TY - GEN
T1 - Predicting student success based on prior performance
AU - Slim, Ahmad
AU - Heileman, Gregory L.
AU - Kozlick, Jarred
AU - Abdallah, Chaouki T.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/13
Y1 - 2015/1/13
N2 - Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses machine learning, and in particular, a Bayesian Belief Network (BBN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.
AB - Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses machine learning, and in particular, a Bayesian Belief Network (BBN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers.
UR - http://www.scopus.com/inward/record.url?scp=84925060989&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84925060989&partnerID=8YFLogxK
U2 - 10.1109/CIDM.2014.7008697
DO - 10.1109/CIDM.2014.7008697
M3 - Conference contribution
AN - SCOPUS:84925060989
T3 - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
SP - 410
EP - 415
BT - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014
Y2 - 9 December 2014 through 12 December 2014
ER -