Effective deep learning based grade prediction system using gated recurrent unit (GRU) with feature optimization using analysis of variance (ANOVA)

Abstract

The prediction of final semester grades is a crucial undertaking in education, offering insights into student performance and enabling timely interventions to support their academic journey. This paper employs a deep learning approach, specifically gated recurrent unit (GRU), in conjunction with feature optimization using analysis of variance (ANOVA), to forecast final semester grades. The predictive model is trained and evaluated on a handcrafted grade prediction dataset, which contains the academic performance of the students during Plus 2 and from Semester 1 to Semester 4 of a group of Computer science and Engineering majors in Kerala. By processing historical academic records and contextual information, the GRU model learns to predict future performance accurately. To enhance the model's efficacy and interpretability, ANOVA is applied to optimize the feature selection process. This statistical technique identifies the most influential factors contributing to final grades, refining the model's predictive power while reducing dimensionality. The experimental results showcase the model's effectiveness in predicting final semester grades, demonstrating superior accuracy and performance compared to grade prediction using CNN with Bayesian optimization and LSTM with L1-Norm optimization.

Keywords

• Student performance prediction
• artificial intelligence
• deep learning
• recurrent neural network
• gated recurrent unit
• feature optimization