Development of a Machine Learning Model for Automated Course Recommendation in Ahmadu Bello University, Zaria, Kaduna State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the Study
Automated course recommendation systems powered by machine learning (ML) have been widely adopted in higher education institutions to assist students in selecting courses based on their academic background, preferences, and career goals. Ahmadu Bello University (ABU) in Zaria, Kaduna State, has faced challenges in helping students choose courses that align with their skills and academic progression. With the increasing number of courses and academic paths available, manual course selection becomes cumbersome and prone to errors. By developing a machine learning model, ABU can provide personalized course recommendations to students, enhancing their academic experience and improving their academic performance (Ali & Musa, 2024).
Statement of the Problem
The current course selection process at Ahmadu Bello University lacks personalization, with students often choosing courses that do not align with their strengths or future career plans. This leads to lower academic satisfaction, poor performance, and inefficiencies in course planning. This study seeks to develop a machine learning model to automate and personalize course recommendations for students, offering tailored suggestions based on academic history, interests, and goals.
Objectives of the Study
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To analyze the current course selection process at Ahmadu Bello University.
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To develop a machine learning model that provides automated, personalized course recommendations.
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To evaluate the accuracy and effectiveness of the machine learning model in improving course selection and student outcomes.
Research Questions
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What are the challenges faced by students at Ahmadu Bello University in selecting courses?
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How can machine learning be used to automate course recommendations based on students’ academic data?
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How effective is the machine learning model in enhancing student course selection and academic success?
Research Hypotheses
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A machine learning-based course recommendation system will improve the course selection process at Ahmadu Bello University.
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Personalized course recommendations will lead to better academic outcomes for students.
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There is a significant difference in academic performance between students using the traditional course selection method and those using the machine learning model.
Significance of the Study
The study will contribute to the development of intelligent academic systems in universities, helping students make better-informed decisions about course selection. It will improve academic efficiency, student satisfaction, and retention rates at Ahmadu Bello University.
Scope and Limitations of the Study
The study will focus on the development of a machine learning model for automated course recommendation at Ahmadu Bello University in Zaria, Kaduna State. Limitations include access to historical student data and potential challenges in ensuring the model's effectiveness across various academic disciplines.
Definitions of Terms
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Machine Learning: A type of artificial intelligence that allows systems to learn from data and make predictions or decisions.
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Course Recommendation: The process of suggesting appropriate academic courses to students based on their preferences and performance.
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Academic Progression: The advancement of a student through their academic career, including course selection and completion.
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