Educational Measurement-Issues and Practice最新文献

The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have unveiled a wealth of opportunities and challenges in assessment. Applying cutting-edge large language models (LLMs) and generative AI to assessment holds great promise in boosting efficiency, mitigating bias, and facilitating customized evaluations. Conversely, these innovations raise significant concerns regarding validity, reliability, transparency, fairness, equity, and test security, necessitating careful thinking when applying them in assessments. In this article, we discuss the impacts and implications of LLMs and generative AI on critical dimensions of assessment with example use cases and call for a community effort to equip assessment professionals with the needed AI literacy to harness the potential effectively.

Short scales are time-efficient for participants and cost-effective in research. However, researchers often mistakenly expect short scales to have the same reliability as long ones without considering the effect of scale length. We argue that applying a universal benchmark for alpha is problematic as the impact of low-quality items is greater on shorter scales. In this study, we proposed simple guidelines for item reduction using the “alpha-if-item-deleted” procedure in scale construction. An item can be removed if alpha increases or decreases by less than .02, especially for short scales. Conversely, an item should be retained if alpha decreases by more than .04 upon its removal. For reliability benchmarks, .80 is relatively safe in most conditions, but higher benchmarks are recommended for longer scales and smaller sample sizes. Supplementary analyses, including item content, face validity, and content coverage, are critical to ensure scale quality.

Measuring opportunities to learn (OTL) is crucial for evaluating education quality and equity, but obtaining accurate and comprehensive OTL data at a large scale remains challenging. We attempt to address this issue by investigating measurement concerns in data collection and sampling. With the primary goal of estimating group-level OTLs for large populations of classrooms and the secondary goal of estimating classroom-level OTLs, we propose forming a teacher panel and using an online log-type survey to collect content and time data on sampled days throughout the school year. We compared various sampling schemes in a simulation study with real daily log data from 66 fourth-grade math teachers. The findings from this study indicate that sampling 1 day per week or 1 day every other week provided accurate group-level estimates, while sampling 1 day per week yielded satisfactory classroom-level estimates. The proposed approach aids in effectively monitoring large-scale classroom OTL.

Educators and researchers strive to build policies and practices on data and evidence, especially on academic achievement scores. When assessment scores are inaccurate for specific student populations or when scores are inappropriately used, even data-driven decisions will be misinformed. To maximize the impact of the research-practice-policy collaborative, every stage of the assessment and research process needs to be critically interrogated. In this paper, we highlight the need to reframe assessment and research for multilingual learners, students with disabilities, and multilingual students with disabilities. We outline a framework that integrates three critical perspectives (QuantCrit, DisCrit, and critical multiculturalism) and discuss how this framework can be applied to assessment creation and research.

The template-based automated item-generation (TAIG) approach that involves template creation, item generation, item selection, field-testing, and evaluation has more steps than the traditional item development method. Consequentially, there is more margin for error in this process, and any template errors can be cascaded to the generated items. Therefore, it is essential to eliminate the source of errors and ensure the quality of the template so items can be problem-free. The article introduces a process to reduce template errors at the early stage of template development, minimize the impact of template errors on generated items, and increase the survival rates of generated items. The article also discusses a statistical method to establish confidence in the quality of the template by systematically examining the quality of the generated items. The proposed method can reduce the review process for some items generated from a template.

I study student characteristics and academic performance at the University of California, where consideration of an applicant's ethnicity has been banned since 1996 and SAT scores were used in admitting students to the university until fall 2021. I show the following: (1) SAT scores were more important than high school grades in predicting first-year university GPA; (2) the use of SAT scores alone or with high school grades in determining admission is biased in favor of admitting underrepresented minorities and students who are socioeconomically disadvantaged; (3) SAT scores are more important and high school grades are less important in predicting GPA for underrepresented minorities and/or those students from low-income families than they are for those students who are white and/or from high-income families; and (4) the University of California found ways to admit a significant number of underrepresented minorities despite many of them having low SAT scores.