Predicting Student Performance Using Personalized Analytics

被引：79

作者：

Elbadrawy, Asmaa ^{[1
]}

Polyzou, Agoritsa ^{[1
]}

Ren, Zhiyun ^{[2
]}

Sweeney, Mackenzie ^{[2
]}

Karypis, George ^{[1
]}

Rangwala, Huzefa ^{[2
]}

机构：

[1] Univ Minnesota, Minneapolis, MN 55455 USA

[2] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA

来源：

COMPUTER | 2016年 / 49卷 / 04期

基金：

美国国家科学基金会;

关键词：

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

D O I：

10.1109/MC.2016.119

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：61 / 69

页数：9

共 12 条

[1]

[Anonymous], BUILD WORKF INF EC

[2]

[Anonymous], 2013, 2013037 NCES US DEP

[3]

[Anonymous], 2015, P 2 2015 ACM C LEARN

[4]

[Anonymous], 2011, EDM

[5]

[Anonymous], 2015, P 5 INT C LEARN AN K, DOI DOI 10.1145/2723576.2723590

[6]

Barber R., 2012, Proceedings of the Second International Conference on Learning Analytics and Knowledge, USA, P259, DOI DOI 10.1145/2330601.2330664

[7] Transfer Learning for Predictive Models in Massive Open Online Courses [J].

Boyer, Sebastien ;

Veeramachaneni, Kalyan .

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 2015, 9112 :54-63

[8]

Pappano L., 2012, The New York Times

[9]

Pardos Z. A, 2013, P 6 INT C ED DAT MIN

[10]

Polyzou A., 2016, P 20 PAC AS IN PRESS

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