Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation

• Published in: Journal of educational data mining Vol. 11; no. 2; pp. 20 - 46
• Main Authors: Morsy, Sara, Karypis, George
• Format: Journal Article
• Language: English
• Published: International Educational Data Mining 01.09.2019
• Subjects: Accuracy; Computer Science - Information Retrieval; Computer Science - Learning; Course Content; Course Selection (Students); Difficulty Level; Educational Benefits; Grade Point Average; Majors (Students); Prediction; Program Effectiveness; Statistics - Machine Learning; Undergraduate Students; Minnesota
• ISSN: 2157-2100; 2157-2100
• DOI: 10.48550/arxiv.1904.11798

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student's GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in the first approach, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses in which they are expected not to perform well in than grade-unaware course recommendation methods.