TY - JOUR
T1 - A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs
AU - Xu, Jie
AU - Moon, Kyeong Ho
AU - Van Der Schaar, Mihaela
N1 - Funding Information:
Manuscript received October 15, 2016; revised February 16, 2017; accepted February 21, 2017. Date of publication April 7, 2017; date of current version July 18, 2017. This work was supported by National Science Foundation under Grants ECCS 1407712 and PFI:BIC 1533983. The guest editor coordinating the review of this paper and approving for publication was Dr. Richard G. Baraniuk. (Corresponding author: Mihaela van der Schaar.) J. Xu is with the Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146 USA (e-mail: jiexu@miami.edu).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8
Y1 - 2017/8
N2 - Accurately predicting students' future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g., college programs) is much less studied and faces new challenges: 1) Students differ tremendously in terms of backgrounds and selected courses; 2) courses are not equally informative for making accurate predictions; and 3) students' evolving progress needs to be incorporated into the prediction. In this paper, we develop a novel machine learning method for predicting student performance in degree programs that is able to address these key challenges. The proposed method has two major features. First, a bilayered structure comprising multiple base predictors and a cascade of ensemble predictors is developed for making predictions based on students' evolving performance states. Second, a data-driven approach based on latent factor models and probabilistic matrix factorization is proposed to discover course relevance, which is important for constructing efficient base predictors. Through extensive simulations on an undergraduate student dataset collected over three years at University of California, Los Angeles, we show that the proposed method achieves superior performance to benchmark approaches.
AB - Accurately predicting students' future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g., college programs) is much less studied and faces new challenges: 1) Students differ tremendously in terms of backgrounds and selected courses; 2) courses are not equally informative for making accurate predictions; and 3) students' evolving progress needs to be incorporated into the prediction. In this paper, we develop a novel machine learning method for predicting student performance in degree programs that is able to address these key challenges. The proposed method has two major features. First, a bilayered structure comprising multiple base predictors and a cascade of ensemble predictors is developed for making predictions based on students' evolving performance states. Second, a data-driven approach based on latent factor models and probabilistic matrix factorization is proposed to discover course relevance, which is important for constructing efficient base predictors. Through extensive simulations on an undergraduate student dataset collected over three years at University of California, Los Angeles, we show that the proposed method achieves superior performance to benchmark approaches.
KW - Data-driven course clustering
KW - personalized education
KW - student performance prediction
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U2 - 10.1109/JSTSP.2017.2692560
DO - 10.1109/JSTSP.2017.2692560
M3 - Article
AN - SCOPUS:85029505112
VL - 11
SP - 742
EP - 753
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
SN - 1932-4553
IS - 5
M1 - 7894238
ER -