Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning
Abstract
In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.
Cite
@article{arxiv.2304.11739,
title = {Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning},
author = {Mark Abdelshiheed and John Wesley Hostetter and Tiffany Barnes and Min Chi},
journal= {arXiv preprint arXiv:2304.11739},
year = {2023}
}
Comments
This paper demonstrates a cognitive perspective of how adaptive interventions impact policy decisions, students' autonomy, and metacognitive knowledge. Another paper (arXiv:2304.09821) compares adaptive against static interventions. The two papers overlap in the reinforcement learning implementation of adaptive interventions