The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems
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Abstract (arXiv)
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems. It was shown that students who know how and when to use each strategy (StrTime) outperformed those who know neither and stick to the default strategy (Default). In this work, students were trained on a logic tutor that supports a default forward-chaining and a backward-chaining (BC) strategy, then a probability tutor that only supports BC. We investigated three types of interventions on teaching the Default students how and when to use which strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime students received no interventions. Overall, our results show that Nudge outperformed their Default peers and caught up with StrTime on both tutors.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 39 references that are in the map. First posted on arXiv 18 Mar 2023.
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2 of 39 references.
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.
- 2023Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systemscited by 10
- 2024Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievementcited by 3
- 2023Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learningcited by 1
- 2024Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement10 May 2024
- 2023Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning23 Apr 2023
- 2023Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems17 Apr 2023