Category: Focus Area: Data Science Education
We’re developing an innovative curriculum and datathon aimed at preparing women and minorities to become engineers, researchers, and healthcare professionals who write algorithms, design research methods, and collect data, ultimately reducing bias in AI and medical databases.
We are addressing the critical need for accessible data science tools in K-12 education by developing a plugin for CODAP that will utilize an AI-powered assistant to facilitate sensemaking with data for blind and low-vision students.
We are working to develop the CODAP ecosystem by engaging user communities, refining development infrastructure, establishing effective governance models, and exploring sustainability options to ensure CODAP’s future.
In collaboration with the University of Florida, Texas Tech University, WestEd, and Florida Virtual School, this project is developing and researching a supplemental program for students to explore AI concepts in connection with their math course.
In collaboration with Carnegie Mellon University and North Carolina State University, we are designing and researching curriculum modules for secondary math, English language arts, and history to incorporate AI education across the curriculum.
In collaboration with James Madison University and Northwestern University, we are investigating how learners make sense of spatiotemporal data and how technology-based affordances can support learners in understanding and analyzing spatiotemporal data.
In collaboration with EL Education and researchers at the University of Colorado Boulder, DataPBL aims to co-design two data-enhanced interdisciplinary modules with middle school teachers and research student agency and positive identities related to data.
Two new projects focused on grades 3-5 and 6-8 are supporting Yup’ik students in Hooper Bay, AK. We are engaging community partners, teachers, and students in adapting Concord Consortium STEM units by including local phenomena and Universal Design for Learning (UDL) features.
We’re developing informal data science learning experiences for middle school-aged girls, using an online, multiplayer game designed to support girls’ identity-aligned work with data and promote their interest in data-rich futures.
Multidimensional Data aims to explore how students reason with hierarchical (multidimensional) datasets and how they might be aided in their reasoning. Drawing on our research findings, we will iteratively develop technologies to support students in representing data structures.