Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 7894238 |
| Pages (from-to) | 742-753 |
| Number of pages | 12 |
| Journal | IEEE Journal on Selected Topics in Signal Processing |
| Volume | 11 |
| Issue number | 5 |
| DOIs | |
| State | Published - Aug 1 2017 |
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Keywords
- Data-driven course clustering
- personalized education
- student performance prediction
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs. / Xu, Jie; Moon, Kyeong Ho; Van Der Schaar, Mihaela.
In: IEEE Journal on Selected Topics in Signal Processing, Vol. 11, No. 5, 7894238, 01.08.2017, p. 742-753.Research output: Contribution to journal › Article
}
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
PY - 2017/8/1
Y1 - 2017/8/1
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
UR - http://www.scopus.com/inward/record.url?scp=85029505112&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029505112&partnerID=8YFLogxK
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 -