Science

Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors.

Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors.

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. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool.

In this article, we present a framework of culturally relevant science and mathematics pedagogy (CRSMP), which is grounded in the tenets of culturally relevant pedagogy. It delineates practices ranging from the most accessible and easy-to-implement, to the most challenging and often contentious ways to teach mathematics and science.

Building on the literature, we designed a practical framework to support attention to equity and justice in science teacher education coursework. This framework presents four approaches for including justice moves in elementary science lessons, from increasing opportunity and access in science, to increasing identity and representation in science, to expanding what counts as science, to seeing science as a part of justice movements. We analyzed the lesson plans of 16 preservice elementary teachers who were using the practical justice framework in their very first lesson planning experience.

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.

Elementary teachers require support through professional learning activities to enhance their climate change literacy and bolster their self-efficacy for teaching climate change. This study explores methods for supporting in-service elementary teachers’ self-efficacy in climate change teaching by examining the impact of professional learning activities that incorporate learning technologies on climate change literacy.

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. Our paper therefore examines how a 7th grade science class and their teacher leverage moments of uncertainty in their work within a digital geohazard curriculum to learn about wildfire risk and impact.

We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently.

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.