| Summary: | Warning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:(1)these works fail to handle unrecorded/missing data when this part is sparse; (2)these works ignore the complex relationship between courses. The different courses are used as the attention weight vector for abnormality prediction, but they do not notice the mutual influence between courses. To this end, we contribute a Hybrid Neural Network Model based on High-Order Attention Mechanism, called HHA, to address the academic abnormality prediction problem. Specifically, we first exploit Generative Adversarial Network(GAN) to mine hidden factors in the unrecorded/missing data reasonably by simulating student behavior. Thereafter, a high-order attention mechanism is proposed to measure the importance of course and course combination. Lastly, a multi-layer projection abstracts feature and classifies whether the student is abnormal. By experimenting on real-world dataset, we demonstrate the effectiveness and rationality of our proposed model.
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