Back to Search
Start Over
Predicting Student Performance Using Personalized Analytics.
- Source :
-
Computer (00189162) . Apr2016, Vol. 49 Issue 4, p61-69. 9p. - Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189162
- Volume :
- 49
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Computer (00189162)
- Publication Type :
- Academic Journal
- Accession number :
- 114640262
- Full Text :
- https://doi.org/10.1109/MC.2016.119