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Noticing is often studied at one time point or with a focus on how one notices a particular practice. Fewer studies have focused on how noticing shifts from before to after professional development (PD), and researchers have yet to commonly study what participants provide as rationale for changes in noticing. We engaged 18 practicing teachers in one year of PD, including a course, video coaching clubs, and coaching. We asked them to annotate video clips before and after the PD to identify what and how they noticed. We interviewed teachers about their noticing and how it related to the PD.

Now more than ever, the world needs citizens comfortable with interdisciplinary problem- solving in Science, Technology, Engineering, and Mathematics (STEM) to address global environmental and health challenges, including extreme weather, habitat destruction, and air pollution, among others. However, many countries report an insufficient number of individuals with experience in STEM problem solving. In addition, recent research indicates low interest and knowledge in STEM fields.

Research on mathematics teacher professional learning indicates that careful analysis of evidence of students’ reasoning and participation can prompt generative inquiry into instruction. Evidence of students’ own perceptions of instruction is noticeably absent in the literature. This absence is consequential, given the well-documented finding that mathematics teachers’ instructional decisions are shaped by assumptions they make about their students. Guided by an interpretive perspective on teaching and the literature on mathematics teachers’ professional learning, this study explores U.S.

Research on mathematics teacher professional learning indicates that careful analysis of evidence of students’ reasoning and participation can prompt generative inquiry into instruction. Evidence of students’ own perceptions of instruction is noticeably absent in the literature. This absence is consequential, given the well-documented finding that mathematics teachers’ instructional decisions are shaped by assumptions they make about their students. Guided by an interpretive perspective on teaching and the literature on mathematics teachers’ professional learning, this study explores U.S.

Research on mathematics teacher professional learning indicates that careful analysis of evidence of students’ reasoning and participation can prompt generative inquiry into instruction. Evidence of students’ own perceptions of instruction is noticeably absent in the literature. This absence is consequential, given the well-documented finding that mathematics teachers’ instructional decisions are shaped by assumptions they make about their students. Guided by an interpretive perspective on teaching and the literature on mathematics teachers’ professional learning, this study explores U.S.

Research on mathematics teacher professional learning indicates that careful analysis of evidence of students’ reasoning and participation can prompt generative inquiry into instruction. Evidence of students’ own perceptions of instruction is noticeably absent in the literature. This absence is consequential, given the well-documented finding that mathematics teachers’ instructional decisions are shaped by assumptions they make about their students. Guided by an interpretive perspective on teaching and the literature on mathematics teachers’ professional learning, this study explores U.S.

Fostering youth voice means supporting young people in expressing their ideas, taking ownership of their learning, and engaging with their communities in meaningful and impactful ways. Out-of-school-time (OST) science, technology, engineering, and math (STEM) programs have long provided these opportunities, empowering youth to drive their learning forward and see themselves as active contributors to the world around them.

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.

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.