Predicting Students' Success Rate Using Deep Learning Techniques

Abstract

Large amounts of data can be reevaluated with the assistance of machine learning to identify certain patterns that may not be immediately evident or recognized by humans. This can be done to improve the accuracy of the analysis. This is something that can be done to find answers to questions or solutions to issues. In recent years, there has been an increase in the application of ML techniques to the analysis of educational data, such as the performance of students in their classes. One example of this type of data includes the grades that students receive. During the time they are enrolled in this class, successful students take charge of their own education by utilizing a variety of different techniques, and the purpose of this study is to analyze those strategies. In this study, supervised and unsupervised machine learning (ML) techniques are utilized to determine the significant characteristics that a successful learner frequently demonstrates while taking a computer class. The purpose of this study is to determine the significant characteristics that a successful learner frequently demonstrates while taking a computer class. The goal of this study is to identify the major traits that a successful learner frequently shows while they are enrolled in a computer class so that appropriate instructional measures can be taken. In a class that was supposed to serve as an introduction to computers, many of the students had trouble understanding the material that was being presented to them on a regular basis.