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Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement
We found AUCs of 0. 63 for talk moves, 0. 66 for ITS, and 0. 77 for their combination, suggesting interactivity among the two feature sources.
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement 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).
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
In two consecutive semesters, we conducted two classroom experiments: Exp.
The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems.
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer
In this work, students were trained on a logic tutor that supports a default forward-chaining (FC) and a backward-chaining (BC) strategy.
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving
Backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty.
