Researchers have developed EduRiskX, a novel neuro-symbolic framework designed to predict academic risk in online education. This system combines a Transformer-based neural network for analyzing student activity sequences with F-Logic symbolic reasoning, grounded in established educational theories. Experiments on the Open University Learning Analytics Dataset (OULAD) demonstrated EduRiskX's superior performance in accuracy and F1-score compared to state-of-the-art models, while also providing interpretable, rule-based explanations for its predictions. AI
IMPACT This framework offers a more interpretable and accurate approach to identifying at-risk students in online learning environments.
RANK_REASON The cluster contains an academic paper detailing a new AI framework and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
- CNN
- EduRiskX
- F-Logic
- iTransformer
- LSTM
- Open University Learning Analytics Dataset
- PatchTST
- Transformer
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