Design of AI-Based Student Course Load Balancing Systems in Kano State Polytechnic, Kano State
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- NGN 5000
Background of the Study
Course load balancing is an essential aspect of academic planning in higher education institutions, particularly in polytechnics. Students are often required to select courses based on their individual study plans, but they may face difficulties in managing their course load due to overlapping schedules, excessive credit hours, or the complexity of their academic programs. Traditional course registration systems do not provide intelligent recommendations that can help students balance their course load efficiently. An AI-based student course load balancing system can optimize the allocation of courses by analyzing students' past performance, course difficulty, and time constraints, suggesting an optimal course schedule. This system can help students manage their academic workload more effectively, reducing stress and improving academic success. Kano State Polytechnic faces challenges in course load management, and this study aims to develop an AI-powered system to address these issues.
Statement of the Problem
At Kano State Polytechnic, students often struggle with balancing their course load, leading to issues such as overloading or underloading, which can impact their academic performance and well-being. The traditional registration system does not provide personalized recommendations for students to manage their course load, nor does it account for factors such as academic performance, prerequisite requirements, and personal schedules. This lack of intelligent course load balancing results in inefficiencies and may contribute to student stress and lower academic success. An AI-based system can provide personalized course load recommendations that optimize students' schedules, ensuring a balanced workload that is conducive to academic achievement.
Objectives of the Study
1. To design and develop an AI-based student course load balancing system for Kano State Polytechnic.
2. To evaluate the effectiveness of the AI-based system in helping students balance their course load more efficiently.
3. To assess the impact of the AI-based course load balancing system on student performance and well-being.
Research Questions
1. How effective is the AI-based course load balancing system in helping students at Kano State Polytechnic balance their academic workload?
2. What factors are considered by the AI system when recommending optimal course loads to students?
3. How does the AI-based system affect students' academic performance and stress levels?
Research Hypotheses
1. The AI-based course load balancing system will lead to more balanced course loads for students compared to traditional methods.
2. The AI system will improve students' academic performance by optimizing their course schedules.
3. Students using the AI-based system will experience lower levels of academic stress due to better course load management.
Significance of the Study
This study will contribute to the development of AI-based solutions for course load management in higher education. The findings can provide valuable insights for other institutions seeking to implement similar systems, ultimately enhancing students' academic performance and well-being.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of an AI-based student course load balancing system at Kano State Polytechnic. Limitations include potential challenges in integrating the AI system with existing infrastructure and the availability of data for training the system.
Definitions of Terms
• AI-Based Course Load Balancing: A system that uses artificial intelligence algorithms to recommend an optimal course load for students based on various factors such as past performance, course difficulty, and scheduling constraints.
• Academic Performance: A measure of students’ academic achievements, typically assessed through grades and exams.
• Course Load: The number of academic credits or courses a student is enrolled in during a semester.
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