A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel
Titel	A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Markov Processes; Models; Teaching Methods; Decision Making; Learning Processes; Individual Differences; Individualized Instruction; Problem Solving; Computation + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Markowscher Prozess; Analogiemodell; Teaching method; Lehrmethode; Unterrichtsmethode; Decision-making; Entscheidungsfindung; Learning process; Lernprozess; Individueller Unterschied; Individualisierender Unterricht; Problemlösen
Abstract	Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally and greedily but that are computationally more efficient while requiring a simpler learner model. Even with the availability of data from several tutoring systems, it is never possible to have a highly accurate student model or one that is tuned for each particular student. We study what is the impact of the quality of the student model on the final results obtained with the two algorithms. Our hypothesis is that the higher flexibility of multi-armed bandits in terms of the complexity and precision of the student model will compensate for the lack of longer term planning featured in POMDPs. We present several simulated results showing the limits and robustness of each approach and a comparison of heterogeneous populations of students. [For the full proceedings, see ED592609.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01