Can the Paths of Successful Students Help Other Students with Their Course Enrollments? [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Wagner, Kerstin; Merceron, Agathe; Sauer, Petra; Pinkwart, Niels
Titel	Can the Paths of Successful Students Help Other Students with Their Course Enrollments? [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	College Freshmen; At Risk Students; Dropouts; Dropout Programs; Success; Academic Achievement; Course Selection (Students); Artificial Intelligence; Information Systems; Technology Uses in Education; Decision Support Systems; Low Achievement; Program Effectiveness; Incidence; Enrollment; Predictor Variables; Peer Influence + Suchen Sie Ihr Suchwort? Studienanfänger; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Erfolg; Schulleistung; Course selection; Student; Students; Kurswahl; Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Entscheidungshilfe; Unterdurchschnittliche Leistung; Vorkommen; Einschulung; Prädiktor
Abstract	In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design and which is based on the explainable k-nearest neighbor algorithm, recommends a set of courses that have been passed by the majority of the student's nearest neighbors who have completed their studies. The present evaluation is based on the data of students from three different study programs. One result is that the recommendations do lower the dropout risk. We also discovered that while the recommended courses differed from those taken by students who dropped out, they matched quite well with courses taken by students who completed the degree program. Although the course recommender system targets primarily students at risk, students doing well could use it. Furthermore, we found that the number of recommended courses for struggling students is less than the number of courses they actually enrolled in. This suggests that the recommendations given indicate a different and hopefully feasible path through the study program for students at risk of dropping out. [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