Predicting Student Performance Using Personalized Analytics Article

Elbadrawy, A, Polyzou, A, Ren, Z et al. (2016). Predicting Student Performance Using Personalized Analytics . 49(4), 61-69. 10.1109/MC.2016.119

cited authors

  • Elbadrawy, A; Polyzou, A; Ren, Z; Sweeney, M; Karypis, G; Rangwala, H

fiu authors

abstract

  • 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.

publication date

  • April 1, 2016

Digital Object Identifier (DOI)

start page

  • 61

end page

  • 69

volume

  • 49

issue

  • 4