Science

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.

Science learning is an important part of the K-12 educational experience, as well as in the lives of students. This study considered students’ science learning as they engaged in the instruction of scientific issues with social relevance. With classroom environments radically changing during the COVID-19 pandemic, our study adapted to teachers and students as they were forced to change from more traditional, in-person instructional settings to virtual, online instruction settings.

Big ideas in science education are meant to be interpretive frameworks that empower student learning. Unfortunately, outside of the broad conception of scientific evaluation, there are few theoretical explanations of how this might happen. Therefore, we contribute one such explanation, an instructional concept called integrative analysis wherein students use a big idea to interconnect isolated scenarios and enrich their meanings. We illustrate the characteristics and value of integrative analysis within an empirical study of student learning in 9th-grade biology.

Big ideas in science education are meant to be interpretive frameworks that empower student learning. Unfortunately, outside of the broad conception of scientific evaluation, there are few theoretical explanations of how this might happen. Therefore, we contribute one such explanation, an instructional concept called integrative analysis wherein students use a big idea to interconnect isolated scenarios and enrich their meanings. We illustrate the characteristics and value of integrative analysis within an empirical study of student learning in 9th-grade biology.

Big ideas in science education are meant to be interpretive frameworks that empower student learning. Unfortunately, outside of the broad conception of scientific evaluation, there are few theoretical explanations of how this might happen. Therefore, we contribute one such explanation, an instructional concept called integrative analysis wherein students use a big idea to interconnect isolated scenarios and enrich their meanings. We illustrate the characteristics and value of integrative analysis within an empirical study of student learning in 9th-grade biology.

This study explores how the interplay between data and model design shifts 6th graders’ students' ideas about diffusion as they build a range of models (“paper and pencil” and computational models). We present a new web-based environment and approach that integrates model-based and data-based features in the same display which facilitates the comparison of models and real-world data. Further, we illustrate how this environment and approach lead students to converge on one canonical scientific model.