Yesterday at @researchEDWarr I tried to make the point that the “love story" between cognitive science and education is exciting because it has reached the point where it’s not just about isolated “quality ingredients”, but about the entire “dish” and even a whole “meal”.
I’ve put 4 things on the table, 4 points that I find central and essential for making cog sci useful in education
1. It’s essential to acknowledge the limitations of the cognitive systems:
📌Attention and WM are limited in capacity, the bottleneck of processing.
📌LTM is “Blackboxy” – we don’t really know how it works, parts of it are unconscious and we are biased as a result
2.
I still think that it’s important to take into consideration and plan according to these four stages of the learning process, making sure we choose an effective sequence of strategies for everyone.
3.
But no matter how effective these strategies are, they are not intuitively chosen by either learners or teachers. The main reason is our cognitive biases that intervene in every step of the way:
The Bjorks coined the term Desirable Difficulties, we can also call it the Ice Cream- Broccoli dilemma: what would you choose?
And how can we prepare a secret sauce that will help learners choose more broccoli over time, and adopt healthier eating (or learning) habits?
4.
We should consider the entire meal (not just the broccoli), and plan sequences that take into consideration students’ motivation, meta-cognition, and habits, aligning them along the cognitive axis at the right points, and "cooking" it adjust our teaching context.
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Learning how to learn & teach
It’s been a ~decade of accelerating bidirectional communication between cognitive science and classroom teaching. And it seems that the field is ready for a leap forward. Some thoughts on the basis of recent selected key publications🧵⬇️
3 They review metacognitive barriers to implementation (e.g. misconceptions, greater effort) and suggest that to develop self-regulated learners we should plan means to promote conceptual change and drive behavior on the basis of a 4-component framework.➡️
1/ How Predicting is different from guessing? and from practicing retrieval? All three strategies are based on generating a response based on semantic elaboration, but how do they differ, and why prediction is worth special attention as a standalone strategy? A thread:
2/ In this new paper @garvin_brod suggests that Prediction deserves the attention, of both teachers and researchers, as an effective strategy for learning, and I agree. Why?
3/ Prediction is different from guessing because it's based on relevant prior knowledge and is associated with higher confidence. This leads to curiosity and surprise if feedback reveals a violation. Surprise enhances focusing attention and encoding the new info as valuable.