Technology

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

MindHive is an online, open science, citizen science platform co-designed by a team of educational researchers, teachers, cognitive and social scientists, UX researchers, community organizers, and software developers to support real-world brain and behavior research for (a) high school students and teachers who seek authentic STEM research experiences, (b) neuroscientists and cognitive/social psychologists who seek to address their research questions outside of the lab, and (c) community-based organizations who seek to conduct grassroots, science-based research for policy change.

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.

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.