Abstract
Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generatingktests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.
Highlights
In the education sector, evaluation of students’ study progress is important and mandatory. ere are many methods such as oral tests or writing tests to evaluate their knowledge and understanding about subjects
The process of finding new solutions using Simulated Annealing (SA) is improved by moving Pbest to Gbest using the received information about the location of Gbest. e Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) with SA is described by a pseudocode in Algorithm 2
E smaller standard deviation values and smaller average fitness values mean that we less likely need to rerun the MMPSO with SA algorithms many times to get the generated tests that better fit the test requirements. e reason is that the generated tests we obtain at the first run are likely close to the requirements and the chance that we obtain those generated tests with less fit to requirements is low
Summary
Evaluation of students’ study progress is important and mandatory. ere are many methods such as oral tests or writing tests to evaluate their knowledge and understanding about subjects. Many researchers have invested their efforts to make computers automate the process of creating multiple-choice tests using available question banks, as in the work of Cheng et al [2]. Bui et al [3] proposed the use of particle swarm optimization to generate tests with approximating difficulties to the required levels from users. Previous works only focused on solving a single objective of the extracting test based on the difficulty level requirement of the user defined. We propose a new approach that uses Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) to extract k tests in a single run with multiple objectives. Correlated studies of normal multiswarm multiobjective PSO and multiswarm multiobjective PSO with SA for the problem of extracting k tests from question banks are presented in Sections 4 and 5.
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