An Investigation of Data Mining Techniques for Identifying At-Risk Students: A Case Study of Taraba State University (Jalingo LGA, Taraba State)
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Background of the Study
Data mining involves the use of computational techniques to uncover patterns and relationships within large datasets. In education, data mining can be used to identify trends and insights that help institutions support students more effectively. Identifying at-risk students is a critical issue for universities, as early detection of students who are struggling can lead to timely interventions, preventing academic failure and dropouts. Taraba State University, located in Jalingo, is no exception, as it faces challenges in identifying students who may be at risk of academic underperformance. By applying data mining techniques to student data, such as academic performance, attendance records, and demographic information, it is possible to develop predictive models that can accurately identify students who need support.
Data mining techniques, such as classification, clustering, and regression, have proven effective in identifying patterns in educational data. In the case of Taraba State University, the application of these techniques could assist in pinpointing factors that contribute to student failure and inform the development of personalized support programs. The goal of this study is to explore how data mining techniques can be applied to identify at-risk students, providing insights into the factors that contribute to academic success and failure at Taraba State University.
Statement of the Problem
At Taraba State University, there is a lack of effective tools for identifying students at risk of academic failure. The university struggles with large amounts of student data and does not have a system in place to analyze this data and detect early warning signs of academic underperformance. As a result, many students who are at risk of failing or dropping out are not identified until it is too late to provide the necessary support. This study aims to address the gap by applying data mining techniques to the university’s student data to improve the identification of at-risk students and facilitate timely academic interventions.
Objectives of the Study
1. To apply data mining techniques to identify at-risk students at Taraba State University.
2. To evaluate the effectiveness of different data mining techniques in predicting academic performance and identifying students at risk of failure.
3. To develop a predictive model that can be used to identify at-risk students and inform academic support strategies at Taraba State University.
Research Questions
1. What data mining techniques are most effective in identifying at-risk students at Taraba State University?
2. How accurate are the predictive models developed using data mining techniques in forecasting student academic performance?
3. How can data mining be used to improve academic support interventions for at-risk students at Taraba State University?
Research Hypotheses
1. Data mining techniques can accurately identify at-risk students at Taraba State University.
2. There is a significant relationship between the predictive models developed using data mining techniques and actual student academic performance.
3. The use of data mining techniques will enhance the effectiveness of academic interventions for at-risk students at Taraba State University.
Significance of the Study
This study will help Taraba State University in identifying at-risk students early and developing appropriate academic interventions. By leveraging data mining techniques, the university can improve student retention rates, reduce dropout rates, and enhance overall academic performance. The findings of this study could also be applied to other higher education institutions facing similar challenges in identifying at-risk students.
Scope and Limitations of the Study
This study will focus on the use of data mining techniques to identify at-risk students at Taraba State University, located in Jalingo LGA, Taraba State. The study will analyze data related to student performance, attendance, and demographics, but will not include external factors such as personal issues or financial challenges. The scope is limited to a specific cohort of students and does not account for broader institutional issues that may impact student performance.
Definitions of Terms
• Data Mining: The process of extracting useful information and patterns from large datasets using computational techniques.
• At-Risk Students: Students who are at a high risk of failing academically or dropping out.
• Predictive Modeling: A data mining technique used to predict future outcomes based on historical data.
• Classification: A data mining technique used to categorize data into predefined groups based on specific attributes.
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