Investigating Course Choice Motivations in University Environments

Autor/inn/en	Ma, Boxuan; Lu, Min; Taniguchi, Yuta; Konomi, Shin'ichi
Titel	Investigating Course Choice Motivations in University Environments
Quelle	In: Smart Learning Environments, 8 (2021), Artikel 31 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ma, Boxuan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2196-7091
DOI	10.1186/s40561-021-00177-4
Schlagwörter	College Students; Course Selection (Students); Decision Making; Student Attitudes; Student Motivation; Instructional Design; Educational Technology; Grades (Scholastic); Teacher Characteristics; Peer Influence; College Faculty + Suchen Sie Ihr Suchwort? Collegestudent; Course selection; Student; Students; Kurswahl; Decision-making; Entscheidungsfindung; Schülerverhalten; Schulische Motivation; Lesson concept; Lessonplan; Unterrichtsentwurf; Unterrichtsmedien; Notenspiegel; Fakultät
Abstract	Recommendation systems need a deeper understanding of users and their motivations to improve recommendation quality and provide more personalized suggestions. This is especially true in the education domain, the more about the student is known, the more useful recommendations can be made. However, although many studies on the course recommendation exist, studies on the students' course selection motivations in universities are limited. This study investigates the factors that contribute to students' choice when selecting courses in universities to better understand student perceptions, attitudes, and needs and leverage data-driven approaches for recommending and explaining the recommendations in university environments. A qualitative interview for university students (N = 10) comprised of open-ended questions as well as a questionnaire for students (N = 81) was conducted, aiming to investigate the main reasons behind their choices. The results of this study show that students highly value the course contents and the benefits of the course towards their future careers. Furthermore, students are influenced by other reasons such as the possibility of obtaining a higher grade, the popularity of professors, and recommendations from peers. Next, we extract the main categories of students' motivations and analyzed the questionnaire data by employing statistical analysis methods as well as the k-means clustering algorithm to identify different types of students in terms of course selection. Based on our findings, we discuss implications for designing more personalized course recommendation systems. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01