Course recommendation as graphical analysis

Connor Bridges, James Jared, Joshua Weissmann, Astrid Montanez-Garay, Jonathan Spencer, Christopher G. Brinton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Publication series

Name2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Bridges, C., Jared, J., Weissmann, J., Montanez-Garay, A., Spencer, J., & Brinton, C. G. (2018). Course recommendation as graphical analysis. In 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018 (pp. 1-6). (2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2018.8362325