Predicting Students’ Performance Using Machine Learning Algorithms: A Review

Abstract

Educational Data Mining is a discipline focused on developing ways for studying the unique and increasingly large-scale data generated by educational settings and applying those methods to better understand students and the environments in which they learn. Predicting student performance is one of the most critical concerns in educational data mining, which is gaining popularity. Student performance prediction attempts to forecast a student's grade before enrolling in a course or completing an exam. The goal of this paper is to present a systematic literature review on predicting student performance using machine learning techniques and how the prediction algorithm can be used to identify the most important attribute(s) in a student's data. The study showed that neural networks is the most used classifier for predicting students’ academic results and also provided the best results in terms of accuracy. Also, 87% of the algorithms used were supervised learning as compared to 13% for unsupervised learning and 59% of the studies employed various feature selection methods to improve the performance of the machine learning models.

Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis

“…To make recommendations based on interpretations, the findings are presented as charts and reports. (10) Machine learning techniques can be utilized to predict the output of the students and identifying the at risk students as early as possible so appropriate actions can be taken to enhance their performance. The student's performance prediction is an essential area as it can help teachers identify students that need additional academic assistance.…”

Student Performance Estimation Through Innovative Classification Techniques in Education

“…The literature review revealed the high effectiveness of ML methods for assessing and appraising students' performance [30]. Although various studies [31], [32], [33], [34], [35] have employed diverse models, each with distinct conditions and characteristics tailored to the specific problem context, also, there are gaps in the literature in the field of utilizing GPC model in integration with several optimization algorithms. Therefore, the main purpose of this study is to succeed in a framework to forecast students' academic performance by amalgamating ML models with meta-heuristic algorithms, www.ijacsa.thesai.org considering the unique educational circumstances throughout their academic journey.…”