Journal of Educational Measurement最新文献

In computerized adaptive testing, item exposure control methods are often used to provide a more balanced usage of the item pool. Many of the most popular methods, including the restricted method (Revuelta and Ponsoda), use a single maximum exposure rate to limit the proportion of times that each item is administered. However, Barrada et al. showed that by using multiple maximum exposure rates, it is possible to obtain an even more balanced usage of the item pool. Therefore, in this paper, we develop four extensions of the restricted method that involve the use of multiple maximum exposure rates. A detailed simulation study reveals that (a) all four of the new methods improve item pool utilization and (b) three of the new methods also improve measurement accuracy. Taken together, these results are highly encouraging, as they reveal that it is possible to improve both types of outcomes simultaneously.

Item response theory (IRT) encompasses a broader class of measurement models than is commonly appreciated by practitioners in educational measurement. For measures of vocabulary and its development, we show how psychological theory might in certain instances support unipolar IRT modeling as a superior alternative to the more traditional bipolar IRT models fit in practice. Although corresponding model choices make unipolar IRT statistically equivalent with bipolar IRT, adopting the unipolar approach substantially alters the resulting metric for proficiency. This shift can have substantial implications for educational research and practices that depend heavily on interval-level score interpretations. As an example, we illustrate through simulation how the perspective of unipolar IRT may account for inconsistencies seen across empirical studies in the observation (or lack thereof) of Matthew effects in reading/vocabulary development (i.e., growth being positively correlated with baseline proficiency), despite theoretical expectations for their presence. Additionally, a unipolar measurement perspective can reflect the anticipated diversification of vocabulary as proficiency level increases. Implications of unipolar IRT representations for constructing tests of vocabulary proficiency and evaluating measurement error are discussed.

This study examined the widely used threshold of .2 for Yen's Q3, an index for violations of local independence. Specifically, a simulation was conducted to investigate whether Q3 values were related to the magnitude of bias in estimates of reliability, item parameters, and examinee ability. Results showed that Q3 values below the typical cut-off yielded meaningful bias in estimates. Practical implications and limitations are discussed.

Item nonresponses frequently occurs in educational and psychological assessments, and if not appropriately handled, it can undermine the reliability of the results. This study introduces a missing data model based on the missing not at random (MNAR) mechanism, incorporating the monotonic missingness assumption to capture individual-level missingness patterns and behavioral dynamics. In specific, the cumulative number of missing indicators allows to consider the tendency of current item's missingness based on the previous missingnesses, which reduces the number of nuisance parameters for modeling missing data mechanisms. Two Bayesian model evaluation criteria were developed to distinguish between missing at random (MAR) and MNAR mechanisms by imposing specific parameter constraints. Additionally, the study introduces a highly efficient Bayesian slice sampling algorithm to estimate the model parameters. Four simulation studies were conducted to show the performance of the proposed model. The PISA 2015 science data was carried out to further illustrate the application of the proposed approach.

Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of their performance warrant examination. In this study, we explore the potential for examinees to inflate their scores by gaming the ACTA automated scoring system. We explore a range of strategies including responding with words selected from the item stem and responding with multiple answers. These responses would be easily identified as incorrect by a human rater but may result in false-positive classifications from an automated system. Our results show that the rate at which these strategies produce responses that are scored as correct varied across items and across strategies but that several vulnerabilities exist.

In item difficulty modeling (IDM), item parameters are predicted from the items' linguistic features, aiming to ultimately render item calibration redundant. Current IDM applications, however, commonly do not yield the required prediction accuracy. To immediately exploit even somewhat inaccurate IDM predictions, we blend IDM with established Bayesian estimation techniques. We propose a two-step approach where (1) IDM predictions are obtained and (2) employed to construct informative prior distributions. We evaluate the approach in a case study on small-sample calibration of the 3PL in a high-stakes test. First, concerning implementation, we find computationally efficient penalized maximum likelihood estimation to be comparable to the best-performing MCMC-based approach. Second, we investigate sample size reductions achievable with state-of-the-art IDM predictions, finding negligible gains compared to merely considering the historical distribution of parameters. Third, we evaluate the prediction accuracy required for a targeted sample size reduction by gradually increasing simulated IDM prediction accuracies. We find that required accuracies can counterbalance each other, allowing calibration sample size to be reduced when either high-quality item difficulty predictions or good predictions of item discriminations and pseudo-guessing parameters are available. We argue that these evaluations provide new, portable IDM benchmarks quantifying performance in terms of achievable sample size reductions.