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Stratified Item Selection Methods in Cognitive Diagnosis computerized adaptive testing

APPLIED PSYCHOLOGICAL MEASUREMENT 2020年第5期44卷 346-361页

作者： Yang, Jing Chang, Hua-Hua Tao, Jian Shi, Ningzhong Northeast Normal Univ Changchun Jilin Peoples R China Purdue Univ W Lafayette IN 47907 USA

Cognitive diagnostic computerized adaptive testing (CD-CAT) aims to obtain more useful diagnostic information by taking advantages of computerized adaptive testing (CAT). Cognitive diagnosis models (CDMs) have been developed to classify examinees into the correct proficiency classes so as to get more efficient remediation, whereas CAT tailors optimal items to the examinee's mastery profile. The item selection method is the key factor of the CD-CAT procedure. In recent years, a large number of parametric/nonparametric item selection methods have been proposed. In this article, the authors proposed a series of stratified item selection methods in CD-CAT, which are combined with posterior-weighted Kullback-Leibler (PWKL), nonparametric item selection (NPS), and weighted nonparametric item selection (WNPS) methods, and named S-PWKL, S-NPS, and S-WNPS, respectively. Two different types of stratification indices were used: original versus novel. The performances of the proposed item selection methods were evaluated via simulation studies and compared with the PWKL, NPS, and WNPS methods without stratification. Manipulated conditions included calibration sample size, item quality, number of attributes, number of strata, and data generation models. Results indicated that the S-WNPS and S-NPS methods performed similarly, and both outperformed the S-PWKL method. And item selection methods with novel stratification indices performed slightly better than the ones with original stratification indices, and those without stratification performed the worst.

关键词： cognitive diagnostic assessment computerized adaptive testing nonparametric item selection method stratification indices

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computerized adaptive testing for Cognitively Based Multiple-Choice Data

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APPLIED PSYCHOLOGICAL MEASUREMENT 2019年第5期43卷 388-401页

作者： Yigit, Hulya D. Sorrel, Miguel A. de la Torre, Jimmy Univ Illinois Champaign IL USA Univ Autonoma Madrid Madrid Spain Univ Hong Kong Hong Kong Peoples R China

Cognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen-Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy "and" gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities.

关键词： cognitive diagnosis models computerized adaptive testing MC-DINA G-DINA item selection methods JSD GDI

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Developing an Item Bank of computerized adaptive testing for Eating Disorders in Chinese University Students

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SAGE OPEN 2022年第4期12卷

作者： Liu, Kai Zhang, Longfei Tu, Dongbo Cai, Yan Jiangxi Normal Univ Nanchang Peoples R China Jiangxi Normal Univ Sch Psychol 99 Ziyang Ave Nanchang 330022 Jiangxi Peoples R China

We aimed to develop an item bank of computerized adaptive testing for eating disorders (CAT-ED) in Chinese university students to increase measurement precision and improve test efficiency. A total of 1,025 Chinese undergraduate respondents answered a series of questions about eating disorders in a paper-pencil test. A total of 133 items from four well-validated Chinese-version scales of eating disorders were used to construct the item bank of CAT-ED with the following analysis. First, unidimensionality, model fit, local independence, item ?t, discrimination and differential item functioning (DIF) were tested. Then, two simulation studies were applied to test the CAT-ED's effectivity and rationality by calculating concurrent criterion-related validity, sensitivity and speci?city. The ?nal item bank comprised 77 items, which met the requirements of local independence, item ?t, high discrimination and no differential item functioning in CAT. The mean number of administered items in CAT with the stopping rule fixed at SE <= 0.3 was 11 items. The obtained results showed that CAT-ED had acceptable reliability, validity, sensitivity and speci?city.

关键词： computerized adaptive testing eating disorders item response theory psychological assessment screening

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Online Calibration of a Joint Model of Item Responses and Response Times in computerized adaptive testing

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JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2020年第2期45卷 175-208页

作者： Kang, Hyeon-Ah Zheng, Yi Chang, Hua-Hua Univ Texas Austin 1912 SpeedwayStop D5800 Austin TX 78712 USA Arizona State Univ 1050 S Forest Mall Tempe AZ 85281 USA Purdue Univ Educ Beering Hall 5166 W Lafayette IN 47907 USA

With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates their performance along with optimal sampling procedures. An extensive simulation study indicates that the proposed online calibration strategies perform well with relatively small samples (e.g., 500 similar to 800 examinees). The analysis of estimated parameters suggests that response time information can be used to improve the recovery of the response model parameters. Among a number of sampling methods investigated, A-optimal sampling was found most advantageous when the item parameters were weakly correlated. When the parameters were strongly correlated, D-optimal sampling tended to achieve the most accurate parameter recovery. The study provides guidelines for deciding sampling design under a specific goal of online calibration given the characteristics of field-testing items.

关键词： online calibration item response theory response time optimal sampling computerized adaptive testing

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A Dynamic Stratification Method for Improving Trait Estimation in computerized adaptive testing Under Item Exposure Control

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APPLIED PSYCHOLOGICAL MEASUREMENT 2020年第3期44卷 182-196页

作者： Chen, Jyun-Hong Chao, Hsiu-Yi Chen, Shu-Ying Soochow Univ Taipei Taiwan Natl Chung Cheng Univ Chiayi Taiwan

When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses dominance curves to stratify an item pool into strata with the number being equal to the test length to precisely and accurately increase the quality of the administered items as the test progresses, reducing the likelihood that a high-discrimination item will be administered to an examinee whose ability is not close to the item difficulty. Furthermore, the SDC incorporates a dynamic process for on-the-fly item-stratum adjustment to optimize the use of quality items. Simulation studies were conducted to investigate the performance of the SDC in CAT under item exposure control at different levels of severity. According to the results, the SDC can efficiently improve trait estimation in CAT through greater precision and more accurate trait estimation than those generated by other methods (e.g., the maximum Fisher information method) in most conditions.

关键词： computerized adaptive testing a-stratified method item exposure control dominance curves high-stakes testing

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Advances in computerized adaptive testing

Advances in Computerized Adaptive Testing

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International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)

作者： Yuan, Yijun Xia, He Han, Yong Hu, Maorong Jiangxi Univ Tradit Chinese Med Coll Humanities Nanchang Jiangxi Peoples R China Nanchang Univ Affiliated Hosp 1 Dept Psychosomat Med Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665423168

computerized adaptive testing (CAT) is a testing mode based on measurement theory and computer technology. Firstly, this article briefly introduces the definition and advantages of computerized adaptive testing. Moreover, this study uses web of science retrieval tools and CiteSpace software to analyze the literature related to CAT technology from 1990 to 2020, and the problems and countermeasures in CAT application are briefly described, Finally, the future development trend of computer adaptive testing is depicted.

关键词： computerized adaptive testing literature research CiteSpace

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Quality meets Diversity: A Model-Agnostic Framework for computerized adaptive testing 20

Quality meets Diversity: A Model-Agnostic Framework for Comp...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Bi, Haoyang Ma, Haiping Huang, Zhenya Yin, Yu Liu, Qi Chen, Enhong Su, Yu Wang, Shijin Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Big Data Anal & Applicat Hefei Peoples R China Anhui Univ Hefei Peoples R China IFLYTEK Res Hefei Peoples R China

ISBN: (纸本)9781728183169

computerized adaptive testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic adaptive testing (MAAT) for CAT solution. where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： computerized adaptive testing Model-Agnostic Quality Diversity

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A hybrid model based on learning automata and cuckoo search for optimizing test item selection in computerized adaptive testing

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Jin, Chanjuan Pan, Weiming Jinhua Adv Res Inst Jinhua 321013 Zhejiang Peoples R China Jinhua Univ Vocat Technol Jinhua 321017 Zhejiang Peoples R China

computerized adaptive testing (CAT) has become one of the most effective methods of providing a customized approach to assessment. Nevertheless, the identification of student ability and the choice of appropriate test items is still a problem. In this paper, a new design of CAT strategy is proposed based on reinforcement learning, machine learning, and multi-objective optimization. A learning automaton is used to feed a neural network with the ability to predict a student's ability based on their response history. A multi-objective cuckoo search algorithm then decides the test strategy, between content coverage and level compliance. Compared with the traditional CAT methods, our approach gives better ability estimates and selects test items that are most appropriate for each student. The findings of the study show that the efficiency, accuracy and fairness of the tests have improved through experimentation.

关键词： computerized adaptive testing Learning automata Machine learning Artificial neural network Test

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Statistical Foundations for computerized adaptive testing with Response Revision

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PSYCHOMETRIKA 2019年第2期84卷 375-394页

作者： Wang, Shiyu Fellouris, Georgios Chang, Hua-Hua Univ Georgia Athens GA 30602 USA Univ Illinois Champaign IL USA Purdue Univ W Lafayette IN 47907 USA

The compatibility of computerized adaptive testing (CAT) with response revision has been a topic of debate in psychometrics for many years. The problem is to provide test takers opportunities to change their answers during the test, while discouraging deceptive strategies from their side and preserving the statistical efficiency of the traditional CAT. The estimating approach proposed in Wang et al. (Stat Sin 27(4):1987-2010, 2017), based on the nominal response model, allows test takers to provide more than one answer to each item during the test, which they all contribute to the interim and final ability estimation. This approach is here reformulated, extended to incorporate a larger class of polytomous and dichotomous item response theory models, and investigated with simulation studies under different test-taking strategies.

关键词： computerized adaptive testing item response theory (IRT) dichotomous IRT models polytomous IRT models response revision large sample property

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The Asymptotic Distribution of Average Test Overlap Rate in computerized adaptive testing

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PSYCHOMETRIKA 2019年第4期84卷 1129-1151页

作者： Choe, Edison M. Chang, Hua-Hua GMAC 11921 Freedom DrSuite 300 Reston VA 20190 USA Purdue Univ W Lafayette IN 47907 USA

The average test overlap rate is often computed and reported as a measure of test security risk or item pool utilization of a computerized adaptive test (CAT). Despite the prevalent use of this sample statistic in both literature and operations, its sampling distribution has never been known nor studied in earnest. In response, a proof is presented for the asymptotic distribution of a linear transformation of the average test overlap rate in fixed-length CAT. The theoretical results enable the estimation of standard error and construction of confidence intervals. Moreover, a practical simulation study demonstrates the statistical comparison of average test overlap rates between two CAT designs with different exposure control methods.

关键词： test overlap item exposure test security pool utilization computerized adaptive testing asymptotic theory

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