Abstract
Selecting courses in an open-curriculum education program is a difficult task for students and academic advisors. Course recommendation systems nowadays can be used to reduce the complexity of this task. To control the recommendation error, we argue that course recommendations need to be provided together with statistical confidence. The latter can be used for computing a statistically valid set of recommended courses that contains courses a student is likely to take with a probability of at least 1-e for a user-specified significance level e. For that purpose, we introduce a generic algorithm for course recommendation based on the conformal prediction framework. The algorithm is used for implementing two conformal course recommender systems. Through experimentation, we show that these systems accurately suggest courses to students while maintaining statistically valid sets of courses recommended.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020, Fully virtual conference, July 10-13, 2020 |
| Editors | Anna N. Rafferty, Jacob Whitehill, Cristobal Romero, Violetta Cavalli-Sforza |
| Publisher | International Educational Data Mining Society |
| Pages | 509-515 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781733673617 |
| Publication status | Published - 1 Jan 2020 |
| Event | 13th International Conference on Educational Data Mining, EDM 2020 - Virtual, Montrea, Canada Duration: 10 Jul 2020 → 13 Jul 2020 https://educationaldatamining.org/edm2020/ |
Conference
| Conference | 13th International Conference on Educational Data Mining, EDM 2020 |
|---|---|
| Abbreviated title | EDM2020 |
| Country/Territory | Canada |
| City | Montrea |
| Period | 10/07/20 → 13/07/20 |
| Internet address |
Keywords
- Conformal Prediction
- Course Recommendation
- Recommender Systems