Analyzing Factors that Influence Student Performance in Academic

Abstract

Student performance analysis is a complex and popular study area in educational data mining. Multiple factors affect performance in nonlinear ways, making this topic more appealing to academics. The broad availability of educational datasets adds to this interest, particularly in online learning. Although previous studies have focused on analyzing and predicting students' performance based on their classroom activities, this study did not take into account student's outside conditions, such as sleep hours, extracurricular activities, and a sample of question papers that they had practiced.  These three variables are included among others in our study. In this paper, we describe an analysis of 10,000 student records, with each record containing information on numerous predictors and a performance index. The dataset intends to shed light on the relationship between predictor variables and the performance indicator. To create the correlation variable heatmap, we use both univariate and bivariate studies to produce a linear equation. Following that, we perform data preprocessing and modeling to facilitate predictive analysis. Finally, we showed the outcomes of actual and expected student performance using the model we constructed. The findings demonstrate that our prediction model was 98% accurate, with a mean absolute error of 1.62.