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Evaluation of the Role of AI-Powered Recommendation Systems in E-Learning Platforms in Ilorin East Local Government Area, Kwara State

  • Project Research
  • 1-5 Chapters
  • Abstract : Available
  • Table of Content: Available
  • Reference Style:
  • Recommended for :
  • NGN 5000

Background of the Study

In the rapidly evolving field of education, AI-powered recommendation systems have gained prominence for their ability to personalize learning experiences. These systems use algorithms to analyze students' behaviors, learning patterns, and preferences, offering tailored suggestions for content, resources, and activities. In e-learning platforms, AI recommendation systems aim to enhance the learning experience by providing students with relevant learning materials based on their individual needs, strengths, and weaknesses.

In Ilorin East Local Government Area, Kwara State, secondary schools face challenges related to engaging students and ensuring that they receive the most relevant educational content. While e-learning platforms are increasingly being adopted, many of these platforms still lack effective personalization that could enhance learning outcomes. AI-powered recommendation systems can address this gap by analyzing student data to suggest customized resources, which can enhance engagement, improve academic performance, and support self-directed learning.

The application of AI in educational platforms holds significant promise, but there is limited research on its impact in the Nigerian secondary school context. This study aims to evaluate the role of AI-powered recommendation systems in e-learning platforms and their influence on students' learning experiences and academic performance in Ilorin East.

Statement of the Problem

Despite the potential of AI-powered recommendation systems, their effectiveness in e-learning platforms within Ilorin East Local Government Area, Kwara State, has not been sufficiently explored. Many students continue to face challenges in accessing the right educational resources, and personalized learning remains underutilized. This study seeks to assess how AI-powered recommendation systems can contribute to improving learning outcomes.

Objectives of the Study

  1. To evaluate the impact of AI-powered recommendation systems on student engagement in e-learning platforms in Ilorin East Local Government Area.

  2. To assess the influence of AI-powered recommendation systems on the academic performance of secondary school students.

  3. To identify the challenges associated with implementing AI-powered recommendation systems in e-learning platforms.

Research Questions

  1. How do AI-powered recommendation systems impact student engagement in e-learning platforms in Ilorin East Local Government Area?

  2. In what ways do AI-powered recommendation systems influence the academic performance of secondary school students?

  3. What challenges are faced in implementing AI-powered recommendation systems in e-learning platforms?

Research Hypotheses

  1. AI-powered recommendation systems significantly improve student engagement in e-learning platforms in Ilorin East.

  2. The use of AI-powered recommendation systems enhances the academic performance of students.

  3. There are significant challenges in implementing AI-powered recommendation systems in secondary schools in Ilorin East.

Significance of the Study

This study will offer valuable insights into the potential of AI-powered recommendation systems to personalize learning and improve academic outcomes. It will help educators and policymakers understand how to integrate such systems into e-learning platforms to enhance student engagement and performance.

Scope and Limitations of the Study

The study will focus on secondary schools in Ilorin East Local Government Area, Kwara State, and will evaluate the role of AI-powered recommendation systems in e-learning platforms. The limitations include varying access to technology and students' familiarity with AI tools.

Definitions of Terms

  • AI-Powered Recommendation Systems: Algorithms that provide personalized content recommendations based on student data and behavior.

  • E-Learning Platforms: Online platforms that provide educational content and resources to students.

  • Student Engagement: The level of interest, motivation, and involvement a student exhibits while participating in learning activities.


 





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