Technology

In this project, we developed, piloted, and studied the use of a set of performance-based tasks delivered within a simulated classroom environment in order to improve preservice elementary teachers' ability to facilitate argumentation-focused discussions in mathematics and science. We conceptualized these simulated discussions as formative assessment opportunities, and studied how teacher educators made use of them within methods courses to support preservice teachers' learning. We also examined evidence of preservice teacher learning via pre/post measures.

The project examines how representations of practice facilitate preservice teachers' professional knowledge for teaching fractions and multiplication/division. The project focuses specifically on: how single and multi-perspective 360 video affects PSTs' professional knowledge; how PSTs use technological scaffolds to engage in 360 video, and its effect on their professional knowledge; and the design of a platform for teacher educators to create their own 360 video immersive experiences

Co-PI(s): Richard E. Ferdig and C. C. Lu, Kent State University

This exploratory project supports the professional development of secondary STEM teachers by providing multiyear training around three specific areas: (1) environmental sciences themed content; (2) technology integration in the classroom, and (3) classroom-based action research within action research communities. Using virtual reality to focus on wetlands and their connection to flooding brings locally relevant STEM concepts in a real-world context that is relatable to minoritized teachers and students living in these areas.

STRIDES supports teachers to customize the curriculum to address diverse students' evolving ideas and achieve the multi-dimensional proficiency called for by the Next Generation Science Standards (NGSS). STRIDES catalyzes a new approach to teachers' curriculum customization. STRIDES will improve the evidence teachers have to make customization decisions by collaborating with the Educational Testing Service (ETS) to advance natural language processing (NLP) methods.

The SDLC project has developed and studied curriculum modules for non-AP high school statistics to promote interest and skills in statistical thinking and data analysis among diverse high school populations. Modules engage students with social-justice-themed data investigations using large-scale socioeconomic data from the U.S. Census Bureau and student-friendly online data visualization tools. Current study findings show growth in student interest and skills in statistical thinking and data analysis following module use.

Streams of Data is pursuing early stage research to address: How can the use of professionally collected, scientific data support the development of data literacy skills in elementary students, and what types of scaffolds are necessary to realize this potential? In the first year, baseline research examined the analytical thinking approaches children intuitively use when making meaning from different types of data with minimal scaffolding? We explored commonplace scenarios of data and conventional data representations.

Every student should have the chance to experience the exciting practice of science. But far too often, students encounter only highly structured “cookbook” labs in their science classrooms. InquirySpace combines a software environment that integrates sensors, simulations, and data exploration capabilities with instructional guidance, and helps students move from fundamental data analysis and scaffolded experiments to open experiments of their own design.

Co-PI(s): Daniel Damelin and Hee-Sun Lee, Concord Consortium; Sam Gweon, Physics Front

We share the conception, design, and some activities from a curriculum based on the use of a global climate model EzGCM in secondary geoscience classrooms. Implemented through the NSF-funded CLiMES (Climate Literacy through Modeling and Epistemology of Science) project, this curriculum facilitated in-depth understanding of climate literacy concepts through model-based reasoning.

Co-PI(s): Mark Chandler, Columbia University