Comparative Study of AI-Based and Traditional Methods for Student Learning Analytics: A Case Study of Taraba State University (Jalingo LGA, Taraba State)
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Background of the Study
Learning analytics refers to the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and improve learning outcomes (Sahin et al., 2024). Traditional methods of learning analytics typically involve manual processes of data collection, including attendance records, exam scores, and surveys, which are often fragmented and limited in scope (Bello et al., 2023). These methods have been criticized for their lack of real-time analysis and predictive capabilities, which hinder effective intervention strategies. In contrast, artificial intelligence (AI)-based learning analytics tools use machine learning algorithms to analyze large volumes of student data in real-time, enabling more personalized learning experiences and timely interventions (Oladeji & Adewale, 2025). AI-based systems can predict student performance, identify at-risk students, and provide personalized recommendations to enhance learning outcomes (Ogunleye & Obafemi, 2024).
Taraba State University, located in Jalingo LGA, Taraba State, offers a useful context for comparing the effectiveness of AI-based and traditional learning analytics methods. The university has a growing number of students, and with limited resources, it is crucial for the institution to adopt efficient methods for tracking student progress and improving learning outcomes. AI-based systems could help the university analyze a broader range of data and offer more effective interventions (Musa & Usman, 2023). By comparing AI-based and traditional learning analytics methods, this study aims to assess the strengths and weaknesses of both approaches and provide recommendations for optimizing student learning analytics at the university.
Statement of the Problem
Taraba State University faces challenges in effectively monitoring and improving student performance due to the limitations of traditional learning analytics methods. The reliance on manual data collection and analysis has led to delays in identifying students who may be struggling academically and hindered the timely delivery of interventions (Haruna & Bawa, 2023). As a result, many students fail to achieve their full academic potential. The university lacks an efficient system for personalized learning and performance prediction, which is essential for addressing the diverse needs of students. This study seeks to explore how AI-based learning analytics can complement or replace traditional methods in improving student learning outcomes at the university.
Objectives of the Study
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To compare the effectiveness of AI-based learning analytics with traditional methods in tracking and improving student performance at Taraba State University.
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To evaluate the accuracy and timeliness of interventions based on AI-based and traditional learning analytics methods.
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To identify the key factors influencing the success of AI-based learning analytics systems in a university setting.
Research Questions
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How do AI-based learning analytics methods compare to traditional methods in tracking student performance at Taraba State University?
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What are the benefits and limitations of using AI-based learning analytics systems for personalized interventions?
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How effective are AI-based methods in providing timely academic interventions compared to traditional methods?
Research Hypotheses
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AI-based learning analytics will significantly outperform traditional methods in tracking and improving student performance at Taraba State University.
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Interventions based on AI-based learning analytics will lead to better academic outcomes for students compared to traditional intervention methods.
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AI-based learning analytics will provide more timely and accurate feedback compared to traditional learning analytics methods.
Significance of the Study
This study will offer valuable insights into the comparative effectiveness of AI-based and traditional learning analytics methods. The findings could help Taraba State University and other Nigerian universities enhance student performance tracking, provide better personalized learning experiences, and optimize academic interventions.
Scope and Limitations of the Study
The study will focus on comparing AI-based and traditional learning analytics methods at Taraba State University, Jalingo, Taraba State. The study will primarily focus on undergraduate students, and the data used for the analysis will be obtained from the university’s learning management systems and student performance records.
Definitions of Terms
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Learning Analytics: The process of collecting, analyzing, and interpreting student data to improve learning outcomes.
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AI-Based Learning Analytics: The use of AI and machine learning algorithms to analyze student data and provide insights into student performance.
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Traditional Learning Analytics: Manual methods of tracking and analyzing student performance based on data such as grades, attendance, and surveys.
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