# Composing Multiple Online Exams: The Bees Algorithm Solution
## 1 Introduction  
Over the past few years, the education system has experienced a significant transformation, primarily attributable to the impact of the COVID-19 pandemic. The shift towards online exams has been motivated by the imperative to prioritize the safety and well-being of both students and staff, while also ensuring the uninterrupted progression of education. Online exams provide students with a secure environment in comparison to traditional modes of examination. In general, from an instructor standpoint, multiple-choice questions (MCQ) can be advantageous with respect to the ease of scoring, perceived objectivity in grading, fast return of scores in large classes, and the capacity to ask more questions [1, 2]. The process of selecting exam questions, though, can be time-consuming, particularly when multiple exam models are necessary. Furthermore, when exams are administered to a large number of students at different times, it becomes essential to have a question bank that encompasses a considerable number of questions. Question banks facilitatethe inclusion of questions in the exam at different intervals, offering a variety of selection criteria. This approach enhances the diversity of the generated exams. However, a challenge faced in both automatic and manual online exam generation is the question bank's size. To ensure exam variety and minimize repetition, having a question bank larger than the total number of questions required is generally recommended [3; 4]. While there is no specific guideline regarding the exact question bank size in relation to the number of questions needed, educational experts and researchers suggest having a substantial number of questions available. A larger question bank offers broader coverage of the curriculum and ensures adequate assessment of all important topics [5; 6]. The specific question bank size will depend on factors such as the subject matter, questions' difficulty level, and desired variety in the exams [3; 4]. Generating various exam models from a question bank can be accomplished through manual or automated methods. The manual process is typically intricate and time-consuming, while employing an algorithm to automate the task allows for precise adherence to specific criteria instead of random question selection. This approach guarantees that the exam models maintain consistent difficulty levels and meet the required constraints. The multiple-exam composition problem has recently attracted the attention of many researchers [2]. The problem at hand involves the automated generation of multiple MCQ exam papers or test sheets. These test sheets cover the same subject matter and maintain an identical or nearly identical difficulty level, ensuring a fair assessment process. It is crucial that the difficulty levels of the generated exams align with the desired test level established by the educator. Furthermore, to promote diversity and mitigate the potential for cheating, the overlap between the different tests, in terms of identical questions, should not exceed a specified percentage. In this study, we propose a method to simultaneously create multiple forms of MCQ exams while adhering to specific criteria, including the difficulty level, avoidance of question repetition, and a predetermined number of questions in each section. Given that traditional search methods that exhaustively examine all feasible solutions are often impractical and time-consuming for larger problem sizes, we introduce a metaheuristic algorithm as a solution, namely, the Bees Algorithm (BA) [7]. In brief, the concept of the BA revolves around a set of initial solutions. The algorithm then focuses on exploiting promising and elite solutions by making modifications to enhance the likelihood of discovering improved solutions. The ultimate objective is to achieve the optimal or near-optimal solution through these iterative refinements [8].