A systematic literature review of practical and explainable AI (XAI) student performance prediction system in higher education institutions
Bibliographic Data
| ID | 22419976 |
|---|---|
| Authors | Xinbing Liu, Sya Azmeela Shariff (0000-0003-2582-712X), Doris Hooi Ten Wong, Zeqiang Xie |
| Year | 2026 |
| Publication date | 2026-07-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational Technology Research and Development (JOURNAL) |
| Journal identifiers | ISSN: 1042-1629 • E-ISSN: 1556-6501 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11423-026-10642-x |
| Language | EN |
| References cited | 93 |
Perceiving Learning at a Glance
Machine learning
Interpreting Black-Box Models
Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
From local explanations to global understanding with explainable AI for trees
Preferred reporting items for systematic reviews and meta-analyses
Analyzing click data with AI
Bringing in the technological, ethical, educational and social-structural for a new education data governance
Towards Intelligent-TPACK
Predicting academic performance of students with machine learning
Applications of Machine Learning for at‐Risk Student Prediction in Online Education
Adopting Digital Tools in Higher Education
Bringing academics on board
The Promises and Challenges of Artificial Intelligence for Teachers
Kanformer
A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management
A Ladder Of Citizen Participation
Participatory learning for sustainable agriculture
Early-predicting dropout of university students
| Citation velocity | historical |
|---|---|
| Highly cited | No |