Technology

To best support students in developing competence, assessments that allow students to use knowledge to solve challenging problems and make sense of phenomena are needed. These assessments need to be designed and tested to validly locate students on the learning progression and hence provide feedback to students and teachers about meaningful next steps in their learning. Yet, such tasks are time-consuming to score and challenging to provide students with appropriate feedback to develop their knowledge to the next level.

To best support students in developing competence, assessments that allow students to use knowledge to solve challenging problems and make sense of phenomena are needed. These assessments need to be designed and tested to validly locate students on the learning progression and hence provide feedback to students and teachers about meaningful next steps in their learning. Yet, such tasks are time-consuming to score and challenging to provide students with appropriate feedback to develop their knowledge to the next level.

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Teachers’ professional noticing has been described as transitioning from descriptions of general pedagogy to analysis of students’ mathematical procedures and conceptual reasoning. Such a shift is described as a transition towards more student-centered noticing. In the present study, we used screen recordings of pre-service teachers’ (PSTs) 360 video viewing to examine the relationship between where and what PSTs’ looked at and what they attended to in writing.

Immersive 360 videos are increasingly being used in pre-service teachers (PST) education. There is preliminary evidence that this technology may benefit future educators’ focus and attention to classroom settings and events. However, more analytical efforts are needed to better understand its potential impact on reported focus of attention (RFA) among future educators. This article addresses this gap by presenting the findings of a study on 360 videos that involved 92 PSTs.

This article presents a vision for 2025 to implement low cost and effective extended reality (XR) technologies to supplement teacher education field experiences, regardless of if and when another global or local crisis occurs (e.g., pandemic, war, weather). In doing so, empirical and theoretical research is presented that argues for teacher educators to seek out and employ more immersive representations of practice that take advantage of the perceptual capacity of XR.

This article introduces a new framework for articulating how educational assessments can be related to teacher uses in the classroom. It articulates three levels of assessment: macro (use of standardized tests), meso (externally developed items), and micro (on-the-fly in the classroom).

This article reports on the first three years of a teacher-led professional development program on the Navajo Nation. We draw on both quantitative and qualitative data from our end-of-year surveys to highlight some of the early lessons we have gathered from the Diné Institute for Navajo Nation Educators (DINÉ). We highlight two guiding principles that have developed through this work, cultural responsiveness and teacher leadership, and we suggest that these guiding principles could be useful for other professional development efforts in Indigenous-serving contexts, many of which would be characterized as “rural.”