Student Subtyping via EM-Inverse Reinforcement Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Yang, Xi; Zhou, Guojing; Taub, Michelle; Azevedo, Roger; Chi, Min
Titel	Student Subtyping via EM-Inverse Reinforcement Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Grouping (Instructional Purposes); Learning Strategies; Artificial Intelligence; Learning Analytics; College Students; Cluster Grouping + Suchen Sie Ihr Suchwort? Grouping; Gruppenbildung; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Künstliche Intelligenz; Collegestudent; Eingruppierung
Abstract	In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student learning processes is often challenging because of the information heterogeneity and temporal dynamics. Various inverse reinforcement learning (IRL) algorithms have been successfully employed in many domains for inducing policies from the trajectories and recently has been applied for analyzing students' temporal logs to identify their domain knowledge patterns. IRL was originally designed to model the data by assuming that all trajectories have a "single" pattern or strategy. Due to the heterogeneity among students, their strategies can vary greatly and the design of traditional IRL may lead to suboptimal performance. In this paper, we applied a novel expectation-maximization IRL (EM-IRL) to extract heterogeneous learning strategies from sequential data collected from three simulation environments and real-world longitudinal students' logs. Experiments on simulation environments showed that EM-IRL can successfully identify different policies from the heterogeneous sequences with different strategies. Furthermore, experimental results from our educational dataset showed that EM-IRL can be used to obtain different student subtypes: a "learning-oriented" subtype who learned the material as much as possible regardless of the time in that they spent significantly more time than the other two subtypes and learned significantly; an "efficient-oriented" subtype who learned efficiently in that they not only learned significantly but also spent less time than the first subtype; a "no learning" subtype who spent less amount of time than first subtype and failed to learn. [For the full proceedings, see ED607784.] (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	2024/1/01