CourseQ: the impact of visual and interactive course recommendation in university environments

Abstract

The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review

“…By learning from previous grades, they estimated the students' grades in future courses. Ma et al (2020) studied the reasons for course selection in universities; they found that getting relatively higher grades was one of the factors that influenced learners' choices. Based on learners' previous grades, they estimated how prepared they were for the upcoming courses.…”

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

“…For instance, in the scenario involving a Career interestbased CRS, the AI could explain to students: "This course is recommended because it aligns with your career interests in machine learning engineering, which could significantly benefit your future career path based on your current academic and extracurricular profile". As students interact with the AI-based CRS's reasoning during evaluations, they will have the chance to develop their own perspectives and improve their capacity to assess and select courses autonomously [48].…”

“…Thirdly, AI-based CRSs should provide unexpected or serendipitous course suggestions. This strategy tackles the filter bubble problem by urging students, especially those uncertain about their academic path, to venture into new domains [48]. These recommendations have the potential to expand students' perspectives, exposing them to unfamiliar subjects and revealing interests they may not have previously considered.…”

AI-based Academic Advising Framework: A Knowledge Management Perspective

“…Study [21] designed an interactive system to recommend suitable courses to university students based on their interest and popularity of the course. The recommendation is based on historical enrollment data, course descriptions, topic, instructor, and time of study.…”

“…Studies have investigated the use of machine learning for recommending courses to students [21] as well as including features personal traits [20], and student preferences [19]. While this type of model is suitable for online courses with a large number of enrollments and course choices, this model does not work for the institution of study.…”