Course Recommendation for University Environments
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| Title: | Course Recommendation for University Environments |
|---|---|
| Language: | English |
| Authors: | Ma, Boxuan, Taniguchi, Yuta, Konomi, Shin'ichi |
| Source: | International Educational Data Mining Society. 2020. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Publication Date: | 2020 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Course Selection (Students), College Students, Online Courses, Student Attitudes, Decision Making, Student Interests, Grade Prediction, Mathematics |
| Abstract: | 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 been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation. [For the full proceedings, see ED607784.] |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | ED607802 |
| Database: | ERIC |
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