Curriculum

Science education literature has highlighted socio-scientific issues (SSI) as an effective pedagogy for teaching science in a social and political context. SSI links science education and real-world problems to engage students in real-world issues, making it ideal for teaching global climate change (GCC). Additionally, technological advances have created a unique opportunity for teaching climate by making previously inaccessible computer-based computational models and data visualizations accessible to the typical K-12 learning environment. Here, we present the findings from the 2020–2021 school year pre-/post-implementation of a 3-week, model-based climate education curriculum module (EzGCM).

Science education literature has highlighted socio-scientific issues (SSI) as an effective pedagogy for teaching science in a social and political context. SSI links science education and real-world problems to engage students in real-world issues, making it ideal for teaching global climate change (GCC). Additionally, technological advances have created a unique opportunity for teaching climate by making previously inaccessible computer-based computational models and data visualizations accessible to the typical K-12 learning environment. Here, we present the findings from the 2020–2021 school year pre-/post-implementation of a 3-week, model-based climate education curriculum module (EzGCM).

When integrated into online curriculum modules for students, educative curriculum materials (ECMs) can enhance teachers’ enactment of these modules. This study investigated (1) the use of digitally enhanced ECMs built into an online plate tectonics curriculum module by teachers with different backgrounds and teaching experience, (2) the relationship between teachers’ use of ECMs and student learning gains, and (3) teacher reflections on the value of the ECMs they used.

Civic Statistics by its nature is highly interdisciplinary. From a cross-curricular perspective, teaching and learning Civic Statistics faces specific challenges related to the preparation of teachers and the design of instruction. This chapter presents examples of how Civic Statistics resources and concepts can be used in different courses and subject areas.

Students need “critical data literacy” skills to help make sense of the multitude of information available to them, especially as it relates to high-stakes issues of social justice. The authors describe two curriculum modules they developed—one on income equality, one on immigration—that help students learn to analyze data in order to shed light on complex social issues and evaluate claims about those issues.

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.