Implementation of a Data Science-Based Course Dropout Prediction Model in Federal University Lokoja, Kogi State
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- NGN 5000
Background of the Study
The increasing rate of course dropout has become a critical challenge for higher education institutions, often leading to loss of valuable human capital and diminished institutional performance. At Federal University Lokoja, Kogi State, traditional approaches to monitor student progress rely heavily on manual assessments and periodic reviews, which tend to be reactive rather than proactive. With the advent of data science, institutions can now harness the power of advanced analytics to predict potential dropouts before they occur. Data science-based models utilize historical academic records, attendance logs, engagement metrics, and demographic information to uncover hidden patterns and identify at-risk students (Adebola, 2023). By implementing machine learning algorithms—such as logistic regression, decision trees, and neural networks—universities can generate predictive models that forecast dropout probabilities with considerable accuracy. These models can be continuously refined as new data become available, enabling dynamic and adaptive intervention strategies. Moreover, integrating data visualization tools helps administrators monitor trends and make data-driven decisions in real time, enhancing the overall academic support system. The ability to predict course dropout not only improves student retention rates but also optimizes resource allocation by focusing intervention efforts where they are most needed. Furthermore, a data-driven approach fosters a culture of accountability and continuous improvement within academic departments, as faculty and administrators can track the effectiveness of various intervention strategies over time (Ibrahim, 2024). The potential benefits of this technology are immense: reducing the financial and social costs associated with high dropout rates, and ensuring that students receive timely support tailored to their unique needs. However, challenges such as data quality, integration of disparate data sources, and ensuring privacy and security of student information remain significant hurdles. This study proposes to develop and implement a robust predictive model using state-of-the-art data science techniques to address these issues, with the ultimate goal of enhancing student success and institutional performance (Chinwe, 2025).
Statement of the Problem
Federal University Lokoja currently relies on conventional monitoring techniques that often fail to identify students at risk of dropping out until significant academic decline has occurred. This reactive approach results in delayed interventions, which in turn exacerbate dropout rates and negatively impact institutional reputation and resource planning (Olufemi, 2023). The existing system is hampered by incomplete data, inconsistent record-keeping, and an inability to process real-time information, all of which contribute to suboptimal decision-making. Moreover, manual tracking methods are labor-intensive and susceptible to human error, further diminishing their reliability. Without an automated, data science-driven predictive model, administrators struggle to pinpoint the specific factors contributing to student disengagement and attrition. Consequently, many students do not receive the tailored academic support they need, leading to a cycle of poor performance and eventual dropout. This study aims to address these issues by developing a comprehensive course dropout prediction model that leverages machine learning algorithms and integrates multiple data sources. The goal is to provide early warning signals that enable timely, targeted interventions, thereby reducing dropout rates and improving overall academic outcomes. The study will also explore the challenges associated with implementing such a system, including data integration, algorithm bias, and privacy concerns, and propose strategies to overcome these hurdles.
Objectives of the Study:
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To develop a predictive model for course dropout using data science techniques.
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To evaluate the model’s accuracy in identifying at-risk students.
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To recommend intervention strategies based on model insights.
Research Questions:
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How effective is the data science-based model in predicting course dropout?
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Which factors most significantly influence student dropout at Federal University Lokoja?
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How can the predictive model inform targeted interventions to reduce dropout rates?
Significance of the Study
This study is significant as it harnesses data science to predict course dropout, enabling early intervention and personalized support for at-risk students. The insights from the predictive model will enhance academic planning and resource allocation, ultimately improving student retention and institutional performance. By addressing data quality and integration challenges, the research contributes to the advancement of educational analytics, providing a scalable framework for other institutions facing similar challenges (Adebola, 2023).
Scope and Limitations of the Study:
The study is limited to the implementation of a data science-based dropout prediction model at Federal University Lokoja, Kogi State, and does not extend to other universities or educational interventions.
Definitions of Terms:
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Data Science: Techniques and methods used to analyze large datasets for predictive insights.
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Predictive Model: A statistical model that forecasts future outcomes based on historical data.
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Dropout: The discontinuation of a course by a student before completion.
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