How Students Can Effectively Choose the Right Courses: Building a Recommendation System to Assist Students in Choosing Courses Adaptively

Autor/inn/en	Chang, Hui-Tzu; Lin, Chia-Yu; Wang, Li-Chun; Tseng, Fang-Ching
Titel	How Students Can Effectively Choose the Right Courses: Building a Recommendation System to Assist Students in Choosing Courses Adaptively
Quelle	In: Educational Technology & Society, 25 (2022) 1, S.61-74 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Course Selection (Students); Blended Learning; Artificial Intelligence; Mathematics; Engineering Education; College Students; Student Interests; Design; Course Content + Suchen Sie Ihr Suchwort? Course selection; Student; Students; Kurswahl; Künstliche Intelligenz; Mathematik; Ingenieurausbildung; Collegestudent; Studieninteresse; Kursprogramm
Abstract	In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students' interests, abilities and career development. To meet students' individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students' past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students' actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01