Optimization of E-Learning Platforms Using Machine Learning: A Case Study of Usmanu Danfodiyo University, Sokoto (Wamako LGA, Sokoto State)
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Background of the Study
E-learning platforms have gained significant traction in higher education, particularly with the increasing shift toward digital learning environments (Alzoubi & O’Toole, 2024). However, many educational institutions struggle to optimize these platforms for efficient content delivery, learner engagement, and real-time performance feedback (Musa & Akinyele, 2023). Usmanu Danfodiyo University, Sokoto, located in Wamako LGA, Sokoto State, is one of the institutions using e-learning platforms to facilitate learning. The application of machine learning (ML) to optimize these platforms can enhance their functionality by providing personalized learning experiences, improving course recommendations, predicting student performance, and automating administrative tasks (Sana et al., 2025). ML can analyze large amounts of data, such as student interactions and course completion rates, to make accurate predictions and improve user experience.
Statement of the Problem
The e-learning platforms at Usmanu Danfodiyo University, Sokoto, while functional, face several issues such as low user engagement, inefficient course recommendations, and poor personalization of learning materials. The challenge lies in enhancing the platform’s ability to adapt to the needs of individual learners, especially in terms of content delivery, personalized support, and real-time feedback. Machine learning has the potential to address these limitations and optimize platform performance for better student learning outcomes.
Objectives of the Study
- To design a machine learning model for optimizing content delivery on e-learning platforms at Usmanu Danfodiyo University, Sokoto.
- To evaluate the effectiveness of the machine learning model in improving student engagement and course completion rates.
- To assess the impact of personalized learning recommendations on student performance and satisfaction.
Research Questions
- How can machine learning be used to optimize content delivery on e-learning platforms at Usmanu Danfodiyo University, Sokoto?
- What impact does the use of machine learning for personalized learning have on student engagement and course completion rates?
- How does machine learning-based course recommendation influence students' learning performance and satisfaction?
Research Hypotheses
- Machine learning-based optimization will significantly improve content delivery and student engagement on e-learning platforms.
- Personalized learning recommendations through machine learning will lead to higher student performance and satisfaction.
- The integration of machine learning into the e-learning platform will increase the rate of course completion among students.
Significance of the Study
This study aims to contribute to the improvement of e-learning systems at Usmanu Danfodiyo University, Sokoto, by applying machine learning techniques to personalize and optimize learning experiences. The findings can inform better pedagogical strategies, enhance platform usability, and provide a framework for other institutions looking to integrate AI in their digital education systems.
Scope and Limitations of the Study
The study will focus on optimizing the e-learning platform at Usmanu Danfodiyo University, Sokoto, specifically in Wamako LGA. The study will primarily look into machine learning applications for content delivery, course recommendation, and student engagement. It will not extend to non-digital teaching methods or other areas of university management.
Definitions of Terms
E-Learning Platform: A digital platform used for delivering educational content and facilitating communication between students and educators.
Machine Learning (ML): A subset of artificial intelligence (AI) that enables systems to learn from data and improve their performance without explicit programming.
Personalized Learning: Tailoring educational content and experiences to the individual needs, skills, and interests of each student.
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