TY - GEN
T1 - Employing markov networks on curriculum graphs to predict student performance
AU - Slim, Ahmad
AU - Heileman, Gregory L.
AU - Kozlick, Jarred
AU - Abdallah, Chaouki T.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/5
Y1 - 2014/2/5
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 data mining and machine learning techniques, and in particular, linear regression and a Markov network (MN), 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 data mining and machine learning techniques, and in particular, linear regression and a Markov network (MN), 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.
KW - Markov Network
KW - educational analytics
KW - linear regression
KW - student success
UR - https://www.scopus.com/pages/publications/84946687543
UR - https://www.scopus.com/pages/publications/84946687543#tab=citedBy
U2 - 10.1109/ICMLA.2014.74
DO - 10.1109/ICMLA.2014.74
M3 - Conference contribution
AN - SCOPUS:84946687543
T3 - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
SP - 415
EP - 418
BT - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
A2 - Ferri, Cesar
A2 - Qu, Guangzhi
A2 - Chen, Xue-wen
A2 - Wani, M. Arif
A2 - Angelov, Plamen
A2 - Lai, Jian-Huang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Y2 - 3 December 2014 through 6 December 2014
ER -