Name
Abstract | ||
|---|---|---|
This work proposes a method for course recommendation using grade and enrollment data. We analyze the per-semester sequence in which courses are taken in order to create a personalized course transition graph that balances the student's current grades, their expected improvement, and course popularity. Using a dataset of 6000 students and 1500 courses, we compare the recommended trajectories of top performing and low performing students to show that popularity alone is a strong heuristic for recommending successful trajectories. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CISS.2018.8362325 | 2018 52nd Annual Conference on Information Sciences and Systems (CISS) |
Keywords | Field | DocType |
course popularity,low performing students,course recommendation,graphical analysis,enrollment data,per-semester sequence,grade data,students current grades,course transition graph | Recommender system,Graph,Mathematical optimization,Heuristic,Graphical analysis,Computer science,Popularity,Schedule,Artificial intelligence,Machine learning,Trajectory,Market research | Conference |
ISBN | Citations | PageRank |
978-1-5386-0580-6 | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Connor Bridges | 1 | 0 | 0.34 |
| James Jared | 2 | 0 | 0.34 |
| Joshua Weissmann | 3 | 0 | 0.34 |
| Astrid Montanez-Garay | 4 | 0 | 0.34 |
| jonathan spencer | 5 | 2 | 1.05 |
| Christopher G. Brinton | 6 | 118 | 15.23 |