knowledge is more important than marks essay

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Essay on Knowledge is Power: Samples in 100, 200, 300 Words

Updated on
Dec 15, 2023

‘ Knowledge is power’ phrase is derived from a Latin term, which is attributed to Sir Francis Bacon, a well-known essayist of all times. Knowledge is power has been accepted widely and timelessly as it underscores the significance of knowledge in empowering people, societies and countries .

Benjamin Franklin once said, ‘An investment in knowledge pays the best interest.’ Knowledge not only improves a person’s understanding of the world but also teaches them life lessons to develop decision-making skills and contribute to the betterment of society. Below we have discussed some essays on knowledge is power in different word limits.

This Blog Includes:

Essay on knowledge is power in 100 words, essay on knowledge is power in 200 words, essay on knowledge is power in 300 words.

Also Read: Traditions and Celebrations for Christmas Around the World

Shiva Tyagi

With an experience of over a year, I've developed a passion for writing blogs on wide range of topics. I am mostly inspired from topics related to social and environmental fields, where you come up with a positive outcome.

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In This Article Expand or collapse the "in this article" section Value of Knowledge

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Value of Knowledge by Erik J. Olsson LAST REVIEWED: 24 July 2018 LAST MODIFIED: 26 August 2013 DOI: 10.1093/obo/9780195396577-0008

Everyone agrees that it is good to know. If you know that it will rain tomorrow, you can adapt your traveling plans accordingly; if you know that the euro crisis will soon be over, you can make a fortune buying euros; if you know a lot about philosophy, you can become a highly regarded teacher; and so on. Knowledge is clearly valuable in the sense of securing success in practical life, or at least making success more likely. Even philosophers, who disagree about many other things, do not normally debate the proposition that knowledge is of great value in practical terms. Moreover, they normally do not dispute the claim that knowledge is, in some ways, more valuable than other, lesser things, such as mere true belief. But this is where agreement usually ends. Philosophers disagree widely over what it is that makes knowledge more valuable than mere true belief. The question of why knowledge is more valuable than mere true belief, raised with characteristic clarity by Plato in the dialogue Meno , has therefore been in the focus of the epistemological debate. The value of knowledge was long not considered to be a serious epistemological concern until it emerged, in the late 1990s, as the central problem of a new research program with contributions from, among others, Jonathan Kvanvig, Ernest Sosa, Richard Swinburne, and Linda Zagzebski. Other authors followed, for example, John Greco, Wayne Riggs, and Duncan Pritchard, marking what has been referred to as a “value turn” in epistemology. A characteristic feature of this movement is that the value problem is used to guide inquiry into the traditionally more debated issue regarding the nature of knowledge. Thus, authors in the value tradition tend to think that any reasonable definition of knowledge should satisfy the condition that knowledge comes out as being distinctively valuable. These authors generally believe, moreover, that the reliabilist account of knowledge, according to which knowledge amounts to reliably produced true belief, does not satisfy this condition because of the so-called swamping problem: if a belief is true, the fact that it was reliably acquired does not seem to add value. Hence, they are inclined to reject the reliabilist theory in favor of other definitions of knowledge, such as a definition that explicates knowledge in terms of intellectual (epistemic) virtue or some variation on that theme.

There are not too many books that deal exclusively with the value of knowledge. The most well-known book-length study is Kvanvig 2003 . Kvanvig’s book was instrumental in setting the agenda for the value debate, and it continues to be one of the most cited texts in this area of epistemology. Published in 2003, it is still a useful introductory text starting with classical approaches and leading up to contemporary work. Classical and early responses to the value problem are dismissed in the first chapter (for reasons that later authors have sometimes contested). It can also be used as a textbook if supplemented with articles that provide different outlooks on the topic, such as the overviews Olsson 2011 , Pritchard 2007 , and Pritchard and Turri 2011 .

Kvanvig, Jonathan. The Value of Knowledge and the Pursuit of Understanding . Cambridge, UK: Cambridge University Press, 2003.

DOI: 10.1017/CBO9780511498909

Kvanvig argues that virtue epistemology can solve the value problem as that problem was understood by Plato. But he also thinks that the problem in its most general form—showing why knowledge is more valuable than its conceptual parts—does not admit of a plausible solution. Instead, he argues that understanding, not knowledge, is what has distinctive value.

Olsson, Erik J. “The Value of Knowledge.” Philosophy Compass 6.12 (2011): 874–883.

DOI: 10.1111/j.1747-9991.2011.00425.x

This article provides an overview of the area, starting with historical figures and early work. The contemporary debate is surveyed and some recent developments are highlighted, including recent criticisms of virtue epistemology. The emphasis is on classical and reliabilist-externalist responses.

Pritchard, Duncan H. “ Recent Work on Epistemic Value .” American Philosophical Quarterly 44 (2007): 85–110.

Focusing on virtue epistemology, Pritchard’s extensive survey covers most works on the value of knowledge that had been published up to 2007. It also treats some nonstandard, though related, subjects such as the relation between epistemic value and the problem of skepticism, and the value of true belief.

Pritchard, Duncan H., and John Turri. “ The Value of Knowledge .” In The Stanford Encyclopedia of Philosophy . Edited by Edward N. Zalta. 2011.

This is a useful overview of the problem of the value of knowledge, covering a number of the most significant and useful debates and positions.

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Grades vs Educational Knowledge

By Madeleine on March 26, 2021 |Tagged with: learning , motivation , skills for class , Student Reflections

As a university student it is important to recognize that there is a difference between grades and educational knowledge. While the two do go hand in hand quite nicely, there is a fine line that separates them and it is important to understand what differentiates the two. It took me a long time to really understand this. I remember getting back exams and seeing a grade that was lower than what I expected, and resorting to thinking that I really did not know anything. Sometimes it was true that I did not understand it, but other times it was just one too many simple mistakes that landed me with a grade that did not represent how much I really knew. It was a really important lesson for me to learn that what I actually knew and understood was not always the same as the grade I got. Similarly, I learned the hard the way that getting a good grade on something does not necessarily mean that I actually know it. Just because I got a good grade on my homework did not mean I actually understood it, and sitting clueless in my midterm exam really showed me that. I realized there is a very important distinction between grades and knowledge, and I am going to explain it to you.

This post will discuss the difference between grades and knowledge, how to reflect on when and how you may have gained knowledge, and to explain the relationship between achieving good grades and gaining knowledge. While most people know that you can achieve both of these things together, it is also important to understand that you can achieve grades without knowledge, and gain knowledge without achieving good grades. And most importantly, gaining knowledge can help you achieve good grades, and the effort you put into achieving good grades can help you gain knowledge. If knowledge helps you get good grades, and working to get good grades help you gain knowledge, you might find yourself thinking which came first? The knowledge or the grade?

But what are grades and what is knowledge? The Oxford Languages dictionary has several definitions for both. I’d like to share the first two of each with you.

Acts, information, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject.
Awareness or familiarity gained by experience of a fact or situation.
A particular level of rank, quality, proficiency, intensity, or value
A mark indicating the quality of a student’s work.

Now, if I may, I would like to share MY personal definitions for each, or rather, what each means to me.

The information that is in your head that you really understand and know, that you can apply to other things and use in your life, for any purpose. Proper dribbling technique in field hockey is very important knowledge to me, even if I would never be graded on it.

A representation of how well you can learn a lot of material, in any way possible, often in a short amount of time. To me this represents my study habits, my ability to stay on track of my workload, my notetaking, and frankly just how well I study. Typically grades represent your performance in a program or course.

The biggest difference, in my opinion, is that grades can be measured concretely, while knowledge is much harder to calculate. You know very easily what your grades are simply by looking at your transcript, or by getting your tests, quizzes, projects, or papers back. It is also quite easy to feel overwhelmed by your grades, whether they are what you were expecting or not. Knowledge, however, goes much more frequently unrecognized. However, while it may be difficult to measure, it is not the case that we should therefore not seek to recognize our knowledge. Educational knowledge comes in forms such as organizing your time, keeping up with your workload, and managing your stress. It is important not to assume that these skills are less important than your grades.

I think it is very important to reflect on our knowledge so when can understand just how much we have learned. In my personal experience, it tends to take me a long time to fully understand that I have actually learned something, that I have gained knowledge. I often feel like after all my final exams I immediately forget everything I have just learned, and I think, what a waste of time that was, seeing as I learned nothing. But I did not learn nothing! That is the tricky part to realize, which requires reflection. For me I find it helps to talk to a friend or a family member, and explain to them what I did in a certain course. Usually what happens for me is that the person I am talking to goes, “slow down, what was that word you just said? I’ve never heard of that before,” and a lot of the time I think “hey, I did not know that word before this term either,” in which case, I realize I MUST have learned something.

Knowing when you have gained knowledge is something you have to think about, and it is something you realize much more easily and more quickly over time. A fun way of knowing, in my opinion, whether you have gained knowledge, is to write a list of questions you have about a course or concept at the beginning or middle of term. Note a number of things that you do not fully understand, from words, to symbols, to whole processes and concepts. Then, at the end of term, just see how many of those things are so obvious to you now. To me, it is a very positive and uplifting way to reflect on what you know now that you did not know before.

The importance of educational knowledge

Educational knowledge is important because it stays with you forever. There are a few ways to think about this, and they are all extremely valuable. The most obvious, I think, is the fact that if you learn a lot in your 102 class, you will be very well prepared for your 202 class in the same subject. The material you learned gets carried over and built upon, and how well you actually know the material, regardless of your grade, will help you with the new material. So, if you got a 90% but it was because there was a really smart person in your group project, or you got lucky on the multiple choice, this grade will not help you going forward if you do not really know the material. On the other hand, if you really knew the material really well but you got a terrible sleep the night before the final and wrote a terrible exam, that bad grade does not stop you from knowing the material. This has been a really important thing for me to realize, after not doing well on exams I really thought I should have done well on. It is important for me to go over the exam and ask myself if I could have gotten the answer correct. Was I completely lost or was I just missing one little thing? Accessing your knowledge is really important.

Another way to think about this is the experiential knowledge you gain. Even if you do not gain factual knowledge, like above, you might have gained knowledge in regard to your skills and technique. This is not surprising at all, since for every year of school, since we were five, our workload increases more and more each year. There are bigger projects and longer essays and harder books to read. This is because we learn from doing. Even if you got a 55% grade on your term, you still had really great practice in writing the essay. Your study habits get better every year just from continuing to study, and this is extremely valuable knowledge. However, if you just memorize something rather than take the time to practice or understand it, once you forget it, it is as though you never had that information. If you fully understand something, even if you forget it, you still have that knowledge, and this is why knowledge is so important.

Importance of grades

Many of us have heard, for all our lives, “your grades are very important”. For me, when I was applying to university, all I ever heard about was how my grades are of the utmost importance. While it is important to also consider educational knowledge, there is also a lot of truth in that grades are of equal importance. If I had not cared about my grades, even if I was learning, and let them drop, I may not have gotten into university. Then, even if I did want to keep learning, I would have no choice since I would not have been accepted into school. Grades are highly important once you are in university too. There are many situations in which your grades are highly dependent on what you are able to do next. Your major might require a certain average to get into it, or a course might require that you got a certain grade in the prerequisites. Grades are a very good concrete way to measure where you stand with respect to other students in your class, or how much you have improved since last year’s grades. Grades matter in the bigger picture too. If you want to apply to graduate school, you apply with your grades. And if you are in a special program, you know that you must keep your grades up to stay in the program. I am sure many of you know that, at UBC, if you want to participate in an exchange program, you must have a certain average to apply. Once you graduate, your degree will allow you to get a good job, and you only receive your degree by getting good enough grades. So now you know that knowledge is very important, but that grades are also very important. So how do they work together?

How grades and knowledge complement each other

Grades act as a key to get in the door for opportunities, and they continue to act as that whether you learned the material or not. As long as you receive a certain percentage, that is good enough to be accepted to whatever your next step is. But once you are through the door, you must rely on your knowledge. Not having knowledge could prevent you from getting the grades to act as the next key to the next step in your education or career path. To give a more concrete example: if you graduate with good grades, you present yourself with many work opportunities with your degree. Of course, your personality and professional experience plays a significant role, but your degree- and, depending on your field, the grades you receive in important courses- also acts as a key in the door. Supposing you get hired but you do not really know the material well that applies from your degree to your area of work, it will not be long before you are forced to look for a new job. Contrarily, if you happen to have fantastic knowledge but no grades to prove it- perhaps you were self-taught in this field- it might be challenging to find a good job that takes your word for it and does not require any academic or professional experience to attest to it. I think everyone knows that grades and knowledge work together, but it is really important to understand how different they are and in what ways they complement each other.

I think a good way to summarize things is as follows: grades and educational knowledge are of equal importance, but at different times and for different things. Grades are short term important, while knowledge is long term important. Grades matter at certain times and are essential to making bigger steps in your academic path. Knowledge matters consistently through time, and helps you get good grades, but knowledge is also an authentic reflection of your genuine learning. It is important to understand the differences between them and to appreciate how they complement each other. If you are unsure about the difference between your grades and your personal knowledge in a subject, I challenge you to reflect on your grades and ask yourself if you feel your grade is reflective of your knowledge on the subject, or if (and, if so, why) there are discrepancies between the two.

One response to “Grades vs Educational Knowledge”

Super fantastic bruh

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The Analysis of Knowledge

For any person, there are some things they know, and some things they don’t. What exactly is the difference? What does it take to know something? It’s not enough just to believe it—we don’t know the things we’re wrong about. Knowledge seems to be more like a way of getting at the truth. The analysis of knowledge concerns the attempt to articulate in what exactly this kind of “getting at the truth” consists.

More particularly, the project of analysing knowledge is to state conditions that are individually necessary and jointly sufficient for propositional knowledge, thoroughly answering the question, what does it take to know something? By “propositional knowledge”, we mean knowledge of a proposition—for example, if Susan knows that Alyssa is a musician, she has knowledge of the proposition that Alyssa is a musician. Propositional knowledge should be distinguished from knowledge of “acquaintance”, as obtains when Susan knows Alyssa. The relation between propositional knowledge and the knowledge at issue in other “knowledge” locutions in English, such as knowledge-where (“Susan knows where she is”) and especially knowledge-how (“Susan knows how to ride a bicycle”) is subject to some debate (see Stanley 2011 and his opponents discussed therein).

The propositional knowledge that is the analysandum of the analysis of knowledge literature is paradigmatically expressed in English by sentences of the form “ S knows that p ”, where “ S ” refers to the knowing subject, and “ p ” to the proposition that is known. A proposed analysis consists of a statement of the following form: S knows that p if and only if j , where j indicates the analysans: paradigmatically, a list of conditions that are individually necessary and jointly sufficient for S to have knowledge that p .

It is not enough merely to pick out the actual extension of knowledge. Even if, in actual fact, all cases of S knowing that p are cases of j , and all cases of the latter are cases of the former, j might fail as an analysis of knowledge. For example, it might be that there are possible cases of knowledge without j , or vice versa. A proper analysis of knowledge should at least be a necessary truth. Consequently, hypothetical thought experiments provide appropriate test cases for various analyses, as we shall see below.

Even a necessary biconditional linking knowledge to some state j would probably not be sufficient for an analysis of knowledge, although just what more is required is a matter of some controversy. According to some theorists, to analyze knowledge is literally to identify the components that make up knowledge—compare a chemist who analyzes a sample to learn its chemical composition. On this interpretation of the project of analyzing knowledge, the defender of a successful analysis of knowledge will be committed to something like the metaphysical claim that what it is for S to know p is for some list of conditions involving S and p to obtain. Other theorists think of the analysis of knowledge as distinctively conceptual —to analyse knowledge is to limn the structure of the concept of knowledge. On one version of this approach, the concept knowledge is literally composed of more basic concepts, linked together by something like Boolean operators. Consequently, an analysis is subject not only to extensional accuracy, but to facts about the cognitive representation of knowledge and other epistemic notions. In practice, many epistemologists engaging in the project of analyzing knowledge leave these metaphilosophical interpretive questions unresolved; attempted analyses, and counterexamples thereto, are often proposed without its being made explicit whether the claims are intended as metaphysical or conceptual ones. In many cases, this lack of specificity may be legitimate, since all parties tend to agree that an analysis of knowledge ought at least to be extensionally correct in all metaphysically possible worlds. As we shall see, many theories have been defended and, especially, refuted, on those terms.

The attempt to analyze knowledge has received a considerable amount of attention from epistemologists, particularly in the late 20 th Century, but no analysis has been widely accepted. Some contemporary epistemologists reject the assumption that knowledge is susceptible to analysis.

1.1 The Truth Condition

1.2 the belief condition, 1.3 the justification condition, 2. lightweight knowledge, 3. the gettier problem, 4. no false lemmas, 5.1 sensitivity, 5.3 relevant alternatives, 6.1 reliabilist theories of knowledge, 6.2 causal theories of knowledge, 7. is knowledge analyzable, 8. epistemic luck, 9. methodological options, 10.1 the “aaa” evaluations, 10.2 fake barn cases, 11. knowledge first, 12. pragmatic encroachment, 13. contextualism, other internet resources, related entries, 1. knowledge as justified true belief.

There are three components to the traditional (“tripartite”) analysis of knowledge. According to this analysis, justified, true belief is necessary and sufficient for knowledge.

S believes that p ;
S is justified in believing that p .

The tripartite analysis of knowledge is often abbreviated as the “JTB” analysis, for “justified true belief”.

Much of the twentieth-century literature on the analysis of knowledge took the JTB analysis as its starting-point. It became something of a convenient fiction to suppose that this analysis was widely accepted throughout much of the history of philosophy. In fact, however, the JTB analysis was first articulated in the twentieth century by its attackers. [ 1 ] Before turning to influential twentieth-century arguments against the JTB theory, let us briefly consider the three traditional components of knowledge in turn.

Most epistemologists have found it overwhelmingly plausible that what is false cannot be known. For example, Hillary Clinton did not win the 2016 US Presidential election. Consequently, nobody knows that Hillary Clinton won the election. One can only know things that are true.

Sometimes when people are very confident of something that turns out to be wrong, we use the word “knows” to describe their situation. Many people expected Clinton to win the election. Speaking loosely, one might even say that many people “knew” that Clinton would win the election—until she lost. Hazlett (2010) argues on the basis of data like this that “knows” is not a factive verb. [ 2 ] Hazlett’s diagnosis is deeply controversial; most epistemologists will treat sentences like “I knew that Clinton was going to win” as a kind of exaggeration—as not literally true.

Something’s truth does not require that anyone can know or prove that it is true. Not all truths are established truths. If you flip a coin and never check how it landed, it may be true that it landed heads, even if nobody has any way to tell. Truth is a metaphysical , as opposed to epistemological , notion: truth is a matter of how things are , not how they can be shown to be. So when we say that only true things can be known, we’re not (yet) saying anything about how anyone can access the truth. As we’ll see, the other conditions have important roles to play here. Knowledge is a kind of relationship with the truth—to know something is to have a certain kind of access to a fact. [ 3 ]

The belief condition is only slightly more controversial than the truth condition. The general idea behind the belief condition is that you can only know what you believe. Failing to believe something precludes knowing it. “Belief” in the context of the JTB theory means full belief, or outright belief. In a weak sense, one might “believe” something by virtue of being pretty confident that it’s probably true—in this weak sense, someone who considered Clinton the favourite to win the election, even while recognizing a nontrivial possibility of her losing, might be said to have “believed” that Clinton would win. Outright belief is stronger (see, e.g., Fantl & McGrath 2009: 141; Nagel 2010: 413–4; Williamson 2005: 108; or Gibbons 2013: 201.). To believe outright that p , it isn’t enough to have a pretty high confidence in p ; it is something closer to a commitment or a being sure. [ 4 ]

Although initially it might seem obvious that knowing that p requires believing that p , a few philosophers have argued that knowledge without belief is indeed possible. Suppose Walter comes home after work to find out that his house has burned down. He says: “I don’t believe it”. Critics of the belief condition might argue that Walter knows that his house has burned down (he sees that it has), but, as his words indicate, he does not believe it. The standard response is that Walter’s avowal of disbelief is not literally true; what Walter wishes to convey by saying “I don’t believe it” is not that he really does not believe that his house has burned down, but rather that he finds it hard to come to terms with what he sees. If he genuinely didn’t believe it, some of his subsequent actions, such as phoning his insurance company, would be rather mysterious.

A more serious counterexample has been suggested by Colin Radford (1966). Suppose Albert is quizzed on English history. One of the questions is: “When did Queen Elizabeth die?” Albert doesn’t think he knows, but answers the question correctly. Moreover, he gives correct answers to many other questions to which he didn’t think he knew the answer. Let us focus on Albert’s answer to the question about Elizabeth:

(E) Elizabeth died in 1603.

Radford makes the following two claims about this example:

Albert does not believe (E).
Albert knows (E).

Radford’s intuitions about cases like these do not seem to be idiosyncratic; Myers-Schutz & Schwitzgebel (2013) find evidence suggesting that many ordinary speakers tend to react in the way Radford suggests. In support of (a), Radford emphasizes that Albert thinks he doesn’t know the answer to the question. He doesn’t trust his answer because he takes it to be a mere guess. In support of (b), Radford argues that Albert’s answer is not at all just a lucky guess. The fact that he answers most of the questions correctly indicates that he has actually learned, and never forgotten, such historical facts.

Since he takes (a) and (b) to be true, Radford holds that belief is not necessary for knowledge. But either of (a) and (b) might be resisted. One might deny (a), arguing that Albert does have a tacit belief that (E), even though it’s not one that he thinks amounts to knowledge. David Rose and Jonathan Schaffer (2013) take this route. Alternatively, one might deny (b), arguing that Albert’s correct answer is not an expression of knowledge, perhaps because, given his subjective position, he does not have justification for believing (E). The justification condition is the topic of the next section.

Why is condition (iii) necessary? Why not say that knowledge is true belief? The standard answer is that to identify knowledge with true belief would be implausible because a belief might be true even though it is formed improperly. Suppose that William flips a coin, and confidently believes—on no particular basis—that it will land tails. If by chance the coin does land tails, then William’s belief was true; but a lucky guess such as this one is no knowledge. For William to know, his belief must in some epistemic sense be proper or appropriate: it must be justified . [ 5 ]

Socrates articulates the need for something like a justification condition in Plato’s Theaetetus , when he points out that “true opinion” is in general insufficient for knowledge. For example, if a lawyer employs sophistry to induce a jury into a belief that happens to be true, this belief is insufficiently well-grounded to constitute knowledge.

1.3.1 Approaches to Justification

There is considerable disagreement among epistemologists concerning what the relevant sort of justification here consists in. Internalists about justification think that whether a belief is justified depends wholly on states in some sense internal to the subject. According to one common such sense of “internal”, only those features of a subject’s experience which are directly or introspectively available count as “internal”—call this “access internalism”. According to another, only intrinsic states of the subject are “internal”—call this “state internalism”. See Feldman & Conee 2001 for the distinction.

Conee and Feldman present an example of an internalist view. They have it that S ’s belief that p is justified if and only if believing that p is the attitude towards p that best fits S ’s evidence, where the latter is understood to depend only on S ’s internal mental states. Conee and Feldman call their view “evidentialism”, and characterize this as the thesis that justification is wholly a matter of the subject’s evidence. Given their (not unsubstantial) assumption that what evidence a subject has is an internal matter, evidentialism implies internalism. [ 6 ] Externalists about justification think that factors external to the subject can be relevant for justification; for example, process reliabilists think that justified beliefs are those which are formed by a cognitive process which tends to produce a high proportion of true beliefs relative to false ones. [ 7 ] We shall return to the question of how reliabilist approaches bear on the analysis of knowledge in §6.1 .

1.3.2 Kinds of Justification

It is worth noting that one might distinguish between two importantly different notions of justification, standardly referred to as “propositional justification” and “doxastic justification”. (Sometimes “ ex ante ” justification and “ ex post ” justification, respectively.) [ 8 ] Unlike that between internalist and externalist approaches to justification, the distinction between propositional and doxastic justification does not represent a conflict to be resolved; it is a distinction between two distinct properties that are called “justification”. Propositional justification concerns whether a subject has sufficient reason to believe a given proposition; [ 9 ] doxastic justification concerns whether a given belief is held appropriately. [ 10 ] One common way of relating the two is to suggest that propositional justification is the more fundamental, and that doxastic justification is a matter of a subject’s having a belief that is appropriately responsive to or based on their propositional justification.

The precise relation between propositional and doxastic justification is subject to controversy, but it is uncontroversial that the two notions can come apart. Suppose that Ingrid ignores a great deal of excellent evidence indicating that a given neighborhood is dangerous, but superstitiously comes to believe that the neighborhood is dangerous when she sees a black cat crossing the street. Since forming beliefs on the basis of superstition is not an epistemically appropriate way of forming beliefs, Ingrid’s belief is not doxastically justified; nevertheless, she does have good reason to believe as she does, so she does have propositional justification for the proposition that the neighborhood is dangerous.

Since knowledge is a particularly successful kind of belief, doxastic justification is a stronger candidate for being closely related to knowledge; the JTB theory is typically thought to invoke doxastic justification (but see Lowy 1978).

Some epistemologists have suggested that there may be multiple senses of the term “knowledge”, and that not all of them require all three elements of the tripartite theory of knowledge. For example, some have argued that there is, in addition to the sense of “knowledge” gestured at above, another, weak sense of “knowledge”, that requires only true belief (see for example Hawthorne 2002 and Goldman & Olsson 2009; the latter contains additional relevant references). This view is sometimes motivated by the thought that, when we consider whether someone knows that p , or wonder which of a group of people know that p , often, we are not at all interested in whether the relevant subjects have beliefs that are justified; we just want to know whether they have the true belief. For example, as Hawthorne (2002: 253–54) points out, one might ask how many students know that Vienna is the capital of Austria; the correct answer, one might think, just is the number of students who offer “Vienna” as the answer to the corresponding question, irrespective of whether their beliefs are justified. Similarly, if you are planning a surprise party for Eugene and ask whether he knows about it, “yes” may be an appropriate answer merely on the grounds that Eugene believes that you are planning a party.

One could allow that there is a lightweight sense of knowledge that requires only true belief; another option is to decline to accept the intuitive sentences as true at face value. A theorist might, for instance, deny that sentences like “Eugene knows that you are planning a party”, or “eighteen students know that Vienna is the capital of Austria” are literally true in the envisaged situations, explaining away their apparent felicity as loose talk or hyperbole.

Even among those epistemologists who think that there is a lightweight sense of “knows” that does not require justification, most typically admit that there is also a stronger sense which does, and that it is this stronger state that is the main target of epistemological theorizing about knowledge. In what follows, we will set aside the lightweight sense, if indeed there be one, and focus on the stronger one.

Few contemporary epistemologists accept the adequacy of the JTB analysis. Although most agree that each element of the tripartite theory is necessary for knowledge, they do not seem collectively to be sufficient . There seem to be cases of justified true belief that still fall short of knowledge. Here is one kind of example:

Imagine that we are seeking water on a hot day. We suddenly see water, or so we think. In fact, we are not seeing water but a mirage, but when we reach the spot, we are lucky and find water right there under a rock. Can we say that we had genuine knowledge of water? The answer seems to be negative, for we were just lucky. (quoted from Dreyfus 1997: 292)

This example comes from the Indian philosopher Dharmottara, c. 770 CE. The 14 th -century Italian philosopher Peter of Mantua presented a similar case:

Let it be assumed that Plato is next to you and you know him to be running, but you mistakenly believe that he is Socrates, so that you firmly believe that Socrates is running. However, let it be so that Socrates is in fact running in Rome; however, you do not know this. (from Peter of Mantua’s De scire et dubitare , given in Boh 1985: 95)

Cases like these, in which justified true belief seems in some important sense disconnected from the fact, were made famous in Edmund Gettier’s 1963 paper, “Is Justified True Belief Knowledge?”. Gettier presented two cases in which a true belief is inferred from a justified false belief. He observed that, intuitively, such beliefs cannot be knowledge; it is merely lucky that they are true.

In honour of his contribution to the literature, cases like these have come to be known as “Gettier cases”. Since they appear to refute the JTB analysis, many epistemologists have undertaken to repair it: how must the analysis of knowledge be modified to accommodate Gettier cases? This is what is commonly referred to as the “Gettier problem”.

Above, we noted that one role of the justification is to rule out lucky guesses as cases of knowledge. A lesson of the Gettier problem is that it appears that even true beliefs that are justified can nevertheless be epistemically lucky in a way inconsistent with knowledge.

Epistemologists who think that the JTB approach is basically on the right track must choose between two different strategies for solving the Gettier problem. The first is to strengthen the justification condition to rule out Gettier cases as cases of justified belief. This was attempted by Roderick Chisholm; [ 11 ] we will refer to this strategy again in §7 below. The other is to amend the JTB analysis with a suitable fourth condition, a condition that succeeds in preventing justified true belief from being “gettiered”. Thus amended, the JTB analysis becomes a JTB+ X account of knowledge, where the “ X ” stands for the needed fourth condition.

Let us consider an instance of this attempt to articulate a “degettiering” condition.

According to one suggestion, the following fourth condition would do the trick:

S ’s belief that p is not inferred from any falsehood. [ 12 ]

In Gettier’s cases, the justified true belief is inferred from a justified false belief. So condition (iv) explains why it isn’t knowledge. However, this “no false lemmas” proposal is not successful in general. There are examples of Gettier cases that need involve no inference; therefore, there are possible cases of justified true belief without knowledge, even though condition (iv) is met. Suppose, for example, that James, who is relaxing on a bench in a park, observes an apparent dog in a nearby field. So he believes

There is a dog in the field.

Suppose further that the putative dog is actually a robot dog so perfect that it could not be distinguished from an actual dog by vision alone. James does not know that such robot dogs exist; a Japanese toy manufacturer has only recently developed them, and what James sees is a prototype that is used for testing the public’s response. Given these assumptions, (d) is of course false. But suppose further that just a few feet away from the robot dog, there is a real dog, concealed from James’s view. Given this further assumption, James’s belief in (d) is true. And since this belief is based on ordinary perceptual processes, most epistemologists will agree that it is justified. But as in Gettier’s cases, James’s belief appears to be true only as a matter of luck, in a way inconsistent with knowledge. So once again, what we have before us is a justified true belief that isn’t knowledge. [ 13 ] Arguably, this belief is directly justified by a visual experience; it is not inferred from any falsehood. If so, then the JTB account, even if supplemented with (iv) , gives us the wrong result that James knows (d).

Another case illustrating that clause (iv) won’t do the job is the well-known Barn County case (Goldman 1976). Suppose there is a county in the Midwest with the following peculiar feature. The landscape next to the road leading through that county is peppered with barn-facades: structures that from the road look exactly like barns. Observation from any other viewpoint would immediately reveal these structures to be fakes: devices erected for the purpose of fooling unsuspecting motorists into believing in the presence of barns. Suppose Henry is driving along the road that leads through Barn County. Naturally, he will on numerous occasions form false beliefs in the presence of barns. Since Henry has no reason to suspect that he is the victim of organized deception, these beliefs are justified. Now suppose further that, on one of those occasions when he believes there is a barn over there, he happens to be looking at the one and only real barn in the county. This time, his belief is justified and true. But since its truth is the result of luck, it is exceedingly plausible to judge that Henry’s belief is not an instance of knowledge. Yet condition (iv) is met in this case. His belief is not the result of any inference from a falsehood. Once again, we see that (iv) does not succeed as a general solution to the Gettier problem.

5. Modal Conditions

Another candidate fourth condition on knowledge is sensitivity . Sensitivity, to a first approximation, is this counterfactual relation:

S ’s belief that p is sensitive if and only if, if p were false, S would not believe that p . [ 14 ]

A sensitivity condition on knowledge was defended by Robert Nozick (1981). Given a Lewisian (Lewis 1973) semantics for counterfactual conditionals, the sensitivity condition is equivalent to the requirement that, in the nearest possible worlds in which not- p , the subject does not believe that p .

One motivation for including a sensitivity condition in an analysis of knowledge is that there seems to be an intuitive sense in which knowledge requires not merely being correct, but tracking the truth in other possible circumstances. This approach seems to be a plausible diagnosis of what goes wrong in at least some Gettier cases. For example, in Dharmottara’s desert water case, your belief that there is water in a certain location appears to be insensitive to the fact of the water. For if there were no water there, you would have held the same belief on the same grounds— viz. , the mirage.

However, it is doubtful that a sensitivity condition can account for the phenomenon of Gettier cases in general. It does so only in cases in which, had the proposition in question been false, it would have been believed anyway. But, as Saul Kripke (2011: 167–68) has pointed out, not all Gettier cases are like this. Consider for instance the Barn County case mentioned above. Henry looks at a particular location where there happens to be a barn and believes there to be a barn there. The sensitivity condition rules out this belief as knowledge only if, were there no barn there, Henry would still have believed there was. But this counterfactual may be false, depending on how the Barn County case is set up. For instance, it is false if the particular location Henry is examining is not one that would have been suitable for the erecting of a barn façade. Relatedly, as Kripke has also indicated (2011: 186), if we suppose that barn facades are always green, but genuine barns are always red, Henry’s belief that he sees a red barn will be sensitive, even though his belief that he sees a barn will not. (We assume Henry is unaware that colour signifies anything relevant.) Since intuitively, the former belief looks to fall short of knowledge in just the same way as the latter, a sensitivity condition will only handle some of the intuitive problems deriving from Gettier cases.

Most epistemologists today reject sensitivity requirements on knowledge. The chief motivation against a sensitivity condition is that, given plausible assumptions, it leads to unacceptable implications called “abominable conjunctions”. [ 15 ] To see this, suppose first that skepticism about ordinary knowledge is false—ordinary subjects know at least many of the things we ordinarily take them to know. For example, George, who can see and use his hands perfectly well, knows that he has hands. This is of course perfectly consistent with a sensitivity condition on knowledge, since if George did not have hands—if they’d been recently chopped off, for instance—he would not believe that he had hands.

Now imagine a skeptical scenario in which George does not have hands. Suppose that George is the victim of a Cartesian demon, deceiving him into believing that he has hands. If George were in such a scenario, of course, he would falsely believe himself not to be in such a scenario. So given the sensitivity condition, George cannot know that he is not in such a scenario.

Although these two verdicts—the knowledge-attributing one about ordinary knowledge, and the knowledge-denying one about the skeptical scenario—are arguably each intuitive, it is intuitively problematic to hold them together. Their conjunction is, in DeRose’s term, abominable: “George knows that he has hands, but he doesn’t know that he’s not the handless victim of a Cartesian demon”. A sensitivity condition on knowledge, combined with the nonskeptical claim that there is ordinary knowledge, seems to imply such abominable conjunctions. [ 16 ]

Most contemporary epistemologists have taken considerations like these to be sufficient reason to reject sensitivity conditions. [ 17 ] However, see Ichikawa (2011a) for an interpretation and endorsement of the sensitivity condition according to which it may avoid commitment to abominable conjunctions.

Although few epistemologists today endorse a sensitivity condition on knowledge, the idea that knowledge requires a subject to stand in a particular modal relation to the proposition known remains a popular one. In his 1999 paper, “How to Defeat Opposition to Moore”, Ernest Sosa proposed that a safety condition ought to take the role that sensitivity was intended to play. Sosa characterized safety as the counterfactual contrapositive of sensitivity.

Sensitivity: If p were false, S would not believe that p .

Safety: If S were to believe that p , p would not be false. [ 18 ]

Although contraposition is valid for the material conditional \((A \supset B\) iff \(\mathord{\sim} B \supset \mathord{\sim}A)\), Sosa suggests that it is invalid for counterfactuals, which is why sensitivity and safety are not equivalent. An example of a safe belief that is not sensitive, according to Sosa, is the belief that a distant skeptical scenario does not obtain. If we stipulate that George, discussed above, has never been at risk of being the victim of a Cartesian demon—because, say, Cartesian demons do not exist in George’s world—then George’s belief that he is not such a victim is a safe one, even though we saw in the previous section that it could not be sensitive. Notice that although we stipulated that George is not at risk of deceit by Cartesian demons, we did not stipulate that George himself had any particular access to this fact. Unless he does, safety, like sensitivity, will be an externalist condition on knowledge in the “access” sense. It is also externalist in the “state” sense, since the truth of the relevant counterfactuals will depend on features outside the subject.

Characterizing safety in these counterfactual terms depends on substantive assumptions about the semantics of counterfactual conditionals. [ 19 ] If we were to accept, for instance, David Lewis’s or Robert Stalnaker’s treatment of counterfactuals, including a strong centering condition according to which the actual world is always uniquely closest, all true beliefs would count as safe according to the counterfactual analysis of safety. [ 20 ] Sosa intends the relevant counterfactuals to be making a stronger claim, requiring roughly that in all nearby worlds in which S believes that p , p is not false.

Rather than resting on a contentious treatment of counterfactuals, then, it may be most perspicuous to understand the safety condition more directly in these modal terms, as Sosa himself often does:

In all nearby worlds where S believes that p , p is not false.

Whether a JTB+safety analysis of knowledge could be successful is somewhat difficult to evaluate, given the vagueness of the stated “nearby” condition. The status of potential counterexamples will not always be straightforward to apply. For example, Juan Comesaña (2005) presents a case he takes to refute the requirement that knowledge be safe. In Comesaña’s example, the host of a Halloween party enlists Judy to direct guests to the party. Judy’s instructions are to give everyone the same directions, which are in fact accurate, but that if she sees Michael, the party will be moved to another location. (The host does not want Michael to find the party.) Suppose Michael never shows up. If a given guest does not, but very nearly does, decide to wear a very realistic Michael costume to the party, then his belief, based in Judy’s testimony, about the whereabouts of the party will be true, but could, Comesaña says, easily have been false. (Had he merely made a slightly different choice about his costume, he would have been deceived.) Comesaña describes the case as a counterexample to a safety condition on knowledge. However, it is open to a safety theorist to argue that the relevant skeptical scenario, though possible and in some sense nearby, is not near enough in the relevant respect to falsify the safety condition. Such a theorist would, if she wanted the safety condition to deliver clear verdicts, face the task of articulating just what the relevant notion of similarity amounts to (see also Bogardus 2014).

Not all further clarifications of a safety condition will be suitable for the use of the latter in an analysis of knowledge. In particular, if the respect of similarity that is relevant for safety is itself explicated in terms of knowledge, then an analysis of knowledge which made reference to safety would be in this respect circular. This, for instance, is how Timothy Williamson characterizes safety. He writes, in response to a challenge by Alvin Goldman:

In many cases, someone with no idea of what knowledge is would be unable to determine whether safety obtained. Although they could use the principle that safety entails truth to exclude some cases, those are not the interesting ones. Thus Goldman will be disappointed when he asks what the safety account predicts about various examples in which conflicting considerations pull in different directions. One may have to decide whether safety obtains by first deciding whether knowledge obtains, rather than vice versa. (Williamson 2009: 305)

Because safety is understood only in terms of knowledge, safety so understood cannot serve in an analysis of knowledge. Nor is it Williamson’s intent that it should do so; as we will see below, Williamson rejects the project of analyzing knowledge. This is of course consistent with claiming that safety is a necessary condition on knowledge in the straightforward sense that the latter entails the former.

A third approach to modal conditions on knowledge worthy of mention is the requirement that for a subject to know that p , she must rule out all “relevant alternatives” to p . Significant early proponents of this view include Stine 1976, Goldman 1976, and Dretske 1981. The idea behind this approach to knowledge is that for a subject to know that p , she must be able to “rule out” competing hypotheses to p —but that only some subset of all not- p possibilities are “relevant” for knowledge attributions. Consider for example, the differences between the several models that have been produced of Apple’s iPhone. To be able to know by sight that a particular phone is the 6S model, it is natural to suppose that one must be able to tell the difference between the iPhone 6S and the iPhone 7; the possibility that the phone in question is a newer model is a relevant alternative. But perhaps there are other possibilities in which the belief that there is an iPhone 6S is false that do not need to be ruled out—perhaps, for instance, the possibility that the phone is not an iPhone, but a Chinese knock-off, needn’t be considered. Likewise for the possibility that there is no phone at all, the phone-like appearances being the product of a Cartesian demon’s machinations. Notice that in these cases and many of the others that motivate the relevant-alternatives approach to knowledge, there is an intuitive sense in which the relevant alternatives tend to be more similar to actuality than irrelevant ones. As such, the relevant alternatives theory and safety-theoretic approaches are very similar, both in verdict and in spirit. As in the case of a safety theorist, the relevant alternatives theorist faces a challenge in attempting to articulate what determines which possibilities are relevant in a given situation. [ 21 ]

6. Doing Without Justification?

As we have seen, one motivation for including a justification condition in an analysis of knowledge was to prevent lucky guesses from counting as knowledge. However, the Gettier problem shows that including a justification condition does not rule out all epistemically problematic instances of luck. Consequently, some epistemologists have suggested that positing a justification condition on knowledge was a false move; perhaps it is some other condition that ought to be included along with truth and belief as components of knowledge. This kind of strategy was advanced by a number of authors from the late 1960s to the early 1980s, although there has been relatively little discussion of it since. [ 22 ] Kornblith 2008 provides a notable exception.

One candidate property for such a state is reliability . Part of what is problematic about lucky guesses is precisely that they are so lucky: such guesses are formed in a way such that it is unlikely that they should turn out true. According to a certain form of knowledge reliabilism, it is unreliability, not lack of justification, which prevents such beliefs from amounting to knowledge. Reliabilist theories of knowledge incorporate this idea into a reliability condition on knowledge. [ 23 ] Here is an example of such a view:

Simple K-Reliabilism:

S knows that p iff

S ’s belief that p was produced by a reliable cognitive process.

Simple K-Reliabilism replaces the justification clause in the traditional tripartite theory with a reliability clause. As we have seen, reliabilists about justification think that justification for a belief consists in a genesis in a reliable cognitive process. Given this view, Simple K-Reliabilism and the JTB theory are equivalent. However, the present proposal is silent on justification. Goldman 1979 is the seminal defense of reliabilism about justification; reliabilism is extended to knowledge in Goldman 1986. See Goldman 2011 for a survey of reliabilism in general.

In the following passage, Fred Dretske articulates how an approach like K-reliabilism might be motivated:

Those who think knowledge requires something other than , or at least more than , reliably produced true belief, something (usually) in the way of justification for the belief that one’s reliably produced beliefs are being reliably produced, have, it seems to me, an obligation to say what benefits this justification is supposed to confer…. Who needs it, and why? If an animal inherits a perfectly reliable belief-generating mechanism, and it also inherits a disposition, everything being equal, to act on the basis of the beliefs so generated, what additional benefits are conferred by a justification that the beliefs are being produced in some reliable way? If there are no additional benefits, what good is this justification? Why should we insist that no one can have knowledge without it? (Dretske 1989: 95)

According to Dretske, reliable cognitive processes convey information, and thus endow not only humans, but (nonhuman) animals as well, with knowledge. He writes:

I wanted a characterization that would at least allow for the possibility that animals (a frog, rat, ape, or my dog) could know things without my having to suppose them capable of the more sophisticated intellectual operations involved in traditional analyses of knowledge. (Dretske 1985: 177)

It does seem odd to think of frogs, rats, or dogs as having justified or unjustified beliefs. Yet attributing knowledge to animals is certainly in accord with our ordinary practice of using the word “knowledge”. So if, with Dretske, we want an account of knowledge that includes animals among the knowing subjects, we might want to abandon the traditional JTB account in favor of something like K-reliabilism.

Another move in a similar spirit to K-Reliabilism replaces the justification clause in the JTB theory with a condition requiring a causal connection between the belief and the fact believed; [ 24 ] this is the approach of Goldman (1967, 1976). [ 25 ] Goldman’s own causal theory is a sophisticated one; we will not engage with its details here. See Goldman’s papers. Instead, consider a simplified causal theory of knowledge, which illustrates the main motivation behind causal theories.

Simple Causal Theory of Knowledge:

S ’s belief that p is caused by the fact that p .

Do approaches like Simple K-Reliabilism or the Simple Causal Theory fare any better than the JTB theory with respect to Gettier cases? Although some proponents have suggested they do—see e.g., Dretske 1985: 179; Plantinga 1993: 48—many of the standard counterexamples to the JTB theory appear to refute these views as well. Consider again the case of the barn facades. Henry sees a real barn, and that’s why he believes there is a barn nearby. This belief is formed by perceptual processes, which are by-and-large reliable: only rarely do they lead him into false beliefs. So it looks like the case meets the conditions of Simple K-Reliabilism just as much as it does those of the JTB theory. It is also a counterexample to the causal theory, since the real barn Henry perceives is causally responsible for his belief. There is reason to doubt, therefore, that shifting from justification to a condition like reliability will escape the Gettier problem. [ 26 ] Gettier cases seem to pose as much of a problem for K-reliabilism and causal theories as for the JTB account. Neither theory, unless amended with a clever “degettiering” clause, succeeds in stating sufficient conditions for knowledge. [ 27 ]

Gettier’s paper launched a flurry of philosophical activity by epistemologists attempting to revise the JTB theory, usually by adding one or more conditions, to close the gap between knowledge and justified true belief. We have seen already how several of these attempts failed. When intuitive counterexamples were proposed to each theory, epistemologists often responded by amending their theories, complicating the existing conditions or adding new ones. Much of this dialectic is chronicled thoroughly by Shope 1983, to which the interested reader is directed.

After some decades of such iterations, some epistemologists began to doubt that progress was being made. In her 1994 paper, “The Inescapability of Gettier Problems”, Linda Zagzebski suggested that no analysis sufficiently similar to the JTB analysis could ever avoid the problems highlighted by Gettier’s cases. More precisely, Zagzebski argued, any analysans of the form JTB+ X , where X is a condition or list of conditions logically independent from justification, truth, and belief, would be susceptible to Gettier-style counterexamples. She offered what was in effect a recipe for constructing Gettier cases:

(1) Start with an example of a case where a subject has a justified false belief that also meets condition X .
(2) Modify the case so that the belief is true merely by luck.

Zagzebski suggests that the resultant case will always represent an intuitive lack of knowledge. So any non-redundant addition to the JTB theory will leave the Gettier problem unsolved. [ 28 ] We may illustrate the application of the recipe using one of Zagzebski’s own examples, refuting Alvin Plantinga’s (1996) attempt to solve the Gettier problem by appending to the JTB analysis a condition requiring that the subject’s faculties be working properly in an appropriate environment.

In step one of Zagzebski’s procedure, we imagine a case in which a subject’s faculties are working properly in an appropriate environment, but the ensuing belief, though justified, is false. Zagzebski invites us to imagine that Mary has very good eyesight—good enough for her cognitive faculties typically to yield knowledge that her husband is sitting in the living room. Such faculties, even when working properly in suitable environments, however, are not infallible—if they were, the condition would not be independent from truth—so we can imagine a case in which they go wrong. Perhaps this is an unusual instance in which Mary’s husband’s brother, who looks a lot like the husband, is in the living room, and Mary concludes, on the basis of the proper function of her visual capacity, that her husband is in the living room. This belief, since false, is certainly not knowledge.

In step two, we imagine Mary’s misidentification of the occupant of the living room as before, but add to the case that the husband is, by luck, also in the living room. Now Mary’s belief is true, but intuitively, it is no more an instance of knowledge than the false belief in the first step was.

Since the recipe is a general one, it appears to be applicable to any condition one might add to the JTB theory, so long as it does not itself entail truth. The argument generalizes against all “non-redundant” JTB+ X analyses.

One potential response to Zagzebski’s argument, and the failure of the Gettier project more generally, would be to conclude that knowledge is unanalyzable. Although it would represent a significant departure from much analytic epistemology of the late twentieth century, it is not clear that this is ultimately a particularly radical suggestion. Few concepts of interest have proved susceptible to traditional analysis (Fodor 1998). One prominent approach to knowledge in this vein is discussed in §11 below.

Another possible line is the one mentioned in §2 —to strengthen the justification condition to rule out Gettier cases as justified. In order for this strategy to prevent Zagzebski’s recipe from working, one would need to posit a justification condition that precludes the possibility of step one above—the only obvious way to do this is for justification to entail truth. If it does, then it will of course be impossible to start with a case that has justified false belief. This kind of approach is not at all mainstream, but it does have its defenders—see e.g., Sturgeon 1993 and Merricks 1995. Sutton 2007 and Littlejohn 2012 defend factive approaches to justification on other grounds.

A third avenue of response would be to consider potential analyses of knowledge that are not of the nonredundant form JTB+ X . Indeed, we have already seen some such attempts, albeit unsuccessful ones. For instance, the causal theory of knowledge includes a clause requiring that the belief that p be caused by the fact that p . This condition entails both belief and truth, and so is not susceptible to Zagzebski’s recipe. (As we’ve seen, it falls to Gettier-style cases on other grounds.) One family of strategies along these lines would build into an analysis of knowledge a prohibition on epistemic luck directly; let us consider this sort of move in more detail.

If the problem illustrated by Gettier cases is that JTB and JTB+ analyses are compatible with a degree of epistemic luck that is inconsistent with knowledge, a natural idea is to amend one’s analysis of knowledge by including an explicit “anti-luck” condition. Zagzebski herself outlines this option in her 1994 (p. 72). Unger 1968 gives an early analysis of this kind. For example:

S ’s belief is not true merely by luck.

The first thing to note about this analysis is that it is “redundant” in the sense described in the previous section; the fourth condition entails the first two. [ 29 ] So its surface form notwithstanding, it actually represents a significant departure from the JTB+ analyses. Rather than composing knowledge from various independent components, this analysis demands instead that the epistemic states are related to one another in substantive ways.

The anti-luck condition, like the safety condition of the previous section, is vague as stated. For one thing, whether a belief is true by luck comes in degrees—just how much luck does it take to be inconsistent with knowledge? Furthermore, it seems, independently of questions about degrees of luck, we must distinguish between different kinds of luck. Not all epistemic luck is incompatible with having knowledge. Suppose someone enters a raffle and wins an encyclopedia, then reads various of its entries, correcting many of their previous misapprehensions. There is a straightforward sense in which the resultant beliefs are true only by luck—for our subject was very lucky to have won that raffle—but this is not the sort of luck, intuitively, that interferes with the possession of knowledge. [ 30 ] Furthermore, there is a sense in which our ordinary perceptual beliefs are true by luck, since it is possible for us to have been the victim of a Cartesian demon and so we are, in some sense, lucky not to be. But unless we are to capitulate to radical skepticism, it seems that this sort of luck, too, ought to be considered compatible with knowledge. [ 31 ]

Like the safety condition, then, a luck condition ends up being difficult to apply in some cases. We might try to clarify the luck condition as involving a distinctive notion of epistemic luck—but unless we were able to explicate that notion—in effect, to distinguish between the two kinds of luck mentioned above—without recourse to knowledge, it is not clear that the ensuing analysis of knowledge could be both informative and noncircular.

As our discussion so far makes clear, one standard way of evaluating attempted analyses of knowledge has given a central role to testing it against intuitions against cases. In the late twentieth century, the perceived lack of progress towards an acceptable analysis—including the considerations attributed to Zagzebski in §7 above—led some epistemologists to pursue other methodological strategies. (No doubt, a wider philosophical trend away from “conceptual analysis” more broadly also contributed to this change.) Some of the more recent attempts to analyse knowledge have been motivated in part by broader considerations about the role of knowledge, or of discourse about knowledge.

One important view of this sort is that defended by Edward Craig (1990). Craig’s entry-point into the analysis of knowledge was not intuitions about cases, but rather a focus on the role that the concept of knowledge plays for humans. In particular, Craig suggested that the point of using the category of knowledge was for people to flag reliable informants—to help people know whom to trust in matters epistemic. Craig defends an account of knowledge that is designed to fill this role, even though it is susceptible to intuitive counterexamples. The plausibility of such accounts, with a less intuitive extension but with a different kind of theoretical justification, is a matter of controversy.

Another view worth mentioning in this context is that of Hilary Kornblith (2002), which has it that knowledge is a natural kind, to be analysed the same way other scientific kinds are. Intuition has a role to play in identifying paradigms, but generalizing from there is an empirical, scientific matter, and intuitive counterexamples are to be expected.

The “knowledge first” stance is also connected to these methodological issues. See §11 below.

10. Virtue-Theoretic Approaches

The virtue-theoretic approach to knowledge is in some respects similar to the safety and anti-luck approaches. Indeed, Ernest Sosa, one of the most prominent authors of the virtue-theoretic approach, developed it from his previous work on safety. The virtue approach treats knowledge as a particularly successful or valuable form of belief, and explicates what it is to be knowledge in such terms. Like the anti-luck theory, a virtue-theoretic theory leaves behind the JTB+ project of identifying knowledge with a truth-functional combination of independent epistemic properties; knowledge, according to this approach, requires a certain non-logical relationship between belief and truth.

Sosa has often (e.g., Sosa 2007: ch. 2) made use of an analogy of a skilled archer shooting at a target; we may find it instructive as well. Here are two ways in which an archer’s shot might be evaluated:

Was the shot successful? Did it hit its target?
Did the shot’s execution manifest the archer’s skill? Was it produced in a way that makes it likely to succeed?

The kind of success at issue in (1), Sosa calls accuracy . The kind of skill discussed in (2), Sosa calls adroitness . A shot is adroit if it is produced skillfully. Adroit shots needn’t be accurate, as not all skilled shots succeed. And accurate shots needn’t be adroit, as some unskilled shots are lucky.

In addition to accuracy and adroitness, Sosa suggests that there is another respect in which a shot may be evaluated, relating the two. This, Sosa calls aptness .

Did the shot’s success manifest the archer’s skill?

A shot is apt if it is accurate because adroit. Aptness entails, but requires more than, the conjunction of accuracy and adroitness, for a shot might be both successful and skillful without being apt. For example, if a skillful shot is diverted by an unexpected gust of wind, then redirected towards the target by a second lucky gust, its ultimate accuracy does not manifest the skill, but rather reflects the lucky coincidence of the wind.

Sosa suggests that this “AAA” model of evaluation is applicable quite generally for the evaluation of any action or object with a characteristic aim. In particular, it is applicable to belief with respect to its aim at truth:

A belief is accurate if and only if it is true.
A belief is adroit if and only if it is produced skillfully. [ 32 ]
A belief is apt if and only if it is true in a way manifesting, or attributable to, the believer’s skill.

Sosa identifies knowledge with apt belief, so understood. [ 33 ] Knowledge entails both truth (accuracy) and justification (adroitness), on this view, but they are not merely independent components out of which knowledge is truth-functionally composed. It requires that the skill explain the success. This is in some respects similar to the anti-luck condition we have examined above, in that it legislates that the relation between justification and truth be no mere coincidence. However, insofar as Sosa’s “AAA” model is generally applicable in a way going beyond epistemology, there are perhaps better prospects for understanding the relevant notion of aptness in a way independent of understanding knowledge itself than we found for the notion of epistemic luck.

Understanding knowledge as apt belief accommodates Gettier’s traditional counterexamples to the JTB theory rather straightforwardly. When Smith believes that either Jones owns a Ford or Brown is in Barcelona, the accuracy of his belief is not attributable to his inferential skills (which the case does not call into question). Rather, unlucky circumstances (the misleading evidence about Jones’s car) have interfered with his skillful cognitive performance, just as the first diverting gust of wind interfered with the archer’s shot. Compensating for the unlucky interference, a lucky circumstance (Brown’s coincidental presence in Barcelona) renders the belief true after all, similar to the way in which the second gust of wind returns the archer’s arrow back onto the proper path towards the target.

Fake barn cases, by contrast, may be less easily accommodated by Sosa’s AAA approach. When Henry looks at the only real barn in a countryside full of barn facades, he uses a generally reliable perceptual faculty for recognizing barns, and he goes right in this instance. Suppose we say the accuracy of Henry’s belief manifests his competence as a perceiver. If so, we would have to judge that his belief is apt and therefore qualifies as an instance of knowledge. That would be a problematic outcome because the intuition the case is meant to elicit is that Henry does not have knowledge. There are three ways in which an advocate of the AAA approach might respond to this difficulty.

First, AAA advocates might argue that, although Henry has a general competence to recognize barns, he is deprived of this ability in his current environment, precisely because he is in fake barn county. According to a second, subtly different strategy, Henry retains barn-recognition competence, his current location notwithstanding, but, due to the ubiquity of fake barns, his competence does not manifest itself in his belief, since its truth is attributable more to luck than to his skill in recognizing barns. [ 34 ] Third, Sosa’s own response to the problem is to bite the bullet. Judging Henry’s belief to be apt, Sosa accepts the outcome that Henry knows there is a barn before him. He attempts to explain away the counterintuitiveness of this result by emphasizing the lack of a further epistemically valuable state, which he calls “reflective knowledge” (see Sosa 2007: 31–32).

Not every concept is analyzable into more fundamental terms. This is clear both upon reflection on examples—what analysis could be offered of hydrogen , animal , or John F. Kennedy ?—and on grounds of infinite regress. Why should we think that knowledge has an analysis? In recent work, especially his 2000 book Knowledge and Its Limits , Timothy Williamson has argued that the project of analyzing knowledge was a mistake. His reason is not that he thinks that knowledge is an uninteresting state, or that the notion of knowledge is somehow fundamentally confused. On the contrary, Williamson thinks that knowledge is among the most fundamental psychological and epistemological states there are. As such, it is a mistake to analyze knowledge in terms of other, more fundamental epistemic notions, because knowledge itself is, in at least many cases, more fundamental. As Williamson puts it, we should put “knowledge first”. Knowledge might figure into some analyses, but it will do so in the analysans, not in the analysandum. [ 35 ]

There is no very straightforward argument for this conclusion; its case consists largely in the attempted demonstration of the theoretical success of the knowledge first stance. Weighing these benefits against those of more traditional approaches to knowledge is beyond the scope of this article. [ 36 ]

Although Williamson denies that knowledge is susceptible to analysis in the sense at issue in this article, he does think that there are interesting and informative ways to characterize knowledge. For example, Williamson accepts these claims:

Knowledge is the most general factive mental state.
S knows that p if and only if S ’s total evidence includes the proposition that p .

Williamson is also careful to emphasize that the rejection of the project of analyzing knowledge in no way suggests that there are not interesting and informative necessary or sufficient conditions on knowledge. The traditional ideas that knowledge entails truth, belief, and justification are all consistent with the knowledge first project. And Williamson (2000: 126) is explicit in endorsement of a safety requirement on knowledge—just not one that serves as part of an analysis.

One point worth recognizing, then, is that one need not engage in the ambitious project of attempting to analyze knowledge in order to have contact with a number of interesting questions about which factors are and are not relevant for whether a subject has knowledge. In the next section, we consider an important contemporary debate about whether pragmatic factors are relevant for knowledge.

Traditional approaches to knowledge have it that knowledge has to do with factors like truth and justification. Whether knowledge requires safety, sensitivity, reliability, or independence from certain kinds of luck has proven controversial. But something that all of these potential conditions on knowledge seem to have in common is that they have some sort of intimate connection with the truth of the relevant belief. Although it is admittedly difficult to make the relevant connection precise, there is an intuitive sense in which every factor we’ve examined as a candidate for being relevant to knowledge has something to do with truth of the would-be knowledgeable beliefs.

In recent years, some epistemologists have argued that focus on such truth-relevant factors leaves something important out of our picture of knowledge. In particular, they have argued that distinctively pragmatic factors are relevant to whether a subject has knowledge. Call this thesis “pragmatic encroachment”: [ 37 ]

Pragmatic Encroachment:

A difference in pragmatic circumstances can constitute a difference in knowledge.

The constitution claim here is important; it is trivial that differences in pragmatic circumstances can cause differences in knowledge. For example, if the question of whether marijuana use is legal in Connecticut is more important to Sandra than it is to Daniel, Sandra is more likely to seek out evidence, and come to knowledge, than Daniel is. This uninteresting claim is not what is at issue. Pragmatic encroachment theorists think that the practical importance itself can make for a change in knowledge, without reliance on such downstream effects as a difference in evidence-gathering activity. Sandra and Daniel might in some sense be in the same epistemic position , where the only difference is that the question is more important to Sandra. This difference, according to pragmatic encroachment, might make it the case that Daniel knows, but Sandra does not. [ 38 ]

Pragmatic encroachment can be motivated by intuitions about cases. Jason Stanley’s 2005 book Knowledge and Practical Interests argues that it is the best explanation for pairs of cases like the following, where the contrasted cases are evidentially alike, but differ pragmatically:

Low Stakes . Hannah and her wife Sarah are driving home on a Friday afternoon. They plan to stop at the bank on the way home to deposit their paychecks. It is not important that they do so, as they have no impending bills. But as they drive past the bank, they notice that the lines inside are very long, as they often are on Friday afternoons. Realizing that it wasn’t very important that their paychecks are deposited right away, Hannah says, “I know the bank will be open tomorrow, since I was there just two weeks ago on Saturday morning. So we can deposit our paychecks tomorrow morning”.

High Stakes . Hannah and her wife Sarah are driving home on a Friday afternoon. They plan to stop at the bank on the way home to deposit their paychecks. Since they have an impending bill coming due, and very little in their account, it is very important that they deposit their paychecks by Saturday. Hannah notes that she was at the bank two weeks before on a Saturday morning, and it was open. But, as Sarah points out, banks do change their hours. Hannah says, “I guess you’re right. I don’t know that the bank will be open tomorrow”. (Stanley 2005: 3–4)

Stanley argues that the moral of cases like these is that in general, the more important the question of whether p , the harder it is to know that p . Other, more broadly theoretical, arguments for pragmatic encroachment have been offered as well. Fantl & McGrath (2009) argue that encroachment follows from fallibilism and plausible principles linking knowledge and action, while Weatherson 2012 argues that the best interpretation of decision theory requires encroachment.

Pragmatic encroachment is not an analysis of knowledge; it is merely the claim that pragmatic factors are relevant for determining whether a subject’s belief constitutes knowledge. Some, but not all, pragmatic encroachment theorists will endorse a necessary biconditional that might be interpreted as an analysis of knowledge. For example, a pragmatic encroachment theorist might claim that:

S knows that p if and only if no epistemic weakness vis-á-vis p prevents S from properly using p as a reason for action.

This connection between knowledge and action is similar to ones endorsed by Fantl & McGrath (2009), but it is stronger than anything they argue for.

Pragmatic encroachment on knowledge is deeply controversial. Patrick Rysiew (2001), Jessica Brown (2006), and Mikkel Gerken (forthcoming) have argued that traditional views about the nature of knowledge are sufficient to account for the data mentioned above. Michael Blome-Tillmann (2009a) argues that it has unacceptably counterintuitive results, like the truth of such claims as S knows that p , but if it were more important, she wouldn’t know , or S knew that p until the question became important . Stanley (2005) offers strategies for accepting such consequences. Other, more theoretical arguments against encroachment have also been advanced; see for example Ichikawa, Jarvis, and Rubin (2012), who argue that pragmatic encroachment is at odds with important tenets of belief-desire psychology.

One final topic standing in need of treatment is contextualism about knowledge attributions, according to which the word “knows” and its cognates are context-sensitive. The relationship between contextualism and the analysis of knowledge is not at all straightforward. Arguably, they have different subject matters (the former a word, and the latter a mental state). Nevertheless, the methodology of theorizing about knowledge may be helpfully informed by semantic considerations about the language in which such theorizing takes place. And if contextualism is correct, then a theorist of knowledge must attend carefully to the potential for ambiguity.

It is uncontroversial that many English words are context-sensitive. The most obvious cases are indexicals, such as “I”, “you”, “here”, and “now” (David Kaplan 1977 gives the standard view of indexicals).

The word “you” refers to a different person, depending on the conversational context in which it is uttered; in particular, it depends on the person one is addressing. Other context-sensitive terms are gradable adjectives like “tall”—how tall something must be to count as “tall” depends on the conversational context—and quantifiers like “everyone”—which people count as part of “everyone” depends on the conversational context. Contextualists about “knows” think that this verb belongs on the list of context-sensitive terms. A consequence of contextualism is that sentences containing “knows” may express distinct propositions, depending on the conversational contexts in which they’re uttered. This feature allows contextualists to offer an effective, though not uncontroversial, response to skepticism. For a more thorough overview of contextualism and its bearing on skepticism, see Rysiew 2011 or Ichikawa forthcoming-b.

Contextualists have modeled this context-sensitivity in various ways. Keith DeRose 2009 has suggested that there is a context-invariant notion of “strength of epistemic position”, and that how strong a position one must be in in order to satisfy “knows” varies from context to context; this is in effect to understand the semantics of knowledge attributions much as we understand that of gradable adjectives. (How much height one must have to satisfy “tall” also varies from context to context.) Cohen 1988 adopts a contextualist treatment of “relevant alternatives” theory, according to which, in skeptical contexts, but not ordinary ones, skeptical possibilities are relevant. This aspect is retained in the view of Lewis 1996, which characterizes a contextualist approach that is more similar to quantifiers and modals. Blome-Tillmann 2009b and Ichikawa forthcoming-a defend and develop the Lewisian view in different ways.

Contextualism and pragmatic encroachment represent different strategies for addressing some of the same “shifty” patterns of intuitive data. (In fact, contextualism was generally developed first; pragmatic encroachment theorists were motivated in part by the attempt to explain some of the patterns contextualists were interested in without contextualism’s semantic commitments.) Although this represents a sense in which they tend to be rival approaches, contextualism and pragmatic encroachment are by no means inconsistent. One could think that “knows” requires the satisfaction of different standards in different contexts, and also think that the subject’s practical situation is relevant for whether a given standard is satisfied.

Like pragmatic encroachment, contextualism is deeply controversial. Critics have argued that it posits an implausible kind of semantic error in ordinary speakers who do not recognize the putative context-sensitivity—see Schiffer 1996 and Greenough & Kindermann forthcoming—and that it is at odds with plausible theoretical principles involving knowledge—see Hawthorne 2003, Williamson 2005, and Worsnip forthcoming. In addition, some of the arguments that are used to undercut the data motivating pragmatic encroachment are also taken to undermine the case for contextualism; see again Rysiew 2001 and Brown 2006.

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For the 2012 revision, we are grateful to Kurt Sylvan for extremely detailed and constructive comments on multiple drafts of this entry. Thanks also to an anonymous referee for additional helpful suggestions. For the 2017 revision, thanks to Clayton Littlejohn, Jennifer Nagel, and Scott Sturgeon for helpful and constructive feedback and suggestions. Thanks to Ben Bayer, Kenneth Ehrenberg, and Mark Young for drawing our attention to errors in the previous version.

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Essay on Knowledge is Power

500 Words Essay On Knowledge is Power

Knowledge is the most substantial element in the world. It can make or break your life alone. Moreover, knowledge is what differentiates humans from animals . With knowledge, one can utilize their skills and make their lives better. When you have knowledge at your disposal, you can accomplish a lot in your life. The essay on knowledge is power will help you learn more about it.

Knowledge is Treasure

There are some people only who understand how important knowledge is. While every educated person may not be intelligent, it is true that every qualified person has an education .

It may seem like a strange statement but it is true. When you have the treasure of knowledge, you can drive a car or even fly an aeroplane. Similarly, you can crack puzzles and solve riddles with knowledge.

Therefore, it allows you to do the little as well as big things. When you have the knowledge, you can stop yourself from falling into the same trap. Also, you cannot buy knowledge. It is very essential to note this in this essay on knowledge is power.

It is a treasure that cannot be bought. You gain it and you earn it with your hard work. Therefore, the real gem is that of knowledge that will make you a successful person in life and help you gain power and respect.

Get the huge list of more than 500 Essay Topics and Ideas

Knowledge is a Bottomless Ocean

Knowledge is like a bottomless ocean . The more you dive deep into it, the deeper it will appear to you. Thus, there are no limits in the world of knowledge. When you desire knowledge, you thirst for riches unknown.

Once you taste the nectar of knowledge, you cannot restrain your desire for it. You only get the desire to gain more wisdom and acquire more knowledge. There is a proverb that tells us that people will worship the king in his kingdom alone but they will worship a man of knowledge all over the world.

In other words, a person with knowledge can find a home in any part of the world. The ocean of knowledge gives us broad thinking and makes us fearless. Moreover, our vision becomes clear through it.

Also, when you get the knowledge of various things like science, medicine, politics, and more, you can work for the betterment of the world. Knowledge gives birth to inventions and discoveries.

Conclusion of Essay on Knowledge is Power

All in all, knowledge allows people to flourish in life. Similarly, it also helps to hold off wars and abuse. It is responsible for bringing peace to the world and helping nations prosper. It can open doors to success and unite people like never before.

FAQ on Essay on Knowledge is Power

Question 1: What does Knowledge is Power mean?

Answer 1: When we say knowledge is power, we mean that a person with education has the power to control his life by making use of his knowledge. Moreover, it helps us overcome hurdles easily.

Question 2: Why is knowledge so important?

Answer 2: Knowledge improves our thinking and helps us solve problems. It is important because it enhances our reasoning and critical thinking to make better decisions in life and choose the correct path.

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7.2 Knowledge

Learning objectives.

By the end of this section, you will be able to:

Identify and explain the elements of Plato’s traditional account of knowledge.
Describe the Gettier problem.
Recall a Gettier case and explain how it is a counterexample to the traditional account of knowledge.
Identify and explain a way of thinking that attempts to solve the Gettier problem.

What does it mean to say that one knows something? Knowledge is an important concept in all areas of thought. Knowledge is the goal and therefore enjoys a special status. Investigating the nature of knowledge reveals the importance of other concepts that are key to epistemological theorizing—justification in particular.

Plato and the Traditional Account of Knowledge

Plato , one of the most important of the Greek philosophers, hypothesized that knowledge is justified true belief. Plato’s analysis is known as the traditional account of knowledge . Plato’s definition is that a person S knows proposition P if and only if

S believes P, and
S is justified in believing P (Plato 1997b).

Plato’s hypothesis on knowledge, often referred to as the JTB account (because it is “ justified true belief ”), is highly intuitive. To say “John knows P, but he does not believe P” sounds wrong. In order to know something, a subject must first believe it. And one also cannot say “Ali knows P, but P is false.” A person simply cannot have knowledge of false things. Knowledge requires truth. Last, someone should not claim to know P if they have no reason to believe P (a reason to believe being justification for P).

Problems with the Traditional Account of Knowledge

Amazingly, Plato ’s view that knowledge is justified true belief was generally accepted until the 20th century (over 2,000 years!). But once this analysis was questioned, a flurry of developments occurred within epistemology in the latter half of the 20th century. This section discusses the counterexample method at play in the dialectic concerning what knowledge is. Plato’s JTB analysis was the first to come under scrutiny.

In 1963, American philosopher Edmund Gettier (1927–2021) published a short paper titled “Is Justified True Belief Knowledge?,” which upended the JTB canon in Western philosophy. Gettier presents two counterexamples to Plato’s analysis of knowledge. In these counterexamples, a person seems to have a justified true belief, yet they do not seem to have knowledge. While Gettier is credited with the first popular counterexample to the JTB account, he was not the first philosopher to articulate a counterexample that calls into question Plato’s analysis. But because Gettier published the first influential account, any example that seems to undermine Plato’s JTB account of knowledge is called a Gettier case . Gettier cases illustrate the inadequacy of the JTB account—a problem referred to as the Gettier problem .

Dharmakīrti’s Mirage

The earliest known Gettier case, long predating the term, was conceived by the eighth century Indian Buddhist philosopher Dharmakīrti . Dharmakīrti’s case asks one to imagine a weary nomad traveling across the desert in search of water (Dreyfus 1997). The traveler crests a mountain and sees what appears to be an oasis in the valley below, and so comes to believe that there is water in the valley. However, the oasis is just a mirage. Yet there is water in the valley, but it is just beneath the surface of the land where the mirage is. The traveler is justified in believing there is water in the valley due to sensory experience. Furthermore, it is true that there is water in the valley. However, the traveler’s belief does not seem to count as knowledge. Dharmakīrti’s conclusion is that the traveler cannot be said to know there is water in the valley because the traveler’s reason for believing that there is water in the valley is an illusory mirage.

Russell’s Case

Perhaps you’ve heard the phrase “Even a broken clock is right twice a day.” The next case relies on this fact about broken clocks. In 1948, Bertrand Russell offered a case in which a man looks up at a stopped clock at exactly the correct time:

There is the man who looks at a clock which is not going, though he thinks it is, and who happens to look at it at the moment when it is right; this man acquires a true belief as to the time of day, but cannot be said to have knowledge. (Russell 1948, 154)

Imagine that the clock the man looks at is known for its reliability. Hence, the man is justified in believing that the time is, for example, 4:30. And, as the cases supposes, it is true that it is 4:30. However, given that the clock is not working and that the man happens to look up at one of the two times a day that the clock is correct, it is only a matter of luck that his belief happens to be true. Hence, Russell concludes that the man cannot be said to know the correct time.

Fake Barn Country

The last Gettier case we will look at is from American philosopher Carl Ginet (b. 1932) (Goldman 1976). Henry is driving through a bucolic area of farmland and barns. What he doesn’t realize, however, is that the area is currently being used as a movie set, and all the barns save one are actually barn facades. While looking at one of the barns, Henry says to himself, “That is a barn.” Luckily for Henry, the one he points to is the one true barn in the area. Again, all the conditions in Plato’s analysis of knowledge are met. It is true that Henry is looking at a real barn, and he believes it is a barn. Furthermore, he has come to this belief utilizing justifiable means—he is using his vision, in normal lighting, to identify a common object (a barn). Yet one cannot reasonably say that Henry knows the barn is a barn because he could have, by chance, accidentally identified one of the fake barns as a true barn. He fortunately happens to pick the one true barn.

Table 7.2 summarizes the Gettier cases discussed in this chapter.

Fixing Plato’s Traditional Account of Knowledge

Gettier cases demonstrate that Plato ’s traditional account of knowledge as justified true belief is wrong. Specifically, Gettier cases show that a belief being true and justified is not sufficient for that belief to count as knowledge. In all the cases discussed, the subject seems to have a justified true belief but not knowledge. Notice that this does not mean that belief, truth, or justification is not necessary for knowledge. Indeed, when speaking of propositional knowledge, all philosophers grant that belief and truth are necessary conditions for knowledge. A person cannot be said to know a proposition if they do not believe that proposition. And clearly, if a belief is to count as knowledge, then that believe simply cannot be false. Accordingly, attempts to solve the Gettier problem do one of two things: either they replace the justification condition with something more robust, or they add a fourth condition to JTB to make the account sufficient.

No False Premises

In Dharmakīrti’s case, the nomad believes there is water in the valley based on the false belief that a mirage is an oasis. And in Russell’s case, the man bases his true belief about the time on the false belief that the clock he’s looking at is working. In both cases, the inference that leads to the true belief passes through false premises. In response to this fact, American philosopher Gilbert Harman (1928–2021) suggested adding a condition to the JTB account that he termed “no false lemmas” (Harman 1973). A false lemma is a false premise, or step in the reasoning process. Harman’s fourth condition is that a person’s belief cannot be based on an inference that uses false premises. According to Harman, S knows P if and only if (1) P is true, (2) S believes P, (3) S is justified in believing P, and (4) S did not infer P from any falsehoods.

Harman theorized that many counterexamples to the traditional account share a similar feature: the truth of the belief is not appropriately connected to the evidence used to deduce that belief. Going back to Dharmakīrti ’s case, what makes the statement “There’s water in the valley” true is the fact that there is water below the surface. However, the nomad comes to believe that there is water based on the mistaken belief that a mirage is an oasis, so what makes the belief true is not connected to the reason the nomad believes it. If Harman’s condition that the reasoning that leads to belief cannot pass through false steps is added, then the nomad’s belief no longer counts as knowledge.

Harman ’s emendation explains why the nomad does not have knowledge and accounts for the intuition that the man in Russell’s case does not actually know what time it is. However, this cannot take care of all Gettier cases . Consider the case of Henry in fake barn country. Henry comes to believe he is looking at a barn based on his perceptual experience of the barn in front of him. And Henry does look at a real barn. He does not reason through any false premises, such as “All the structures on my drive are barns.” His inference flows directly from his perceptual experience of a real barn. Yet it is a matter of luck that Henry isn’t looking at one of the many barn facades in the area, so his belief still does not seem to count as knowledge. Because Harman’s account is vulnerable to the barn counterexample, it does not solve the Gettier problem.

Ruling Out Defeaters and Alternatives

While driving through fake barn country, Henry happens to form the belief “That is a barn” when looking at the only real barn in the area. While Henry’s belief is not based on false premises, there still seems to be something wrong with it. Why? The problem is that certain facts about Henry’s environment (that it is filled with barn facades), if known, would undermine his confidence in the belief. That the area is predominantly filled with barn facades is what is known as a defeater because it serves to defeat the justification for his belief. Contemporary American philosophers Keith Lehrer and Thomas Paxson Jr. suggest that justified true belief is knowledge as long as there are no existing defeaters of the belief (Lehrer and Paxson 1969). S has knowledge that P if and only if (1) P is true, (2) S believes P, (3) S is justified in believing P, and (4) there exist no defeaters for P. The added fourth condition means that there cannot exist evidence that, if believed by S, would undermine S’s justification.

The “no defeaters” condition solves all three Gettier cases discussed so far because in each case, there exists evidence that, if possessed by the subject, would undermine their justification. Henry cannot be said to know he’s looking at a barn because of the evidence that most of the barns in the area are fake, and Russell’s man doesn’t know the time because the clock is stopped. The “no defeaters” condition thus helps solve many Gettier cases. However, we now need a thorough account of when evidence counts as a defeater . We are told that a defeater is evidence that would undermine a person’s justification but not how it does this. It cannot be that all evidence that weakens a belief is a defeater because this would make knowledge attainment much more difficult. For many of our justified true beliefs , there exists some evidence that we are unaware of that could weaken our justification. For example, we get many beliefs from other people. Research indicates that people tell an average of one lie per day (DePaulo et al. 1996; Serota, Levine, and Boster 2010). So when someone tells you something in conversation, often it is true that the person has lied once today. Is the evidence that a person has lied once today enough evidence to undermine your justification for believing what they tell you?

Notice that because a defeater is evidence that would undermine a person’s justification, what counts as a defeater depends on what justification is. Of the theories of knowledge examined so far, all of them treat justification as basic. They state that a belief must be justified but not how to measure or determine justification.

The Problem with Justification

The traditional analysis of knowledge explains that knowledge is justified true belief. But even if we accept this definition, we could still wonder whether a true belief is knowledge because we may wonder if it is justified. What counts as justification ? Justification is a rather broad concept. Instead of simply stating that justification is necessary for knowledge, perhaps a thorough account of knowledge ought to instead spell out what this means. The next section looks more deeply at how to understand justification and how some theorists suggest replacing the justification condition in order to solve the Gettier problem.

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Economic Sociology & Political Economy

The global community of academics, practitioners, and activists – led by dr. oleg komlik, albert einstein on the power of ideas and imagination in science.

“In nearly every detective novel since the admirable stories of Conan Doyle there comes a time when the investigator has collected all the facts he needs for at least some phase of his problem. These facts often seem quite strange, incoherent, and wholly unrelated. The great detective, however, realizes that no further investigation is needed at the moment, and that only pure thinking will lead to a correlation of the facts collected. So he plays his violin, or lounges in his armchair enjoying a pipe, when suddenly, by Jove, he has it! Not only does he have an explanation for the clues at hand but he knows that certain other events must have happened. Since he now knows exactly where to look for it, he may go out, if he likes, to collect further confirmation for his theory (pp. 4-5)… It is a familiar fact to readers of detective fiction that a false clue muddles the story and postpones the solution (6)… Intuitive conclusions based on immediate observation are not always to be trusted, for they sometimes lead to the wrong clues. But where does intuition go wrong? (7)… In a good mystery story the most obvious clues often lead to the wrong suspects… the most obvious intuitive explanation is often the wrong one (9)… Science must create its own language, its own concepts, for its own use . Scientific concepts often begin with those used in ordinary language for the affairs, of everyday life, but they develop quite differently. They are transformed and lose the ambiguity associated with them in ordinary language, gaining in rigorousness so that they may be applied to scientific thought (14)… Our interest here lies in the first stages of development, in following initial clues, in showing how new… concepts are born in the painful struggle with old ideas. We are concerned only with pioneer work in science, which consists of finding new and unexpected paths of development; with the adventures in scientific thought which create an ever-changing picture of the universe. The initial and fundamental steps are always of a revolutionary character. Scientific imagination finds old concepts too confining, and replaces them by new ones. The continued development along any line already initiated is more in the nature of evolution, until the next turning point is reached when a still newer field must be conquered. In order to understand, however, what reasons and what difficulties force a change in important concepts, we must know not only the initial clues, but also the conclusions which can be drawn (28)… Most of the fundamental ideas of science are essentially simple, and may, as a rule, be expressed in a language comprehensible to everyone. To follow up these ideas demands the knowledge of a highly refined technique of investigation (29)… Nearly every great advance in science arises from a crisis in the old theory, through an endeavour to find a way out of the difficulties created. We must examine old ideas, old theories, although they belong to the past, for this is the only way to understand the importance of the new ones and the extent of their validity. In the first pages of our book we compared the role of an investigator to that of a detective who, after gathering the requisite facts, finds the right solution by pure thinking. In one essential this comparison must be regarded as highly superficial. Both in life and in detective novels the crime is given. The detective must look for letters, fingerprints, bullets, guns, but at least he knows that a murder has been committed. This is not so for a scientist…. For the detective the crime is given, the problem formulated: who killed Cock Robin? The scientist must, at least in part, commit his own crime, as well as carry out the investigation. Moreover, his task is not to explain just one case, but all phenomena which have happened or may still happen (77-8)… Yet we may choose to be conservative and seek a solution [to a result that shakes our belief] within the frame of old ideas. Difficulties of this kind, sudden and unexpected obstacles in the triumphant development of a theory, arise frequently in science. Sometimes a simple generalization of the old ideas seems, at least temporarily, to be a good way out… Very often, however, it is impossible to patch up an old theory, and the difficulties result in its downfall and the rise of a new one (93-4)… The formulation of a problem is often more essential than its solution , which may be merely a matter of… experimental skill. To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science (95)… Creating a new theory is not like destroying an old barn and erecting a sky scraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering new connections between our starting point and its rich environment. But the point from which we started still exists and can be seen, although it appears smaller and forms a tiny part of our broad view gained by the mastery of the obstacles on our way up (159)… Science forces us to create new ideas, new theories. Their aim is to break down the wall of contradictions which frequently blocks the way of scientific progress. All the essential ideas in science were born in a dramatic conflict between reality and our attempts at understanding. Here again is a problem for the solution of which new principles are needed (280)… The association of solved problems with those unsolved may throw new light on our difficulties by suggesting new ideas. It is easy to find a superficial analogy which really expresses nothing. But to discover some essential common features, hidden beneath a surface of external differences, to form, on this basis, a new successful theory, is important creative work ” ( Einstein and Infeld 1938: 286-7 ).

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Which Is More Important: Creativity or Knowledge?

Which is more important: creativity or knowledge? Find here the answer! This creativity vs. knowledge essay explains the relationship between imagination and intelligence and gives examples.

Introduction

Creativity vs. Knowledge

Works Cited

Schools are institutions that are set up with the aim of impacting students with knowledge. This being the primary focus of most education systems, generating new knowledge through creativity becomes secondary to most scholars. This leads to the question of which, between knowledge and creativity, is more important? This question is more relevant to students in higher institutions of learning since this level of learning is developed enough to generate creative thinking, in addition to impacting students with knowledge.

Creativity Is More Important than Knowledge

As such, a college student should ponder on this question, considering that such a student is almost ready for the job market. As a college student, creativity is more important than knowledge since creativity allows one to explore ideas with no boundaries, it gives birth to innovation, and it provides room for developing practical solutions to real life challenges, unlike knowledge which is limited to one’s expertise and experience.

While knowledge is limited to one’s skills, creativity has no boundaries since it goes as far as one’s imagination can reach. Knowledge hardly goes beyond one’s training or experience in a certain field, whereas creativity/imagination follows intuition and transcends one’s acquired skills (Ox and van der Elst 84).

Creative minds do not necessarily focus on achieving good grades in school. This is because good grades do not always imply creativity; instead, good grades are usually a reflection of one’s knowledge in a given subject since schooling systems are more oriented on impacting knowledge than creating an environment that enhances creativity.

Knowledge is determined by set standards and systems, but creativity transcends these systems since a creative mind is more flexible and imaginative. With specific focus on great people like Albert Einstein, who came up with the laws of relativity, it is very clear that Einstein exercised more creativity than knowledge in coming up with the laws (Gardner 108).

Although it is acknowledgeable that his prior knowledge in the field of physical science created an environment for developing the laws of relativity, his sense of imagination was far much important than the acquired knowledge. It is for such a reason that college students should be more creative, other than just acquiring knowledge in their course of learning.

Creativity births innovation since it is not bound by experience, unlike knowledge that is limited to acquired skills and experience. Creativity encompasses the intrinsic motivation to pursue a certain interest, and this gives birth to innovation. Creativity allows college students to think in a flexible and imaginative way such that when a motivating environment is provided, students can end up creating very impressive solutions to problems. Global success is pegged on innovation.

Virtually every sphere of life in the current world is competitive in its own way. This calls for innovative minds in order to emerge successful (HR Focus 8). On the other hand, new ideas cannot be generated by relying on knowledge alone since knowledge is limited to the skills that are acquired through a formal or informal process of learning.

As such, creative thinking remains to be the solution to innovation in the current world. It is said that contemporary organizations are encouraging creative thinking as a way of remaining competitive. College students can supplement the existing gap in innovation by being more creative, in addition to being knowledgeable in their fields of study. To a college student, divergent and convergent thinking are a recipe for success, even outside college (HR Focus 8).

Creativity holds promise to providing solutions to the numerous challenges in the work environment and the larger society. Combining expertise with imagination, which encompasses flexible thinking, would help explore problems beyond the limited human understanding and develop effective solutions.

Colleges, among other institutions of higher learning, should offer the best platform for developing individual creativity. College students have the right environment to develop creativity since university-level education encourages individual learning more than pedagogical structured learning.

The wide access to information and elaborate interaction and networking available in colleges should ignite students to be more creative and become problem solvers. Moreover, college students should realize that they are under preparation for the great roles they will later play in the society, especially in their places of work (Livingston 60). For this reason, creativity is far much important than knowledge to a college student.

Combined knowledge and creativity can generate greater achievement, thus the importance of both cannot be underestimated. For instance, while someone like Einstein used imagination to come up with the laws of relativity, he also relied heavily on his immense knowledge of physical science to draft these laws (Gardner 104). Thus, while it is right to argue that creativity is more important than knowledge to a college student, it does not mean that knowledge has no place in fostering creativity.

In fact, it is right to argue that creativity is boosted by one’s knowledge, thus a very thin line exists between knowledge and creativity. Creativity comprises of expertise, flexible thinking and imagination, and motivation. Hope (39) acknowledges that creative potential is build over time and calls for consistent study with a particular goal in mind. The study must be focused on a specific field for creativity in that field to be developed.

This implies that knowledge must be acquired under certain structures, such as schooling structures, to develop creative potential. An environment that does not foster creative thinking kills creativity. In addition, creativity is developed by acknowledging knowledge in a particular field (Ox and van der Elst 84). As such, college students cannot afford to ignore the need for structured learning and pursue creativity solely. Such an approach may not give birth to creativity since creativity is built on knowledge.

The world today is need of creative and innovative minds for global success to be achieved, and creative college minds can meet this need. To a college student, creativity will help in thinking and developing solutions beyond one’s acquired knowledge and skills.

Moreover, creative college students will find relevance in the present work environment since they will be able to come up with innovations. This notwithstanding, it is important for college student to acquire knowledge since creativity in any field is catalyzed by accumulated knowledge in the field of study.

As a college student, imagination should be given a priority in the course of acquiring knowledge if one wants to be creative. Moreover, college education should foster flexible thinking and provide a motivating environment that will give birth to creativity. Creativity among college students should be encouraged than the sole pursuit for knowledge in order to develop solutions that are so much needed in the working world and the society at large.

Gardner, Howard. Creating Minds: An Anatomy of Creativity Seen Through the Lives of Freud, Einstein, Picasso, Stravinsky, Eliot, Graham, and Gandhi . New York, NY: Basic Books, 2011. Print.

Hope, Samuel. “Creativity, Content, and Policy.” Arts Education Policy Review 111.2 (2010): 39-47. Print.

HR Focus. “Creativity and Innovation: Must-Haves for Global Success.” HR Focus News Briefs (2007): 8. Print.

Livingston, Larry. “Teaching Creativity in Higher Education.” Arts Education Policy Review 111.2 (2010): 59-62. Print.

Ox, Jack, and van der Elst Judith. “How Metaphor Functions as a Vehicle of Thought: Creativity as a Necessity for Knowledge Building and Communication.” Journal of Visual Art Practice 10.1 (2011): 83-102. Print.

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Front Psychol

Is a Long Essay Always a Good Essay? The Effect of Text Length on Writing Assessment

Johanna fleckenstein.

1 Department of Educational Research and Educational Psychology, Leibniz Institute for Science and Mathematics Education, Kiel, Germany

Jennifer Meyer

Thorben jansen.

2 Institute for Psychology of Learning and Instruction, Kiel University, Kiel, Germany

Stefan Keller

3 School of Education, Institute of Secondary Education, University of Applied Sciences and Arts Northwestern Switzerland, Brugg, Switzerland

Olaf Köller

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

The assessment of text quality is a transdisciplinary issue concerning the research areas of educational assessment, language technology, and classroom instruction. Text length has been found to strongly influence human judgment of text quality. The question of whether text length is a construct-relevant aspect of writing competence or a source of judgment bias has been discussed controversially. This paper used both a correlational and an experimental approach to investigate this question. Secondary analyses were performed on a large-scale dataset with highly trained raters, showing an effect of text length beyond language proficiency. Furthermore, an experimental study found that pre-service teachers tended to undervalue text length when compared to professional ratings. The findings are discussed with respect to the role of training and context in writing assessment.

Introduction

Judgments of students’ writing are influenced by a variety of text characteristics, including text length. The relationship between such (superficial) aspects of written responses and the assessment of text quality has been a controversial issue in different areas of educational research. Both in the area of educational measurement and of language technology, text length has been shown to strongly influence text ratings by trained human raters as well as computer algorithms used to score texts automatically ( Chodorow and Burstein, 2004 ; Powers, 2005 ; Kobrin et al., 2011 ; Guo et al., 2013 ). In the context of classroom language learning and instruction, studies have found effects of text length on teachers’ diagnostic judgments (e.g., grades; Marshall, 1967 ; Osnes, 1995 ; Birkel and Birkel, 2002 ; Pohlmann-Rother et al., 2016 ). In all these contexts, the underlying question is a similar one: Should text length be considered when judging students’ writing – or is it a source of judgment bias? The objective of this paper is to investigate to what degree text length is a construct-relevant aspect of writing competence, or to what extent it erroneously influences judgments.

Powers (2005) recommends both correlational and experimental approaches for establishing the relevance of response length in the evaluation of written responses: “the former for ruling out response length (and various other factors) as causes of response quality (by virtue of their lack of relationship) and the latter for establishing more definitive causal links” (p. 7). This paper draws on data from both recommended approaches: A correlational analysis of a large-scale dataset [MEWS; funded by the German Research Foundation (Grant Nr. CO 1513/12-1) and the Swiss National Science Foundation (Grant Nr. 100019L_162675)] based on expert text quality ratings on the one hand, and an experimental study with untrained pre-service teachers on the other. It thereby incorporates the measurement perspective with the classroom perspective. In the past, (language) assessment research has been conducted within different disciplines that rarely acknowledged each other. While some assessment issues are relevant for standardized testing in large-scale contexts only, others pertain to research on teaching and classroom instruction as well. Even though their assessments may serve different functions (e.g., formative vs. summative or low vs. high stakes), teachers need to be able to assess students’ performance accurately, just as well as professional raters in standardized texts. Thus, combining these different disciplinary angles and looking at the issue of text length from a transdisciplinary perspective can be an advantage for all the disciplines involved. Overall, this paper aims to present a comprehensive picture of the role of essay length in human and automated essay scoring, which ultimately amounts to a discussion of the elusive “gold standard” in writing assessment.

Theoretical Background

Writing assessment is about identifying and evaluating features of a written response that indicate writing quality. Overall, previous research has demonstrated clear and consistent associations between linguistic features on the one hand, and writing quality and development on the other. In a recent literature review, Crossley (2020) showed that higher rated essays typically include more sophisticated lexical items, more complex syntactic features, and greater cohesion. Developing writers also show movements toward using more sophisticated words and more complex syntactic structures. The studies presented by Crossley (2020) provide strong indications that linguistic features in texts can afford important insights into writing quality and development. Whereas linguistic features are generally considered to be construct-relevant when it comes to assessing writing quality, there are other textual features whose relevance to the construct is debatable. The validity of the assessment of students’ competences is negatively affected by construct-irrelevant factors that influence judgments ( Rezaei and Lovorn, 2010 ). This holds true for professional raters in the context of large-scale standardized writing assessment as well as for teacher judgments in classroom writing assessment (both formative or summative). Assigning scores to students’ written responses is a challenging task as different text-inherent factors influence the accuracy of the raters’ or teachers’ judgments (e.g., handwriting, spelling: Graham et al., 2011 ; length, lexical diversity: Wolfe et al., 2016 ). Depending on the construct to be assessed, the influence of these aspects can be considered judgment bias. One of the most relevant and well-researched text-inherent factors influencing human judgments is text length. Crossley (2020) points out that his review does “not consider text length as a linguistic feature while acknowledging that text length is likely the strongest predictor of writing development and quality.” Multiple studies have found a positive relationship between text length and human ratings of text quality, even when controlling for language proficiency ( Chenoweth and Hayes, 2001 ; McCutchen et al., 2008 ; McNamara et al., 2015 ). It is still unclear, however, whether the relation between text length and human scores reflects a true relation between text length and text quality (appropriate heuristic assumption) or whether it stems from a bias in human judgments (judgment bias assumption). The former suggests that text length is a construct-relevant factor and that a certain length is needed to effectively develop a point of view on the issue presented in the essay prompt, and this is one of the aspects taken into account in the scoring ( Kobrin et al., 2007 ; Quinlan et al., 2009 ). The latter claims that text length is either completely or partly irrelevant to the construct of writing proficiency and that the strong effect it has on human judgment can be considered a bias ( Powers, 2005 ). In the context of large-scale writing assessment, prompt-based essay tasks are often used to measure students’ writing competence ( Guo et al., 2013 ). These essays are typically scored by professionally trained raters. These human ratings have been shown to be strongly correlated with essay length, even if this criterion is not represented in the assessment rubric ( Chodorow and Burstein, 2004 ; Kobrin et al., 2011 ). In a review of selected studies addressing the relation between length and quality of constructed responses, Powers (2005) showed that most studies found correlations within the range of r = 0.50 to r = 0.70. For example, he criticized the SAT essay for encouraging wordiness as longer essays tend to score higher. Kobrin et al. (2007) found the number of words to explain 39% of the variance in the SAT essay score. The authors argue that essay length is one of the aspects taken into account in the scoring as it takes a certain length to develop an argument. Similarly, Deane (2013) argues in favor of regarding writing fluency a construct-relevant factor (also see Shermis, 2014 ; McNamara et al., 2015 ). In an analytical rating of text quality, Hachmeister (2019) could showed that longer texts typically contain more cohesive devices, which has a positive impact on ratings of text quality. In the context of writing assessment in primary school, Pohlmann-Rother et al. (2016) found strong correlations between text length and holistic ratings of text quality ( r = 0.62) as well as the semantic-pragmatic analytical dimension ( r = 0.62). However, they found no meaningful relationship between text length and language mechanics (i.e., grammatical and orthographical correctness; r = 0.09).

Text length may be considered especially construct-relevant when it comes to writing in a foreign language. Because of the constraints of limited language knowledge, writing in a foreign language may be hampered because of the need to focus on language rather than content ( Weigle, 2003 ). Silva (1993) , in a review of differences between writing in a first and second language, found that writing in a second language tends to be “more constrained, more difficult, and less effective” (p. 668) than writing in a first language. The necessity of devoting cognitive resources to issues of language may mean that not as much attention can be given to higher order issues such as content or organization (for details of this debate, see Weigle, 2003 , p. 36 f.). In that context, the ability of writing longer texts may be legitimately considered as indicative of higher competence in a foreign language, making text length a viable factor of assessment. For example, Ruegg and Sugiyama (2010) showed that the main predictors of the content score in English foreign language essays were first, organization and second, essay length.

The relevance of this issue has further increased as systems of automated essay scoring (AES) have become more widely used in writing assessment. These systems offer a promising way to complement human ratings in judging text quality ( Deane, 2013 ). However, as the automated scoring algorithms are typically modeled after human ratings, they are also affected by human judgment bias. Moreover, it has been criticized that, at this point, automated scoring systems mainly count words when computing writing scores ( Perelman, 2014 ). Chodorow and Burstein (2004) , for example, showed that 53% of the variance in human ratings can be explained by automated scoring models that use only the number of words and the number of words squared as predictors. Ben-Simon and Bennett (2007) provided evidence from National Assessment of Educational Progress (NAEP) writing test data that standard, statistically created e-rater models weighed essay length even more strongly than human raters (also see Perelman, 2014 ).

Bejar (2011) suggests that a possible tendency to reward longer texts could be minimized through the training of raters with responses at each score level that vary in length. However, Barkaoui (2010) and Attali (2016) both compared the holistic scoring of experienced vs. novice raters and – contrary to expectations – found that the correlation between essay length and scores was slightly stronger for the experienced group. Thus, the question of whether professional experience and training counteract or even reinforce the tendency to overvalue text length in scoring remains open.

Compared to the amount of research on the role of essay length in human and automated scoring in large-scale high-stakes contexts, little attention has been paid to the relation of text length and quality in formative or summative assessment by teachers. This is surprising considering the relevance of the issue for teachers’ professional competence: In order to assess the quality of students’ writing, teachers must either configure various aspects of text quality in a holistic assessment or hold them apart in an analytic assessment. Thus, they need to have a concept of writing quality appropriate for the task and they need to be aware of the construct-relevant and -irrelevant criteria (cf. the lens model; Brunswik, 1955 ). To our knowledge, only two studies have investigated the effect of text length on holistic teacher judgments, both of which found that longer texts receive higher grades. Birkel and Birkel (2002) found significant main effects of text length (long, medium, short) and spelling errors (many, few) on holistic teacher judgments. Osnes (1995) reported effects of handwriting quality and text length on grades.

Whereas research on the text length effect on classroom writing assessment is scarce, a considerable body of research has investigated how other text characteristics influence teachers’ assessment of student texts. It is well-demonstrated, for example, that pre-service and experienced teachers assign lower grades to essays containing mechanical errors ( Scannell and Marshall, 1966 ; Marshall, 1967 ; Cumming et al., 2002 ; Rezaei and Lovorn, 2010 ). Scannell and Marshall (1966) found that pre-service teachers’ judgments were affected by errors in punctuation, grammar and spelling, even though they were explicitly instructed to grade on content alone. More recently, Rezaei and Lovorn (2010) showed that high quality essays containing more structural, mechanical, spelling, and grammatical errors were assigned lower scores than texts without errors even in criteria relating solely to content. Teachers failed to distinguish between formal errors and the independent quality of content in a student essay. Similarly, Vögelin et al. (2018 , 2019) found that lexical features and spelling influenced not only holistic teacher judgments of students’ writing in English as a second or foreign language, but also their assessment of other analytical criteria (e.g., grammar). Even though these studies do not consider text length as a potential source of bias, they do show that construct-irrelevant aspects influence judgments of teachers.

This Research

Against this research background, it remains essential to investigate whether the relation between essay length and text quality represents a true relationship or a bias on the part of the rater or teacher ( Wolfe et al., 2016 ). First, findings of correlational studies can give us an indication of the effect of text length on human ratings above and beyond language proficiency variables. Second, going beyond correlational findings, there is a need for experimental research that examines essay responses on the same topic differing only in length in order to establish causal relationships ( Kobrin et al., 2007 ). The present research brings together both of these approaches.

This paper comprises two studies investigating the role of essay length in foreign language assessment using an interdisciplinary perspective including the fields of foreign language education, computer linguistics, educational research, and psychometrics. Study 1 presents a secondary analysis of a large-scale dataset with N = 2,722 upper secondary school students in Germany and Switzerland who wrote essays in response to “independent writing” prompts of the internet-based Test of English as a Foreign Language (TOEFL iBT). It investigates the question of how several indicators of students’ English proficiency (English grade, reading and listening comprehension, self-concept) are related to the length of their essays (word count). It further investigates whether or not essay length accounts for variance in text quality scores (expert ratings) even when controlling for English language proficiency and other variables (e.g., country, gender, cognitive ability). A weak relationship of proficiency and length as well as a large proportion of variance in text quality explained by length beyond proficiency would be in favor of the judgment bias assumption.

Study 2 focused on possible essay length bias in an experimental setting, investigating the effect of essay length on text quality ratings when there was (per design) no relation between essay length and text quality score. Essays from Study 1 were rated by N = 84 untrained pre-service teachers, using the same TOEFL iBT rubric as the expert raters. As text quality scores were held constant within all essay length conditions, any significant effect of essay length would indicate a judgment bias. Both studies are described in more detail in the following sections.

This study investigates the question of judgment bias assumption vs. appropriate heuristic assumption in a large-scale context with professional human raters. A weak relationship between text length and language proficiency would be indicative of the former assumption, whereas a strong relationship would support the latter. Moreover, if the impact of text length on human ratings was significant and substantial beyond language proficiency, this might indicate a bias on the part of the rater rather than an appropriate heuristic. Thus, Study 1 aims to answer the following research questions:

(1) How is essay length related to language proficiency?
(2) Does text length still account for variance in text quality when English language proficiency is statistically controlled for?

Materials and Methods

Sample and procedure.

The sample consisted of N = 2,722 upper secondary students (11th grade; 58.1% female) in Germany ( n = 894) and Switzerland ( n = 1828) from the interdisciplinary and international research project Measuring English Writing at Secondary Level (MEWS; for an overview see Keller et al., 2020 ). The target population were students attending the academic track of general education grammar schools (ISCED level 3a) in the German federal state Schleswig-Holstein as well as in seven Swiss cantons (Aargau, Basel Stadt, Basel Land, Luzern, St. Gallen, Schwyz, Zurich). In a repeated-measures design, students were assessed at the beginning (T1: August/September 2016; M age = 17.34; SD age = 0.87) and at the end of the school year (T2: May/June 2017; M age = 18.04; SD age = 0.87). The students completed computer-based tests on writing, reading and listening skills, as well as general cognitive ability. Furthermore, they completed a questionnaire measuring background variables and individual characteristics.

Writing prompt

All students answered two independent and two integrated essay writing prompts of the internet-based Test of English as a Foreign Language (TOEFL iBT ® ) that is administered by the Educational Testing Service (ETS) in Princeton. The task instruction was as follows: “In the writing task below you will find a question on a controversial topic. Answer the question in an essay in English. List arguments and counter-arguments, explain them and finally make it clear what your own opinion on the topic is. Your text will be judged on different qualities. These include the presentation of your ideas, the organization of the essay and the linguistic quality and accuracy. You have 30 min to do this. Try to use all of this time as much as possible.” This task instruction was followed by the essay prompt. The maximum writing time was 30 min according to the official TOEFL iBT ® assessment procedure. The essays were scored by trained human raters on the TOEFL 6-point rating scale at ETS. In addition to two human ratings per essay, ETS also provided scores from their automated essay scoring system (e-rater ® ; Burstein et al., 2013 ). For a more detailed description of the scoring procedure and the writing prompts see Rupp et al. (2019) and Keller et al. (2020) . For the purpose of this study, we selected the student responses to the TOEFL iBT independent writing prompt “Teachers,” which showed good measurement qualities (see Rupp et al., 2019 ). Taken together, data collections at T1 and T2 yielded N = 2,389 valid written responses to the following prompt: “A teacher’s ability to relate well with students is more important than excellent knowledge of the subject being taught.”

Text quality and length

The rating of text quality via human and machine scoring was done by ETS. All essays were scored by highly experienced human raters on the operational holistic TOEFL iBT rubric from 0 to 5 ( Chodorow and Burstein, 2004 ). Essays were scored high if they were well-organized and individual ideas were well-developed, if they used specific examples and support to express learners’ opinion on the subject, and if the English language was used accurately to express learners’ ideas. Essays were assigned a score of 0 if they were written in another language, were generally incomprehensible, or if no text was entered.

Each essay received independent ratings by two trained human raters. If the two ratings showed a deviation of 1, the mean of the two scores was used; if they showed a deviation of 2 or more, a third rater (adjudicator) was consulted. Inter-rater agreement, as measured by quadratic weighted kappa (QWK), was satisfying for the prompt “Teachers” at both time points (QWK = 0.67; Hayes and Hatch, 1999 ; see Rupp et al., 2019 for further details). The mean text quality score was M = 3.35 ( SD = 0.72).

Word count was used to measure the length of the essays. The number of words was calculated by the e-Rater scoring engine. The mean word count was M = 311.19 ( SD = 81.91) and the number of words ranged from 41 to 727. We used the number of words rather than other measures of text length (e.g., number of letters) as it is the measure which is most frequently used in the literature: 9 out of 10 studies in the research review by Powers (2005) used word count as the criterion (also see Kobrin et al., 2007 , 2011 ; Crossley and McNamara, 2009 ; Barkaoui, 2010 ; Attali, 2016 ; Wolfe et al., 2016 ; Wind et al., 2017 ). This approach ensures that our analyses can be compared with previous research.

English language proficiency and control variables

Proficiency was operationalized by a combination of different variables: English grade, English writing self-concept, reading and listening comprehension in English. The listening and reading skills were measured with a subset of items from the German National Assessment ( Köller et al., 2010 ). The tasks require a detailed understanding of long, complex reading and listening texts including idiomatic expressions and different linguistic registers. The tests consisted of a total of 133 items for reading, and 118 items for listening that were administered in a multi-matrix-design. Each student was assessed with two rotated 15-min blocks per domain. Item parameters were estimated using longitudinal multidimensional two-parameter item response models in M plus version 8 ( Muthén and Muthén, 1998–2012 ). Student abilities were estimated using 15 plausible values (PVs) per person. The PV reliabilities were 0.92 (T1) and 0.76 (T2) for reading comprehension, and 0.85 (T1) and 0.72 (T2) for listening comprehension. For a more detailed description of the scaling procedure see Köller et al. (2019) .

General cognitive ability was assessed at T1 using the subtests on figural reasoning (N2; 25 items) and on verbal reasoning (V3; 20 items) of the Cognitive Ability Test (KFT 4–12 + R; Heller and Perleth, 2000 ). For each scale 15 PVs were drawn in a two-dimensional item response model. For the purpose of this study, the two PVs were combined to 15 overall PV scores with a reliability of 0.86.

The English writing self-concept was measured with a scale consisting of five items (e.g., “I have always been good at writing in English”; Eccles and Wigfield, 2002 ; Trautwein et al., 2012 ; α = 0.90). Furthermore, country (Germany = 0/Switzerland = 1), gender (male = 0/female = 1) and time of measurement (T1 = 0; T2 = 1) were used as control variables.

Statistical Analyses

All analyses were conducted in M plus version 8 ( Muthén and Muthén, 1998–2012 ) based on the 15PV data sets using robust maximum likelihood estimation to account for a hierarchical data structure (i.e., students clustered in classes; type = complex). Full-information maximum likelihood was used to estimate missing values in background variables. Due to the use of 15PVs, all analyses were run 15 times and then averaged (see Rubin, 1987 ).

Confirmatory factor analysis was used to specify a latent proficiency factor. All four proficiency variables showed substantial loadings in a single-factor measurement model (English grade: 0.67; writing self-concept: 0.73; reading comprehension: 0.42; listening comprehension: 0.51). As reading and listening comprehension were measured within the same assessment framework and could thus be expected to share mutual variance beyond the latent factor, their residuals were allowed to correlate. The analyses yielded an acceptable model fit: χ 2 (1) = 3.65, p = 0.06; CFI = 0.998, RMSEA = 0.031, SRMR = 0.006.

The relationship between text length and other independent variables was explored with correlational analysis. Multiple regression analysis with latent and manifest predictors was used to investigate the relations between text length, proficiency, and text quality.

The correlation of the latent proficiency factor and text length (word count) was moderately positive: r = 0.36, p < 0.01. This indicates that more proficient students tended to write longer texts. Significant correlations with other variables showed that students tended to write longer texts at T1 ( r = -0.08, p < 0.01), girls wrote longer texts than boys ( r = 0.11, p < 0.01), and higher cognitive ability was associated with longer texts ( r = 0.07, p < 0.01). However, all of these correlations were very weak as a general rule. The association of country and text length was not statistically significant ( r = -0.06, p = 0.10).

Table 1 presents the results of the multiple linear regression of text quality on text length, proficiency and control variables. The analysis showed that proficiency and the covariates alone explained 38 percent of the variance in text quality ratings, with the latent proficiency factor being by far the strongest predictor (Model 1). The effect of text length on the text quality score was equally strong when including the control variables but not proficiency in the model (Model 2). When both the latent proficiency factor and text length were entered into the regression model (Model 3), the coefficient of text length was reduced but remained significant and substantial, explaining an additional 24% of the variance (ΔR 2 = 0.24 from Model 1 to Model 3). Thus, text length had an incremental effect on text quality beyond a latent English language proficiency factor.

Linear regression of text quality on text length, English language proficiency, and control variables: standardized regression coefficients (β) and standard errors (SE).

Study 1 approached the issue of text length by operationalizing the construct of English language proficiency and investigating how it affects the relationship of text length and text quality. This can give us an idea of how text length may influence human judgments even though it is not considered relevant to the construct of writing competence. These secondary analyses of an existing large-scale dataset yielded two central findings: First, text length was only moderately associated with language proficiency. Second, text length strongly influenced writing performance beyond proficiency. Thus, it had an impact on the assigned score that was not captured by the construct of proficiency. These findings could be interpreted in favor of the judgment bias assumption as text length may include both construct-irrelevant and construct-relevant information.

The strengths of this study were the large sample of essays on the same topic and the vast amount of background information that was collected on the student writers (proficiency and control variables). However, there were three major limitations: First, the proficiency construct captured different aspects of English language competence (reading and listening comprehension, writing self-concept, grade), but that operationalization was not comprehensive. Thus, the additional variance explained by text length may still have been due to other aspects that could not be included in the analyses as they were not in the data. Further research with a similar design (primary or secondary analyses) should use additional variables such as grammar/vocabulary knowledge or writing performance in the first language.

The second limitation was the correlational design, which does not allow a causal investigation of the effect of text length on text quality ratings. Drawing inferences which are causal in nature would require an experimental environment in which, for example, text quality is kept constant for texts of different lengths. For that reason, Study 2 was conducted exactly in such a research design.

Last but not least, the question of transferability of these findings remains open. Going beyond standardized large-scale assessment, interdisciplinary research requires us to look at the issue from different perspectives. Findings pertaining to professional raters may not be transferable to teachers, who are required to assess students’ writing in a classroom context. Thus, Study 2 drew on a sample of preservice English teachers and took a closer look at how their ratings were impacted by text length.

Research Questions

In Study 2, we investigated the judgment bias assumption vs. the appropriate heuristic assumption of preservice teachers. As recommended by Powers (2005) , we conducted an experimental study in addition to the correlational design used in Study 1. As text quality scores were held constant within all essay length conditions, any significant effect of essay length would be in favor of the judgment bias assumption. The objective of this study was to answer the following research questions:

(1) How do ratings of pre-service teachers correspond to expert ratings?
(2) Is there an effect of text length on the text quality ratings of preservice English teachers, when there is (per design) no relation between text length and text quality (main effect)?
(3) Does the effect differ for different levels of writing performance (interaction effect)?

Participants and Procedure

The experiment was conducted with N = 84 pre-service teachers ( M Age = 23 years; 80% female), currently enrolled in a higher education teacher training program at a university in Northern Germany. They had no prior rating experience of this type of learner texts. The experiment was administered with the Student Inventory ASSET ( Jansen et al., 2019 ), an online tool to assess students’ texts within an experimental environment. Participants were asked to rate essays from the MEWS project (see Study 1) on the holistic rubric used by the human raters at ETS (0–5; https://www.ets.org/s/toefl/pdf/toefl_writing_rubrics.pdf ). Every participant had to rate 9 out of 45 essays in randomized order, representing all possible combinations of text quality and text length. Before the rating process began, participants were given information about essay writing in the context of the MEWS study (school type; school year; students’ average age; instructional text) and they were presented the TOEFL writing rubric as the basis for their judgments. They had 15 min to get an overview of all nine texts before they were asked to rate each text on the rubric. Throughout the rating process, they were allowed to highlight parts of the texts.

The operationalization of text quality and text length as categorical variables as well as the procedure of selecting an appropriate essay sample for the study is explained in the following.

Text Length and Text Quality

The essays used in the experiment were selected on the basis of the following procedure, which took both text quality and text length as independent variables into account. The first independent variable of the essay (overall text quality) was operationalized via scores assigned by two trained human raters from ETS on a holistic six-point scale (0–5; see Study 1 and Appendix A). In order to measure the variable as precisely as possible, we only included essays for which both human raters had assigned the same score, resulting in a sample of N = 1,333 essays. As a result, three gradations of text quality were considered in the current study: lower quality (score 2), medium quality (score 3) and higher quality (score 4). The corpus included only few texts (10.4%) with the extreme scores of 0, 1, and 5; these were therefore excluded from the essay pool. We thus realized a 3 × 3 factorial within-subjects design. The second independent variable text length was measured via the word count of the essays, calculated by the e-rater (c) scoring engine. As with text quality, this variable was subdivided in three levels: rather short texts (s), medium-length texts (m), and long texts (l). All available texts were analyzed regarding their word count distribution. Severe outliers were excluded. The remaining N = 1308 essays were split in three even groups: the lower (=261 words), middle (262–318 words) and upper third (=319 words). Table 2 shows the distribution of essays for the resulting combinations of text length and text score.

Distribution of essays in the sample contingent on text quality and text length groupings.

Selection of Essays

For each text length group (s, m, and l), the mean word count across all three score groups was calculated. Then, the score group (2, 3, or 4) with the smallest number of essays in a text length group was taken as reference (e.g., n = 22 short texts of high quality or n = 15 long texts of low quality). Within each text length group, the five essays being – word count-wise – closest to the mean of the reference were chosen for the study. This was possible with mostly no or only minor deviations. In case of multiple possible matches, the essay was selected at random. This selection procedure resulted in a total sample of 45 essays, with five essays for each combination of score group (2, 3, 4) and length group (s, m, l).

A repeated-measures ANOVA with two independent variables (text quality and text length) was conducted to test the two main effects and their interaction on participants’ ratings (see Table 3 ). Essay ratings were treated as a within-subject factor, accounting for dependencies of the ratings nested within raters. The main effect of text quality scores on participants’ ratings showed significant differences between the three text quality conditions ( low , medium , high ) that corresponded to expert ratings; F (2, 82) = 209.04, p < 0.001, d = 4.52. There was also a significant main effect for the three essay length conditions ( short , medium , long ); F (2, 82) = 9.14, p < 0.001, d = 0.94. Contrary to expectations, essay length was negatively related to participants’ ratings, meaning that shorter texts received higher scores than longer texts. The interaction of text quality and text length also had a significant effect; F (4, 80) = 3.93, p < 0.01, d = 0.89. Post-hoc tests revealed that texts of low quality were especially impacted by essay length in a negative way (see Figure 1 ).

Participants’ ratings of text quality: means (M) and standard deviations (SD).

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Visualization of the interaction between text length and text quality.

The experiment conducted in Study 2 found a very strong significant main effect for text quality, indicating a high correspondence of pre-service teachers’ ratings with the expert ratings of text quality. The main effect of text length was also significant, but was qualified by a significant interaction effect text quality x text length, indicating that low quality texts were rated even more negative the longer they were. This negative effect of text length was contrary to expectations: The pre-service teachers generally tended to assign higher scores to shorter texts. Thus, they seemed to value shorter texts over longer texts. However, this was mainly true for texts of low quality.

These findings were surprising against the research background that would suggest that longer texts are typically associated with higher scores of text quality, particularly in the context of second language writing. Therefore, it is even more important to discuss the limitations of the design before interpreting the results: First, the sample included relatively inexperienced pre-service teachers. Further research is needed to show whether these findings are transferable to in-service teachers with reasonable experience in judging students’ writing. Moreover, further studies could use assessment rubrics that teachers are more familiar with, such as the CEFR ( Council of Europe, 2001 ; also see Fleckenstein et al., 2020 ). Second, the selection process of essays may have reduced the ecological validity of the experiment. As there were only few long texts of low quality and few short texts of high quality in the actual sample (see Table 2 ), the selection of texts in the experimental design was – to some degree – artificial. This could also have influenced the frame of reference for the pre-service teachers as the distribution of the nine texts was different from what one would find naturally in an EFL classroom. Third, the most important limitation of this study is the question of the reference norm, a point which applies to studies of writing assessment in general. In our study, writing quality was operationalized using expert ratings, which have been shown to be influenced by text length in many investigations as well as in Study 1. If the expert ratings are biased themselves, the findings of this study may also be interpreted as pre-service teachers (unlike expert raters) not showing a text length bias at all: shorter texts should receive higher scores than longer ones if the quality assigned by the expert raters is held constant. We discuss these issues concerning the reference norm in more detail in the next section.

All three limitations may have affected ratings in a way that could have reinforced a negative effect of text length on text quality ratings. However, as research on the effect of text length on teachers’ judgments is scarce, we should consider the possibility that the effect is actually different from the (positive) one typically found for professional human raters. There are a number of reasons to assume differences in the rating processes that are discussed in more detail in the following section. Furthermore, we will discuss what this means in terms of the validity of the gold standard in writing assessment.

General Discussion

Combining the results of both studies, we have reason to assume that (a) text length induces judgment bias and (b) the effect of text length largely depends on the rater and/or the rating context. More specifically, the findings of the two studies can be summarized as follows: Professional human raters tend to reward longer texts beyond the relationship of text length and proficiency. Compared to this standard, inexperienced EFL teachers tend to undervalue text length, meaning that they sanction longer texts especially when text quality is low. This in turn may be based on an implicit expectation deeply ingrained in the minds of many EFL teachers: that writing in a foreign language is primarily about avoiding mistakes, and that longer texts typically contain more of them than shorter ones ( Keller, 2016 ). Preservice teachers might be particularly afflicted with this view of writing as they would have experienced it as learners up-close and personal, not too long ago. Both findings point toward the judgment bias assumption, but with opposite directions. These seemingly contradictory findings lead to interesting and novel research questions – both in the field of standardized writing assessment and in the field of teachers’ diagnostic competence.

Only if we take professional human ratings as reliable benchmark scores can we infer that teachers’ ratings are biased (in a negative way). If we consider professional human ratings to be biased themselves (in a positive way), then the preservice teachers’ judgments might appear to be unbiased. However, it would be implausible to assume that inexperienced teachers’ judgments are less biased than those of highly trained expert raters. Even if professional human ratings are flawed themselves, they are the best possible measure of writing quality, serving as a reference even for NLP tools ( Crossley, 2020 ). It thus makes much more sense to consider the positive impact of text length on professional human ratings – at least to a degree – an appropriate heuristic. This means that teachers’ judgments would generally benefit from applying the same heuristic when assessing students’ writing, as long as it does not become a bias.

In his literature review, Crossley (2020) sees the nature of the writing task to be among the central limitations when it comes to generalizing findings in the context of writing assessment. Written responses to standardized tests (such as the TOEFL) may produce linguistic features that differ from writing samples produced in the classroom or in other, more authentic writing environments. Moreover, linguistic differences may also occur depending on a writing sample being timed or untimed. Timed samples provide fewer opportunities for planning, revising, and development of ideas as compared to untimed samples, where students are more likely to plan, reflect, and revise their writing. These differences may surface in timed writing in such a way that it would be less cohesive and less complex both lexically and syntactically.

In the present research, such differences may account for the finding that pre-service teachers undervalue text length compared to professional raters. Even though the participants in Study 2 were informed about the context in which the writing samples were collected, they may have underestimated the challenges of a timed writing task in an unfamiliar format. In the context of their own classrooms, students rarely have strict time limitations when working on complex writing tasks. If they do, in an exam consisting of an argumentative essay, for example, it is usually closer to 90 min than to 30 min (at least in the case of the German pre-service teachers who participated in this study). Thus, text length may not be a good indicator of writing quality in the classroom. On the contrary, professional raters may value length as a construct-relevant feature of writing quality in a timed task, for example as an indicator of writing fluency (see Peng et al., 2020 ).

Furthermore, text length as a criterion of quality cannot be generalized over different text types at random. The genres which are taught in EFL courses, or assessed in EFL exams, differ considerably with respect to expected length. In five paragraph essays, for example, developing an argument requires a certain scope and attention to detail, so that text length is a highly salient feature for overall text quality. The same might not be true for e-mail writing, a genre frequently taught in EFL classrooms ( Fleckenstein et al., in press ). E-mails are usually expected to be concise and to the point, so that longer texts might seem prolix, or rambling. Such task-specific demands need to be taken into account when it comes to interpreting our findings. The professional raters employed in our study were schooled extensively for rating five-paragraph essays, which included a keen appreciation of text length as a salient criterion of text quality. The same might not be said of classroom teachers, who encounter a much wider range of genres in their everyday teaching and might therefore be less inclined to consider text length as a relevant feature. Further research should consider different writing tasks in order to investigate whether text length is particularly important to the genre of the argumentative essay.

Our results underscore the importance of considering whether or not text length should be taken into account for different contexts of writing assessment. This holds true for classroom assessment, where teachers should make their expectations regarding text length explicit, as well as future studies with professional raters. Crossley (2020) draws attention to the transdisciplinary perspective of the field as a source for complications: “The complications arise from the interdisciplinary nature of this type of research which often combines writing, linguistics, statistics, and computer science fields. With so many fields involved, it is often easy to overlook confounding factors” (p. 428). The present research shows how the answer to one and the same research question – How does text length influence human judgment? – can be very different from different perspectives and within different areas of educational research. Depending on the population (professional raters vs. pre-service teachers) and the methodology (correlational analysis vs. experimental design), our findings illustrate a broad range of possible investigations and outcomes. Thus, it is a paramount example of why interdisciplinary research in education is not only desirable but imperative. Without an interdisciplinary approach, our view of the text length effect would be uni-dimensional and fragmentary. Only the combination of different perspectives and methods can live up to the demands of a complex issue such as writing assessment, identify research gaps, and challenge research traditions. Further research is needed to investigate the determinants of the strength and the direction of the bias. It is necessary to take a closer look at the rating processes of (untrained) teachers and (trained) raters, respectively, in order to investigate similarities and differences. Research pertaining to judgment heuristics/biases can be relevant for both teacher and rater training. However, the individual concerns and characteristics of the two groups need to be taken into account. This could be done, for example, by directly comparing the two groups in an experimental study. Both in teacher education and in text assessment studies, we should have a vigorous discussion about how appropriate heuristics of expert raters can find their way into the training of novice teachers and inexperienced raters in an effort to reduce judgement bias.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the Ministry of Education, Science and Cultural Affairs of the German federal state Schleswig-Holstein. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

JF analyzed the data and wrote the manuscript. TJ and JM collected the experimental data for Study 2 and supported the data analysis. SK and OK provided the dataset for Study 1. TJ, JM, SK, and OK provided feedback on the manuscript. All authors contributed to the article and approved the submitted version.

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.

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17.6: What are the benefits of essay tests?

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Jennfer Kidd, Jamie Kaufman, Peter Baker, Patrick O'Shea, Dwight Allen, & Old Dominion U students

Jennfer Kidd, Jamie Kaufman, Peter Baker, Patrick O'Shea, Dwight Allen, & Old Dominion U students
Old Dominion University

Learning Objectives

Understand the benefits of essay questions for both Students and Teachers
Identify when essays are useful

Introduction

Essays, along with multiple choice, are a very common method of assessment. Essays offer a means completely different than that of multiple choice. When thinking of a means of assessment, the essay along with multiple choice are the two that most come to mind (Schouller).The essay lends itself to specific subjects; for example, a math test would not have an essay question. The essay is more common in the arts, humanities and the social sciences(Scouller). On occasion an essay can be used used in both physical and natural sciences as well(Scouller). As a future history teacher, I will find that essays will be an essential part of my teaching structure.

The Benefits for Students

By utilizing essays as a mean of assessments, teachers are able to better survey what the student has learned. Multiple choice questions, by their very design, can be worked around. The student can guess, and has decent chance of getting the question right, even if they did not know the answer. This blind guessing does not benefit the student at all. In addition, some multiple choices can deceive the student(Moore). Short answers, and their big brother the essay, work in an entirely different way. Essays remove this factor. in a addition, rather than simply recognize the subject matter, the student must recall the material covered. This challenges the student more, and by forcing the student to remember the information needed, causes the student to retain it better. This in turn reinforces understanding(Moore). Scouller adds to this observation, determining that essay assessment "encourages students' development of higher order intellectual skills and the employment of deeper learning approaches; and secondly, allows students to demonstrate their development."

"Essay questions provide more opportunity to communicate ideas. Whereas multiple choice limits the options, an essay allows the student express ideas that would otherwise not be communicated." (Moore)

The Benefits for Teachers

The matter of preparation must also be considered when comparing multiple choice and essays. For multiple choice questions, the instructor must choose several questions that cover the material covered. After doing so, then the teacher has to come up with multiple possible answers. This is much more difficult than one might assume. With the essay question, the teacher will still need to be creative. However, the teacher only has to come up with a topic, and what the student is expected to cover. This saves the teacher time. When grading, the teacher knows what he or she is looking for in the paper, so the time spent reading is not necessarily more. The teacher also benefits from a better understanding of what they are teaching. The process of selecting a good essay question requires some critical thought of its own, which reflects onto the teacher(Moore).

Multiple Choice. True or False. Short Answer. Essay. All are forms of assessment. All have their pros and cons. For some, they are better suited for particular subjects. Others, not so much. Some students may even find essays to be easier. It is vital to understand when it is best to utilize the essay. Obviously for teachers of younger students, essays are not as useful. However, as the age of the student increase, the importance of the essay follows suit. That essays are utilized in essential exams such as the SAT, SOLs and in our case the PRAXIS demonstrates how important essays are. However, what it ultimately comes down to is what the teacher feels what will best assess what has been covered.

Exercise \(\PageIndex{1}\)

1)What Subject would most benefit from essays?

B: Mathematics for the Liberal Arts

C: Survey of American Literature

2)What is an advantage of essay assessment for the student?

A) They allow for better expression

B) There is little probability for randomness

C) The time taken is less overall

D) A & B

3)What is NOT a benefit of essay assessment for the teacher

A)They help the instructor better understand the subject

B)They remove some the work required for multiple choice

C)The time spent on preparation is less

D) There is no noticeable benefit.

4)Issac is a teacher making up a test. The test will have multiple sections: Short answer, multiple choice, and an essay. What subject does Issac MOST LIKELY teach?

References Cited

1)Moore, S.(2008) Interview with Scott Moore, Professor at Old Dominion University

2)Scouller, K. (1998). The influence of assessment method on students' learning approaches: multiple Choice question examination versus assignment essay. Higher Education 35(4), pp. 453–472

Imagination is More Important than Knowledge: Essay Example

Imagination is more important than knowledge: essay introduction, imagination is better than knowledge: essay body, imagination is more important than knowledge: conclusion.

“Imagination is more important than knowledge,” is a famous quote of Albert Einstein. There are only a couple of words in this line, but if we think logically, it encloses the whole world. Imagination is a bequest of life and is indeed far more significant than knowledge. If we have the capability of imagining things, we can craft our world.

Imagination is not significant just for us as individuals but also for the community in general. It can also be interpreted as the fundamental element of theology and can be better articulated through contemplation. There have been great philosophers in the past who imagined the unattainable, and today our societies have certain values that are very relevant.

Simultaneously to be very dominant, imagination is also very risky. It all depends on the direction towards which we orient our imagination. Just like in the case of fire, if it becomes uncontrollable, it spells havocs, but if it is harnessed properly, it contributes to the development and wellbeing of the people. So our imagination should be oriented towards the positive or constructive direction rather than the negative or destructive one.

On the one hand, where positive approach in imagination improves life values, standards and progress, the negative approach is bound to lead the individuals towards fake things and feelings such as panic, intolerance, nervousness, etc. In the negative imagination, people lose their interface with the truth.

All the inventions and developments that have today become inseparable parts of our lives are results of positive imagination only. Some people imagined these things and converted them into reality. Human beings owe the transformation from Stone Age to being civilized to positive imagination. This is what positive imagination can do.

Knowledge is also important because simply by imagining things, one cannot convert them into reality. An intellectual mind is required for such tasks. But without imagination, knowledge would be of no use. We would be stagnant as far as development is concerned.

Like for instance, if Thomas Alva Edison were aware of the light (current) generating system but didn’t have the foresight to make useful things, then today we would not have the so important thing called bulb. Imagination is the foundation of contentment and pleasure in our lives. It provides us with lots of amusement, leisure and above all makes us more lively and humane.

Knowledge can be gained from various textbooks and lectures, but what about innovation? Unless we combine innovation and knowledge, there is no point in studying. Innovation comes from imagination, and imagination cannot be learned at schools or colleges. To put it more strongly, imagination is a revolution – in a good sense – and dominant, whereas knowledge is merely an attained instrument.

It is always good to acquire knowledge, but having the ability to imagine is far more important and inevitable. By acquiring knowledge, we learn things, but my imagination, we learn how to comprehend the things that we have learned. This comprehension further increases our knowledge.

Above all, the knowledge that we acquire is again a result of imagination. We don’t get knowledge out of knowledge but out of imagination that guides us to knowledge. So imagination is a sort of concierge to knowledge. We cannot gain knowledge unless we have imagination. So imagination is more important than knowledge.

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Essay on Imagination Is More Important Than Knowledge

Students are often asked to write an essay on Imagination Is More Important Than Knowledge in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Imagination Is More Important Than Knowledge

Understanding imagination and knowledge.

Imagination and knowledge are both important. Knowledge is the collection of facts and information, while imagination helps us create new ideas.

Imagination’s Role

Imagination is crucial because it allows us to think beyond what we know. It helps us dream, invent, and solve problems.

Knowledge’s Limitations

Knowledge has limits. It’s confined to what we’ve learned and experienced. It doesn’t allow for new possibilities like imagination does.

While knowledge is important, imagination is more so. It leads us to new discoveries, innovations, and a better future.

250 Words Essay on Imagination Is More Important Than Knowledge

The supremacy of imagination.

Imagination is the driving force behind innovation and advancement. While knowledge is the accumulation of facts and data, imagination transcends the realm of the known, venturing into the universe of possibilities. Albert Einstein famously stated, “Imagination is more important than knowledge. For knowledge is limited, whereas imagination encircles the world.”

Limitations of Knowledge

Knowledge, though vital, is inherently restrictive. It is confined to what is already known, discovered, or understood. Our knowledge is based on past experiences and learned information, which, although crucial, can limit our perspective to the existing reality.

Unleashing Potential with Imagination

Contrarily, imagination is boundless. It enables us to envision scenarios beyond the constraints of reality, paving the way for groundbreaking ideas and extraordinary innovations. Imagination fuels creativity, leading to advancements in diverse fields like technology, arts, and science.

The Interplay of Imagination and Knowledge

Despite their disparities, imagination and knowledge are not mutually exclusive. Knowledge serves as a foundation upon which imagination can build. It provides the raw materials that imagination can transform into novel concepts.

In conclusion, while knowledge equips us with the tools to understand and navigate the world, it is imagination that empowers us to reshape it. Emphasizing the importance of imagination doesn’t undermine the value of knowledge; instead, it encourages us to transcend the known and explore the realm of possibilities. Hence, imagination, with its ability to envision, innovate, and inspire, holds a higher pedestal than knowledge.

500 Words Essay on Imagination Is More Important Than Knowledge

The power of imagination.

Imagination is an integral part of human cognition, serving as a catalyst for creativity, innovation, and problem-solving. It is the mental faculty that allows us to transcend the confines of our immediate reality, enabling us to explore limitless possibilities. Albert Einstein once said, “Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution.”

Imagination Versus Knowledge

Knowledge is undoubtedly crucial. It is the accumulation of facts, information, and skills acquired through experience or education. It empowers us to understand the world around us, make informed decisions, and perform various tasks. However, knowledge is fundamentally limited to what is known and understood.

On the other hand, imagination is boundless. It is not confined to the realm of the known but ventures into the unknown, the unexplored, and the yet-to-be-invented. Imagination fuels innovation, pushing us to challenge the status quo and create something new. It is the driving force behind scientific discoveries, technological advancements, and artistic creations.

The Role of Imagination in Progress

Imagination plays a pivotal role in societal and technological progress. The greatest inventors, scientists, and artists were not just knowledgeable; they were imaginative. They dared to envision a different world and then used their knowledge to make it a reality.

Consider the example of the Wright Brothers. Their knowledge of physics and engineering was essential, but it was their imagination that enabled them to conceive the possibility of human flight. Similarly, Einstein’s theory of relativity was a product of his ‘thought experiments’ – a testament to the power of imagination in scientific discovery.

Imagination in Education

In the realm of education, imagination is equally crucial. It fosters curiosity, critical thinking, and problem-solving skills. It encourages students to approach problems from different perspectives, fostering innovative solutions.

While knowledge provides the foundation, imagination allows students to go beyond rote learning and engage in experiential and creative learning. It promotes a deeper understanding of concepts, facilitating the application of knowledge in real-world scenarios.

In conclusion, while knowledge is essential, it is imagination that truly propels us forward. It is the engine of progress, the catalyst for innovation, and the spark that ignites the flame of discovery. As we navigate an increasingly complex and rapidly changing world, imagination – the ability to envision new possibilities and create novel solutions – will be more important than ever. Thus, we should strive to cultivate not just knowledge but also a rich and vibrant imagination.

That’s it! I hope the essay helped you.

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Value of Knowledge by Erik J. Olsson LAST REVIEWED: 24 July 2018 LAST MODIFIED: 26 August 2013 DOI: 10.1093/obo/9780195396577-0008

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The Analysis of Knowledge

1.1 The Truth Condition

1.3.1 Approaches to Justification

1.3.2 Kinds of Justification

5. Modal Conditions

6. Doing Without Justification?

10. Virtue-Theoretic Approaches

Acknowledgments

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Which Is More Important: Creativity or Knowledge?

Introduction

Works Cited

Creativity Is More Important than Knowledge

Is a Long Essay Always a Good Essay? The Effect of Text Length on Writing Assessment

Jennifer Meyer

Stefan Keller

Olaf Köller

Introduction

Theoretical Background

This Research

Materials and Methods

Writing prompt

Text quality and length

English language proficiency and control variables

Statistical Analyses

Research Questions

Participants and Procedure

Text Length and Text Quality

Selection of Essays

General Discussion

Data Availability Statement

Author Contributions