Evaluation of Data Mining Techniques for Predicting Student Academic Success in Federal University Kashere, Gombe State
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Background of the Study
Data mining techniques have emerged as powerful tools for extracting valuable insights from large datasets, particularly in the field of education. At Federal University Kashere in Gombe State, the ability to predict student academic success is critical for designing targeted interventions and improving overall educational outcomes. By applying data mining methods such as decision trees, neural networks, and association rule mining, educational administrators can analyze patterns in historical student data, including grades, attendance records, and engagement metrics (Olufemi, 2023). These techniques enable the identification of key predictors of academic performance and facilitate early detection of students who may be at risk of underachievement. Recent studies have shown that data mining can significantly enhance the accuracy of predictive models, leading to more effective resource allocation and personalized support for students (Chinwe, 2024). The integration of data mining into academic planning not only supports proactive decision-making but also promotes a culture of continuous improvement within the institution. Furthermore, the development of interactive dashboards and visualization tools can help educators and administrators monitor student performance in real time and adjust their strategies accordingly. However, despite the potential benefits, the application of data mining in educational settings faces challenges such as data quality issues, privacy concerns, and the need for specialized analytical skills. This study aims to evaluate various data mining techniques to determine their effectiveness in predicting student academic success at Federal University Kashere. The research will compare different algorithms, assess their predictive accuracy, and provide recommendations for best practices in utilizing data mining to support academic excellence.
Statement of the Problem
At Federal University Kashere, traditional methods of evaluating student performance are often limited to periodic assessments and subjective evaluations, which do not fully capture the underlying factors that contribute to academic success. This reactive approach can lead to delayed interventions, as educators may not recognize at-risk students until significant issues have already manifested (Ibrahim, 2023). Although data mining techniques offer a promising solution for early prediction of academic outcomes, their application in the university has been limited by challenges such as inconsistent data quality, fragmented data sources, and a lack of technical expertise in advanced analytical methods. These challenges hinder the development of robust predictive models that can accurately identify students in need of support. Furthermore, privacy concerns and data protection regulations add an additional layer of complexity to the process. This study seeks to address these issues by evaluating different data mining techniques for predicting student success and identifying the key variables that impact academic performance. The goal is to develop a predictive framework that enables timely, data-informed interventions, ultimately enhancing student outcomes and reducing dropout rates. By systematically comparing various algorithms and addressing the barriers to their implementation, the research aims to provide a comprehensive solution for improving academic support services at Federal University Kashere (Udo, 2024).
Objectives of the Study:
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To evaluate the predictive accuracy of various data mining techniques.
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To identify key predictors of student academic success.
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To propose a framework for integrating data mining into academic support services.
Research Questions:
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Which data mining techniques yield the highest predictive accuracy for student success?
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What are the most significant factors influencing academic performance at Federal University Kashere?
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How can the predictive framework be effectively implemented to support early interventions?
Significance of the Study
This study is significant as it examines the potential of data mining techniques to predict student academic success at Federal University Kashere. The research aims to provide a data-driven framework for early intervention, improving student retention and academic performance. The findings will be valuable for educators and administrators seeking to leverage analytics for enhanced decision-making and resource allocation.
Scope and Limitations of the Study:
The study is limited to the evaluation of data mining techniques for predicting student academic success at Federal University Kashere in Gombe State and does not extend to other educational institutions.
Definitions of Terms:
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Data Mining: The process of discovering patterns and insights from large datasets using statistical and computational techniques.
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Predictive Modeling: The creation of models to forecast future outcomes based on historical data.
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Academic Success: The achievement of desired educational outcomes, often measured by grades, retention, and graduation rates.
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