Predicting Student Performance Using Personalized Analytics

Asmaa Elbadrawy, Agoritsa Polyzou, Zhiyun Ren, Mac Kenzie Sweeney, George Karypis, Huzefa Rangwala

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


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.

Original languageEnglish (US)
Article number7452320
Pages (from-to)61-69
Number of pages9
Issue number4
StatePublished - Apr 2016


  • big data
  • computing in education
  • data analysis
  • data mining
  • learning-management systems
  • LMSs
  • massive open online courses
  • Matrix factorization
  • MOOCs
  • multilinear regression
  • recommender systems


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