge (Kemp et al., 2009 ). They frequently leave more spelling errors in their texts than writers without such difficulties (e.g., Connelly et al., 2006 ), resulting in lower quality ratings compared to their peers’ work (Bogdanowicz et al., 2014 ; Connelly et al., 2006 ; Sterling et al., 1998). To some extent, these quality differences seem to persist even after texts have been corrected for spelling (Galbraith et al., 2012 ; Gregg et al., 2007 ; Tops et al., 2013 ). It has been suggested that variables such as low fluency causing cognitive overload (e.g., Berninger et al., 2008 ) or avoidance strategies (O’Rourke, 2020 ; Wengelin, 2007 ) resulting in lower lexical diversity (Sumner et al., 2016 ; Wengelin, 2007 ) could account for some of these outcomes, indicating that spelling difficulty is a complex phenomenon that requires understanding not only of the errors that meet the eye but also of the underlying processes. This raises questions about the need to take spelling processes more into account in the assessment and instruction of writing.

One might argue that spelling is not crucial since most texts can still be comprehensible despite a few errors. However, research suggests that spelling mistakes negatively impact the reading speed of fluent adult readers, even if they are not consciously noticed (Melin, 2007 ), and that children find texts with errors less engaging, memorable, well-crafted, and comprehensible (Varnhagen, 2000 ). Moreover, some readers use spelling errors to infer authors’ intelligence (Figueredo & Varnhagen, 2005 ), credibility (Schloneger, 2016), or trustworthiness (Melin, 2007 ). Therefore, it is not surprising that adults, including educators and parents, commonly consider spelling fundamental to good writing (Rankin et al., 1993 ), or that children and adolescents perceive spelling proficiency as crucial for academic success and future career prospects (Rankin et al., 1994 ). Spelling ability is typically assumed to reflect the effort invested in writing, and authors making spelling mistakes are often stigmatized as careless and lazy thinkers (Varnhagen, 2000 ). This is likely one reason why many individuals with reading and writing difficulties continue to struggle with writing well into their later years (e.g., Wengelin, 2007 ).

Let’s consider an exemplary case of struggling writing, drawn from the experiences of 15-year-old Philip, a participant in one of our previous studies (Wengelin et al., 2014 ). Despite initially scoring at Stanine 1 on a spelling test during the screening process, he managed to compose a short argumentative text (57 words long) with just two misspellings. A segment from the keystroke log (Example 1) sheds light on his approach to the task, which he needed a total of 37 min to complete. We zoom in on the third word “engagera” (engage). Numbers within angle brackets (e.g., <2.86>) shows pause lengths in seconds, and BACKSPACE X within angle brackets (e.g., <BACKSPACE4>) demonstrates Philip’s use of the backspace key, including number of characters deleted.

L<2.86>ärarna <42.58>sk<36.79><BACKSPACE2>bör eng<4.88>a<9.27>

<BACKSPACE2>ga<22.96><BACKSPACE4>äng<2.82>agera<2.22><BACKSPACE8>eng<4.75>agera<2.60><135.59>sig.

Philip clearly hesitated about, reread, and revised the word several times before his efforts resulted in a correct spelling. The first phoneme, /ɛ/, which he identified correctly seemed to cause a major challenge, as shown by the way he exchanged the letter “e” for “ä”, both of which can represent the phoneme, and then back to “e”.

One potential explanation for his laborious and slow word processing could be that Philip has fuzzy representations of words. According to this view (Sénéchal et al., 2016 ), mental representations of words are initially constructed as a frame in which consistent graphemes are clearly specified, whereas inconsistent graphemes are more likely to be underspecified, if represented at all. “Fuzziness” may arise from variability in pronunciation, complex morphological structures, or visual similarities. Consequently, uncertainty regarding the sequence or presence of specific letters may arise, leading to slow word production, particularly among children facing reading and writing challenges who struggle with recalling the correct letter combinations.

In combination with the stigma surrounding texts that include spelling errors, difficulties in retrieving the correct spelling from memory may also—with increasing age and awareness—lead to conscious worry about spelling, including increased hesitation, rereading, and revision. Philip’s example is, of course, anecdotal and stems from a relatively dated dataset. Thus, its generalizability to other writers grappling with reading and writing difficulties in today’s digital communication landscape remains unclear. However, a more recent study by Reynolds and Wu ( 2018 ), in which dyslexic young adults self-reported their Facebook usage, suggests that many writers encountering writing difficulties contend with significant stigma regarding their spelling. These writers commented, for example, on how spelling errors were used to discredit arguments or divert debates, causing strong emotional responses toward writing and prompting them to meticulously edit and revise their content until deemed error-free.

There is also evidence from experimental and quasi-experimental research suggesting that those with spelling difficulties often exhibit hesitation and struggle. This line of research typically operationalizes hesitation and struggle in terms of word production speed/fluency, word-level pausing, and revision. While some early studies failed to find significant differences in writing speed or pausing between children with and without dyslexia (Martlewm, 1992 ), or yielded inconclusive results (Søvik et al., 1987), more recent research has demonstrated that both young children (English 9-year-olds by Sumner et al., 2013 ; 2016 , Spanish 8–12-year-olds by Suárez-Coalla et al., 2020 and Afonso et al., 2020 , and French 11-year-olds by Alamargot et al., 2020 ) with dyslexia, and adolescents (Norwegian 17-year-olds by Torrance et al., 2016 , and Swedish 15-year-olds, by Wengelin et al., 2014 ) with reading and writing difficulties, have been reported to require more time to transition from one letter to another and/or to make more word-internal pauses, i.e. hesitate more within words, than comparable control/reference groups. Similar results have been demonstrated for Swedish (Wengelin, 2002 , 2007 ) and Croatian (Tomazin et al., 2023 ) adults, the majority of whom did not possess a university education, and for university students (Afonso et al., 2015 ; O’Rourke, 2020 ; Sumner & Connelly, 2020 ). However, Galbraith et al. ( 2012 ) found no differences in temporal processing between university students with and without dyslexia.

Regarding spelling-related revision, Wengelin’s (2002; 2007) adult dyslexic participants revised their spelling more frequently compared to the reference group, and their spelling-related revisions constituted a higher proportion of their total number of revisions. Notably, only 50% of their spelling revisions were successful. In Sumner and Connelly’s ( 2020 ) research, university students with dyslexia did not revise spelling more frequently than those in the control group, but their spelling revisions did indeed comprise a higher proportion of their overall revisions. Interestingly, in contrast, the adolescents in Torrance et al.’s ( 2016 ) study made slightly fewer word-level revisions than their peers. This could perhaps be attributed to either a lack of concern about errors or difficulty in error detection, possibly due to reading challenges. However, in a masked condition where their participants could not see their texts, they neither revised more nor less. Like Wengelin ( 2002 ; 2007 ), Torrance et al. analysed all word-level revisions, including for example typos, and we have only found one study focusing specifically on spelling error detection by individuals with reading and writing difficulties. O’Rourke ( 2020 ) conducted an experimental sentence-level task for this purpose and found that university students with dyslexia detected and correctly revised fewer spelling errors compared to students without dyslexia.

Although it is well established that texts by writers with spelling difficulties typically receive lower quality assessments, the relationship between their writing processes and the resulting texts is not yet clear. Contemporary models of writing and writing development, such as that of Hayes and Berninger ( 2014 ), the Simple View of Writing (Berninger et al., 2002 ), and the Revised Writer(s) Within Communities (WWC) model by Graham ( 2018 ), suggest that dysfluent and hesitant word processing can create cognitive overload, potentially impeding higher-level processes and thus affecting the content or structure of a text. Interestingly, a recent study by Rønneberg et al. ( 2022 ) that aimed to investigate this in Norwegian 6th-graders without known difficulties found no effects of fluency measures on the quality of completed text, thus not supporting what they termed the “process-disruption hypothesis” for typically developing children writing in a shallow orthography. The authors did note though that their participants may have had sufficient fluency in spelling and typing for this not to be disruptive. They also acknowledged that their results do not negate the relevance of spelling ability to higher-level text in individuals with writing difficulties.

For texts composed by writers with dyslexia, both Sumner et al. ( 2016 ) and Wengelin ( 2007 ) identified associations between spelling-related dysfluencies and lexical diversity. Sumner and Connelly ( 2020 ) also found such associations, albeit in a different manner. Their participants with dyslexia produced texts with lexical diversity equal to that of their peers but seemingly at the expense of spelling accuracy. The authors suggested that these writers had the ability to overlook spelling errors, possibly due to familiarity with spell check. This notion was supported by O’Rourke ( 2020 ) and O’Rourke et al. ( 2020 ) who found that although interrupting writing, the use of spell check by writers with dyslexia to alleviate spelling demands increased lexical diversity. O’Rourke’s result is in line with some previous research showing that dictating by means of speech recognition—thus not having to think about spelling—can facilitate for children with various language difficulties (Higgins & Raskind, 1995 ; Kraft, 2023 ; MacArthur & Cavalier, 2004 ; Quinlan, 2004 ). The suggestion that some writers had the ability to overlook spelling errors could potentially also apply to Torrance et al.’s ( 2016 ) participants, as Norwegian students are accustomed to typing their work and exams on a computer from an early age. Finally, Galbraith et al. ( 2012 ) reported a more complex relationship. Although their research indicated no discrepancies in the proportions of time that dyslexic and non-dyslexic undergraduates dedicated to various writing processes, their results did indicate that these processes were correlated with the final text’s quality in distinct ways for the two groups.

In summary: On an individual level, there appears to be little doubt that qualitative analysis of process data can offer valuable insights to both researchers and educators regarding the types of spelling challenges described above. It illuminates moments of hesitation, worry, error detection, attempted revisions, the success of these revisions, and avoidance strategies. Additionally, process data can reveal which orthographic patterns writers are aware of and find challenging. For instance, Philip demonstrated clear awareness that the phoneme /ɛ/ could be spelled in multiple ways, causing him difficulty. On the group level, while taking into account that the studies accounted for above defined and operationalized fluency and/or dysfluency in different ways, we note that they indicate a certain consistency regarding temporal aspects of word-level writing by individuals with spelling difficulties. The results for spelling-related revision and the relation between process variables and text characteristics are less conclusive. Disparities can probably, to a large extent, be explained by differences in demographics, input methods, tasks, and languages employed throughout the research. The studies accounted for include different age groups, levels of education, input modalities, and languages—including orthographies of varying levels of transparency. Thus, different results are not surprising. It is, for example, conceivable that due to fuzzy representations (e.g., Sénéchal et al., 2016 ), dysfluency in the writing of young children does indeed hinder their higher-level processing only until they reach a certain level of automatization, in accordance with the Simple View of Writing (Berninger et al., 2002 ). However, those who develop reading and writing difficulties may, with age, become increasingly aware of their limited proficiency, and thus become more hesitant, as highlighted by Reynolds and Wu ( 2018 ). This raises questions about the extent to which school children are at risk of developing such writing behaviour, when and how it happens in that case, and how classroom teachers can identify struggling writing in time to prevent it.

Three of the studies referred to in the literature review (Afonso et al., 2020 ; Alamargot et al., 2020 ; Suárez-Coalla et al., 2020 ) investigated children with reading and writing difficulties in the ages of ‘upper elementary school’/‘middle school’—all with a focus on handwriting skills. In the Swedish context, these are the grades (4–6, ages 10–12) where children are expected to move from basic writing skills into more complex spelling and composition. We speculate that these ages may be where differences between children with and without spelling difficulties start to become apparent and thus where their processes may start to deviate from those of their peers. Therefore, our first research question of this paper focuses on fluency and dysfluency in grade 4–6 Swedish children.

Another important aspect of the previous research is how the participants with spelling difficulties are conceptualized. Some studies have recruited participants with a known dyslexia diagnosis, while others have used the broader concept of reading and writing difficulties, and one has specifically focused on poor decoders. All these groups have also been shown to be poor spellers, but differences in dyslexia definitions and diagnoses across time and location aside, this means that they all, including our own research, assume reading difficulty as the default problem. Although, Torrance et al. ( 2016 ), who focused specifically on the writing of poor decoders, concluded that word-level hesitation in their sample appeared to be linked ‘solely to production rather than reading’ (Torrance et al., 2016 , p. 385), as differences between writers with and without reading and writing difficulties persisted in temporal patterns even in a condition where participants were prevented from seeing what they had written. However, inhibiting hesitation and lookbacks for anxious writers may not be easier than inhibiting other types of behaviour that have developed over a long period, and more research is needed to disentangle the influences of spelling and reading abilities on word processing. For example, the results of the studies included in the literature demonstrate the importance of distinguishing between word-level revision caused by hesitation which, as argued by Torrance et al could be merely a question of production, and spelling revision induced by unambiguous error detection, which clearly needs reading skill. As indicated by an eye-tracking study of typically developing adolescents by Beers et al. ( 2010 , p. 768), ‘reading at the inscription point’ was associated with text quality, possibly to ‘review their most recently composed words’. Paradoxically, error detection could be just as important when introducing compensatory tools to facilitate writing, such as spell check or speech recognition. Although experienced as useful by many (but under-researched), these systems are not infallible. In view of the above, we conclude that distinguishing reading and spelling skills may add to our understanding of spelling difficulties, and thus, our second research question focuses on the detection and revision of spelling errors. To understand the contribution of spelling difficulty per se, our main inclusion criterion was based on spelling skill, after which we assigned the participants into either a group with mainly spelling difficulties or a group with both reading and spelling difficulties.

The overarching aim of our paper is to explore the processes of 10–13-year-old (grades 4–6) Swedish children with and without spelling difficulties using keystroke logging, and to discuss to what extent the knowledge gained can be of use for assessment of spelling difficulties. Based on the results by Rønneberg et al. ( 2022 ), we assume that at least older children without reading and writing difficulties in our sample will have reached a certain ‘ceiling level’ of automatization. Younger children and children with reading and writing difficulties may not have done so to the same extent. Therefore, our main approach is comparative rather than correlational, as indicated by the two research questions outlined above. However, for the sake of comparability with other studies and because we are interested in whether and how process data can add to existing assessments, our third question focuses on the relationships between process data, text characteristics, and the outcomes of standardized spelling and word decoding tests. Our specific research questions are:

To what extent do Swedish children with and without spelling difficulties in grades 4–6 demonstrate dysfluency in writing in terms of word-internal pausing and word-level revision?

Can distinctions be identified between children characterized by both reading and spelling difficulties and children facing challenges specifically in spelling, particularly regarding the detection and revision of spelling errors?

Are there any identifiable correlations between data on writing processes, text characteristics, and the outcomes of standardized spelling and word decoding tests?

We expect children with spelling difficulties to be less fluent and produce texts with more spelling errors and of lower quality than typically developing children. We also expect children with both reading and spelling difficulties to detect and revise spelling errors to a lesser extent than the others. The results will be discussed in terms of implications for assessment.

Participants and corpus

Three groups of children aged 10–13 (grades 4–6), one primarily with spelling difficulties ( n  = 16), one with both reading and spelling difficulties ( n  = 15), and one without difficulties ( n  = 16), all of whom had received their entire education in a mainstream Swedish school and used laptops or tablets with physical keyboards daily, were drawn from a project aimed at understanding writing difficulties in children with decoding and spelling challenges and exploring ways to facilitate writing for them, through writing technologies and individual interventions. The project included 40 participants with reading and writing difficulties, but only 31 had complete data sets for the present study. These include a spelling test, a decoding test in two parts, and a writing task carried out on a computer—without compensatory writing technologies. These participants were recruited through special educational experts who were invited to suggest participants with reading and spelling difficulties, focusing specifically on those who would benefit from compensatory support with spelling. Spelling difficulties, as highlighted by Dockrell ( 2009 ), can be identified in a wide range of children, and we adopted an inclusive approach to participation. Autism and/or intellectual disability were exclusion criteria, but beyond that, we welcomed all children Footnote 1 . To confirm group membership, we conducted a standardized spelling test, DLS (Johansson, 1992 ). Only children who scored at stanine 3 or below were included.

Because error detection is an important aspect of this paper, we then used the LäSt (word and non-word) decoding test (Elwér et al., 2011 ) to divide this group of children into two subgroups post hoc: one with clear both reading and spelling difficulties and one with mainly spelling difficulties. Inclusion criteria for the group with both reading and writing difficulties were stanine 3 or lower on both the word-decoding part and the non-word decoding part of the decoding test. Throughout the paper, we will refer to the two groups with spelling difficulties as follows:

Children with mainly spelling difficulties.

Children with both reading and spelling difficulties.

We also initiated the recruitment of typically developing children for a reference group. Unfortunately, this data collection was interrupted by Covid-19, and therefore the reference group in this paper comprises only 16 children who, on average are slightly younger than the children with reading and writing difficulties.

The group with both reading and spelling difficulties comprised the oldest participants and exhibited the poorest performance in both reading and spelling, not only in comparison to their similarly aged peers but also in absolute terms. A Bayesian ANCOVA confirmed that the typically developing children, as expected, demonstrated stronger spelling abilities than participants in the two groups with spelling difficulties—despite being younger. However, we found no evidence for a difference between the two groups with spelling difficulties. That is, they were at similar spelling levels. The majority of children in these two groups performed at or below the expected level of a 4th grader—all of them at least one grade-level below expectations, with most of them falling even further behind.

Regarding non-word decoding, we observed very strong evidence for a group effect (BF 10  = 9410.36). Post hoc tests indicated moderate evidence for a difference between typically developing children and those with primarily spelling difficulties (BF 10  = 3.45). In contrast, there was very strong evidence that participants with both reading and spelling difficulties were weaker readers compared to both of the other two groups (BF 10  = 4071 for the comparison with typically developing children and BF 10  = 110.15 for the comparison with children mainly experiencing spelling difficulties).

For word decoding, the results were similar but demonstrated even higher probabilities (BF 10  = 137302.41 for the model based on Group). In summary, acknowledging substantial individual variation, the two groups with spelling difficulties exhibited similar levels of spelling proficiency but differed significantly in reading skills. Descriptive statistics are shown in Table  1 .

The corpus for the present study consists of 47 text samples containing both product and process data. The finally edited texts comprise a total of 4166 words (1884 by the typically developing children, 1495 by the children with mainly spelling difficulties, and 787 by the children with both spelling and reading difficulties) out of which 356 are misspelled: 64 by the children without difficulties and 292 by the children in the two groups with spelling difficulties. As will be shown later more errors were made during the writing processes, but they were detected and revised accordingly.

Elicitation material, instruments, and procedure

Individual sessions were conducted with a certified speech pathologist, either Author 2 or 3 of this paper, for all testing and text production. The DLS spelling test (Johansson, 1992 ) served as a standardized diagnostic tool, involving a word list of 36 dictated words for the child to spell. Each correctly spelled word earned the child one point, and the raw score was converted into stanine scores based on the child’s grade level (4–6). The test boasts reported reliabilities of 0.90 for grade 4, 0.88 for grade 5, and 0.96 for grade 6. The administration was carried out using pen and paper.

The LäSt decoding test (Elwér et al., 2011 ) functioned as a standardized reading assessment tool, featuring two lists of words: one with non-words and the other with real words. Both lists required the child to read individual units one at a time. The non-word list measured alphabetic reading skills, while the real-word list also evaluated the child’s orthographic reading abilities. The reported reliabilities for the two lists are 0.74 and 0.91, respectively.

The keyboarded texts were created on a MacBook computer equipped with ScriptLog, a keystroke logging program designed for recording typing processes (Wengelin et al., 2019 ). ScriptLog records each keystroke and mouse click, providing them with a timestamp, enabling playback of the writing process, and analysis of temporal patterns, pauses, and revisions. The texts were composed in ScriptLog’s simple editor, which is similar to Windows’ NotePad or TextEdit in Mac OS, and thus does not include spell check. Participants were prompted by short film clips (Berman & Verhoeven, 2002 ) presenting moral dilemmas related to cheating or stealing. They were instructed to present the problem and discuss what their favourite superhero would do in response to the incidents in the films. Participants were asked to write for a maximum of 30 min. The time spent on the task varied widely. One child gave up after 1.82 min, and another (the only one who exceeded the given time) kept writing for 65 min.

We report both product measures, that is, characteristics of the final compositions, and process measures, that is, measures related to temporal aspects of the writing process, and measures related to error detection and revision.

Product measures

We assessed text length, text quality, vocabulary diversity, and proportion of misspelled words in the finally edited texts. Prior to quality rating and calculations of text length and vocabulary diversity, all texts were corrected for spelling. Some words included more than one error but for the analyses in this study we only used number of misspelled words. We operationalized text length as the number of words in the final product, which we calculated using LIX.se, a tool which is similar to textinspector.com but specifically designed for Swedish. To measure vocabulary diversity, we used VocD, a measure that, as distinguished from type/token ratio, is not sensitive to text length (McCarthy & Jarvis, 2010 ). It does, however, require a minimum number of words (50) why not all texts could be included in this analysis. Text quality (holistic) was assessed by means of comparative judgment (using nomoremarking.com) which has been argued to have better reliability than criterion-based marking (Steedle & Ferrara, 2016; Verhavert et al., 2019). The basic idea is that judges compare two randomly selected texts and decide which is better—again and again, until each text has been compared with a large variety of other texts. Based on the recommendations in a meta-analysis by Verhavert et al. (2019) we let each text constitute the basis for comparison 20 times. The system calculates a quality score, between 0 and 100. To ensure acceptable reliability, we used four judges who had not participated in the data collection. Before starting the assessment process, the judges agreed on time slots, and how many assessments they should do during each slot to avoid that fatigue would influence their judging. They also conducted a pilot study assessing texts that were similar to those in the main study. The reliability measure reported by the system, Scale separation reliability Footnote 2 , was 0.92.

Process measures

We examined two types of process measures: (1) those concerning temporal aspects, and (2) those related to error detection and revision. Regarding temporal processing, we report word-internal mean interkey intervals (IKIs), as well as those that could be considered interruptions, here referred to as pauses . The latter necessitates establishing a pause threshold. This is somewhat problematic because pause thresholds set on the group level are by definition arbitrary. While it is essential to choose a pause threshold relevant to the research question, a two-second threshold has frequently been used to mark the point at which an IKI is considered a pause (Strömqvist et al., 2006 ) as seen in earlier keystroke logging research. This threshold was, for example, utilized in both Wengelin ( 2007 ) and Wengelin et al. ( 2014 ). Several subsequent studies that have recorded typing or handwriting have used the same threshold — either by convention or for comparability. Although this practice remains relatively common, variations in threshold choice between studies have become more apparent today. For instance, while Sumner et al. ( 2014 ) used a two-second criterion, Rønneberg et al. ( 2022 ) employed a one-second threshold within words and a two-second threshold before words. In this article, we focused on word-internal pauses and explored two different thresholds. First, we calculated the number of IKIs within words that lasted for 2 s or longer. Notably, two-second pauses are highly unlikely to occur within words during regular typing, even among children (Wengelin & Strömqvist, 2004 ). Although employing a two-second threshold might result in a lower recall rate, it is expected to yield a higher precision level, as the included pauses significantly disrupt the overall word production flow. In other words, while this approach may not capture all instances of word-level hesitation, the included pauses undoubtedly disrupt the general word production flow. Additionally, for comparison with the dataset of Rønneberg et al., which closely resembles our study in terms of recently collected data, keystroke logging usage, a reasonably similar age group, and a transparent orthography, we also present pauses based on a one-second criterion. We will revisit the rationale behind these thresholds in the Discussion section. For revision behavior, we focused on the extent to which children with and without reading and writing difficulties detected and corrected word-level errors during written composition and how frequently this occurred. Two annotators coded all revisions as either word-level revisions (typographical errors and spelling errors) or other revisions (formulation, content, punctuation, or other) and calculated the proportions of word-level revision for each group. We counted revisions of word-level errors as detected errors, regardless of their success, along with errors left in the final text to comprise the total number of errors. To calculate the detection rate, detected errors were then divided by the total number of errors.

Ethical considerations

As already mentioned, data analysed in the current study were obtained from a larger dataset collected as part of a research project which was funded by the Marcus and Amalia Wallenberg Foundation (Ref. No. 2014 − 0122). The data collection was conducted in eight schools in southern and mid-Sweden by the second and third authors. Testing normally took place over two sessions, to avoid exhausting the participants, but if a participant still showed signs of fatigue we interrupted and divided the rest of the data collection from that person in shorter sessions. Ethical approval for the study was granted by the Swedish Ethical Review Authority in Gothenburg (Ref. No. 702 − 17).

Written assent/consent was obtained from both the participants and their caregivers. Participants were informed that they could withdraw from the study at any time without providing a reason.

Statistical analyses

We used Bayesian methods to analyse the data. These methods allow for evidence in favour of both the presence and absence of group differences and correlations because they represent uncertainty about the true value of a parameter using a probability distribution, which can assign non-zero probabilities to both the null hypothesis and alternative hypotheses. Furthermore, Bayesian analyses have been argued to achieve better type I error control than traditional frequentist analyses because Bayesian methods allow for the incorporation of prior knowledge into the analysis, which can reduce the impact of random noise in the data. For all analyses, we report the Bayes factor for the best model in comparison to the null model (BF 10 ), which provides the evidence for the alternative hypothesis over the null hypothesis. For example, if BF 10  = 10, the alternative hypothesis is regarded as ten times as likely as the null hypothesis. In most of the literature, a Bayes factor below 3 is regarded as negligible. In such cases, we only state a lack of evidence for our models. Values between 3 and 10 indicate moderate evidence, and values above 10 indicate strong evidence (Jeffreys, 1961 ).

Because age is not evenly distributed across our three groups, for group comparisons, we performed Bayesian analysis of covariance, ANCOVA (Rouder et al., 2012 ) in Jamovi, version 2.3 (The Jamovi Project, 2022), using its default priors (r scale fixed effects = 0.5, r scale random effects = 1, r scale covariates = 0.354), on the proportion of word-level errors, error detection rate, frequency of word-internal pausing, vocabulary diversity, and text quality as dependent variables, including group as a fixed factor and age as a covariate for each comparison. The Bayesian ANCOVA works by comparing four models with various predictors of the dependent variable, in our case:

A null model, P(M) = 0.250.

A model containing only group as a predictor, P(M) = 0.250.

A model containing only age as a predictor, P(M) = 0.250, and.

A model containing both group and age as predictors, P(M) = 0.250.

Note that all odds were set to be equally likely, a priori (0.250). Posthoc comparisons between the individual groups are in Jamovi based on the t-test with a Cauchy prior r  = 0.707. To account for model uncertainty, we performed Bayesian model averaging to test the effects of both predictors. For relations between different variables, we conducted Bayesian Pearson’s correlations between the different spelling-related variables: spelling test score, proportion of misspellings in the final text, frequency of word-internal pausing, frequency of word-level revision, and between each of those with text quality or vocabulary diversity. Under the null hypothesis, we would expect a correlation of 0 between any two of the variables in any of the pairs in the correlation matrix. The alternative hypothesis is two-sided, and we assigned a uniform prior probability to all correlations between − 1 and + 1.

Since we are interested in whether and how process data can contribute to the assessment of spelling difficulties, we will first compare the product data, that is, the characteristics of the texts produced by the participants, and then the process data. Finally, we will present correlational analysis as follows: (a) correlations between the test results and the product data, (b) correlations between the process data and the product data, and (c) correlations between word-processing variables: spelling test, word-decoding test, proportion of misspelled words in the texts, word-internal pausing, error detection rate, and spelling revision.

Comparing the text characteristics of the three groups

The results for the text characteristics are detailed in Table  2 . For vocabulary diversity the evidence for any effect was negligible. However, as already mentioned, only a subset of the texts was included, and the results must be interpreted with great care. The typically developing children outperformed the other two groups for all the other three variables. For text length and text quality, the Bayesian analysis of covariance indicated strong evidence for the model based on Group only. In the case of the proportion of spelling errors, we found the strongest evidence for the model based on Group + Age (BF 10  = 43.37) but we also found increased odds for the model based on Group only (BF 10  = 38.29). The averaging analysis of the two predictors indicated that the data were more likely under models containing Group as a predictor than when including Age. In all three cases, posthoc tests indicated that typically developing children were likely to produce better results than both the other groups. Only for text length, children with mainly spelling difficulties were likely to perform better than the group with both reading and spelling difficulties.

Analyses of the process variables

Results for the process variables are presented in Table  3 . For the variables related to temporal processes, the Bayesian analysis of covariance indicated the strongest evidence for the model based on Group + Age, but the evidence for pauses longer than one second was negligible.

For the mean duration of word-internal Inter-Keystroke Intervals (IKIs), there was strong evidence for the model based on Group only, but the averaging analysis of the two predictors indicated that data were more likely under models containing Age as a predictor than when including Group. However, posthoc tests for Group indicated moderate (BF 10  = 8.863) evidence for a difference between the typically developing children and the children with both reading and writing difficulties, but not between any of these two groups and the group with only spelling difficulties. For word-internal pauses longer than 2 s, the evidence for models other than the one based on Group + Age was negligible, and once again, the averaging analysis of the two predictors indicated that data were more likely under models containing Age as a predictor than when including Group. Moreover, posthoc tests for Group indicated only negligible effects.

Regarding error detection and revision, the Bayesian analysis of covariance indicated strong evidence for both the model based on Group only and the model based on Group + Age for error detection, but the averaging analysis of the two predictors indicated that data were more likely under models containing Group as a predictor than when including Age. Posthoc tests indicated that the odds for detecting errors were much better for the typically developing children than for those in the two groups with spelling difficulties. With that in mind it was somewhat surprising to find that the probabilities for any effects of Group or Age on revision frequency or the proportion of word-level revision were, more or less, negligible. Instead, we found moderate evidence for the null hypothesis.

In sum, the probabilities for differences between the groups are small in most cases. Although the typically developing children appear to process words faster than the children with both reading and writing difficulties, age appears to be a more important predictor of both these variables and of word-internal pausing. However, visual inspection of the scatterplots (Fig.  1 ) related to these may add some food for thought. The leftmost scatterplot illustrates the mean durations of the word-internal IKIs and indicates that while word-processing speed appear to increase with age for all three groups, the distance between them is relatively stable. The scatterplot for word-internal pausing (> 2 s) on the other hand (the one in the middle) indicates that the less dysfluent the children appear to become with age, the closer the three groups get to each other. But although differences between them decreases accordingly, the participants with both reading and spelling difficulties—who are also the poorest spellers in relation to their ages—stand out as the most dysfluent. For most age levels this group demonstrate the highest number of word-internal pauses > 2 s. We will return to that in the discussion section. The probability that children detect and correctly revise their errors is, on the other hand, much higher for the typically developing children. Interestingly, the children with only spelling difficulties seem unlikely to outperform their peers with both reading and spelling difficulties in speed (mean duration of word-internal IKIs), (word-internal) pausing and revision. However, for error detection rate, the patterns look completely different. While typically developing children and the children with mainly spelling difficulties appear to improve their ability to detect and revise errors, the children with both reading and spelling difficulties demonstrate no such tendencies.

Scatterplots, mean duration of word-internal IKI:s, word-internal pausing, and error detection rate for typically developing children (0), children with mainly spelling difficulties (1), and children with both reading and spelling difficulties (2)

Bayesian Pearson correlations

Our sample is too small for reliable reports of correlations for each group, and therefore, we will focus mainly on correlations for the whole sample. We did, however, also run groupwise correlations, to explore whether some correlations for the whole group may have been driven by group differences rather than by a linear relation, and we will touch upon a couple of these.

We first report correlations between the spelling test and the word decoding test on the one hand and the text characteristic variables on the other. Bayesian correlation pairs are shown in Table  4 . We found no evidence for any correlation with vocabulary diversity and have excluded that variable from the table. Not surprisingly the odds for a strong correlation ( r = −0.735) between the tests and proportion of misspelled words are very high (BF 10  = 3.43e + 6). Furthermore, we found very strong evidence, with Bayes factors around 1000 for moderate to strong correlations ( r  ≈ 0.5) between both decoding and spelling skills on the one hand and text quality on the other. Finally, we also found strong evidence for correlations between decoding skills and text length.

However, individual variation within the groups is large and when we looked at text length and text quality for the three groups separately only one of these correlations held for a single group. The evidence was relatively strong (BF 10  = 13.62) for a correlation ( r  = 0.68) between spelling test results and text quality for the group with mainly spelling difficulties.

We then analysed correlations between spelling process related variables and the two higher-level text characteristics for the whole sample and found only negligible results.

Finally we carried out correlational analyses between the different word-level variables (test results, proportion of misspelled words in the texts, error detection rate, word-internal pausing, error detection rate, and duration of word-internal IKI:s. Since no effects were found in the comparative analysis for revision frequency or proportion word-level revision, these variables were excluded from the correlational analyses.

For word-internal pausing, we chose to include the variable with the strongest results from the comparisons, which was word-internal pausing > 2 s. Numerous correlation models with very large Bayes factors were found for correlations involving test results and word processing variables from the writing data. Table  5 shows the Bayesian Correlation Matrix, and Fig.  2 . shows the correlation plots. Not surprisingly, the spelling test results appear to correlate with both the word decoding test ( r  = 0.69) and the proportion of misspelled words in the text ( r = −0.74), indicating that poor spelling and poor word decoding go hand in hand. Furthermore, those who receive low results in a spelling test also produce many spelling errors during composition. Error detection rate appears to correlate with all three of these variables ( r  = 0.64 for the spelling test, 0.41 for the word decoding test, and −0.80 for the proportion misspelled words in the finally edited texts). This suggests that one of the reasons why poor spellers leave many errors in their texts is that they do not detect them or alternatively do not know how to revise them. However, although relatively strong, the evidence for the correlation between error detection rate and word decoding is considerably lower than for the other models (BF 10  = 10.018).

Correlation plots for word-processing related variables

Regarding the temporal process variables, there is strong evidence for correlations between the mean duration of word-internal IKIs and all the other variables, while for word-internal pausing there is evidence only for a correlation with the proportion of misspelled words ( r  = 0.444). In other words, those who hesitate more within words also tend to make many spelling errors. Just like for the correlation between word decoding skill and error detection rate, the evidence for this model is lower than that of the others, but still with a Bayes factor of 20.7, meaning that it is 20 times more probable than the null model. However, as shown in the correlation plot for word-internal pausing, the participants are scattered around one end of the regression line. Moreover, when conducting the Bayesian correlation analyses with this variable for each group separately, we found no evidence for such a correlation, so the result should be interpreted with care.

In our study, we embarked on a journey to deepen our understanding of how spelling data derived from children’s composition processes could enhance our comprehension of spelling difficulty beyond traditional spelling test results and observations from free writing samples by children with reading and writing difficulties. We were interested in whether such analyses could contribute to the assessment of spelling. Our primary focus was on (a) spelling difficulties in general and (b) the role of reading difficulties in combination with spelling difficulties.

Our first question aimed to determine whether 10–13-year-old Swedish children with spelling difficulties exhibit comparable hesitation and avoidance behaviours to those observed in adults and adolescents in previous studies. The succinct response to this query appears to be negative, or at least not to a significant extent. While typically developing children outperformed others for all product variables except vocabulary diversity, we found no indications that the groups with spelling difficulties displayed distinct patterns of revision or pauses exceeding one second. For both mean word-internal IKIs and word-internal pauses > 2 s, the best model was the one based on Group + Age, and the averaging analysis of the two predictors indicated that data were most likely under models containing Age as a predictor. These results contrast with those of Sumner et al. ( 2013 ), who showed that children with dyslexia made more word-internal pauses during handwriting. Since our group of children was based on spelling difficulty in a broad sense, these results are not too surprising. A very tentative and premature interpretation of these results could be that it is not any spelling difficulty that drives dysfluency in writing, but rather an underlying disorder, such as dyslexia. However, more research and larger samples would be needed to support that notion.

The effect of age was also clearly visible in the scatterplots in Fig.  1 . Within each group, the speed of word-level processing, as shown by the word internal IKIs, increased, and the number of long word-internal interruptions, in terms of pauses exceeding 2 s., decreased. This could to a certain extent be taken to support the claim by Rønneberg et al. ( 2022 ) that word-internal pausing did not distinguish weaker spellers from stronger in typically developing children of the same age. It does, however, also raise the question of what constitutes a dysfluency, since they only measured pauses > 1 s. within words. Like these authors, we found no evidence for either age or group effects on 1-s pauses. The suggested explanation for the results of Rønneberg et al. was that perhaps the participants in their study had already automatized typing and spelling. This interpretation seems reasonable in the light of the behaviour of the typically developing children in our data. However, for the children with spelling difficulties automatization appear to happen considerably later. While the gap between the groups narrowed considerably with age for pauses > 2 s., the gaps between the groups for mean word internal IKI:s appeared to remain more or less consistent. These findings have the potential to support the idea that fuzzy representations of words (Perfetti, 2007; Sénéchal et al., 2016 ) diminish with age in typically developing children but persist, or at least decrease at a slower rate, for children with spelling difficulties.

Our second inquiry aimed to investigate the extent to which reading difficulty influenced the observed patterns. Interestingly, we found evidence only for one clear distinction between the two groups with spelling difficulties and that related to text length. Children with mainly spelling difficulties produced considerably more text than those with both spelling and reading difficulties. In fact, the subcorpus based on texts by children with mainly spelling difficulties was about twice the size of that by children with both reading and spelling difficulties. The explanation for this is unclear. Since the children with both reading and spelling difficulties were older than those with mainly spelling difficulties, we certainly did not anticipate this difference. A potential explanation could have been a high degree of dysfluency, but as already discussed, our analyses provided very moderate evidence for this, primarily in terms of longer word internal IKIs, and very little effect for pausing or revision. Another possibility is that the children who demonstrate both reading and spelling difficulties, and thus were more “dyslexic-like”, than those with mainly spelling difficulties, had other underlying language difficulties, and/or as suggested by Torrance et al. ( 2016 ), less prior knowledge due to limited print exposure.

Of particular interest for the distinction between children with mainly spelling difficulties and those with both reading and spelling difficulties was of course the question of error detection. Surprisingly, while the evidence was strong for the typically developing children outperforming the children with spelling difficulties, we found no statistical evidence that reading difficulty played a role on the group level. However, it may be worth directing some attention to the rightmost scatterplot in Fig. 1 . Although visual observations of scatterplots should never substantiate any scientific claim, this plot does seem to tell a story that merits further research. While error detection seems to improve with age for both typically developing children and the group with mainly spelling difficulties, this improvement does not appear to be evident for the group with both reading and spelling difficulties. To our knowledge, this has not been investigated before.

Our third question related to the correlations between variables. As already mentioned, and in accordance with previous research, we found strong evidence for a correlation between spelling test results and text quality. Furthermore, despite not finding any group differences for error detection between the groups with spelling difficulties, we did observe some evidence for a correlation between the word decoding test and error detection. Therefore, we are not yet prepared to rule out the influence of reading difficulty on error detection, but should rather re-evaluate our inclusion and exclusion criteria. Is there also a relation between process and text characteristics? Whereas we did find strong evidence for a relation between test data and text characteristics, we found no correlations between the process data and product data for these young writers, except for the correlation between percentage of spelling errors, and duration of word internal IKIs, indicating on the one hand that spelling does indeed influence the processes, but that at least for the children in this study, without consequences for the text characteristics. Hence, once again our results support the claims by Rønneberg et al. (2020) that word-level dysfluency does not necessarily impede higher processes of writing in young writers—with or without reading and writing difficulties. We found this result slightly surprising, yet hopeful. It could mean that on a general level these children have not (yet?) developed the types of writing behaviour demonstrated by Philip and that described by the participants of Reynolds and Wu ( 2018 ) and thus that that type of stigma can be prevented.

Finally, we return to our final question and the title of this paper. What can writing-process data add to the assessment of spelling difficulties? At first sight: perhaps very little—at least for these young children. We have not been able to show that children with spelling difficulties consistently demonstrate process patterns that predicts their difficulties better than a traditional spelling test. Is then the problem with cognitive overload, caused by difficulties with spelling and other lower order processes, overstated, as indicated by Torrance et al. ( 2016 )? Perhaps, and that is encouraging. It could be the case that writing instruction has improved, or that better writing tools have become more accessible, or something else that have changed the conditions for writing development. However, as already mentioned, the longer mean durations of the word-internal IKI:s do indeed suggest that children with reading and spelling difficulties process words more slowly than the typically developing children, even if they don’t make long word-internal pauses, and this could support the theory of fuzzy representations (cf. Perfetti, 2007 Sénéchal et al., 2016 ), rather than conscious hesitations and coping strategies. On the other hand, the fact that not all the gaps between the groups appear to narrow, and that the group with both reading and spelling difficulties show different profiles for some variables while simultaneously being older than the others indicate that there could still be a definable group out there to identify in order to prevent stigmas related to writing, even if we didn’t manage to capture that in our current analyses. More research is needed, on larger groups, other age spans, more orthographies, and the effects of writing technologies to support struggling writers. One of several limitations with our study is the limited number of participants in each group.

In the Swedish context, the transition between grade 6 and 7 (age 12–13, accidentally the age of many of the poorest writers in our sample) should most likely be in focus for our next study. In this transition children leave upper elementary and move to secondary school, which not only constitutes a different school form, but is also frequently located at a different geographical location, with new teachers—who typically expects that the elementary school teachers have “fixed” their pupils’ reading and writing skills.

Moreover, during our explorations and analyses of the data we have noticed that there are individuals with weak spelling test results who manage to produce text with very few errors and vice versa (see Fig.  2 ). In fact, a couple of students with spelling and decoding difficulties, as assessed by the tests, produced texts that were completely free from spelling errors. During the analyses of our data, we also noticed how some children were very successful in their spelling revisions while revision for others rather made the text worse, and how some children succeeded in revising certain types of spelling errors but not others. While process data may not be the best screening tool for these ages yet, slow word writing may be a first warning, and a signal to educators to look out for and attempt to prevent more dysfluent writing processes. As shown by Afonso et al. ( 2020 ), Spanish children with dyslexia in the same age span as our participants, displayed similar patterns of slow word processing during handwriting. For a review of the relation between processes in handwriting and typing, see Feng et al. ( 2019 ). These types of measures can relatively easily be detected by means of keystroke logging or handwriting recordings. We suggest that researchers and educators alike, embrace qualitative analyses of individual cases, and use these types of information to gain valuable insights into the formative assessment (cf. Skar et al., 2022 ) of the writing processes of struggling students, to acquire knowledge of where and when bottle necks can occur and to foster more effective and targeted interventions. While these suggestions are language independent, we also encourage continued research on spelling processes and their relation to higher-level processes in different orthographies.

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Acknowledgements

This research was funded by the Swedish Research Council (VR-2009-02004), the Marcus and Amalia Wallenberg Foundation (Ref. No. 2014–0122), and Riksbankens Jubileumsfond (SAB20-0018_RJ). Thanks to Petter Åberg, Celia Wik Mergulhão and Ingrid Henriksson for contributing with text quality assessments.

Open access funding provided by University of Gothenburg.

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How light can vaporize water without the need for heat

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It’s the most fundamental of processes — the evaporation of water from the surfaces of oceans and lakes, the burning off of fog in the morning sun, and the drying of briny ponds that leaves solid salt behind. Evaporation is all around us, and humans have been observing it and making use of it for as long as we have existed.

And yet, it turns out, we’ve been missing a major part of the picture all along.

In a series of painstakingly precise experiments, a team of researchers at MIT has demonstrated that heat isn’t alone in causing water to evaporate. Light, striking the water’s surface where air and water meet, can break water molecules away and float them into the air, causing evaporation in the absence of any source of heat.

The astonishing new discovery could have a wide range of significant implications. It could help explain mysterious measurements over the years of how sunlight affects clouds, and therefore affect calculations of the effects of climate change on cloud cover and precipitation. It could also lead to new ways of designing industrial processes such as solar-powered desalination or drying of materials.

The findings, and the many different lines of evidence that demonstrate the reality of the phenomenon and the details of how it works, are described today in the journal PNAS, in a paper by Carl Richard Soderberg Professor of Power Engineering Gang Chen, postdocs Guangxin Lv and Yaodong Tu, and graduate student James Zhang.

The authors say their study suggests that the effect should happen widely in nature— everywhere from clouds to fogs to the surfaces of oceans, soils, and plants — and that it could also lead to new practical applications, including in energy and clean water production. “I think this has a lot of applications,” Chen says. “We’re exploring all these different directions. And of course, it also affects the basic science, like the effects of clouds on climate, because clouds are the most uncertain aspect of climate models.”

A newfound phenomenon

The new work builds on research reported last year , which described this new “photomolecular effect” but only under very specialized conditions: on the surface of specially prepared hydrogels soaked with water. In the new study, the researchers demonstrate that the hydrogel is not necessary for the process; it occurs at any water surface exposed to light, whether it’s a flat surface like a body of water or a curved surface like a droplet of cloud vapor.

Because the effect was so unexpected, the team worked to prove its existence with as many different lines of evidence as possible. In this study, they report 14 different kinds of tests and measurements they carried out to establish that water was indeed evaporating — that is, molecules of water were being knocked loose from the water’s surface and wafted into the air — due to the light alone, not by heat, which was long assumed to be the only mechanism involved.

One key indicator, which showed up consistently in four different kinds of experiments under different conditions, was that as the water began to evaporate from a test container under visible light, the air temperature measured above the water’s surface cooled down and then leveled off, showing that thermal energy was not the driving force behind the effect.

Other key indicators that showed up included the way the evaporation effect varied depending on the angle of the light, the exact color of the light, and its polarization. None of these varying characteristics should happen because at these wavelengths, water hardly absorbs light at all — and yet the researchers observed them.

The effect is strongest when light hits the water surface at an angle of 45 degrees. It is also strongest with a certain type of polarization, called transverse magnetic polarization. And it peaks in green light — which, oddly, is the color for which water is most transparent and thus interacts the least.

Chen and his co-researchers have proposed a physical mechanism that can explain the angle and polarization dependence of the effect, showing that the photons of light can impart a net force on water molecules at the water surface that is sufficient to knock them loose from the body of water. But they cannot yet account for the color dependence, which they say will require further study.

They have named this the photomolecular effect, by analogy with the photoelectric effect that was discovered by Heinrich Hertz in 1887 and finally explained by Albert Einstein in 1905. That effect was one of the first demonstrations that light also has particle characteristics, which had major implications in physics and led to a wide variety of applications, including LEDs. Just as the photoelectric effect liberates electrons from atoms in a material in response to being hit by a photon of light, the photomolecular effect shows that photons can liberate entire molecules from a liquid surface, the researchers say.

“The finding of evaporation caused by light instead of heat provides new disruptive knowledge of light-water interaction,” says Xiulin Ruan, professor of mechanical engineering at Purdue University, who was not involved in the study. “It could help us gain new understanding of how sunlight interacts with cloud, fog, oceans, and other natural water bodies to affect weather and climate. It has significant potential practical applications such as high-performance water desalination driven by solar energy. This research is among the rare group of truly revolutionary discoveries which are not widely accepted by the community right away but take time, sometimes a long time, to be confirmed.”

Solving a cloud conundrum

The finding may solve an 80-year-old mystery in climate science. Measurements of how clouds absorb sunlight have often shown that they are absorbing more sunlight than conventional physics dictates possible. The additional evaporation caused by this effect could account for the longstanding discrepancy, which has been a subject of dispute since such measurements are difficult to make.

“Those experiments are based on satellite data and flight data,“ Chen explains. “They fly an airplane on top of and below the clouds, and there are also data based on the ocean temperature and radiation balance. And they all conclude that there is more absorption by clouds than theory could calculate. However, due to the complexity of clouds and the difficulties of making such measurements, researchers have been debating whether such discrepancies are real or not. And what we discovered suggests that hey, there’s another mechanism for cloud absorption, which was not accounted for, and this mechanism might explain the discrepancies.”

Chen says he recently spoke about the phenomenon at an American Physical Society conference, and one physicist there who studies clouds and climate said they had never thought about this possibility, which could affect calculations of the complex effects of clouds on climate. The team conducted experiments using LEDs shining on an artificial cloud chamber, and they observed heating of the fog, which was not supposed to happen since water does not absorb in the visible spectrum. “Such heating can be explained based on the photomolecular effect more easily,” he says.

Lv says that of the many lines of evidence, “the flat region in the air-side temperature distribution above hot water will be the easiest for people to reproduce.” That temperature profile “is a signature” that demonstrates the effect clearly, he says.

Zhang adds: “It is quite hard to explain how this kind of flat temperature profile comes about without invoking some other mechanism” beyond the accepted theories of thermal evaporation. “It ties together what a whole lot of people are reporting in their solar desalination devices,” which again show evaporation rates that cannot be explained by the thermal input.

The effect can be substantial. Under the optimum conditions of color, angle, and polarization, Lv says, “the evaporation rate is four times the thermal limit.”

Already, since publication of the first paper, the team has been approached by companies that hope to harness the effect, Chen says, including for evaporating syrup and drying paper in a paper mill. The likeliest first applications will come in the areas of solar desalinization systems or other industrial drying processes, he says. “Drying consumes 20 percent of all industrial energy usage,” he points out.

Because the effect is so new and unexpected, Chen says, “This phenomenon should be very general, and our experiment is really just the beginning.” The experiments needed to demonstrate and quantify the effect are very time-consuming. “There are many variables, from understanding water itself, to extending to other materials, other liquids and even solids,” he says.

“The observations in the manuscript points to a new physical mechanism that foundationally alters our thinking on the kinetics of evaporation,” says Shannon Yee, an associate professor of mechanical engineering at Georgia Tech, who was not associated with this work. He adds, “Who would have thought that we are still learning about something as quotidian as water evaporating?”

“I think this work is very significant scientifically because it presents a new mechanism,” says University of Alberta Distinguished Professor Janet A.W. Elliott, who also was not associated with this work. “It may also turn out to be practically important for technology and our understanding of nature, because evaporation of water is ubiquitous and the effect appears to deliver significantly higher evaporation rates than the known thermal mechanism. …  My overall impression is this work is outstanding. It appears to be carefully done with many precise experiments lending support for one another.”

The work was partly supported by an MIT Bose Award. The authors are currently working on ways to make use of this effect for water desalination, in a project funded by the Abdul Latif Jameel Water and Food Systems Lab and the MIT-UMRP program.

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Researchers at MIT have discovered that “light in the visible spectrum is enough to knock water molecules loose at the surface where it meets air and send them floating away,” reports Michael Franco for New Atlas . “While the distinction between light-caused evaporation and heat-caused evaporation might not seem like a big one, the researchers say it could not only have a big impact on the way future evaporative projects are executed, but that it could also explain a long-standing discrepancy involving clouds,” writes Franco.

Interesting Engineering

Interesting Engineering reporter Rizwan Choudhury spotlights a new study by MIT researchers that finds light can cause evaporation of water from a surface without the need for heat. The photomolecular effect “presents exciting practical possibilities,” writes Choudhury. “Solar desalination systems and industrial drying processes are prime candidates for harnessing this effect. Since drying consumes significant industrial energy, optimizing this process using light holds immense promise.”

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

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States find a downside to mandatory reporting laws meant to protect children

Kristin Jones

More than 60 years ago, policymakers in Colorado embraced the idea that early intervention could prevent child abuse and save lives. The state's requirement that certain professionals tell officials when they suspect a child has been abused or neglected was among the first mandatory reporting laws in the nation.

Since then, mandatory reporting laws have expanded nationally to include more types of maltreatment — including neglect, which now accounts for most reports — and have increased the number of professions required to report. In some states, all adults are required to report what they suspect may be abuse or neglect.

But now there are efforts in Colorado and other states – including New York and California — to roll back these laws, saying the result has been too many unfounded reports, and that they disproportionately harm families that are poor, Black, or Indigenous, or have members with disabilities.

"There's a long, depressing history based on the approach that our primary response to a struggling family is reporting," says Mical Raz, a physician and historian at the University of Rochester in New York. "There's now a wealth of evidence that demonstrates that more reporting is not associated with better outcomes for children."

Seeking balance

Stephanie Villafuerte, Colorado's child protection ombudsman, oversees a task force to reexamine the state's mandatory reporting laws. She says the group is seeking to balance a need to report legitimate cases of abuse and neglect with a desire to weed out inappropriate reports.

"This is designed to help individuals who are disproportionately impacted," Villafuerte says. "I'm hoping it's the combination of these efforts that could make a difference."

Some critics worry that changes to the law could result in missed cases of abuse. Medical and child care workers on the task force have expressed concern about legal liability. While it's rare for people to be criminally charged for failure to report, they can also face civil liability or professional repercussions, including threats to their licenses.

Being reported to child protective services is becoming increasingly common. More than 1 in 3 children in the United States will be the subject of a child abuse and neglect investigation by the time they turn 18, according to the most frequently cited estimate, a 2017 study funded by the Department of Health and Human Services' Children's Bureau.

Black and Native American families, poor families, and parents or children with disabilities experience even more oversight. Research has found that, among these groups, parents are more likely to lose parental rights and children are more likely to wind up in foster care.

In an overwhelming majority of investigations, no abuse or neglect is substantiated. Nonetheless, researchers who study how these investigations affect families describe them as terrifying and isolating.

In Colorado, the number of child abuse and neglect reports has increased 42% in the past decade and reached a record 117,762 last year, according to state data . Roughly 100,000 other calls to the hotline weren't counted as reports because they were requests for information or were about matters like child support or adult protection, say officials from the Colorado Department of Human Services.

No surge in substantiated cases of abuse

The increase in reports can be traced to a policy of encouraging a broad array of professionals — including school and medical staff, therapists, coaches, clergy members, firefighters, veterinarians, dentists, and social workers — to call a hotline whenever they have a concern.

These calls don't reflect a surge in mistreatment. More than two-thirds of the reports received by agencies in Colorado don't meet the threshold for investigation. Of the children whose cases are assessed, 21% are found to have experienced abuse or neglect. The actual number of substantiated cases has not risen over the past decade.

While studies do not demonstrate that mandatory reporting laws keep children safe, the Colorado task force reported in January , there is evidence of harm. "Mandatory reporting disproportionately impacts families of color" — initiating contact between child protection services and families who routinely do not present concerns of abuse or neglect, the task force said.

The task force says it is analyzing whether better screening might mitigate "the disproportionate impact of mandatory reporting on under-resourced communities, communities of color and persons with disabilities."

The task force pointed out that the only way to report concerns about a child is with a formal report to a hotline. Yet many of those calls are not to report abuse at all but rather attempts to connect children and families with resources like food or housing assistance.

Hotline callers may mean to help, but the families who are the subjects of mistaken reports of abuse and neglect rarely see it that way.

That includes Meighen Lovelace, a rural Colorado resident who asked KFF Health News not to disclose their hometown for fear of attracting unwanted attention from local officials. For Lovelace's daughter, who is neurodivergent and has physical disabilities, the reports started when she entered preschool at age 4 in 2015. The teachers and medical providers making the reports frequently suggested that the county human services agency could assist Lovelace's family. But the investigations that followed were invasive and traumatic.

"Our biggest looming fear is, 'Are you going to take our children away?'" says Lovelace, who is an advocate for the Colorado Cross-Disability Coalition, an organization that lobbies for the civil rights of people with disabilities. "We're afraid to ask for help. It's keeping us from entering services because of the fear of child welfare."

State and county human services officials said they could not comment on specific cases.

A 'warmline' to connect families to services

The Colorado task force plans to suggest clarifying the definitions of abuse and neglect under the state's mandatory reporting statute. Mandatory reporters should not "make a report solely due to a family/child's race, class or gender," nor because of inadequate housing, furnishings, income or clothing. Also, there should not be a report based solely on the "disability status of the minor, parent or guardian," according to the group's draft recommendation.

The task force plans to recommend additional training for mandatory reporters, help for professionals who are deciding whether to make a call, and an alternative phone number, or "warmline," for cases in which callers believe a family needs material assistance, rather than surveillance.

Critics say such changes could leave more children vulnerable to unreported abuse.

"I'm concerned about adding systems such as the warmline, that kids who are in real danger are going to slip through the cracks and not be helped," says Hollynd Hoskins, an attorney who represents victims of child abuse. Hoskins has sued professionals who fail to report their suspicions.

The Colorado task force includes health and education officials, prosecutors, victim advocates, county child welfare representatives and attorneys, as well as five people who have experience in the child welfare system. It intends to finalize its recommendations by early next year in the hope that state legislators will consider policy changes in 2025. Implementation of any new laws could take several years.

Other places have recently considered changes to restrain, rather than expand, reporting of abuse. In New York City, teachers are being trained to think twice before making a report, while New York state introduced a warmline to help connect families with resources like housing and child care. In California, a state task force aimed at shifting "mandated reporting to community supporting" is planning recommendations similar to Colorado's .

Among those advocating for change are people with experience in the child welfare system. They include Maleeka Jihad , who leads the Denver-based MJCF Coalition, which advocates for the abolition of mandatory reporting along with the rest of the child welfare system, citing its damage to Black, Native American, and Latino communities.

"Mandatory reporting is another form of keeping us policed and surveillanced by whiteness," says Jihad, who as a child was taken from the care of a loving parent and placed temporarily into the foster system. Reform isn't enough, she says. "We know what we need, and it's usually funding and resources."

Some of these resources — like affordable housing and child care — don't exist at a level sufficient for all the Colorado families that need them, Jihad says.

Other services are out there, but it's a matter of finding them. Lovelace says the reports ebbed after the family got the help it needed, in the form of a Medicaid waiver that paid for specialized care for their daughter's disabilities. Their daughter is now in seventh grade and doing well.

None of the caseworkers who visited the family ever mentioned the waiver, Lovelace says. "I really think they didn't know about it."

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF — the independent source for health policy research, polling, and journalism.

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‘Huge disappointment’ as UK delays bottle deposit plan and excludes glass

Scheme for plastic bottles and cans put back to 2027 while environment minister says glass recycling ‘unduly’ complex

A UK deposit return scheme for recycling drinks bottles has been delayed to 2027, meaning it will not be in place until almost a decade after it was proposed.

Campaigners say the delay is a “huge disappointment”, adding they are doubly dismayed that the plan will not include glass bottles.

The environment minister Robbie Moore told parliament on Wednesday that the scheme would not include glass because glass recycling would “create undue complexity for the drinks industry and it increases storage and handling costs for retailers”. Large drinks companies have been lobbying the government to remove glass from the scheme across the UK.

He said the delay was because additional time was needed to synchronise the policy of the devolved administrations in Northern Ireland, Scotland and Wales with that in England. Scotland has agreed to remove glass from its scheme after being asked to by the UK government, but Wales is still including it.

Moore said: “We will continue our conversations with the Welsh government, but if their position does not change, we will reiterate the duty to protect the UK internal market and facilitate free trade within the UK so businesses can continue trading unhindered across the UK and ensure better prices and choice for consumers.”

UK consumers use an estimated 13bn plastic drinks bottles a year. Only 7.5bn are recycled, with the remaining 5.5bn sent to landfill, littered or incinerated. The scheme is intended to cut litter on land and sea by paying consumers a small cash sum to return their bottles and cans. Once returned, retailers are responsible for properly recycling the containers. Deposit return schemes have increased recycling rates to more than 90% in other countries.

Sandy Luk, the chief executive of the Marine Conservation Society, said: “It’s a huge disappointment that the new scheme isn’t going to start for another three years and isn’t going to cover glass bottles. For our ocean’s sake, we can’t keep kicking the can – or bottle – down the road. We call on the UK government to speed up this law and to follow Wales’s ambition to include plastic, metal and glass.”

The charity Keep Britain Tidy estimates 25bn bottles and cans will be littered between now and the start of the scheme. Allison Ogden-Newton, the head of the charity, said: “This delay means oceans of bottles and cans will continue to needlessly pile up in bins and continue to be strewn on roadsides and in our green spaces, rather than being recycled.

“The exclusion of glass is hugely disappointing. Glass containers start fires and cause harm to people, pets and wildlife. This is why 78% of people want to see it included in a deposit return scheme. We are pleased that Wales look determined to pursue their best-in-class scheme, and encourage the rest of the UK to follow suit.”

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Steve Reed, the shadow environment secretary, said: “We’ve gone through four prime ministers since the Conservative government first promised a deposit return scheme for recycling bottles. And yet it will be nearly a decade until they have something to show for it. The Conservatives simply don’t care that plastic bottles end up littering our streets, parks, rivers and seas. Labour will work across Britain and with business to bring in a deposit return scheme that will stop this waste and clean up our environment.”

• Ethical and green living
• Food & drink industry

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