Multi-Swarm Optimization for Extracting Multiple-Choice Tests From Question Banks

Abstract

In this study, a novel method for generating multiple-choice tests is presented, which extracts the required number of tests of the same levels of difficulty in a single attempt and approximates the difficulty level requirement given by users. We propose an approach using parallelism and Pareto optimization for multi-swarm migration in a particle swarm optimization (PSO) algorithm. Multi-PSO is proposed for shortening the computing time. The proposed migration of PSOs increases the diversity of tests and controls the overlap of extracted tests. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the developed method is shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, a simulated annealing algorithm (SA), random methods and PSO-based approaches in terms of the number of successful solutions, accuracy, standard deviation, search speed, and the number of questions overlapping between the exam questions, as well as for changing the search space, changing the number of individuals, changing the number of swarms, and changing the difficulty requirements. INDEX TERMS multiple-choice tests, multi-swarm optimization, multi-objective optimization, parallelism

Composing Multiple Online Exams: The Bees Algorithm Solution

“…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 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.…”

“…Moreover, for the multiple-exam composition problem specifically, we opted for the BA for two primary reasons. Firstly, despite its popularity and simplicity, the BA has not been previously applied to solve this particular problem, unlike other well-known methods such as Simulated Annealing (SA) [9], the Genetic Algorithm (GA) [10], Particle Swarm Optimization (PSO) [2,11,12], and Ant Colony Optimization (ACO) [13]. Secondly, we believe that the BA possesses an inherent advantage over other methods which directly aligns with the multiple-exam composition problem.…”

“…Specifically, it naturally selects the best and elite solutions as part of its optimization process, thereby readily generating multiple exams (i.e., solutions) in each run. In other words, the BA does not require any additional steps to generate multiple tests simultaneously, as seen in other algorithms, such as the random algorithm [14], SA [9], GA [10], and ACO [13], which involve repeating runs and/or sorting the population to select the best individuals, or incorporating parallelization, as in PSO [2,11,12].…”

Proposing an algorithm for selecting and evaluating the level of questions for Gifted Schools

“…To find the best position for all the swarm particles, the Global Best Position (GP) requires the existence of a Fitness Function for each particle of the swarm and through it. We can know the global GB best position for all of the swarm particles , since the global best position corresponds to the highest value of the efficiency function [3] [6] [8].…”