Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm

Chang, Ting‐Yi; Shiu, You-Fu

doi:10.1007/s10489-011-0308-x

Cited by 23 publications

(9 citation statements)

References 25 publications

(35 reference statements)

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“…Furthermore, alternative approaches to MIP have been proposed in the past, e.g., heuristic algorithms, which are also capable of solving automated test assembly problems. Examples include genetic algorithms [32][33][34] or simulated annealing [35]. For a short but comprehensive overview see van der Linden [1].…”

Section: Alternativesmentioning

confidence: 99%

Automated Test Assembly in R: The eatATA Package

Becker

Debeer

Sachse

et al. 2021

Psych

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Combining items from an item pool into test forms (test assembly) is a frequent task in psychological and educational testing. Although efficient methods for automated test assembly exist, these are often unknown or unavailable to practitioners. In this paper we present the R package eatATA, which allows using several mixed-integer programming solvers for automated test assembly in R. We describe the general functionality and the common work flow of eatATA using a minimal example. We also provide four more elaborate use cases of automated test assembly: (a) The assembly of multiple test forms for a pilot study; (b) the assembly of blocks of items for a multiple matrix booklet design in the context of a large-scale assessment; (c) the assembly of two linear test forms for individual diagnostic purposes; (d) the assembly of multi-stage testing modules for individual diagnostic purposes. All use cases are accompanied with example item pools and commented R code.

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Section: Alternativesmentioning

confidence: 99%

Automated Test Assembly in R: The eatATA Package

Becker

Debeer

Sachse

et al. 2021

Psych

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“…Both works use a real 540 items pool to test their proposed method. Chang & Shiu (2012) proposes a method to construct multiple tests based on CLONALG, an algorithm based on the clonal selection principle in a biological 160 immune system. The authors report results with a 4.000 items pool used to construct 5 parallel tests.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Among these characteristics, we would like to highlight the joint evaluation of multiple ability levels of the recipients on a single unified test (van der Linder, 2005;Chang & Shiu, 2012;Nguyen & Fong, 840 2013) or the inclusion of additional constraints, such as incompatibilities among questions of different sets or limiting the number of questions with specific attributes (Hwang et al, 2008;Yao, 2014;Yao et al, 2014). These additional characteristics are usually present in many real situations and would impose challenging changes to both the bin-packing-based formulation as well as to the neighborhood structures proposed in this work.…”

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confidence: 99%

Variable neighborhood search heuristics for a test assembly design problem

Pereira

Vilà

2015

Expert Systems with Applications

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Test assembly design problems appear in the areas of psychology and education, among others. The goal of these problems is to construct one or multiple tests to evaluate some criteria. This paper studies a recent formulation of the problem known as the one-dimensional minimax bin-packing problem with bin size constraints (MINIMAX BSC). In the MINIMAX BSC, items are initially divided into groups and multiple tests need to be constructed using a single item from each group, while minimizing the difference among the tests. We first show that the problem is NP-Hard, which remained an open question. Second, we propose three different local search neighborhoods derived from the exact resolution of special cases of the problem, and combine them into a Variable Neighborhood Search (VNS) metaheuristic. Finally, we test the proposed algorithm using real-life-based instances. The results show that the algorithm is able to obtain optimal or near-optimal solutions for instances with item pools with up to 60.000 items. Consequently, the algorithm is a viable option to design large-scale tests, as well as to provide tests for online small-sized situations such as those found in e-learning platforms.

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“…To equilibrate the ability measurement accuracy, van der Linden and Ameda [15], Sun et al [18], Songmuang and Ueno [19], Boekooi-Timminga [26], Armstrong et al [27], Armstrong et al [28], Chang and Shiu [29], and Pereira and Vila [30] formulated an assembly of uniform tests as a largescale IP that directly minimizes the differences of the ability measurement accuracies among tests. However, these optimizations have extremely large computational costs.…”

Section: Introductionmentioning

confidence: 99%