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A Hierarchical Bayesian Model of Adaptive Teaching.
Alicia M Chen1, Andrew Palacci2, Natalia Vélez3
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Cognitive Science
|July 9, 2024
Summary
Teachers adapt their teaching based on student knowledge and feedback. Students strategically provide feedback when lessons don't align with their background knowledge, improving the collaborative learning process.
Area of Science:
- Cognitive Science
- Educational Psychology
- Computational Pedagogy
Background:
- Understanding how teachers gauge student understanding is crucial for effective instruction.
- Learner feedback is vital for tailoring educational content to individual needs and prior knowledge.
Purpose of the Study:
- To formalize the collaborative reasoning between teachers and learners using a computational model.
- To investigate how teachers utilize learner background knowledge and feedback.
- To examine how learners strategically provide feedback.
Main Methods:
- Developed a hierarchical Bayesian model of pedagogy.
- Conducted two online behavioral experiments with 312 adult participants.
- Analyzed teacher example selection and learner feedback patterns.
Main Results:
- Teachers selected examples aligned with learners' background knowledge.
- Teachers adjusted examples based on learner feedback.
- Learners provided more feedback when examples were incongruent with their background knowledge.
Conclusions:
- The study provides a computational framework for understanding teacher-learner interaction.
- Findings highlight the strategic nature of information exchange in collaborative learning.
- Results support extending computational models of pedagogy to interactive settings.
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