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Leveraging Predictive Analytics to Identify At-Risk Students in Secondary Mathematics: An Intervention Framework for Urban and Rural School Districts
Subject area: Science,Engineering and Technology · Area of research: Maths Education
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.
References
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[2] Shiraishi, R., & Munashe Naphtali Mupa. (2025). Cross-Border Tax Structuring and Valuation Optimization in Energy-Sector M&A: A U.S.-Japan Perspective. 8(11), 126–135. https://www.researchgate.net/publication/391485061_Cross-Border_Tax_Structuring_and_Valuation_Optimization_in_Energy-Sector_MA_A_US-Japan_Perspective
[3] Khodani Netshifhefhe, Magalane Vivian Netshifhefhe, Munashe Naphtali Mupa, Artwell Kudakwashe, & Murapa. (2024). Integrating Internal Auditing and Legal Compliance: A Strategic Approach to Risk Management. 8(4), 446–465. https://www.researchgate.net/publication/385318623_Integrating_Internal_Auditing_and_Legal_Compliance_A_Strategic_Approach_to_Risk_Management
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How to cite this paper
@article{1709553,
author = {Wendy Matende, Tichaona Remias, Shelter Muguti, Cosmas Mutoto},
title = {Leveraging Predictive Analytics to Identify At-Risk Students in Secondary Mathematics: An Intervention Framework for Urban and Rural School Districts},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {1},
pages = {381-387},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709553.pdf},
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.},
month = {July},
}