TY - GEN
T1 - Course recommendation as graphical analysis
AU - Bridges, Connor
AU - Jared, James
AU - Weissmann, Joshua
AU - Montanez-Garay, Astrid
AU - Spencer, Jonathan
AU - Brinton, Christopher G.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/21
Y1 - 2018/5/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048532738&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048532738&partnerID=8YFLogxK
U2 - 10.1109/CISS.2018.8362325
DO - 10.1109/CISS.2018.8362325
M3 - Conference contribution
AN - SCOPUS:85048532738
T3 - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
SP - 1
EP - 6
BT - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd Annual Conference on Information Sciences and Systems, CISS 2018
Y2 - 21 March 2018 through 23 March 2018
ER -