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 â€œcookbookâ€ 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
InSTEP is developing an online personalized professional learning platform to support teachers' growth in providing students learning opportunities in statistics and data science using key practices and processes with data. We are creating a scalable, accessible, and flexible approach aligned with research-based principles of effective professional learning. We use design principles for online teacher learning, and our materials are based on research on students' and teachers' learning in statistics and data science education.
This project aims to investigate needs and challenges in developing an informed public able to evaluate empirical evidence generated from scientific activities. This includes understanding teachers' epistemic goals and practices and how to provide professional development (PD) to improve instruction. The resulting instruction will offer new affordances to advance students' and teachers' learning.
Co-PI(s): Clark Chinn, Rutgers University
The A-STEP project fosters collaboration between university faculty and pathway partners to implement common set of tools (Next Gen ASET Toolkit) across a science teacher training and development pathway. Partnerships across steps function under shared goals and paradigm shifts for pedagogical reform along the teacher pathway. A-STEP promotes change across our Networked Improvement Community (NIC) and the local pathway partners working with each university, ultimately impacting the enactment of the NGSS in respective K-12 classrooms.
The purpose of this project is the design and development of a K-12 classroom observation protocol for integrated STEM instruction (STEM-OP). Using exploratory and confirmatory factor analysis, the STEM-OP will be a valid and reliable instrument for use in a variety of educational contexts. The STEM-OP and associated training materials will be available for use by education stakeholders, (e.g., K-12 teachers and district administrators), through a publicly available online platform.
The goal of this planning grant, which is based on the Cultural-Historical Activity Theory (CHAT). is to explicitly focus on broadening participation in the K-12 STEM teaching workforce, with the theory of action that diversifying the K-12 STEM teaching workforce would in the long term help more students see STEM as accessible to them and then be more likely to choose a STEM degree or career. This grant is also funded by NSF INCLUDES.
Co-PI(s): Helen Bond and Marilyn M Irving, Howard University; Hyunju Lee and Amy L D'Amico, Smithsonian Institution