Key factors influencing students’ academic performance

• Published in: Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 41 - 18
• Main Authors: Suleiman, Ibrahim Baba, Okunade, Oluwasogo Adekunle, Dada, Emmanuel Gbenga, Ezeanya, Uchenna Christiana
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Academic achievement; Academic performance; Artificial Intelligence; College students; Data-driven education; Education; Educational objectives; Electrical Engineering; Electrical Machines and Networks; Engineering; Extracurricular activities; Grade prediction; Information Systems and Communication Service; Learning; Linear regression; Mechanical Engineering; Performance evaluation; Performance prediction; Power Electronics; Predictive modeling; Regression analysis; Regression models; Secondary schools; Socioeconomic factors; Socioeconomic status; Statistical analysis; Student assessment; Students; Success; Teachers; Visual aids; Visual aspects; Predictive modeling; Data-driven education; Linear regression; Grade prediction; Student assessment; Academic performance
• DOI: 10.1186/s43067-024-00166-w
• ISSN: 2314-7172

Academic achievement is a multifaceted outcome influenced by a multitude of factors spanning across educational, socioeconomic, and individual characteristics. Understanding the key determinants of students’ academic performance is paramount for educators, policymakers, and institutions to enhance learning outcomes and facilitate targeted interventions. The absence of a cohesive framework for understanding academic achievement poses a barrier to effective interventions and policies, hindering educators and policymakers from implementing targeted strategies for student improvement. To address this challenge, it is crucial to conduct a thorough examination of the factors influencing academic performance, prioritizing their identification, exploring interactions, and examining their relative impact. Through this research, educational stakeholders can gain insights to develop evidence-based strategies that foster equitable learning outcomes and pave the way for academic success for all students. In this research, linear regression is chosen as the primary model due to its ability to model relationships between variables, making it suitable for assessing the impact of multiple factors on academic performance. Simulation results show that the proposed model achieved 98% accuracy in predicting the factors that are affecting students’ academic performance. The findings derived from the linear regression analysis offer statistical insights into the key factors influencing students’ academic performance. It also incorporates visual aids to enhance the clarity and interpretability of the results. This research interprets the implications of these key factors and discusses their broader significance in the context of students’ academic performance.