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Getting Meta About Metacognition

This post was originally published on April 25, 2021 on Psychology in Action.


Think about the following situations:

  1. You have been preparing for your midterm exam all night, and finally feel like you know the material well enough to stop studying.

  2. As you are warming up to play your first concert as part of an orchestra, you decide to practice the hardest pieces first to make sure you have them down.

  3. You feel like you learn more from testing yourself with flashcards than just rereading your notes, so you make some flashcards to prepare for your final exam.

What do they have in common?


In all of these scenarios, you are making a decision based on your own awareness of your knowledge and learning, also known as metacognition (Flavell, 1979).

Metacognition is a critical aspect of good learning, and includes into two major components: monitoring of your own learning and decisions about your learning practices (Nelson & Narens, 1994).


Monitoring has to do with your real-time assessment of your learning. For example, how well you think you have learned a particular fact about Marie Curie, or whether you believe you will be able to solve a math problem using the Pythagorean Theorem in the future.


Learning decisions are then based on your monitoring of your knowledge (Nelson & Leonesio, 1988). If you determine that you know a fact about Marie Curie pretty well, you may decide to stop studying that topic and move on to a new subject. In contrast, if you think that you need more practice on the Pythagorean Theorem, then you will probably do more practice problems.


Most of the time, your metacognition lines up with your actual learning (Kornell & Metcalfe, 2006; Metcalfe & Kornell, 2005).


However, there are times that metacognition can go awry. Students often believe that studying small stacks of flashcards is better for learning than studying big stacks of flashcards, even though spacing out your study with bigger stacks of flashcards is better for learning (Kornell, 2009). Similarly, learners can believe that they know something better than they actually do, and stop studying too early (Kornell & Bjork, 2008).


How is it that metacognition can be inaccurate?


One hypothesis (Koriat, 1997) suggests that metacognition can be inaccurate because we do not have direct access to our own knowledge and learning. Instead, we have to use different aspects of the material, our internal states, or even the environment, which are also referred to as cues, when monitoring or managing our learning.

That is, instead of having a thermometer available which allows us to directly read our level of learning:






We instead have to make use of other factors to make a best guess.

If we use cues that predict our own state of learning, then our metacognition is likely to be pretty accurate:



If we use cues that do not predict our own state of learning, then our metacognition is likely to be inaccurate.



Therefore, the key to accurate metacognition is to make judgments of our learning that are based on relevant cues.


How can we improve our metacognition?

  1. Seek out feedback when learning. This feedback can come from instructors (Miller & Geraci, 2011) or from other students during group study sessions (Wissman & Rawson, 2016).

  2. Use the wait-generate-validate method (Hausman et al., 2021). After learning something, wait (a few minutes to even a few days), try to remember what you learned by writing it down or taking a practice test (generate), and then check your work (validate).

  3. Learn about the “quirks” of memory that can sometimes lead our feelings of learning to be a mismatch with our actual learning. The following resources can be a great start in learning about your own memory:

  4. https://www.learningscientists.org/blog/2016/5/10-1

  5. https://www.retrievalpractice.org/

  6. https://psychology-inaction.squarespace.com/psychology-in-action-1/2018/10/22/the-dangers-of-fluency

References

  1. Flavell, J. (1979). Metacognition and cognitive monitoring: A new area of cognitive-development inquiry. American Psychologist, 34, 906-911. https://doi.org/10.1111/j.1529-1006.2004.00018.x

  2. Hausman, H., Myers, S. J., & Rhodes, M. G. (2021). Improving metacognition in the classroom. Journal of Psychology, 229(2), 89-103. https://doi.org/10.1027/2151-2604/a000440

  3. Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4), 349-370. https://doi.org/10.1037/0096-3445.126.4.349

  4. Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. https://doi.org/10.1002/acp.1537

  5. Kornell, N., & Bjork, R. A. (2008). Optimising self-regulated study: The benefits--and costs-- of dropping flashcards. Memory, 16(2), 125-136. https://doi.org/10.1080/09658210701763899

  6. Kornell, N., & Metcalfe, J. (2006). Study efficacy and region of proximal learning framework. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 609-622. https://doi.org/10.1037/0278-7393.32.3.609

  7. Metcalfe, J., & Kornell, N. (2005). A region of proximal learning model of study time allocation. Journal of Memory and Language, 52, 463-477. https://doi.org/10.1016/j.jml.2004.12.001

  8. Miller, T. M., & Geraci, L. (2011). Training metacognition in the classroom: The influence of incentives and feedback on exam predictions. Metacognition Learning, 6, 303-314. https://doi.org/s11409-011-9083-7

  9. 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(4), 676-686.

  10. Nelson, T. O., & Narens, L. (1994). Why investigate metacognition? In J. Metcalfe & A. P. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 1-25). Cambridge, MA: MIT Press.

  11. Wissman, K. T., & Rawson, K. A. (2016). How do students implement collaborative testing in real-world contexts? Memory, 24(2), 223-239. https://doi.org/10.1080/09658211.2014.999792

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