Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 0 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Academic Achievement | 1 |
| Bayesian Statistics | 1 |
| Data Analysis | 1 |
| Feedback (Response) | 1 |
| Instructional Materials | 1 |
| Learning | 1 |
| Models | 1 |
| Prediction | 1 |
| Programming Languages | 1 |
| Students | 1 |
| Tests | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Author
| Brusilovsky, Peter | 1 |
| Lin, Yu-Ru | 1 |
| Sahebi, Shaghayegh | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textSahebi, Shaghayegh; Lin, Yu-Ru; Brusilovsky, Peter – International Educational Data Mining Society, 2016
We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students' attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require…
Descriptors: Data Analysis, Prediction, Models, Learning
