Enhancing Online Learning Platforms with AI-Powered Recommendation Systems: A Case Study of Federal University, Birnin Kebbi (Birnin Kebbi LGA, Kebbi State)
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- NGN 5000
Background of the Study
With the growing popularity of online learning platforms, there is an increasing need to provide students with personalized content that matches their learning pace and preferences. AI-powered recommendation systems can help students navigate through vast amounts of educational content by offering tailored suggestions based on their interests, past interactions, and learning needs. This study explores the use of AI-based recommendation systems to enhance online learning platforms at the Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State.
Statement of the Problem
While online learning platforms offer a wide variety of courses and materials, students often struggle to find relevant content, leading to disengagement and poor learning outcomes. AI-powered recommendation systems can address this challenge by personalizing content recommendations. However, their effectiveness in the context of Nigerian universities, such as Federal University, Birnin Kebbi, has not been fully investigated.
Objectives of the Study
1. To evaluate the effectiveness of AI-powered recommendation systems in enhancing online learning experiences at Federal University, Birnin Kebbi.
2. To analyze the impact of personalized content recommendations on student engagement, retention, and academic performance.
3. To identify the challenges associated with implementing AI-powered recommendation systems in online learning platforms.
Research Questions
1. How effective are AI-powered recommendation systems in providing personalized learning content for students at Federal University, Birnin Kebbi?
2. What impact does personalized content have on student engagement, retention, and academic performance in online learning platforms?
3. What challenges are involved in integrating AI-based recommendation systems into online learning platforms at Federal University, Birnin Kebbi?
Research Hypotheses
1. AI-powered recommendation systems will enhance the personalization of learning content and improve student engagement and retention.
2. Students who receive personalized content recommendations will perform better academically compared to those who follow generic course suggestions.
3. Challenges in implementing AI-based recommendation systems will include issues related to data privacy, system integration, and infrastructure limitations.
Significance of the Study
This study will provide valuable insights into the role of AI-powered recommendation systems in improving online learning experiences. The findings will contribute to enhancing the use of personalized learning pathways in Nigerian universities, thereby improving student outcomes and satisfaction.
Scope and Limitations of the Study
The study will focus on AI-powered recommendation systems specifically for online learning platforms at Federal University, Birnin Kebbi. Limitations include the need for adequate data to train the recommendation algorithms and potential resistance to technology adoption by faculty and students.
Definitions of Terms
• Artificial Intelligence (AI): Technology that enables machines to simulate human-like intelligence.
• Recommendation Systems: AI algorithms designed to suggest personalized content based on users' preferences and behaviors.
• Online Learning Platforms: Digital platforms where students access educational content and resources for learning.
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