Leveraging Predictive Analytics to Identify At-Risk Students in Secondary Mathematics: An Intervention Framework for Urban and Rural School Districts

• Author(s): Wendy Matende ; Tichaona Remias ; Shelter Muguti ; Cosmas Mutoto
• Paper ID: 1709553
• Page: 381-387
• Published Date: 10-07-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 1 July-2025

Abstract

Predictive analytics in education has come widely in focus as a mechanism for identifying at-risk students and structuring interventions that are yet to make academic failure a foregone conclusion. This paper explores how predictive analytics can support the secondary mathematics education system that will identify at-risk students with an intervention regulation to be applied in the school districts (urban and rural). With the combination of data on academic performance with behavioural factors and socio-demographic variables, predictive models can provide information on those students who may fare poorly in mathematics. Relying on the results obtained in multiple fields, including corporate governance and risk management, this paper creates a broad intervention plan that serves the specific needs of students in different settings. A particular focus is on equal access to learning resources and proactive data utilization to reduce the risk and enable students to succeed.

Citations

IRE Journals:
Wendy Matende , Tichaona Remias , Shelter Muguti , Cosmas Mutoto "Leveraging Predictive Analytics to Identify At-Risk Students in Secondary Mathematics: An Intervention Framework for Urban and Rural School Districts" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 381-387

IEEE:
Wendy Matende , Tichaona Remias , Shelter Muguti , Cosmas Mutoto "Leveraging Predictive Analytics to Identify At-Risk Students in Secondary Mathematics: An Intervention Framework for Urban and Rural School Districts" Iconic Research And Engineering Journals, 9(1)