Multiple-objective optimization applied in extracting multiple-choice tests

Nguyen, Tram Thi Bao; Bui, Toan Duc; Fujita, Hamido; Hong, Tzung-Pei; Loc, Ho Dac; Snåšel, Václav; Vo, Bay

doi:10.1016/j.engappai.2021.104439

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…The selection of answer choices has a dual impact: it not only influences the overall quality of the test but also affects the likelihood of guessing (Esmaeeli et al, 2021;Haladyna & Downing, 1993;Lord, 1977;Nguyen et al, 2021). Beyond these considerations, the number of choices also bears psychological implications, affecting the cognitive load on the test-taker.…”

Section: Objective and Scope Of Multiple-choice Test Designmentioning

confidence: 99%

5 vs 4: A Quantitative Investigation into the Quality Metrics of Different Multiple-Choice Test Formats

Sukkaew,

Chumkaew

2023

ABAC J

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This study employed quantitative methods to address two primary objectives: 1) to compare the quality of 5-choice and 4-choice multiple-choice tests, and 2) to evaluate the discriminant power of these formats using test response theory with kernel smoothing. Data were collected from 1,966 students at Sukhothai Thammathirat Open University who took a 120-question multiple-choice exam during the second semester of 2019. Four test configurations were analyzed: the Initial Case utilized the original 5-choice format; Case 1 randomly omitted one option from the 5-choice test, excluding the correct answer; Case 2 randomly omitted one option, including the correct answer; and Case 3 adapted the options based on the test-taker’s proficiency level. The study employed Cronbach’s Alpha (denoted as raw_alpha) as a reliability metric, discovering varying levels of reliability across the four cases. The highest reliability was observed in Case 3 with a raw_alpha value of 0.87. There were no differences in the difficulty values or discriminatory power across all cases. The mean scores indicated that students generally performed better on the 4-choice tests in Cases 1-3 than on the original 5-choice format, referred to as the Initial Case. These findings have significant implications for test design, suggesting that 4-choice tests can achieve comparable reliability and discriminatory power to traditional 5-choice tests.

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Section: Objective and Scope Of Multiple-choice Test Designmentioning

confidence: 99%

5 vs 4: A Quantitative Investigation into the Quality Metrics of Different Multiple-Choice Test Formats

Sukkaew,

Chumkaew

2023

ABAC J

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“…In 2021, Zamfirache proposed a new reinforcement learningbased control method using policy iteration (PI) and grey wolf optimization (GWO) for training neural networks [14]. In the same years, the combination of Nguyen's improved genetic algorithm (GA) using a parallel strategy and simulated annealing efficiently generated practical tests that meet specific requirements [15]. In 2020, Beldjilali proposed a system to solve the vehicle routing problem with the time window (VRPTW) problem.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction

Zhang

Zhou

Huang

et al. 2022

Int J Comput Intell Syst

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Extreme learning machine (ELM) is popular as a method of training single hidden layer feedforward neural networks. However, the ELMs optimized by the traditional gradient descent algorithms cannot fundamentally solve the influence of the random selection of the input weights and biases. Therefore, this paper proposes a method of extreme learning machine optimized by an enhanced salp search algorithm (NSSA-ELM). Salp search algorithm (SSA) is a metaheuristic algorithm, to improve the performance of SSA exploration and avoid getting stuck in local optima, the neighborhood centroid opposite‑based learning is used to optimize SSA. This method maintains the diversity of the population, which is conducive to avoid local optimization and accelerate convergence. This paper performs classification tests on NSSA and other metaheuristic-optimized ELMs on ten datasets, and regression tests on 5 datasets. Finally, the prediction ability of dew point temperature is evaluated. The meteorological data of five climatically representative cities in China from 2016 to 2022 were collected to predict the dew point temperature. The experimental results show that the NSSA-ELM is the best model, and its generalization performance and accuracy are better than other models.

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Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine

Guo

Zhu²,

Hao³

et al. 2022

Appl Intell

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Multiple-objective optimization applied in extracting multiple-choice tests

Cited by 6 publications

References 24 publications

5 vs 4: A Quantitative Investigation into the Quality Metrics of Different Multiple-Choice Test Formats

5 vs 4: A Quantitative Investigation into the Quality Metrics of Different Multiple-Choice Test Formats

Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction

Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine

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