Predicting Student Performance Using Personalized Analytics
Article
Elbadrawy, A, Polyzou, A, Ren, Z et al. (2016). Predicting Student Performance Using Personalized Analytics
. COMPUTER, 49(4), 61-69. 10.1109/MC.2016.119
Elbadrawy, A, Polyzou, A, Ren, Z et al. (2016). Predicting Student Performance Using Personalized Analytics
. COMPUTER, 49(4), 61-69. 10.1109/MC.2016.119
To help solve the ongoing problem of student retention, new expected performance-prediction techniques are needed to facilitate degree planning and determine who might be at risk of failing or dropping a class. Personalized multiregression and matrix factorization approaches based on recommender systems, initially developed for e-commerce applications, accurately forecast students' grades in future courses as well as on in-class assessments.
