To What Extent Can a Student’s Academic Performance be Predicted Based on Socioeconomic Status and Daily Habits Using Data Science Techniques?

Abstract

Despite having the same teachers and similar methods of education, students studying in a specific school produce diverse academic results. This, in turn, results from their diverse backgrounds, personal behaviour, and socio-economic factors. To understand the extent to which these factors can affect a student’s academic performance, this study, with a sample size of 100 boarding school students, identifies the relationships between them using data science techniques. By distributing forms and collecting some responses online, this study has utilised data science methods to analyse trends and present them through various graphs. By identifying patterns, this research provides valuable insights for teachers and educators to develop specific strategies for targeted students that can improve their performances. Analysis of this research show that while socio-economic factors play an important role in shaping students’ performance, other factors like personal well-being and sleep are also pivotal. This highlights the need of promoting balanced schedules alongside healthy study habits to support the overall student performance and well-being.