Academic warning plays an essential role in delivering personalized intervention for the student early and constructing a sound educational management system. However, practical explorations are usually limited by two challenges:1) Many influenced factors (e.g., curriculum setting) vary from the time, causing the change of data distribution; 2) The abnormal samples are generally rare compared with normal samples, and the available dataset tends to be imbalanced. To handle the challenges above, this paper proposes a Weighted Incremental Learning Scheme, namely WILS, to realize the effective academic warning of undergraduates. WILS consists of two essential components:1) An incremental learning mechanism that supports the changes of data distribution and features setting; 2) A weighted loss function that will assign a higher weight to the abnormal category. To evaluate the effectiveness of WILS, we conduct experiments on real dataset with 2 275 undergraduates and public-available dataset with 1 000 students. Experimental results demonstrate that WILS is significantly more accurate and efficient compared with other methods.
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