Optimization of AI-Based Educational Data Mining Techniques for Student Performance Analysis in Kano State Polytechnic, Kano State
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- NGN 5000
Background of the Study
Educational data mining is the process of analyzing large datasets from educational systems to extract useful information and patterns that can improve learning outcomes and decision-making. AI-based educational data mining techniques leverage machine learning and data mining algorithms to analyze students' academic data, identifying patterns and factors that contribute to their performance. By applying AI to educational data, institutions can gain insights into student behavior, identify at-risk students, and develop strategies for improving overall academic success.
Kano State Polytechnic, located in Kano State, is one of the major technical institutions in Nigeria. However, the institution faces challenges in utilizing its student performance data to inform decision-making. AI-based educational data mining techniques could optimize the analysis of this data, providing valuable insights that can guide interventions aimed at improving student outcomes. This study seeks to explore the potential of AI-based educational data mining to enhance student performance analysis at Kano State Polytechnic.
Statement of the Problem
At Kano State Polytechnic, there is a large volume of student data that is not fully utilized for analyzing academic performance or improving teaching strategies. Traditional methods of performance analysis are limited in scope and may not effectively identify students at risk of failure or provide actionable insights. AI-based data mining techniques offer the potential to uncover hidden patterns in student performance data, enabling the institution to develop more targeted interventions. However, the application of AI in educational data mining within the polytechnic context has not been adequately explored.
Objectives of the Study
1. To apply AI-based educational data mining techniques to analyze student performance at Kano State Polytechnic.
2. To evaluate the effectiveness of AI-based data mining in identifying factors influencing student performance.
3. To explore the challenges and benefits of implementing AI-based educational data mining techniques in Nigerian polytechnics.
Research Questions
1. How effective are AI-based educational data mining techniques in identifying factors influencing student performance at Kano State Polytechnic?
2. What are the perceptions of students and instructors regarding the insights generated from AI-based data mining?
3. What challenges are encountered in implementing AI-based educational data mining techniques in polytechnic institutions?
Research Hypotheses
1. AI-based educational data mining techniques provide more accurate insights into student performance compared to traditional methods.
2. Students and instructors perceive the results from AI-based data mining as valuable for improving academic outcomes.
3. The implementation of AI-based data mining techniques faces challenges related to data quality, system integration, and staff training.
Significance of the Study
This study will contribute to the optimization of educational performance analysis at Kano State Polytechnic by utilizing AI-based data mining techniques. The insights gained from the study will help the institution improve academic outcomes, support at-risk students, and enhance the overall quality of education.
Scope and Limitations of the Study
The study will focus on the application of AI-based educational data mining techniques at Kano State Polytechnic. Limitations include challenges related to data quality, privacy concerns, and the integration of AI systems with existing institutional data management frameworks.
Definitions of Terms
• AI-Based Educational Data Mining: The application of machine learning and data mining algorithms to analyze educational data and extract insights about student performance.
• Student Performance Analysis: The process of evaluating students' academic outcomes based on various factors, such as grades, attendance, and participation.
• Data Mining: The process of discovering patterns and knowledge from large datasets using statistical and computational techniques.
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