MULTISCALE ATTENTION-BASED DEEP LEARNING FOR STUDENTS’ ACADEMIC GRADES PREDICTION
Abstract
One of the most vital tasks in the domain of Educational Data Mining (EDM) is predicting students' academic success at an early stage of a semester. For this purpose, various Artificial Intelligence (AI) algorithms have been developed in the past years. Amongst, Deep Neural Network (DNN) can construct an effective predictive system by taking the academic features associated with the student’s previous grades in the particular courses. On the other hand, it was unable to capture the multiscale temporal features of the time series data for students’ success. Therefore, this article presents a new Multi-Scale Attention Deep Convolutional Neural Network (MSA-DCNN) to predict students’ performance by extracting the academic and temporal features at different scales. Initially, the raw academic dataset is pre-processed by using different methods to convert it into the proper format. Then, the obtained dataset is fed to the MSA-DCNN, which applies the multiscale convolution to extract the academic and temporal features at multiple scales along with the occasion by creating various scales of feature maps. Also, an attention strategy is employed to decide relevant feature maps and reduce the feature dimensionality by learning the significance of all feature maps in an automated way. Further, the softmax classifier is used to predict the performance of students studying in a specific year. Finally, the experimental results illustrate that the MSA-DCNN achieves 94.9% of accuracy than the other state-of-the-art prediction models.
Keyword : Educational data mining, Artificial Intelligence, DNN, Academic features, Time series data, Temporal features, Multi-scale attention strategy, DCNN
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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