Thurstonian forced-choice modeling is considered to be a powerful new tool to estimate item and person parameters while simultaneously testing the model fit. This assessment approach is associated with the aim of reducing faking and other response tendencies that plague traditional self-report trait assessments. As a result of major recent…

The study aims to introduce catIRT tools which facilitates researchers' Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) simulations. catIRT tools provides an interface for mirt and catR packages through the shiny package in R. Through this interface, researchers can apply IRT calibration and CAT simulations although they do not…

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…

Process Analysis is an emerging approach to discover meaningful knowledge from temporal educational data. The study presented in this paper shows how we used Process Analysis methods on the National Assessment of Educational Progress (NAEP) test data for modeling and predicting student test-taking behavior. Our process-oriented data exploration…

Allowance for multiple chances to answer constructed response questions is a prevalent feature in computer-based homework and exams. We consider the use of item response theory in the estimation of item characteristics and student ability when multiple attempts are allowed but no explicit penalty is deducted for extra tries. This is common…

This research derived information functions and proposed new scalar information indices to examine the quality of multidimensional forced choice (MFC) items based on the RANK model. We also explored how GGUM-RANK information, latent trait recovery, and reliability varied across three MFC formats: pairs (two response alternatives), triplets (three…

Assessing job applicants' general mental ability online poses psychometric challenges due to the necessity of having brief but accurate tests. Recent research (Myszkowski & Storme, 2018) suggests that recovering distractor information through Nested Logit Models (NLM; Suh & Bolt, 2010) increases the reliability of ability estimates in…

This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…

The present study examines the relative importance of attributes within and across items by applying four cognitive diagnostic assessment models. The current study utilizes the function of the models that can indicate inter-attribute relationships that reflect the response behaviors of examinees to analyze scored test-taker responses to four forms…

Item development is a time- and resource-intensive process. Automatic item generation integrates cognitive modeling with computer technology to systematically generate test items. To date, however, items generated using cognitive modeling procedures have received limited use in operational testing situations. As a result, the psychometric…

This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

Item response theory (IRT) becomes an increasingly important tool when analyzing "big data" gathered from online educational venues. However, the mechanism was originally developed in traditional exam settings, and several of its assumptions are infringed upon when deployed in the online realm. For a large-enrollment physics course for…

If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

This study investigates the effect of fitting a unidimensional IRT model to multidimensional data in content-balanced computerized adaptive testing (CAT). Unconstrained CAT with the maximum information item selection method is chosen as the baseline, and the performances of three content balancing procedures, the constrained CAT (CCAT), the…

In this research, we investigated the suitability of implementing "e-rater"® automated essay scoring in a high-stakes large-scale English language testing program. We examined the effectiveness of generic scoring and 2 variants of prompt-based scoring approaches. Effectiveness was evaluated on a number of dimensions, including agreement…