An Investigation of AI-Based Student Performance Analytics in Federal Polytechnic Kaura Namoda, Zamfara State
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- NGN 5000
Background of the Study
Accurate student performance analytics are essential for understanding learning outcomes and improving academic programs. At Federal Polytechnic Kaura Namoda in Zamfara State, traditional assessment methods are often insufficient for providing deep insights into student performance trends. The emergence of artificial intelligence (AI) offers advanced techniques to analyze complex educational data and generate actionable insights. AI-based analytics can process diverse datasets—including grades, attendance records, and behavioral indicators—to identify patterns and predict future academic performance (Olufemi, 2023). Machine learning algorithms such as decision trees, neural networks, and clustering methods can segment students based on performance levels, highlighting at-risk groups and enabling personalized interventions (Ibrahim, 2024). These systems offer the advantage of continuous learning and adaptation, meaning that as more data is collected, the predictive accuracy improves. Moreover, AI-driven performance analytics can integrate real-time data streams, providing educators with up-to-date information that informs curriculum adjustments and teaching strategies. The use of such technology also enhances transparency in academic evaluations and supports evidence-based decision-making at the institutional level. However, challenges related to data quality, privacy, and the need for substantial computational resources persist. This study aims to investigate the implementation of AI-based student performance analytics at Federal Polytechnic Kaura Namoda, evaluate its effectiveness in predicting academic outcomes, and determine how it can be integrated into existing academic support frameworks to enhance student success (Chinwe, 2025).
Statement of the Problem
Federal Polytechnic Kaura Namoda currently faces challenges in effectively monitoring and predicting student performance due to the limitations of conventional assessment methods, which are often manual and time-consuming. This traditional approach fails to capture the complexity of factors that influence academic success, leading to delayed interventions for underperforming students (Adebola, 2023). The absence of an AI-driven analytics system means that critical patterns in student data remain undetected, thereby impeding efforts to implement timely and personalized support strategies. Furthermore, inconsistencies in data collection and record-keeping exacerbate these issues, resulting in unreliable performance indicators. Without advanced analytics, faculty and administrators are unable to make informed decisions regarding curriculum adjustments or resource allocation, which ultimately affects overall academic outcomes. This study seeks to address these challenges by developing an AI-based analytics framework that processes and analyzes student performance data to identify at-risk students and forecast future academic trends. By comparing various AI algorithms and validating their predictive power, the research aims to establish a robust model that can be integrated into the institution’s academic management system, thereby enhancing the effectiveness of intervention strategies and improving student success rates.
Objectives of the Study:
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To develop an AI-based framework for analyzing student performance.
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To evaluate the predictive accuracy of different AI algorithms in forecasting academic outcomes.
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To recommend strategies for integrating AI analytics into academic support systems.
Research Questions:
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How effective is AI in predicting student performance at the polytechnic?
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Which AI techniques yield the highest predictive accuracy?
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How can the integration of AI analytics improve intervention strategies for at-risk students?
Significance of the Study
This study is significant as it leverages AI to provide advanced performance analytics, offering a data-driven approach to enhance academic support at Federal Polytechnic Kaura Namoda. The predictive insights generated will help educators implement timely interventions, thereby improving student outcomes and overall institutional performance. The research contributes to the broader field of educational analytics, demonstrating the potential of AI to transform traditional assessment systems (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the use of AI-based performance analytics in Federal Polytechnic Kaura Namoda, Zamfara State, and does not extend to other polytechnics or higher education institutions.
Definitions of Terms:
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Artificial Intelligence (AI): The simulation of human intelligence processes by computer systems.
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Performance Analytics: The systematic analysis of data to evaluate and predict student academic outcomes.
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Predictive Modeling: The use of statistical techniques to forecast future events based on historical data.
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