Implementation of AI-Based Learning Analytics for Student Performance Prediction in Federal University Gashua, Yobe State
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- NGN 5000
Background of the Study
Predicting student performance is essential for facilitating timely interventions and improving academic outcomes. At Federal University Gashua, Yobe State, traditional methods of performance assessment are often reactive and provide limited insights into individual student progress. The advent of AI-based learning analytics has introduced new possibilities for real-time performance prediction by leveraging machine learning algorithms and advanced statistical techniques to analyze data from various academic activities (Olu, 2023). These AI-based systems integrate information from online assessments, classroom interactions, and digital learning platforms to generate continuous insights into student performance. By identifying patterns and anomalies in learning behaviors, the system can predict which students are likely to underperform, thereby enabling proactive measures and personalized support (Adebayo, 2024). Furthermore, the adaptive nature of AI models ensures that the prediction system evolves as new data becomes available, enhancing its accuracy over time. However, the implementation of such systems is challenged by issues related to data integration, the quality and consistency of input data, and concerns over student data privacy (Balogun, 2025). Despite these challenges, AI-based learning analytics holds significant promise for transforming student support and academic planning. This study aims to evaluate the effectiveness of an AI-based learning analytics system in predicting student performance, comparing its predictive accuracy with traditional methods, and providing recommendations for improving data integration and privacy safeguards.
Statement of the Problem
Federal University Gashua currently faces challenges in accurately predicting student performance using traditional methods that rely on periodic assessments and manual data analysis (Olu, 2023). These conventional approaches are reactive, often leading to delayed interventions for students at risk of underperformance. While AI-based learning analytics offers the potential for real-time, continuous performance monitoring, its implementation is constrained by several obstacles. Key issues include the integration of diverse data sources from online learning platforms and classroom assessments, the variability in data quality, and significant concerns regarding the privacy of student information (Adebayo, 2024). Additionally, there is skepticism among educators about the transparency and reliability of AI algorithms in predicting academic outcomes. The lack of a standardized framework for evaluating these systems further exacerbates the problem, making it difficult to gauge their effectiveness compared to traditional methods. Without a robust system in place, the university risks missing critical opportunities to intervene and support students, potentially leading to poorer academic performance and higher dropout rates. This study seeks to address these challenges by developing and validating an AI-based system for real-time student performance prediction, thereby enabling timely and effective academic interventions (Balogun, 2025).
Objectives of the Study:
- To design an AI-based learning analytics system for real-time performance prediction.
- To compare the system’s predictive accuracy with traditional assessment methods.
- To propose strategies for improving data integration and ensuring data privacy.
Research Questions:
- How effective is the AI-based system in predicting student performance in real time?
- What limitations exist in traditional performance monitoring methods?
- How can data privacy and integration challenges be addressed in AI-based learning analytics?
Significance of the Study
This study is significant as it explores the application of AI-based learning analytics to predict student performance, enabling timely interventions and enhancing academic outcomes at Federal University Gashua. The findings will offer valuable insights into integrating advanced analytics into academic monitoring systems, ultimately contributing to improved student support and educational quality (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of student performance prediction systems at Federal University Gashua, Yobe State.
Definitions of Terms:
• AI-Based Learning Analytics: The use of AI to analyze educational data for insights into student performance (Adebayo, 2024).
• Real-Time Performance Prediction: The continuous forecasting of academic outcomes as data is generated (Olu, 2023).
• Data Integration: The process of combining data from multiple sources into a unified system (Balogun, 2025).
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