a systematic review on educational data mining

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A Systematic Review on Educational Data Mining

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Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

• clustering methods
• Data mining
• educational technology
• systematic review

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• 10.1109/ACCESS.2017.2654247

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T1 - A Systematic Review on Educational Data Mining

AU - Dutt, Ashish

AU - Ismail, Maizatul Akmar

AU - Herawan, Tutut

N1 - Funding Information: This work was supported by the University of Malaya research under Grant RP028B-14AET. Publisher Copyright: © 2013 IEEE.

N2 - Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

AB - Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

KW - clustering methods

KW - Data mining

KW - educational technology

KW - systematic review

UR - http://www.scopus.com/inward/record.url?scp=85028347409&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2017.2654247

DO - 10.1109/ACCESS.2017.2654247

M3 - Review Article

AN - SCOPUS:85028347409

SN - 2169-3536

JO - IEEE Access

JF - IEEE Access

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Information Discovery and Delivery

ISSN : 2398-6247

Article publication date: 19 May 2020

Issue publication date: 10 October 2020

Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education.

Design/methodology/approach

This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research.

Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward.

Research limitations/implications

Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals.

Originality/value

This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

• Educational data mining
• Learning analytics
• Systematic review
• Prediction of performance
• Decision support
• Artificial intelligence

Acknowledgements

Conflict of interest: The authors have declared no conflicts of interest for this article.

This study was supported by National Natural Science Foundation of China Under Grant No. 61877027.

Du, X. , Yang, J. , Hung, J.-L. and Shelton, B. (2020), "Educational data mining: a systematic review of research and emerging trends", Information Discovery and Delivery , Vol. 48 No. 4, pp. 225-236. https://doi.org/10.1108/IDD-09-2019-0070

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A Systematic Review on Educational Data Mining

In this paper, implementing K-Means clustering algorithm for analyzing the particular dataset and data mining. The main purpose is WEKA process. In Weka process we can get perfect graph, accuracy and random process. The Pre-processing was important concept it may clear a null values, removes a unwanted data and unwanted memory space. In Data mining analyzing data set. In Data mining implementing two methods classification, clustering process. By using classification, clustering we get flexible result and large amount of database. Here, weka process and K-means algorithm going to compare whether both graphs are accurate manner.

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A Systematic Review on Data Mining for Mathematics and Science Education

• Published: 14 May 2020
• Volume 19 , pages 639–659, ( 2021 )

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• Jaekwoun Shim 1  

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Educational data mining is used to discover significant phenomena and resolve educational issues occurring in the context of teaching and learning. This study provides a systematic literature review of educational data mining in mathematics and science education. A total of 64 articles were reviewed in terms of the research topics and data mining techniques used. This review revealed that data mining in mathematics and science education has been commonly used to understand students’ behavior and thinking process, identify factors affecting student achievements, and provide automated assessment of students’ written work. Recently, researchers have tended to use such data mining techniques as text mining to develop learning systems for supporting teachers’ instruction and students’ learning. We also found that classification, text mining, and clustering are major data mining techniques researchers have used. Studies using data mining were more likely to be conducted in the field of science education than in the field of mathematics education. We discuss the main results of our review in comparison with the previous reviews of educational data mining (EDM) literature and with EDM studies conducted in the context of science and mathematics education. Finally, we provide implications for research and teaching and learning of science and mathematics and suggest potential research directions.

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Abidi, S., Hussain, M., Xu, Y., & Zhang, W. (2019). Prediction of confusion attempting algebra homework in an intelligent tutoring system through machine learning techniques for educational sustainable development. Sustainability . Advance online publication. https://doi.org/10.3390/su11010105 .

Aiken, J. M., Henderson, R., & Caballero, M. D. (2019). Modeling student pathways in a physics bachelor’s degree program. Physical Review Physics Education Research, Advance online publication . https://doi.org/10.1103/PhysRevPhysEducRes.15.010128 .

Akgün, E., & Demir, M. (2018). Modeling course achievements of elementary education teacher candidates with artificial neural networks. International Journal of Assessment Tools in Education, 5 (3), 491–509.

Article   Google Scholar  

Aksoy, E., Narli, S., & Idil, F. H. (2016). Using data mining techniques examination of the middle school students’ attitude towards mathematics in the context of some variables. International Journal of Education in Mathematics Science and Technology, 4 (3), 210–228.

Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21stcentury higher education: A review and synthesis. Telematics and Informatics, 37 , 13–46.

Araya, R., Jiménez, A., Bahamondez, M., Calfucura, P., Dartnell, P., & Soto-Andrade, J. (2014). Teaching modeling skills using a massively multiplayer online mathematics game. World Wide Web, 17 (2), 213–227.

Bağ, H., & Çalık, M. (2017). A thematic review of argumentation studies at the K-8 level. Education and Science, 42 (190), 281–303.

Google Scholar  

Barnhart, T., & van Es, E. (2015). Studying teacher noticing: Examining the relationship among pre-service science teachers’ ability to attend, analyze and respond to student thinking. Teaching and Teacher Education, 45 , 83–93.

Beggrow, E. P., Ha, M., Nehm, R. H., Pearl, D., & Boone, W. J. (2014). Assessing scientific practices using machine-learning methods: How closely do they match clinical interview performance? Journal of Science Education and Technology, 23 (1), 160–182.

Bywater, J. P., Chiu, J. L., Hong, J., & Sankaranarayanan, V. (2019). The teacher responding tool: Scaffolding the teacher practice of responding to student ideas in mathematics classrooms. Computers & Education, 139 , 16–30.

Cai, W., Grossman, J., Lin, Z., Sheng, H., Wei, J. T. Z., Williams, J. J., & Goel, S. (2019). MathBot: A personalized conversational agent for learning math . Retrieved from https://footprints.stanford.edu/papers/mathbot.pdf . Accessed 16 Jan 2020.

Çalık, M., & Sözbilir, M. (2014). Parameters of content analysis. Education and Science, 39 (174), 33–38.

Chen, C. T., & Chang, K. Y. (2017). A study on the rare factors exploration of learning effectiveness by using fuzzy data mining. EURASIA Journal of Mathematics, Science and Technology Education, 13 (6), 2235–2253.

Chen, J., Zhang, Y., Wei, Y., & Hu, J. (2019). Discrimination of the contextual features of top performers in scientific literacy using a machine learning approach. Research in Science Education . Advanced online publication. https://doi.org/10.1007/s11165-019-9835-y .

Cheon, J., Lee, S., Smith, W., Song, J., & Kim, Y. (2013). The determination of children’s knowledge of global lunar patterns from online essays using text mining analysis. Research in Science Education, 43 (2), 667–686.

Choi, Y., Lim, Y., & Son, D. (2017). A semantic network analysis on the recognition of STEAM by middle school students in South Korea. EURASIA Journal of Mathematics, Science and Technology Education, 13 (10), 6457–6469.

Cooper, C. I., & Pearson, P. T. (2012). A genetically optimized predictive system for success in general chemistry using a diagnostic algebra test. Journal of Science Education and Technology, 21 (1), 197–205.

Depren, S. K. (2018). Prediction of students’ science achievement: An application of multivariate adaptive regression splines and regression trees. Journal of Baltic Science Education, 17 (5), 887–903.

Depren, S. K., Aşkın, Ö. E., & Öz, E. (2017). Identifying the classification performances of educational data mining methods: A case study for TIMSS. Educational Sciences: Theory & Practice, 17 (5), 1605–1623.

Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5 , 15991–16005.

Duzhin, F., & Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education in Science, 8 (1), 15. https://doi.org/10.3390/educsci9040263 .

English, L. D., & King, D. (2019). STEM integration in sixth grade: Desligning and constructing paper bridges. International Journal of Science and Mathematics Education, 17 (5), 863–884.

Figueiredo, M., Esteves, L., Neves, J., & Vicente, H. (2016). A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation. Chemistry Education Research and Practice, 17 (1), 156–171.

Filiz, E., & Oz, E. (2019). Finding the best algorithms and effective factors in classification of Turkish science student success. Journal of Baltic Science Education, 18 (2), 239–253.

Gabriel, F., Signolet, J., & Westwell, M. (2018). A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy. International Journal of Research & Method in Education, 41 (3), 306–327.

Gobert, J. D., Kim, Y. J., Sao Pedro, M. A., Kennedy, M., & Betts, C. G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18 , 81–90.

Goggins, S. P., Xing, W., Chen, X., Chen, B., & Wadholm, B. (2015). Learning analytics at “small” scale: Exploring a complexity-grounded model for assessment automation. Journal of Universal Computer Science, 21 (1), 66–92.

Gorostiaga, A., & Rojo-Álvarez, J. L. (2016). On the use of conventional and statistical-learning techniques for the analysis of PISA results in Spain. Neurocomputing, 171 , 625–637.

Günel, K., Polat, R., & Kurt, M. (2016). Analyzing learning concepts in intelligent tutoring systems. International Arab Journal of Information Technology, 13 (2), 281–286.

Ha, M., & Nehm, R. H. (2016). The impact of misspelled words on automated computer scoring: A case study of scientific explanations. Journal of Science Education and Technology, 25 (3), 358–374.

Ha, M., Nehm, R. H., Urban-Lurain, M., & Merrill, J. E. (2011). Applying computerized-scoring models of written biological explanations across courses and colleges: Prospects and limitations. CBE Life Sciences Education, 10 (4), 379–393.

Hershkovitz, A., de Baker, R. S. J., Gobert, J., Wixon, M., & Pedro, M. S. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57 (10), 1480–1499.

Hodgen, J., Küchemann, D., Brown, M., & Coe, R. (2009). Children’s understandings of algebra 30 years on. Research in Mathematics Education, 11 (2), 193–194.

Hossain, Z., Bumbacher, E., Brauneis, A., Diaz, M., Saltarelli, A., Blikstein, P., & Riedel-Kruse, I. H. (2018). Design guidelines and empirical case study for scaling authentic inquiry-based science learning via open online courses and interactive biology cloud labs. International Journal of Artificial Intelligence in Education, 28 (4), 478–507.

Howard, E., Meehan, M., & Parnell, A. (2018). Live lectures or online videos: Students’ resource choices in a first-year university mathematics module. International Journal of Mathematical Education in Science and Technology, 49 (4), 530–553.

Huang, C. J., Wang, Y. W., Huang, T. H., Chen, Y. C., Chen, H. M., & Chang, S. C. (2011). Performance evaluation of an online argumentation learning assistance agent. Computers & Education, 57 (1), 1270–1280.

Ismail, S., & Abdulla, S. (2015). Design and implementation of an intelligent system to predict the student graduation AGPA. Australian Educational Computing, 30 (2). Retrieved from http://journal.acce.edu.au/index.php/AEC/article/view/53 . Accessed 16 Jan 2020.

Jacobs, V. R., Lamb, L. L., & Philipp, R. A. (2010). Professional noticing of children’s mathematical thinking. Journal for Research in Mathematics Education, 41 (2), 169–202.

Kilic, H. (2018). Pre-service mathematics teachers’ noticing skills and scaffolding practices. International Journal of Science and Mathematics Education, 16 (2), 377–400.

Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers & Education, 127 , 233–251.

Kinnebrew, J. S., Killingsworth, S. S., Clark, D. B., Biswas, G., Sengupta, P., Minstrell, J., . . . Krinks, K. (2016). Contextual markup and mining in digital games for science learning: Connecting player behaviors to learning goals. IEEE Transactions on Learning Technologies, 10 (1), 93–103.

Kirby, N., & Dempster, E. (2015). Not the norm: The potential of tree analysis of performance data from students in a foundation mathematics module. African Journal of Research in Mathematics, Science and Technology Education, 19 (2), 131–142.

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3) . Keele University and Durham University.

Lamb, R., Annetta, L., Vallett, D., & Sadler, T. (2014). Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames. Computers & Education, 70 , 92–104.

Lamb, R., Cavagnetto, A., & Akmal, T. (2016). Examination of the nonlinear dynamic systems associated with science student cognition while engaging in science information processing. International Journal of Science and Mathematics Education, 14 (1), 187–205.

Lavie Alon, N., & Tal, T. (2015). Student self-reported learning outcomes of field trips: The pedagogical impact. International Journal of Science Education, 37 (8), 1279–1298.

Lee, H. S., Pallant, A., Pryputniewicz, S., Lord, T., Mulholland, M., & Liu, O. L. (2019). Automated text scoring and real-time adjustable feedback: Supporting revision of scientific arguments involving uncertainty. Science Education, 103 (3), 590–622.

Lee, Y. (2019). Using self-organizing map and clustering to investigate problem-solving patterns in the massive open online course: An exploratory study. Journal of Educational Computing Research, 57 (2), 471–490.

Levy, S. T., & Wilensky, U. (2011). Mining students’ inquiry actions for understanding of complex systems. Computers & Education, 56 (3), 556–573.

Liu, S. H., & Lee, G. G. (2013). Using a concept map knowledge management system to enhance the learning of biology. Computers & Education, 68 , 105–116.

Liu, O. L., Rios, J. A., Heilman, M., Gerard, L., & Linn, M. C. (2016). Validation of automated scoring of science assessments. Journal of Research in Science Teaching, 53 (2), 215–233.

Liu, X., & Whitford, M. (2011). Opportunities-to-learn at home: Profiles of students with and without reaching science proficiency. Journal of Science Education and Technology, 20 (4), 375–387.

Magana, A. J., Elluri, S., Dasgupta, C., Seah, Y. Y., Madamanchi, A., & Boutin, M. (2019). The role of simulation-enabled design learning experiences on middle school students’ self-generated inherence heuristics. Journal of Science Education and Technology, 28 (4), 1–17.

Malmberg, J., Järvenoja, H., & Järvelä, S. (2013). Patterns in elementary school students’ strategic actions in varying learning situations. Instructional Science, 41 (5), 933–954.

Martin, T., Petrick Smith, C., Forsgren, N., Aghababyan, A., Janisiewicz, P., & Baker, S. (2015). Learning fractions by splitting: Using learning analytics to illuminate the development of mathematical understanding. Journal of the Learning Sciences, 24 (4), 593–637.

Masci, C., Johnes, G., & Agasisti, T. (2018). Student and school performance across countries: A machine learning approach. European Journal of Operational Research, 269 (3), 1072–1085.

McConney, A., & Perry, L. B. (2010). Science and mathematics achievement in Australia: The role of school socioeconomic composition in educational equity and effectiveness. International Journal of Science and Mathematics Education, 8 (3), 429–452.

National Council of Teachers of Mathematics. (2014). Principles to actions: Ensuring mathematical success for all . Reston, VA: Author.

National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas . Washington, DC: National Academies Press.

Nehm, R. H., Ha, M., & Mayfield, E. (2012). Transforming biology assessment with machine learning: Automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21 (1), 183–196.

Nehm, R. H., & Haertig, H. (2012). Human vs. computer diagnosis of students’ natural selection knowledge: Testing the efficacy of text analytic software. Journal of Science Education and Technology, 21 (1), 56–73.

NGSS Lead States. (2013). Next generation science standards: For states, by states . Washington, DC: National Academies Press.

Northcutt, C. G., Ho, A. D., & Chuang, I. L. (2016). Detecting and preventing “multiple-account” cheating in massive open online courses. Computers & Education, 100 , 71–80.

Owens, M. T., Seidel, S. B., Wong, M., Bejines, T. E., Lietz, S., Perez, J. R., . . . Balukjian, B. (2017). Classroom sound can be used to classify teaching practices in college science courses. Proceedings of the National Academy of Sciences, 114 (12), 3085–3090.

Pantziara, M., & Philippou, G. N. (2015). Students’ motivation in the mathematics classroom. Revealing causes and consequences. International Journal of Science and Mathematics Education, 13 (2), 385–411.

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17 (4), 49–64.

Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41 (4), 1432–1462.

Prevost, L. B., Smith, M. K., & Knight, J. K. (2016). Using student writing and lexical analysis to reveal student thinking about the role of stop codons in the central dogma. CBE Life Sciences Education, 15 (4), ar65. https://doi.org/10.1187/cbe.15-12-0267 .

Rao, D. C., & Saha, S. K. (2019). An immersive learning platform for efficient biology learning of secondary school-level students. Journal of Educational Computing Research . Advanced online publication. https://doi.org/10.1177/0735633119854031 .

Reitsma, R., Marshall, B., & Chart, T. (2012). Can intermediary-based science standards crosswalking work? Some evidence from mining the standard alignment tool (SAT). Journal of the American Society for Information Science and Technology, 63 (9), 1843–1858.

Roberts, J. D., Chung, G. K., & Parks, C. B. (2016). Supporting children’s progress through the PBS KIDS learning analytics platform. Journal of Children and Media, 10 (2), 257–266.

Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35 (6), 1701–1717.

Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33 (1), 135–146.

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state-of-the-art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40 (6), 601–618.

Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3 (1), 12–27.

Saa, A. A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: A systematic review of predictive data mining techniques. Technology, knowledge and learning . Advanced online publication. doi: https://doi.org/10.1007/s10758-019-09408-7 .

Sánchez-Matamoros, G., Fernández, C., & Llinares, S. (2015). Developing pre-service teacher’ noticing of students’ understanding of the derivative concept. International Journal of Science and Mathematics Education, 13 (6), 1305–1329.

Scarpello, G. (2007). Helping students get past math anxiety. Techniques: Connecting Education and Careers, 82 (6), 34–35.

Schwarz, B. B., Prusak, N., Swidan, O., Livny, A., Gal, K., & Segal, A. (2018). Orchestrating the emergence of conceptual learning: A case study in a geometry class. International Journal of Computer-Supported Collaborative Learning, 13 (2), 189–211.

Sergis, S., Sampson, D. G., Rodríguez-Triana, M. J., Gillet, D., Pelliccione, L., & de Jong, T. (2019). Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education. Computers in Human Behavior, 92 , 724–738.

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72 , 414–422.

She, H.-C., Lin, H.-s., & Huang, L.-Y. (2019). Reflections on and implications of the programme for international student assessment 2015 performance of students in Taiwan: The role of epistemic beliefs about science in scientific literacy. Journal of Research in Science Teaching . Advanced online publication. https://doi.org/10.1002/tea.21553 .

Sieke, S. A., McIntosh, B. B., Steele, M. M., & Knight, J. K. (2019). Characterizing students’ ideas about the effects of a mutation in a noncoding region of DNA. CBE Life Sciences Education, 18 (2), ar18. https://doi.org/10.1187/cbe.18-09-0173 .

Suh, S. C., Upadhyaya, A., & Nadig, A. (2019). Analyzing personality traits and external factors for stem education awareness using machine learning. International Journal of Advanced Computer Science and Applications, 10 (5), 1–4.

Tawfik, A. A., Reeves, T. D., Stich, A. E., Gill, A., Hong, C., McDade, J., . . . Giabbanelli, P. J. (2017). The nature and level of learner–learner interaction in a chemistry massive open online course (MOOC). Journal of Computing in Higher Education, 29 (3), 411–431.

Tissenbaum, M., & Slotta, J. D. (2019). Developing a smart classroom infrastructure to support real-time student collaboration and inquiry: A 4-year design study. Instructional Science . Advanced online publication , 47 , 423–462. https://doi.org/10.1007/s11251-019-09486-1 .

Wahlberg, S. J., & Gericke, N. M. (2018). Conceptual demography in upper secondary chemistry and biology textbooks’ descriptions of protein synthesis: A matter of context ? CBE Life Sciences Education, 17 (3), ar51. https://doi.org/10.1187/cbe.17-12-0274 .

Wang, X. (2016). Course-taking patterns of community college students beginning in STEM: Using data mining techniques to reveal viable STEM transfer pathways. Research in Higher Education, 57 (5), 544–569.

Wiley, J., Hastings, P., Blaum, D., Jaeger, A. J., Hughes, S., Wallace, P., ... & Britt, M. A. (2017). Different approaches to assessing the quality of explanations following a multiple-document inquiry activity in science. International Journal of Artificial Intelligence in Education, 27 (4), 758–790.

Zhang, W., Qin, S., Jin, H., Deng, J., & Wu, L. (2017). An empirical study on student evaluations of teaching based on data mining. EURASIA Journal of Mathematics, Science and Technology Education, 13 (8), 5837–5845.

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Shin, D., Shim, J. A Systematic Review on Data Mining for Mathematics and Science Education. Int J of Sci and Math Educ 19 , 639–659 (2021). https://doi.org/10.1007/s10763-020-10085-7

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Received : 12 November 2019

Accepted : 18 March 2020

Published : 14 May 2020

Issue Date : April 2021

DOI : https://doi.org/10.1007/s10763-020-10085-7

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