Analysis of educational data enabled by deep learning to increase student success

Abstract

A key component of improving educational quality is identifying pupils who are at a high risk of doing poorly academically as early as feasible. To accomplish this, most studies now in existence have used conventional Deep learning (DL) algorithms to forecast students' academic progress based on their behavior data, from which behavior elements are manually identified owing to the professional expertise and knowledge. Nevertheless, it has become increasingly difficult to recognize finely constructed handcrafted traits as a result of a rise in the types and quantities of behavioral data. The Enriched Plant Growth Optimized Artificial Neural Network (EPGO-ANN) technique enabled data analysis of educational data which is a viable tactic that may be used to improve student accomplishment in educational settings as we suggested in this research. The optimization predicts the academic success by autonomously extracting characteristics from student behavior data from several heterogeneous sources. This model's novelty employs recording the in-built time-series data for each kind of activity and uses EPGO-ANN to extract correlation features between various behaviors. The results of the experiments show that the suggested deep model approach outperforms several DL methods. The results of experiments show that the EPGO-ANN technique is superior to other DL methods.