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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.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

We need STEM knowledge programs in formal and informal settings that guide learners in applying STEM learning to the creation of solutions.

In this paper, we present an overview of the design research used to develop a digital collaborative environment with an embedded problem-based curriculum. We then discuss the student and teacher features of the environment that promote inquiry-based learning and teaching.

While tragedy has struck an inordinate number of students in the past several years, not all areas of the country are at risk for every natural hazard all the time. To avoid having students feel like Chicken Little under a falling sky, the GeoHazard project uses simulations, data, experimentation, and scientific argumentation to teach about risk and uncertainty. We have created three scaffolded online modules focused on hurricanes, wildfires, and inland flooding to help teach these concepts. Through investigations using both simulations and real-world data, these curriculum units introduce students to the scientific factors responsible for these hazards and provide practice in interpreting forecasts.