Assessment

Engaging students in scientific modeling practice is critical for developing their competence in using scientific knowledge to explain phenomena and design solutions. Student-drawn models are frequently used to investigate students’ proficiency in scientific modeling. However, scoring student-drawn models is time-consuming and requires technical expertise. The recently released GPT-4V(ision) provides a unique opportunity to facilitate the automatic scoring of scientific models with its image classification capability.

The significance of children’s mathematical competence during the early years is well established; however, the methods for developing such competencies remain less understood. Specifically, there is a need to identify what constitutes high-quality educational environments and effective instruction. This study employed innovative analytical techniques to evaluate the scoring and interpretation of an existing domain-specific observational measure: the Classroom Observation of Early Mathematics Environment and Teaching (COEMET).

For the promotion of youth voice to be successful, out-of-school-time (OST) program facilitators and classroom teachers need a common understanding of what quality looks and sounds like and support for implementing higher-quality instructional strategies. For well over a decade, the Dimensions of Success (DoS) observation system has provided such support in OST settings and, more recently, in middle-grade classrooms. In this article, we first demonstrate how DoS defines quality Youth Voice in OST and classroom settings through four vignettes based on observations of grade 5–8 classrooms and OST program observations, then provide strategies for educators to promote higher-quality Youth Voice by building on youth ideas and encouraging decision-making that drives their STEM learning forward.

This study advances understandings of formative assessment by introducing the Culturally and Linguistically Sustaining Formative Assessment (CLSA) framework, grounded in relational and embodied perspectives and culturally sustaining pedagogy. While formative assessment is widely recognized as a process for supporting learning, less is known about how it can be enacted in culturally and linguistically sustaining ways.

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy.

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy.

We present findings from an analysis of tests of teacher mathematical knowledge identified over a 20-year period of mathematics education literature, and report on how these tests are discussed in the literature, with a focus on validity arguments and evidence.

As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination.

As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination.

Testing is a part of education around the world; however, there are concerns that consequences of testing is underexplored within current educational scholarship. Moreover, usability studies are rare within education. One aim of the present study was to explore the usability of a mathematics problem-solving test called the Problem Solving Measures–Computer-Adaptive Test (PSM-CAT) designed for grades six to eight students (ages 11–14). The second aim of this mixed-methods research was to unpack consequences of testing validity evidence related to the results and test interpretations, leveraging the voices of participants.