Middle

Controversial topics that arise in science classrooms, especially those of social relevance (e.g., the climate crisis), provide opportunities to help students learn about and discuss contradictory ideas they may encounter in their everyday experiences. Such topics may also be challenging to teach, but scaffolding may facilitate effective instruction. We describe one type of instructional scaffolding, the Model-Evidence Link (MEL) activity, that supports students’ reasoning when evaluating connections between lines of evidence and competing explanations about phenomena.

Controversial topics that arise in science classrooms, especially those of social relevance (e.g., the climate crisis), provide opportunities to help students learn about and discuss contradictory ideas they may encounter in their everyday experiences. Such topics may also be challenging to teach, but scaffolding may facilitate effective instruction. We describe one type of instructional scaffolding, the Model-Evidence Link (MEL) activity, that supports students’ reasoning when evaluating connections between lines of evidence and competing explanations about phenomena.

Controversial topics that arise in science classrooms, especially those of social relevance (e.g., the climate crisis), provide opportunities to help students learn about and discuss contradictory ideas they may encounter in their everyday experiences. Such topics may also be challenging to teach, but scaffolding may facilitate effective instruction. We describe one type of instructional scaffolding, the Model-Evidence Link (MEL) activity, that supports students’ reasoning when evaluating connections between lines of evidence and competing explanations about phenomena.

Integrating science education with social justice is vital for preparing students to critically address significant societal issues like climate change and pandemics. This study examines the effectiveness of socioscientific system modeling as a tool within Justice-Centered Science Pedagogy (JCSP) to enhance middle school students’ understanding of social justice science issues. It focuses on how system modeling can scaffold students’ reasoning about complex social systems, informed by their lived experiences, cultural backgrounds, and social identities.

This paper explores the transformative impact of generative artificial intelligence (GenAI) on teachers’ roles and agencies in education, presenting a comprehensive framework that addresses teachers’ perceptions, knowledge, acceptance, and practices of GenAI. As GenAI technologies, such as ChatGPT, become increasingly integrated into educational settings, both in-service and future teachers are required to adapt to evolving classroom dynamics, where AI plays a significant role in content creation, personalized learning, and student engagement.

Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system.

This study employs the Experiential Learning Theory framework to investigate students’ use of a wildfire simulation. We analyzed log files automatically generated by middle and high school students (n = 1515) as they used a wildfire simulation and answered associated prompts in three simulation-based tasks.

A classroom study was conducted to understand how to engage in responsive teaching with 18 seventh grade students at three stages of units coordination during a unit on proportional reasoning co-taught by the first author and classroom teacher. In the unit, students worked on making two cars travel the same speed. Students at all three stages of units coordination learned to do so, as reported elsewhere (Hackenberg et al., 2023). This paper focuses on the practice of inquiring responsively in small groups.

The changing landscape of geoscience learning has initiated growing interest in engaging science learners with climate data. One approach to teaching climate is the application of broadly accessible digital science curricula, which often include data tools such as visualizations, data representations, and simulations embedded within digital science curricula. We are specifically interested in how students and teachers grapple with scientific uncertainty in digital curricula.

In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration. Students write an initial explanation, respond to two adaptive prompts in the dialog, and write a revised explanation. The NLP models score the KI level of the initial and revised explanations. They also detect the ideas in the explanations and the dialog responses.