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A novel transfer learning approach to predict student performance based on CNN-GWO

Educational data mining (EDM), which assists educators to gain the features from related student information, has made it possible that the performance of students can be efficiently predicted. However, traditional approaches usually just focus on how to classify the performance but ignore the problem of the shortage of data. Thus, a novel transfer learning approach to predict student performance based on CNN-GWO is exhibited. Firstly, the CNN-FC structure is employed for establishing the pre-trained model. Then, the deficient data of the target prediction task is used for retraining the fully connection (FC) layer of CNN-FC optimized by the Grey Wolf optimizer (GWO) for constructing the transfer learning model. It is exhibited in the paper that the proposed approach promotes the accuracy of prediction of the student performance. The Ermse and Emae of the prediction of the target task are reduced by 27.21%, 22.99% compared to the traditional methods without transfer learning.