Middle

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.

Coordinating modeling and real-world data is central to building scientific theories. This paper examines how a complementary focus on modeling and data contributed to 8th grade students’ learning of mechanisms underlying wildfire smoke spread in MoDa, a web-based environment that integrates computational modeling side-by-side with real-world data for comparison and validation.

This study explored the use of a three-part suite of practice-based activities -- one- and two-player online simulations, an avatar-based simulation, and a virtual teaching simulator—for supporting preservice teachers in learning how to facilitate argumentation-focused discussions in elementary mathematics and science.

Eliciting and interpreting students’ ideas are essential skills in teaching, yet pre-service teachers (PSTs) rarely have adequate opportunities to develop these skills. In this study, we examine PSTs’ patterns of discourse and perceived learning through engaging in an interactive digital simulation called Eliciting Learner Knowledge (ELK). ELK is a seven-minute, chat-based virtual role play between a PST playing a “teacher” and a PST playing a “student” where the goal is for the teacher to find out what the student knows about a topic.

Facilitating discussions is a key approach that science teachers use to engage students in scientific argumentation. However, learning how to facilitate argumentation-focused discussions is an ambitious teaching practice that can be difficult to learn how to do well, especially for preservice teachers (PSTs) who typically have limited opportunities to tryout and refine this teaching practice. This study examines secondary PSTs’ perceptions and engagement with a science performance task—used within an online, simulated classroom consisting of five middle school student avatars—to practice this ambitious teaching practice.

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.