Predicting Student Performance Using Personalized Analytics

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

doi:10.1109/MC.2016.119

Predicting Student Performance Using Personalized Analytics

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

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

129 Scopus citations

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.

Original language	English (US)
Article number	7452320
Pages (from-to)	61-69
Number of pages	9
Journal	Computer
Volume	49
Issue number	4
DOIs	https://doi.org/10.1109/MC.2016.119
State	Published - Apr 2016

Keywords

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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10.1109/MC.2016.119

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Predicting Student Performance Using Personalized Analytics

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Keywords

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