Student Performance Prediction Using Machine Learning
Keywords:
Student Performance, Machine Learning, Academic Prediction, Data Preprocessing, Classification, Educational Data MiningAbstract
Student academic performance is an important indicator of learning progress and can be influenced by several factors, including attendance, internal marks, previous academic performance, assignments and study habits. Traditional evaluation generally depends on manual analysis of student records, which can become time-consuming when the number of students increases. This paper proposes a Student Performance Prediction System using Machine Learning to analyse relevant academic data and provide an estimated prediction of a student's expected performance. The proposed approach includes data collection, preprocessing, model training, testing and evaluation, selection of a suitable machine learning model, and prediction for new student records. Previous studies have applied algorithms such as Naïve Bayes, Decision Tree, Support Vector Machine, K-Nearest Neighbour, Random Forest and other ensemble or hybrid approaches for student performance prediction. The proposed system focuses on a simple and efficient approach that can assist in identifying students who may require academic support at an early stage. The prediction is intended as an estimate and is not a guaranteed result.
References
Pallathadka et al., “Classification and Prediction of Student Performance Data Using Various Machine Learning Algorithms,” 2021.
Alsariera et al., “Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance,” 2022.
Hussain & Khan, “Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine Learning,” 2021.
Alsumaidaie et al., “Intelligent System for Student Performance Prediction Using Machine Learning,” 2024.
“Student Performance Prediction using AI,” 2022.
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