Name
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 futur... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MC.2016.119 | Computer |
Keywords | Field | DocType |
Predictive models,Big data,Data retention,Data models,Recommender systems,Servers,Education | Recommender system,Data science,Data modeling,Data retention,Computer science,Matrix decomposition,Server,Analytics,Big data | Journal |
Volume | Issue | ISSN |
49 | 4 | 0018-9162 |
Citations | PageRank | References |
16 | 1.21 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asmaa Elbadrawy | 1 | 84 | 4.64 |
| Agoritsa Polyzou | 2 | 34 | 3.85 |
| Zhiyun Ren | 3 | 27 | 3.17 |
| Mackenzie Sweeney | 4 | 16 | 1.21 |
| George Karypis | 5 | 15691 | 1171.82 |
| Huzefa Rangwala | 6 | 435 | 57.50 |