Enhancing Learning Outcome Predictions Using AI-Based Data Analytics in Modibbo Adama University, Yola, Adamawa State
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- NGN 5000
Background of the Study
Learning outcome predictions are fundamental in shaping educational strategies and improving student performance. At Modibbo Adama University, Yola, Adamawa State, the integration of AI-based data analytics provides a cutting-edge approach to forecast academic achievements and identify at-risk students. Traditional methods of predicting learning outcomes rely on periodic assessments and historical trends, which often fail to capture the dynamic nature of student learning. AI-based analytics, on the other hand, utilize machine learning algorithms to process diverse data sets including grades, attendance records, engagement metrics, and demographic information, providing more accurate and personalized predictions (Oluwaseun, 2023). These systems can identify complex patterns and correlations that are not evident through conventional statistical methods, allowing for real-time interventions and tailored instructional strategies (Ibrahim, 2024). The predictive models developed through AI-based analytics also facilitate proactive planning by enabling educators to simulate various scenarios and assess the potential impact of different teaching methodologies. By continuously updating the predictive model with new data, the system adapts to changes in student performance over time, thereby enhancing its accuracy. Additionally, the use of interactive dashboards enables administrators to visualize key performance indicators and make data-driven decisions regarding resource allocation and curriculum adjustments. This approach aligns with global trends in educational data analytics, which emphasize the importance of personalized learning and early intervention. However, challenges such as data quality, integration issues, and the need for technical expertise remain significant obstacles. This study aims to evaluate the effectiveness of AI-based data analytics in predicting learning outcomes, ultimately contributing to improved academic performance and more efficient educational planning (Chinwe, 2025).
Statement of the Problem
Modibbo Adama University currently faces challenges in accurately predicting student performance due to the limitations of traditional evaluation methods. Conventional techniques rely heavily on historical data and periodic assessments, which do not account for the dynamic variables influencing student learning (Abdullahi, 2023). This reactive approach often results in delayed interventions and suboptimal educational strategies. Furthermore, the lack of an integrated data analytics system hinders the ability to monitor and predict learning outcomes in real time. Inaccurate predictions contribute to inefficient resource allocation and inadequate support for struggling students. Additionally, issues such as incomplete data records, inconsistencies in measurement, and the absence of sophisticated analytical tools further exacerbate these challenges. As a result, educators are unable to tailor their teaching methods to meet individual student needs effectively. This study seeks to address these issues by employing AI-based data analytics to develop a predictive model that accurately forecasts learning outcomes. By integrating various data sources and utilizing advanced machine learning algorithms, the research aims to identify critical predictors of academic success and provide actionable insights for early intervention. The ultimate goal is to enhance student performance through personalized learning plans and timely support, thereby improving overall educational outcomes at Modibbo Adama University.
Objectives of the Study:
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To develop an AI-based predictive model for learning outcomes using diverse student data.
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To evaluate the model’s accuracy and its impact on early intervention strategies.
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To propose recommendations for integrating AI analytics into educational planning.
Research Questions:
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How effectively can AI-based data analytics predict learning outcomes?
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What key factors significantly influence student academic performance?
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How can the predictive model be integrated into current educational planning to enhance student support?
Significance of the Study
This study is significant as it explores the use of AI-based data analytics to improve learning outcome predictions at Modibbo Adama University. The research will enable more precise forecasting of student performance, facilitating early interventions and personalized learning strategies that can enhance academic success. The findings provide critical insights for educators and policymakers, contributing to the digital transformation of educational planning and resource allocation (Oluwaseun, 2023).
Scope and Limitations of the Study:
The study is limited to the application of AI-based data analytics for predicting learning outcomes at Modibbo Adama University, Yola, Adamawa State, and does not extend to other academic institutions or subjects.
Definitions of Terms:
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AI-Based Data Analytics: The use of artificial intelligence and machine learning techniques to analyze and predict trends based on large datasets.
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Learning Outcome: The measurable educational results that indicate student achievement.
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Predictive Model: A mathematical model used to forecast future events based on historical data.
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