THE EFFECT OF PREDICTIVE ANALYTICS FOR IMPROVING STUDENT RETENTION: A CASE STUDY OF UMARU MUSA YAR’ADUA UNIVERSITY, KATSINA STATE
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- NGN 5000
Background of the Study
Student retention is a critical concern for higher education institutions worldwide. High attrition rates not only affect institutional performance and funding but also undermine students' chances of achieving their academic and career goals. In Nigeria, student retention poses unique challenges, including socio-economic factors, academic preparedness, and institutional constraints. Umaru Musa Yar’adua University, Katsina State, is no exception, with many students dropping out before completing their programs.
Predictive analytics, a subset of Artificial Intelligence (AI), offers an innovative solution to address student retention issues. By analyzing historical and real-time data, predictive analytics can identify at-risk students, uncover patterns associated with attrition, and provide actionable insights to improve retention strategies. Factors such as attendance, grades, financial aid usage, and socio-economic backgrounds can be integrated into predictive models to anticipate and address potential dropout risks proactively.
This study explores the role of predictive analytics in improving student retention at Umaru Musa Yar’adua University. It examines the effectiveness of predictive models in identifying at-risk students, enhancing academic support services, and creating data-driven strategies to foster retention.
Statement of the Problem
Despite various interventions, Umaru Musa Yar’adua University faces persistent challenges with student attrition, leading to adverse impacts on institutional reputation and student outcomes. Current retention strategies often lack the precision and proactivity needed to address these challenges effectively. This study investigates the application of predictive analytics as a tool for improving student retention.
Aim and Objectives of the Study
Aim:
To evaluate the effect of predictive analytics on improving student retention at Umaru Musa Yar’adua University, Katsina State.
Objectives:
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To identify factors contributing to student attrition at Umaru Musa Yar’adua University.
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To explore the application of predictive analytics in identifying at-risk students.
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To assess the impact of predictive analytics on designing effective retention strategies.
Research Questions
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What factors contribute to student attrition at Umaru Musa Yar’adua University?
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How can predictive analytics enhance the identification of at-risk students and improve retention efforts?
Research Hypotheses
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Predictive analytics significantly improves the identification of at-risk students.
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The use of predictive models reduces student attrition rates.
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Data-driven retention strategies are more effective than traditional approaches in improving student retention.
Significance of the Study
This study provides valuable insights into the potential of predictive analytics to revolutionize student retention strategies at Umaru Musa Yar’adua University. It contributes to the broader discourse on data-driven decision-making in higher education and offers practical recommendations for enhancing retention efforts.
Scope and Limitation of the Study
The study focuses on the application of predictive analytics for improving student retention at Umaru Musa Yar’adua University, Katsina State. Limitations include the availability and quality of data for analysis and the study’s concentration on a single institution.
Definition of Terms
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Predictive Analytics: The use of statistical techniques and machine learning models to analyze current and historical data to predict future outcomes.
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Student Retention: The ability of an institution to retain students from admission through graduation.
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Attrition: The process by which students leave an institution before completing their academic program.
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