An Investigation of Data Mining Techniques for Detecting Patterns in Student Learning Behaviors: A Case Study of Federal University, Kashere (Kashere LGA, Gombe State)
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- NGN 5000
Background of the Study
Student learning behaviors can provide valuable insights into academic performance and engagement. Federal University, Kashere, located in Kashere LGA, Gombe State, aims to improve student retention and success by better understanding these behaviors. However, the vast amounts of data collected through online learning platforms and student interactions are often underutilized, and traditional analysis methods are insufficient to identify meaningful patterns.
Data mining techniques, which extract useful information from large datasets, can be applied to analyze student learning behaviors, uncovering patterns that may indicate factors influencing academic performance. This study aims to investigate the use of data mining techniques to identify patterns in student learning behaviors at Federal University, Kashere.
Statement of the Problem
Despite the availability of data on student activities, the university does not currently have an effective system to analyze and interpret this data. Identifying patterns in student learning behaviors can help predict academic outcomes, personalize learning experiences, and improve student support. There is a need to explore data mining techniques that can uncover hidden patterns in student learning behaviors.
Objectives of the Study
1. To apply data mining techniques to analyze student learning behaviors at Federal University, Kashere.
2. To identify key patterns and trends that influence academic performance and engagement.
3. To develop recommendations for improving student learning outcomes based on the findings from the data mining analysis.
Research Questions
1. How can data mining techniques be applied to detect patterns in student learning behaviors?
2. What are the key factors that influence student learning behaviors and performance?
3. How can the insights gained from data mining improve academic support and interventions?
Research Hypotheses
1. Data mining techniques will reveal significant patterns in student learning behaviors that can predict academic performance.
2. Identified patterns will provide insights into factors that influence student success and engagement.
3. The application of data mining will lead to more targeted and effective academic interventions for students at Federal University, Kashere.
Significance of the Study
This study will assist Federal University, Kashere in utilizing data mining to enhance its understanding of student behaviors, leading to more personalized and effective interventions. The findings will also contribute to the growing field of educational data mining and its applications in improving student outcomes.
Scope and Limitations of the Study
The study will focus on the analysis of student learning behaviors at Federal University, Kashere, using data mining techniques. It will not address other aspects of academic performance or broader educational data applications.
Definitions of Terms
• Data Mining: The process of discovering patterns and relationships in large datasets.
• Learning Behaviors: Actions and interactions of students during the learning process, such as time spent on tasks, participation in discussions, and content engagement.
• Educational Data Mining: The application of data mining techniques to educational data to uncover patterns and predict outcomes.
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