Analysis of Deep Reinforcement Learning for Optimizing University Course Registration: A Case Study of Kano State University of Science and Technology (Wudil LGA, Kano State)
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Background of the Study
Course registration in universities is a critical process that impacts both students' academic progression and the university's resource allocation. At Kano State University of Science and Technology, Wudil LGA, Kano State, the course registration process is often inefficient, with students facing difficulties in selecting courses, scheduling conflicts, and the overbooking of popular courses. These issues result in frustration, delays in graduation, and underutilization of resources.
Deep reinforcement learning (DRL), a subfield of machine learning, can be applied to optimize course registration systems by learning the best actions based on students' preferences and available resources. DRL models learn by interacting with the environment, receiving feedback, and adjusting their strategies to maximize long-term rewards. By using DRL, the university can dynamically allocate courses, manage student choices, and avoid scheduling conflicts, ensuring that students' needs are met while optimizing resource usage.
Statement of the Problem
The current course registration system at Kano State University of Science and Technology does not efficiently manage the allocation of courses, leading to issues such as overbooked courses, scheduling conflicts, and a lack of personalized course recommendations for students. There is a need for an AI-driven system that can optimize course selection and allocation, improving the overall student experience and resource utilization.
Objectives of the Study
1. To analyze the application of deep reinforcement learning in optimizing the course registration process at Kano State University of Science and Technology.
2. To develop a deep reinforcement learning model to recommend optimal course schedules for students while minimizing scheduling conflicts.
3. To evaluate the effectiveness of the DRL-based system in improving course allocation and student satisfaction during the registration process.
Research Questions
1. How can deep reinforcement learning be applied to optimize the course registration process at Kano State University of Science and Technology?
2. What impact will the DRL-based system have on minimizing course scheduling conflicts and optimizing course allocations?
3. How do students perceive the effectiveness of the AI-based course registration system compared to the traditional method?
Research Hypotheses
1. The deep reinforcement learning model will significantly reduce course scheduling conflicts and improve course allocation efficiency at Kano State University of Science and Technology.
2. The implementation of the DRL-based system will lead to higher student satisfaction in the course registration process.
3. The DRL-based system will perform better than traditional registration methods in terms of resource utilization and efficiency.
Significance of the Study
This study will provide valuable insights into the application of deep reinforcement learning for optimizing course registration systems in universities. The findings will benefit Kano State University of Science and Technology by enhancing the efficiency of course allocation, reducing scheduling conflicts, and improving the overall registration experience for students. The research may also serve as a model for other institutions looking to implement AI-driven systems for academic administration.
Scope and Limitations of the Study
The study will focus on the development and evaluation of a deep reinforcement learning model for optimizing the course registration process at Kano State University of Science and Technology, located in Wudil LGA, Kano State. The scope of the study is limited to the course registration system and does not address other academic administrative functions.
Definitions of Terms
• Deep Reinforcement Learning (DRL): A machine learning technique that enables systems to learn optimal actions through interaction with the environment and feedback to maximize cumulative rewards.
• Course Registration: The process by which students select and enroll in courses for an academic term.
• Scheduling Conflicts: Instances where students are unable to enroll in courses due to overlapping time slots or limited course availability.
• Resource Allocation: The process of assigning resources, such as classrooms, instructors, and course slots, to meet the demands of students.
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