The previous blog in this two-part series described the role of metacognition in the context of adaptive learning. In this blog, we discuss how the use of metacognitive strategies can encourage more effective learning behaviors.
How do we encourage learners to use metacognitive strategies?
Before a learning session begins, there are a number of things that a learner can do to prepare themselves for learning. First off, choosing what and how to study. This necessary step is often overlooked, as content is often assigned to students, and learning systems can be inflexible to different approaches. When relevant, however, this promotes forethought. Choosing how to study would traditionally be between flashcards, mind maps and so on. In the case of adaptive learning systems, it may be choosing which system to use – or which type of exercise, if a system includes different ways to learn. By providing them with these choices, we require learners to think about the pros and cons to each possible approach, and which would be most effective for this particular learning session, depending on the learner’s specific goals. Also, knowing which resources are available and how to use them can give the students more options while planning. Bear in mind that often, deadlines drive study behaviour more than optimal planning, so it is recommended to design for flexibility and make this process as easy as possible.
While learning, you tend to notice what you are doing right and wrong more often if you are actively thinking about your own cognitive processes. By this logic, having learners ask themselves questions like “is this the best approach?” or “do I understand how this works?” can help them direct their attention to making small changes and improving their method of learning. Self-feedback involves improving work over time by learning from mistakes and successes. Discussing these mistakes, successes and any confusions with other learners means the learners can hear many new perspectives. This can be a monitoring mechanism, during a group learning session, or an evaluative mechanism, after the learning is complete. (Taylor, 2021)
Judgements of learning are made all the time by learners. These are assessments of how well particular information has been learned – “will I remember this in the exam?”. They play a major role in people’s study choices – what to learn, when to stop learning it. However, they are also known to be fickle (Bjork, et al., 2013). Requiring learners to actively retrieve information; as opposed to practising recognition or some other form of memory; can improve their metacognitive accuracy (Sense, et al., 2021) as well as the accuracy of their judgments of learning (Kalinko, 2022).
Learners’ beliefs about intelligence can affect their goals and ultimately their learning outcomes. Specifically, learners who view intelligence as something that can be improved with effort and practice are more likely to take on challenging tasks and persist through difficulties, which can improve their learning and memory. Sincerely reminding learners that intelligence is flexible, at the start of a learning session or as part of feedback, can bolster their ability to persist at tasks within the difficult Zone of Proximal Development. (Metcalfe, 2009; Peng & Tullis, 2020)
After a learning session, self-evaluation of performance and progress is a vital step that is often absent – we tend to call it a day once the learning is finished. Taking a moment to consider “What am I doing well? What am I doing poorly? What reasons can I give for this?” can have a very real impact on the choices a learner makes when preparing for their next learning session, thereby directing them towards more optimal learning behaviours. For example, my two-year old nephew (who has just discovered the word “boom”) is currently learning to run. If I were to ask him these questions, he would probably say something like: “I can go fast! But then I go boom! and fall on my face. When I go ‘round a corner, I still go boom! I go faster if I lean forward, but that’s also why I go boom!”
If he was capable of metacognitive thought, he might consider this before hurling himself around our corridor, and it may direct him to slowing down before turning, a more optimal learning behaviour. Particularly for younger learners, making these considerations might not be obvious. Whenever possible, prompt learners to take some time for reflection, exploring their emotions, what happened during the learning session and where they could use this new information in the outside world or new settings.
In interactive learning sessions, teachers can incorporate metacognition into their instruction in many ways. Subtle language changes can make a world of difference. Instead of “explain your answer,” you could say “what was your main reason for choosing this answer” which encourages them to analyse their thought process rather than simply engage with the content. Similarly, showing metacognitive thinking in the way you answer questions can give learners an example of what it looks like to be aware of your own thinking. An example would be “First, I think about x, because it is related to y and z.”
The age of learners is also something to consider. The ages from 7 to 12 are a critical period in the development of conceptual thinking. The concept of “thinking about thinking” can be difficult to grasp for younger learners. As such, 12-year-old learners see significantly greater benefits to metacognitive strategies than younger groups (van Loon & Oeri, 2023; Zhao et al., 2022). Once conceptual thinking is up and running in the early teens, the benefits of metacognitive thinking are robust and stable for the rest of our lives.
For some learners, this can take longer than others. Young learners with Special Educational Needs (SENs) often struggle with the self-regulatory components of metacognitive learning. Very broadly speaking, as SENs take many forms, these learners do not struggle with learning metacognitive strategies, but rather with independently applying them. The Irish National Council for Special Education (2009) recommends explicitly cueing learners to do the following: To analyse a task before it is approached; to decide on the best strategy to apply to a task; and to take a “step back” from a problem and think about the bigger picture. With the necessary support and explicit, frequent reminders of these strategies, learners with SENs can achieve metacognitive mastery.
The Main Takeaway
Learners’ ability to think about their own thinking is a resource to be used to help direct them towards the best content to learn, and the best ways to learn it. Specifically, this all comes down to preparation before learning, self-monitoring during learning and evaluating after learning. As in physical learning environments (Baars, et al., 2022), digital learning environments should include features that prompt a learner to consider their own learning process. In doing so, learners become active participants in their own learning journey, able to navigate the seas of unknown knowledge with confidence, guided by their own metacognitive compass.
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Baars, S., Schellings, G.L.M., Joore, J.P. et al. (2020). Physical learning environments’ supportiveness to innovative pedagogies: students’ and teachers’ experiences. Learning Environ Res. https://doi.org/10.1007/s10984-022-09433-x
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annual Review of Psychology, 64(1), 417–444. https://doi.org/10.1146/annurev-psych-113011-143823
Kalinko, N. (2022). Do subjective item difficulty estimates correlate with the objective item estimates of an adaptive learning algorithm? (Unpublished doctoral dissertation or master’s thesis). Rijksuniversiteit Groningen, Groningen, the Netherlands.
Metacognition | National Council for Special Education – CPD and In-School Support. (2009). https://www.sess.ie/resources/metacognition
Metcalfe, J. (2009). Metacognitive Judgments and Control of Study. Current Directions in Psychological Science, 18(3), 159–163. https://doi.org/10.1111/j.1467-8721.2009.01628.x
Peng, Y., & Tullis, J. G. (2020). Theories of intelligence influence self-regulated study choices and learning. Journal of Experimental Psychology: Learning, Memory and Cognition. https://doi.org/10.1037/xlm0000740
Sense, F., van der Velde, M., & van Rijn, H. (2021). Predicting University Students’ Exam Performance Using a Model-Based Adaptive Fact-Learning System. Journal of Learning Analytics, 8(3), 155–169. https://doi.org/10.18608/jla.2021.6590
Taylor, Tricia. (2021). GUEST POST: THE BIG REVEAL: Showing Students How Metacognition Works — The Learning Scientists. The Learning Scientists. https://www.learningscientists.org/blog/2021/6/10-1
van Loon, M. H., & Oeri, N. S. (2023). Examining on-task regulation in school children: Interrelations between monitoring, regulation, and task performance. Journal of Educational Psychology. Advance online publication.
Zhao, W., Li, B., Shanks, D. R., Zhao, W., Zheng, J., Hu, X., Su, N., Fan, T., Yin, Y., Luo, L., & Yang, C. (2022). When judging what you know changes what you really know: Soliciting metamemory judgments reactively enhances children’s learning. Child Development, 93(2), 405–417. https://doi.org/10.1111/cdev.13689