Investigation into the Role of Data Science in Predicting University Dropout Rates in University of Jos, Plateau State
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Background of the Study
The phenomenon of student dropout remains a persistent challenge for higher education institutions globally. At the University of Jos, Plateau State, there is an increasing interest in applying data science techniques to predict and mitigate dropout rates. By analyzing a range of factors such as academic performance, socio-economic background, attendance records, and engagement levels, data science models can identify students at risk of discontinuing their studies (Chukwu, 2023). These predictive models, often powered by machine learning algorithms and statistical analyses, provide early warning signals that enable timely intervention strategies. The integration of data science into dropout prediction not only facilitates a proactive approach to student retention but also helps institutions to allocate resources more effectively (Okoro, 2024).
Data science-driven approaches to dropout prediction involve the use of big data analytics, data mining, and predictive modeling. This technology can sift through large volumes of heterogeneous data, uncover hidden patterns, and forecast dropout risks with a high degree of accuracy. Recent studies have demonstrated that these models, when fine-tuned with local contextual data, can significantly reduce dropout rates by identifying critical risk factors and suggesting targeted interventions (Udo, 2025). The University of Jos stands to benefit from such an approach by improving student support services and enhancing overall academic performance. Furthermore, the application of data science in this context aligns with global trends toward evidence-based decision-making in education, where the focus is on using empirical data to drive policy and practice improvements (Chukwu, 2023).
Statement of the Problem
Despite the potential benefits of data science in predicting dropout rates, the University of Jos faces several challenges in implementing these techniques. A primary issue is the variability and quality of the data available. Inconsistent data collection methods and missing information on critical factors such as socio-economic status and student engagement undermine the predictive accuracy of data science models (Chukwu, 2023). Moreover, the integration of data from disparate sources, such as academic records, attendance logs, and counseling reports, presents technical challenges in ensuring data compatibility and reliability (Okoro, 2024).
Additionally, there is a lack of expertise in data analytics within the institution, which hinders the effective deployment and maintenance of predictive models. Resistance from administrative staff, who may be wary of relying on algorithmic predictions for student retention strategies, further complicates the adoption of these technologies (Udo, 2025). Ethical concerns, including the privacy of student data and the potential for stigmatization of at-risk individuals, also contribute to the challenges faced by the university. These issues collectively limit the ability of data science techniques to provide accurate, actionable insights for reducing dropout rates. This study aims to identify these challenges and propose robust methodologies to enhance data collection, integration, and analysis, thereby enabling more effective early intervention measures (Chukwu, 2023).
Objectives of the Study:
- To develop a predictive model using data science techniques to forecast dropout rates at the University of Jos.
- To identify key factors contributing to student dropout and evaluate their predictive significance.
- To propose intervention strategies based on the predictive model to reduce dropout rates.
Research Questions:
- How can data science be utilized to accurately predict dropout rates at the University of Jos?
- What are the main factors influencing student dropout in this context?
- How can predictive insights be translated into effective intervention strategies?
Significance of the Study
This study is significant as it applies data science to predict student dropout rates at the University of Jos, providing valuable insights into the risk factors and enabling early intervention strategies. By identifying key determinants of dropout, the research supports evidence-based improvements in student retention initiatives. The findings will help policymakers and educators develop targeted programs that enhance student support and academic success (Udo, 2025).
Scope and Limitations of the Study:
This study is limited to the application of data science techniques in predicting dropout rates at the University of Jos, Plateau State, and does not extend to other predictive factors or institutions.
Definitions of Terms:
- Data Science: An interdisciplinary field that uses statistical and computational methods to extract insights from large datasets (Chukwu, 2023).
- Dropout Rate: The percentage of students who discontinue their studies before completing their academic program (Okoro, 2024).
- Predictive Modeling: The process of using data, statistical algorithms, and machine learning techniques to forecast future outcomes (Udo, 2025).
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