Session-Based Course Recommendation Frameworks Using Deep Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Khan, Md Akib Zabed; Polyzou, Agoritsa
Titel	Session-Based Course Recommendation Frameworks Using Deep Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Course Selection (Students); Learning Analytics; Academic Advising; Decision Making; Higher Education; Academic Achievement; Educational Experience; Required Courses; Elective Courses; Models; Universities; Longitudinal Studies; Sequential Approach; Learning Management Systems; College Students; Computer Software; Preferences; Florida + Suchen Sie Ihr Suchwort? Course selection; Student; Students; Kurswahl; Akademischer Rat; Decision-making; Entscheidungsfindung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Schulleistung; Bildungserfahrung; Pflichtkurs; Elective course; Wahlkurs; Analogiemodell; University; Universität; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Schrittfolge; Collegestudent
Abstract	Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to be explored is the suitability of the recommended courses taken together in a semester. Students may face more difficulty coping with the workload of courses if there is no relationship among courses taken within a semester. To address this problem, we propose to employ session-based approaches to recommend a set of courses for the next semester. In particular, we test two session-based recommendation models, CourseBEACON and CourseDREAM. Our experimental evaluation shows that session-based methods outperform existing popularity-based, sequential, and non-sequential recommendation approaches. Accurate course recommendation can lead to better student advising, which, in turn, can lead to better student performance, lower dropout rates, and better overall student experience and satisfaction. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01