Olivia Croley and Dillon Murphy

Have you ever tried to predict the grade you would receive on an exam? If so, how did you do it? The answer is through metacognition. Metacognition refers to the awareness and understanding of one’s learning and memory processes. More specifically, metacognition can be considered in terms of both monitoring (the self-assessment of how well something has been learned) and control (study decisions informed by monitoring; see Rhodes, 2016). These metacognitive abilities are known to be related to students’ academic performance (Hsu & Hsieh, 2014; Ward & Butler, 2019). Thus, it is essential that students accurately monitor their understanding of course material.

When students decide how and when to study, they rely on metacognitive monitoring to inform their decisions. Students who accurately monitor their learning will identify and direct more resources toward the encoding and mastery of yet-to-be-learned concepts while students who inaccurately monitor their understanding may dismiss confusing concepts and focus on concepts that they already understand which would not be as beneficial for exam performance (Nelson & Leonesio, 1988). However, accurate monitoring can be challenging. For example, when students assess their learning based on their familiarity with concepts instead of their ability to recall the concepts, this can lead to overconfidence and later forgetting (Stanton et al., 2021). To improve metacognitive monitoring and learning, it is important to understand which study habits encourage such errors and how students can be better aware of what will be remembered.

Instances of overconfidence in memory performance can arise from common studying methods like rereading or highlighting course materials. For example, a recent study found that 47% of the recruited college students greatly valued highlighting as a learning tool (Yue et al., 2015). However, the participants who valued highlighting and used highlighting to study a to-be-remembered passage performed worse on a test than participants who did not value highlighting and did not highlight the passage. Thus, the act of highlighting seems to make students feel like they are learning the information but little effortful encoding or learning usually occurs. Rather, the act of highlighting can mislead students by providing a feeling of familiarity which can lead to overconfidence in their ability to recall the highlighted material (Sungkhasettee et al., 2011).

Another instance when metacognition can be fallible is the “illusion of competence,” or the cognitive bias of making overconfident learning predictions. Koriat and Bjork (2005) demonstrated this illusion by having participants study word pairs and provide a prediction of their future memory performance for each pair. The word pairs were either unrelated to one another (e.g., car-apple), weakly related (e.g., laugh-humor), or highly related (e.g., mother-father). Results revealed that participants generally expected to remember the weakly related pairs as well as the highly related pairs (memory was expected to be worst for unrelated pairs). However, the weakly related word pairs were recalled significantly less well than expected as compared to highly related pairs. The inflated judgments of learning for the weakly related pairs relative to recall performance indicate that these pairs triggered weak associations that were perceived as easily learned and memorable. However, during testing (when only half a word pair was presented), these word pairs were poorly remembered because it is more difficult for the participants to engage in retrieval for weakly related than highly related pairs because they made superficial connections with the to-be-learned material.

Outside of the lab, this illusion of competence can be seen in the cognitive processes and errors that can occur when students attempt to learn course material by highlighting or re-reading it instead of interacting with the information in a more effortful way. When students restudy, they feel like they can recall the information because the material feels fluent and familiar when it is in front of them. However, during a test when students must recall information from memory, it is often difficult to remember the information because they only processed the information in a shallow way which often does not lead to successful memory retrieval (see Craik & Lockhart, 1972). Instead, more accurate metacognitive monitoring could be achieved by testing yourself because these self-tests will reveal what you know and what you do not know.

To avoid the costs of inaccurate metacognition, we suggest that students use “desirable difficulties” as a study strategy to improve learning and encourage more accurate metacognitive monitoring. Desirable difficulties are strategies whereby more effortful encoding processes enhance long-term memory (see Bjork & Bjork, 2014, 2020). For example, testing instead of restudying, spacing rather than massed studying, interleaved practice rather than blocked practice, and generating information rather than restudying it can all help long-term memory. Narrowing in on the testing effect, which is one of the most robust effects in memory research, this technique can be easily accomplished by using flashcards or taking practice exams rather than passively reviewing course material (Rowland, 2014). When a learner self-tests, the learner needs to recall information that is not in front of them, compared to a strategy like restudying, where the correct answer is always present. This form of retrieval practice can facilitate more accurate monitoring since the learner is assessing their learning after every question or practice test (i.e., testing makes it apparent what information is known or unknown; Stanton et al., 2021). When a question is answered incorrectly, the learner can then prioritize studying that specific material, which is more helpful for improving learning than restudying information that is already known.

Another way to apply desirable difficulties is by distributing, rather than massing, self-testing (Rohrer et al., 2020). The spacing study strategy involves spreading a learner’s total study time across multiple learning sessions, such as self-testing for an hour on Monday, Wednesday, and Friday, instead of 3 hours on Friday. Spacing allows for the forgetting of the material in between study sessions, which can initially reduce students’ ability to recall information. However, forgetting information in between spaced study sessions can encourage learners to assess what they remember well and what they do not remember well from the last study session, and to use this assessment to direct their current study session.

In sum, using desirable difficulties like self-testing and spacing may be more effective for learning than restudying or re-reading because they potentiate memory but also encourage the student to monitor what they have successfully learned. However, students are often unaware of the benefits of desirable difficulties, and students often prefer studying methods that do not require effortful processing which can lead to the unexpected forgetting of information on a test (Hui et al., 2022). To avoid the adverse effects of inaccurate metacognition, we recommend that students turn to desirable difficulties. When a student knows how to properly study and employ methods that encourage more accurate metacognitive monitoring, they can perform well on a test with confidence.

 

References

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Bjork, R. A., & Bjork, E. L. (2020). Desirable difficulties in theory and practice. Journal of Applied Research in Memory and Cognition, 9, 475-479.

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Hsu, L., & Hsieh, S. (2014). Factors affecting metacognition of undergraduate nursing students in a blended learning environment. International Journal of Nursing Practice, 20, 233-241.

Hui, L., de Bruin, A. B., Donkers, J., & van Merriënboer, J. J. (2022). Why students do (or do not) choose retrieval practice: Their perceptions of mental effort during task performance matter. Applied Cognitive Psychology, 36, 433-444.

Koriat, A., & Bjork, R. A. (2005). Illusions of competence in monitoring one’s knowledge during study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 187-194.

Nelson, T. O., & Leonesio, R. J. (1988). Allocation of self-paced study time and the “labor-in-vain effect.”. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 676-686.

Rhodes, M. G. (2016). Judgments of learning. In J. Dunlosky and S. K. Tauber (Eds.), The Oxford handbook of metamemory (pp. 65-80). New York: Oxford University Press.

Rohrer, D., Dedrick, R. F., Hartwig, M. K., & Cheung, C. (2020). A randomized controlled trial of interleaved mathematics practice. Journal of Educational Psychology, 112, 40-52.

Rowland, C. A. (2014). The effect of testing versus restudy on retention: a meta-analytic review of the testing effect. Psychological Bulletin, 140, 1432-1463.

Stanton, J. D., Sebesta, A. J., & Dunlosky, J. (2021). Fostering metacognition to support student learning and performance. CBE Life Sciences Education, 20, 1-7.

Sungkhasettee, V. W., Friedman, M. C., & Castel, A. D. (2011). Memory and metamemory for inverted words: Illusions of competency and desirable difficulties. Psychonomic Bulletin & Review, 18, 973-978.

Ward, R. T., & Butler, D. L. (2019). An investigation of metacognitive awareness and academic performance in college freshmen. Education, 139, 120-126.

Yue, C. L., Storm, B. C., Kornell, N., & Bjork, E. L. (2015). Highlighting and its relation
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