Self-Regulation and Collaborative Learning
Topic 2: Metacognition in SRL
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My Reflections
My Reflections
Winne’s (2017) argument that “the same fundamental cognitive processes are used in cognition and in metacognition” (p.38) brought a new representation of cognition and metacognition as a multi-level system. This point, connected to other points in the article where computational logic and concepts of computer programming were mentioned, contributed in shaping a cross-disciplinary viewpoint on those aspects of learning. For example, Winne & Hadwin’s (1998) SRL model was described as a model with both sequential and recursive phases. Iteration and recursion are fundamental concepts of algorithms. When discussing Nelson and Narens’s (1990) principles (p.39), the if-then representation was proposed. Conditional logic is a construct extensively used in Computer Science. Similarly, in p.42, the concept of “state” was mentioned. In object-oriented programming, the state of an object is the combination of its original values plus any modifications.
These points validated my thoughts about how closely learning and computer sciences are interrelated and evoked an idea about utilizing this connection for the benefit of both learning sciences and computer science students in upper education. Specifically, for the former, providing a computational representation of learning processes, for example via a simulation tool and inviting students to think of them with a level of abstraction would benefit the students in my opinion by a) raising the interactivity level in their learning, b) connecting theoretical literature concepts to a concrete and c) targeting their computational thinking skills. Also, utilizing software for purposes other than the most common existing ones (e.g learning analytics, feedback mechanisms) is a promising direction for technology-enhanced learning in general. For the latter, contextualizing students’ assignments and inviting them to create, for example, a system for representing SRL models could be a multi-disciplinary attempt in a) bridging the gap between formal and social sciences, b) raising students’ awareness about their own cognitive and metacognitive experiences and c) providing them with a meaningful and relevant context for their learning.
Through my experience in Informatics in my bachelor’s degree, it was often that we were involved in programming course assignments that had a very rigid scientific context which lacked the human element. For example,we would be asked to create a program that would solve mathematical riddles automatically, or that would optimize the complexity of an algorithm. This was useful in the discipline that we were exploring but had no further connections to our experiences as learners. Since the present and future of education revolves around technology, computational thinking, it seems like a logical step to try to bridge those disciplines in every way possible.
Lastly, while reading through Winne’s (2017) article, the consideration of learners as learning scientists (p. 40) aligned a lot with my thoughts while being on the LET Master’s program. It’s often that I catch myself observing this duality, that while I’m an active learner, exploring the different aspects of learning through literature and gaining insight on the work of learning scientists and how this insight can be influential to other people’s education, I’m simultaneously becoming a learning scientist myself for my own learning, collecting and analyzing data about my approaches, observing my strategies and evolving my tactics and skills, overall evolving from a novice learner to an expert one.
Related Readings
Related Readings
Winne, P. H. (2017). Cognition and metacognition within self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 36–48). Routledge. https://doi.org/10.4324/9781315697048-3
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