Educational Measurement-Issues and Practice最新文献

With the increased restrictions on physical distancing due to the COVID-19 pandemic, remote proctoring has emerged as an alternative to traditional onsite proctoring to ensure the continuity of essential assessments, such as computer-based medical licensing exams. Recent literature has highlighted the significant impact of different proctoring modalities on examinees’ test experience, including factors like response-time data. However, the potential influence of these differences on test performance has remained unclear. One limitation in the current literature is the lack of a rigorous learning analytics framework to evaluate the comparability of computer-based exams delivered using various proctoring settings. To address this gap, the current study aims to introduce a machine-learning-based framework that analyzes computer-generated response-time data to investigate the association between proctoring modalities in high-stakes assessments. We demonstrated the effectiveness of this framework using empirical data collected from a large-scale high-stakes medical licensing exam conducted in Canada. By applying the machine-learning-based framework, we were able to extract examinee-specific response-time data for each proctoring modality and identify distinct time-use patterns among examinees based on their proctoring modality.

This article presents the consensus of an National Council on Measurement in Education Presidential Task Force on Foundational Competencies in Educational Measurement. Foundational competencies are those that support future development of additional professional and disciplinary competencies. The authors develop a framework for foundational competencies in educational measurement, illustrate how educational measurement programs can help learners develop these competencies, and demonstrate how foundational competencies continue to develop in educational measurement professions. The framework introduces three foundational competency domains: Communication and Collaboration Competencies; Technical, Statistical, and Computational Competencies; and Educational Measurement Competencies. Within the Educational Measurement Competency domain, the authors identify five subdomains: Social, Cultural, Historical, and Political Context; Validity, Validation, and Fairness; Theory and Instrumentation; Precision and Generalization; and Psychometric Modeling.

Perceptions of fairness are fundamental in building cooperation and trust, undermining conflicts, and gaining legitimacy in teacher-student relationships in classroom assessment. However, perceptions of unfairness in assessment can undermine students’ mental well-being, increase antisocial behaviors, increase psychological disengagement with learning, and threaten the belief in a fair society, fundamental to engaging in civic responsibilities. Despite the crucial role of perceived fairness in assessment, there are widespread experiences of unfairness reported by students internationally. To undermine these widespread unfair experiences, limited explicit education on promoting fairness in assessment is being delivered in graduate, preservice, and in-service training. However, it seems that explicit education is the first step in capacity building for reducing unfair perceptions and related undesirable outcomes. The purpose of this module is thus to share the findings drawn from theoretical and empirical research from various countries to provide a space for further critical reflection on best practices in enhancing fairness in classroom assessment contexts.

In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set of candidate items whose expected performance matches the examinee's current performance is selected. In the second stage, an item that is approximately matched to the examinee's observed performance is selected from the candidate set. The expected performance of an examinee on an item is predicted by autoencoders. Experiment results show that the proposed algorithm outperforms existing item selection algorithms in terms of item exposure while incurring only a small loss in measurement precision.

The increasing use of computerization in the testing industry and the need for items potentially measuring higher-order skills have led educational measurement communities to develop technology-enhanced (TE) items and conduct validity studies on the use of TE items. Parallel to this goal, the purpose of this study was to collect validity evidence comparing item information functions, expected information values, and measurement efficiencies (item information per time unit) between multiple-choice (MC) and technology-enhanced (TE) items. The data came from K–12 mathematics large-scale accountability assessments. The study results were mainly interpreted descriptively, and the presence of specific patterns between MC and TE items was examined across grades and depth of knowledge levels. Although many earlier researchers pointed out that TE items were not as efficient as MC items, the results from the study point to ways that TE items might provide more information and were more than or equally efficient as MC items overall.