Predicting Student Performance Using Personalized Analytics

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 sys...

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Bibliographic Details
Published inComputer (Long Beach, Calif.) Vol. 49; no. 4; pp. 61 - 69
Main Authors Elbadrawy, Asmaa, Polyzou, Agoritsa, Zhiyun Ren, Sweeney, Mackenzie, Karypis, George, Rangwala, Huzefa
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Analytics
Assessments
Big data
computing in education
data analysis
data mining
Data models
Data retention
Education
Electronic commerce
Factorization
Internet
learning-management systems
LMSs
massive open online courses
Matrix
matrix factorization
MOOCs
multilinear regression
Personalized
Predictive models
Recommender systems
Regression analysis
Risk
Risk assessment
Servers
Student retention
Students
Online AccessGet full text

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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2016.119