Journal of Educational Measurement最新文献

Eye-tracking procedures generate copious process data that could be valuable in establishing the response processes component of modern validity theory. However, there is a lack of tools for assessing and visualizing response processes using process data such as eye-tracking fixation sequences, especially those suitable for young children. This study, which explored student responses to a spatial reasoning task, employed eye tracking and social network analysis to model, examine, and visualize students' visual transition patterns while solving spatial problems to begin to elucidate these processes. Fifty students in Grades 2–8 completed a spatial reasoning task as eye movements were recorded. Areas of interest (AoIs) were defined within the task for each spatial reasoning question. Transition networks between AoIs were constructed and analyzed using selected network measures. Results revealed shared transition sequences across students as well as strategic differences between high and low performers. High performers demonstrated more integrated transitions between AoIs, while low performers considered information more in isolation. Additionally, age and the interaction of age and performance did not significantly impact these measures. The study demonstrates a novel modeling approach for investigating visual processing and provides initial evidence that high-performing students more deeply engage with visual information in solving these types of questions.

Group differences in test scores are a key metric in education policy. Response time offers novel opportunities for understanding these differences, especially in low-stakes settings. Here, we describe how observed group differences in test accuracy can be attributed to group differences in latent response speed or group differences in latent capacity, where capacity is defined as expected accuracy for a given response speed. This article introduces a method for decomposing observed group differences in accuracy into these differences in speed versus differences in capacity. We first illustrate in simulation studies that this approach can reliably distinguish between group speed and capacity differences. We then use this approach to probe gender differences in science and reading fluency in PISA 2018 for 71 countries. In science, score differentials largely increase when males, who respond more rapidly, are the higher performing group and decrease when females, who respond more slowly, are the higher performing group. In reading fluency, score differentials decrease where females, who respond more rapidly, are the higher performing group. This method can be used to analyze group differences especially in low-stakes assessments where there are potential group differences in speed.

Hurtz, G.M., & Mucino, R. (2024). Expanding the lognormal response time model using profile similarity metrics to improve the detection of anomalous testing behavior. Journal of Educational Measurement, 61, 458–485. https://doi.org/10.1111/jedm.12395

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The emergence of sophisticated AI tools such as ChatGPT, coupled with the transition to remote delivery of educational assessments in the COVID-19 era, has led to increasing concerns about academic integrity and test security. Using AI tools, test takers can produce high-quality texts effortlessly and use them to game assessments. It is thus critical to detect these nonauthentic texts to ensure test integrity. In this study, we leveraged keystroke logs—recordings of every keypress—to build machine learning (ML) detectors of nonauthentic texts in a large-scale writing assessment. We focused on investigating the fairness of the detectors across demographic subgroups to ensure that nongenuine writing can be predicted equally well across subgroups. Results indicated that keystroke dynamics were effective in identifying nonauthentic texts. While the ML models were slightly more likely to misclassify the original responses submitted by male test takers as consisting of nonauthentic texts than those submitted by females, the effect sizes were negligible. Furthermore, balancing demographic distributions and class labels did not consistently mitigate detector bias across predictive models. Findings of this study not only provide implications for using behavioral data to address test security issues, but also highlight the importance of evaluating the fairness of predictive models in educational contexts.

Traditional IRT and IRTree models are not appropriate for analyzing the item that simultaneously consists of multiple-choice (MC) task and constructed-response (CR) task in one item. To address this issue, this study proposed an item response tree model (called as IRTree-MR) to accommodate items that contain different response types at different steps and multiple different cognitive processes behind each score to effectively investigate the cognitive process and achieve a more accurate evaluation of examinees. The proposed model employs appropriate processing function for each task and allows multiple paths to an observed outcome. The simulation studies were conducted to evaluate the performance of the proposed IRTree-MR, and results show the proposed model outperforms the traditional IRT model in terms of parameters recovery and model-fit. Moreover, an empirical study was carried out to verify the advantages of the proposed model.