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.
Highlights
Current studies on course recommendation use datasets collected in physical university environments, they rely on recommendation approaches that are similar to the ones used in recommending MassiveOpen Online Courses (MOOCs) courses without fully considering the versatile nature of the reasons involved in course selection in university environments
We could inform the design of alternative course recommendation systems that may consider the versatile nature of reasons and students’ different demands involved in course selection
Results of our studies show that different students may have completely different orientations based on their own reasons, which serve as different criteria for course selection and those should be considered in course recommendation systems in physically-based university environments
Summary
Current studies on course recommendation use datasets collected in physical university environments, they rely on recommendation approaches that are similar to the ones used in recommending MOOC courses without fully considering the versatile nature of the reasons involved in course selection in university environments. This amounts to a collaborative recommendation of the nature of “most people like you did X next.”. A few existing works consider the students’ motivations in university environments, they tend to make simplistic assumptions about learners and their contexts, thereby merely recommending the whole sequence of courses that satisfy the degree requirements (Parameswaran et al, 2011), or predicting the performance of students and give recommendations based on predicted results (Elbadrawy & Karypis, 2016; Elbadrawy et al, 2015; Hu & Rangwala, 2018; Sweeney et al, 2016)
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