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In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (\textit{DRL}) in providing \textit{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 \textit{how} and \textit{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.
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Experiential Instruction of Metacognitive Strategies
Learners often have metacognitive deficits that limit their ability to select material at appropriate levels in independent studying situations. The increasing prevalence of intelligent recommender systems can assume this role, while also fostering a kind of experiential meta-instruction. The creation of hybrid tutors (federated systems of both adaptive and static learning resources with a single interface and learning record store) provides an opportunity to test this experiential instruction of metacognitive strategies. As a test case, we examine the hybrid tutor ElectronixTutor, which has two distinct intelligent recommender engines corresponding to distinct use cases. Each of these constitutes a method of providing scaffolding to learners so that they can internalize the principled, theoretically informed reasons for the order of their progression through learning content. However, the learning described is speculative and requires evaluation. By examining expected efficacy, perceived efficacy, actual efficacy, and especially the relationships among these three concepts, actionable insights should arise pertaining to adaptive instructional system design, learning science generally, and other areas
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- Award ID(s):
- 1918751
- PAR ID:
- 10207664
- Editor(s):
- Sottilare, Robert A.; Schwarz, Jessica
- Date Published:
- Journal Name:
- Proceedings of the Second International Conference on Adaptive Instructional Systems
- Page Range / eLocation ID:
- 108 - 116
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-50788-6_8
