# Composing Multiple Online Exams: The Bees Algorithm Solution
## 6 Conclusions  
Automatically generated online exams have garnered significant interest among researchers and educators. However, there is a scarcity of resources that can produce multiple versions of the same exam automatically. Educators require such tools for fair assessments and to prevent cheating during exams. This study aims to contribute to the field of education by providing a method to quickly generate unique and fair exams compared to manual solutions. We propose the utilization of the Bees Algorithm (BA) to compose multiple exams from a question bank, with a focus on achieving the desired difficulty level as the primary objective. Each generated exam also adheres to several constraints defined by the educators to ensure an equitable examination process. The contribution of this research lies in presenting a customized version of the BA, specifically designed to address the problem at hand. Our proposed method enables the generation of multiple online tests simultaneously, effectively meeting user requirements without the need for parallelization techniques. Additionally, we introduce innovative problem-specific neighborhood moves that are designed to adhere to the constraints of the problem. Through extensive testing on benchmark data and comparison with the four rival methods reported in [2], the experimental results indicate that our algorithm outperforms the competing methods in terms of the primary objective, i.e., the difficulty level of the exams, in the majority of test cases. Furthermore, the algorithm exhibits a small standard deviation across all experiments, demonstrating its robustness. However, it has a slightly lower success rate compared to the Particle Swarm Optimization (PSO) methods, although it outperforms the Random and Simulated Annealing (SA) algorithms in this aspect. Furthermore, the BA fell short of attaining the targeted overlap percentage, as assumed in [2]. For future work, we aim to enhance the design of the BA by employing smarter neighborhood moves, particularly when dealing with overlapping questions between exams. Fine-tuning this approach and exploring alternative methods may mitigate the overlap percentage and further improve the algorithm's performance in terms of exam construction. One possible approach is to utilize question selection heuristics instead of randomly selecting replacement questions. An example heuristic is the Least Recently Used (LRU) heuristic, which prioritizes questions that have been used the least number of times in previous exams. By favoring less recently used questions, the LRU heuristic can maximize question diversity and minimize overlap. Additionally, runtime improvements can be achieved through parallelization or other optimization techniques (e.g., adaptive parameter tuning). Moreover, we intend to test the algorithm's scalability by evaluating larger-sized instances. In conclusion, the BA is a metaheuristic algorithm that proves useful in solving our problem. It stands out for its simplicity, adaptability, and ease of use. We believe that our BA competes well with other metaheuristic algorithms that have been successfully applied to this problem. Our research findings hold promise for improving educational outcomes by reducing the workload for instructors, enhancing student satisfaction, and optimizing resource allocation. Conceptualization, M.H. ; methodology, M.H. ; validation, M.H. and R.H.; formal analysis, M.H. ; investigation, M.H. and R.H; resources, M.H. and N.A. ; data curation, M.H and R.H.; writing--original draft preparation, R.H.; writing--review and editing, M.H. and N.A. ; visualization, M.H. ; supervision, M.H. ; project administration, M.H; funding acquisition N.A. All authors have read and agreed to the published version of the manuscript. This work has been funded by King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project number (RSPD2023R857). Not applicable.