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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hampton, A., & Tawfik, A. (2020). Experiential instruction of metacognitive strategies. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12214 LNCS, 108-116. https://doi.org/10.1007/978-3-030-50788-6_8