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Ma, Boxuan; Lu, Min; Taniguchi, Yuta; Konomi, Shin'ichi – Research and Practice in Technology Enhanced Learning, 2021
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…
Descriptors: Universities, College Students, Course Selection (Students), Visualization
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Ma, Boxuan; Taniguchi, Yuta; Konomi, Shin'ichi – International Educational Data Mining Society, 2020
Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have…
Descriptors: Course Selection (Students), College Students, Online Courses, Student Attitudes
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Ma, Boxuan; Lu, Min; Taniguchi, Yuta; Konomi, Shin'ichi – Smart Learning Environments, 2021
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…
Descriptors: College Students, Course Selection (Students), Decision Making, Student Attitudes