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Journal of Information Science and Engineering, Vol. 34 No. 6, pp. 1405-1423

Application of Particle Swarm Optimization to Create Multiple-Choice Tests

1Faculty of Information Technology, Ho Chi Minh City University of Technology
Ho Chi Minh City, 70000 Vietnam

2Faculty of Information Technology, Nong Lam University
Ho Chi Minh City, 70000 Vietnam

3Department of Computer Science, Faculty of Electrical Engineering and Computer Science
VŠB  Technical University of Ostrava
Ostrava-Poruba, 708 33 Czech Republic

4Division of Data Science, Ton Duc Thang University
Ho Chi Minh City, 70000 Vietnam

5Faculty of Information Technology, Ton Duc Thang University
Ho Chi Minh City, 70000 Vietnam

6Department of Electrical and Computer Engineering
University of Alberta
Edmonton, T6R 2V4 AB Canada

7Department of Electrical and Computer Engineering
Faculty of Engineering
King Abdulaziz University
Jeddah, 21589 Saudi Arabia

8Systems Research Institute
Polish Academy of Sciences
Warsaw, 01-447 Poland

Generating tests from question banks by using manually extracted items or involving random method consumes a great deal of time and effort. At the same time, the quality of the resulting tests is often not high. The generated tests may not entirely meet the requirements formulated in advance. Therefore, this study develops innovative ways to enhance this process by optimizing the execution time and generating results that closely meet the extraction requirements. The paper proposes the use of Particle Swarm Optimization (PSO) to generate multiple-choice tests based on assumed objective levels of difficulty. The experimental results reveal that PSO speed-ups the extraction process, and improves the quality of tests in comparison with the results produced by previously used methods such as Random or Genetic Algorithm (GA) optimized methods. In addition, PSO shows to be more efficient than GA and random selection in most criteria, such as execution time, search space, stability, and standard deviation.

Keywords: test question bank, multiple-choice tests, genetic algorithms, particle swarm optimization, creating tests

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