Design of an Automated Course Allocation System for Lecturers: A Case Study of Federal University, Kashere, Akko LGA
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Background of the Study
Efficient course allocation is crucial for balancing faculty workload and ensuring optimal teaching outcomes in higher education. At Federal University, Kashere, Akko LGA, an automated course allocation system is being designed to streamline the assignment of courses to lecturers. Traditional manual allocation methods are often subjective, time‑consuming, and prone to errors that lead to imbalanced workloads and suboptimal course coverage. The proposed system leverages advanced algorithms and data analytics to match lecturers’ expertise, teaching history, and departmental needs with course requirements (Musa, 2023; Okafor, 2024). By integrating real‑time data from academic records, departmental schedules, and lecturer preferences, the system aims to generate equitable and efficient course assignments. This digital solution also incorporates predictive analytics to forecast future course demands and adjust allocations accordingly, thereby enhancing long‑term academic planning. The system’s design emphasizes user‑friendliness, transparency, and scalability, ensuring that it can adapt to changes in academic programs and faculty composition. Moreover, the automated process minimizes human bias and administrative workload, allowing academic leaders to focus on strategic decision‑making rather than routine tasks. Despite these advantages, the implementation of an automated course allocation system poses challenges such as data standardization, integration with existing administrative systems, and the need for continuous updates based on evolving academic policies. Pilot projects in similar institutions have demonstrated significant improvements in workload balance and resource utilization (Chinwe, 2025). This study examines how the system can be effectively designed, implemented, and evaluated at Federal University, Kashere, with the goal of improving academic efficiency and lecturer satisfaction.
Statement of the Problem
The current course allocation process at Federal University, Kashere, is heavily reliant on manual methods that lead to inefficient and often inequitable assignment of courses. This traditional approach is characterized by lengthy administrative procedures, subjective decision‑making, and an increased risk of workload imbalances among lecturers. The lack of automation in the allocation process results in delayed scheduling, misaligned teaching assignments, and inadequate matching of lecturer expertise with course content. Although an automated course allocation system offers a promising alternative, its implementation faces significant challenges. These include issues of data inconsistency, integration with legacy systems, and resistance from staff who are accustomed to traditional processes. Concerns about the transparency and fairness of algorithm‑based decisions further complicate adoption. Additionally, the dynamic nature of academic schedules and changing departmental needs require a flexible system capable of real‑time adjustments. This study aims to address these challenges by designing and evaluating an automated course allocation system that leverages data analytics and machine learning. The research will assess the system’s effectiveness in improving allocation accuracy, reducing administrative burdens, and enhancing overall lecturer satisfaction. By comparing the automated approach with existing manual methods, the study will identify key areas for improvement and propose strategies to overcome technical and operational barriers, ultimately ensuring a more balanced and efficient course allocation process (Okafor, 2024).
Objectives of the Study
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To design and implement an automated course allocation system that matches lecturer expertise with course requirements.
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To evaluate the system’s performance in terms of accuracy and efficiency.
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To propose strategies for overcoming integration and data standardization challenges.
Research Questions -
How does the automated system improve course allocation compared to manual methods?
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What technical issues hinder the system’s performance?
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Which measures can enhance fairness and user acceptance?
Significance of the Study
This study is significant as it addresses inefficiencies in course allocation at Federal University, Kashere by implementing an automated system that enhances fairness, reduces administrative workload, and improves lecturer satisfaction. The insights gained will aid academic administrators in adopting data‑driven solutions to optimize teaching assignments and overall academic planning (Musa, 2023).
Scope and Limitations of the Study
This study is limited to the design and implementation of an automated course allocation system at Federal University, Kashere, Akko LGA.
Definitions of Terms -
Course Allocation System: A digital platform that assigns courses to lecturers based on predetermined criteria.
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Automated System: A technology‑driven process that operates with minimal human intervention.
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Data Standardization: The process of ensuring consistency in data formats across different systems.
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