Journal Article10.3758/s13428-023-02319-7
Ant colony optimization for parallel test assembly.
Luc Zimny,Ulrich Schroeders,O. Wilhelm +2 more
5
TL;DR: This article assembled three 12-item tests that adequately cover the construct at the domain level, follow a unidimensional measurement model, allow reliable and precise measurement of factual knowledge, and are gender-fair.
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Abstract: Abstract Ant colony optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to construct multiple parallel short scales that adhere to several competing and interacting criteria simultaneously. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at the domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of factual knowledge, and (e) are gender-fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions to metaheuristic test assembly and the equivalence of parallel knowledge tests in general.
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Citations
Is It Just a Game?
Ulrich Schroeders,Ariane Loos,Sebastian Wiedemann,Kristin Jankowsky +3 more
TL;DR: Researchers developed a deductive reasoning test based on Mastermind, finding it to be a reliable and efficient measure of reasoning, with item difficulty predicted by an entropy-based index, and related to other cognitive abilities and need for cognition.
Numerical Harmonic Modelling of Low Wattage LED Lamps Based on Parameter Estimation Algorithms
Hasan A. Abdulmajeed Salbi,Fahad Al-Amyal,Kiss Péter +2 more
The ACO-BmTSP to Distribute Meals Among the Elderly
Sílvia de Castro Pereira,Eduardo J. Solteiro Pires +1 more
Abstract: The aging of the Portuguese population is a multifaceted challenge that requires a coordinated and comprehensive response from society. In this context, social service institutions play a fundamental role in providing aid and support to the elderly, ensuring that they can enjoy a dignified and fulfilling life even in the face of the challenges of aging. This research proposes a Balanced Multiple Traveling Salesman Problem based on the Ant Colony Optimization algorithm (ACO-BmTSP) to solve a distribution of meals problem in the municipality of Mogadouro, Portugal. The Multiple Traveling Salesman Problem (mTSP) is an NP-complete problem where m salesmen perform a shortest tour between different cities, visiting each only once. The primary purpose is to minimize the sum of all distance traveled by all salesmen keeping the tours balanced. This paper shows the results of computing obtained for three, four, and five agents with this new approach and their comparison with other approaches like the standard Particle Swarm Optimization and Ant Colony Optimization algorithms. As can be seen, the ACO-BmTSP, in addition to obtaining much more equitable paths, also achieves better results in lower total costs. In conclusion, some benchmark problems were used to evaluate the efficiency of ACO-BmTSP, and the results clearly indicate that this algorithm represents a strong alternative to be considered when the problem size involves fewer than one hundred locations.
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TL;DR: This study develops a load management system to ensure power supply security, leveraging an improved Ant Colony Optimization (ACO) algorithm to analyze and manage provincial load data, promoting grid interaction and alleviating supply-demand imbalances.
Sample Size Planning in Item Response Theory: A Tutorial
Ulrich Schroeders,Timo Gnambs +1 more
- 24 Jul 2024
TL;DR: This tutorial reevaluates IRT's underutilization, highlighting its advantages over classical test theory and factor analysis, and provides a practical introduction to simulation-based sample size estimation in IRT for accurate parameter estimation and model fit.
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