Sequence-based Approaches to Course Recommender Systems
- Ren Wang
- Osmar R. Zaiane, University of Alberta (Database)
The scope and order of courses to take to graduate are typically defined, but liberal programs encourage flexibility and may generate many possible paths to graduation. Students and course counselors struggle with the question of choosing a suitable course at a proper time. Many researchers have focused on making course recommendations with traditional data mining techniques, yet failed to take a student’s sequence of past courses into consideration. In this paper, we study sequence-based approaches for the course recommender system. First, we implement a course recommender system based on three different sequence related approaches: process mining, dependency graph and sequential pattern mining. Then, we evaluate the impact of the recommender system. The result shows that all can improve the performance of students while the approach based on dependency graph contributes most.
Citation
R. Wang, O. Zaiane. "Sequence-based Approaches to Course Recommender Systems". International Conference on Database and Expert Systems Applications (DEXA), Regensburg, Germany, September 2018.| Keywords: | Recommender Systems, Dependency Graph, Process Mining |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Wang+Zaiane:DEXA18,
author = {Ren Wang and Osmar R. Zaiane},
title = {Sequence-based Approaches to Course Recommender Systems},
booktitle = {International Conference on Database and Expert Systems
Applications (DEXA)},
year = 2018,
}Last Updated: November 03, 2019Submitted by Sabina P
