UC Davis

In a data-driven age, K–12 science education must evolve to foster data literacy among learners. This goal relies heavily on educators’ ability to integrate authentic data experiences into instruction. This two-part study first investigates the level at which educators practice data-rich pedagogy in their science classrooms and their ability to make sense of data when connecting to science content areas. An additional study then investigates how structured professional learning and one-on-one mentoring can enhance educators’ data-rich pedagogy (DRP) and data fluency (DF). Over the course of 2 years, data were collected from K–12 science educators in both studies through surveys, interviews, and classroom observations. Results reveal a notable discrepancy between educators’ perceived proficiency with data and the depth of their classroom integration of data-rich practices. Although participants demonstrated enthusiasm for using a variety of data resources available to them through National Aeronautics and Space Administration (NASA) and Global Learning and Observation to Benefit the Environment (GLOBE), they reported challenges, including limited time, insufficient formal training in data science, and constrained curricular flexibility. Professional learning opportunities—particularly individualized mentoring—proved highly effective in helping educators understand how to navigate data resources while developing data-rich strategies. Also, when educators made sense of data sources through their interactions with data, they advanced their development of data fluency. This resulted in greater educator confidence in delivering data-centric lessons, providing learners with data literacy skills, and using data more iteratively and intentionally with them. Both studies identify the gap in the literature on the critical role of sustained, targeted professional development in bridging the gap between educators’ intentions and enactment of data-rich instruction. Perhaps this gap in educational science research exists because scaling such professional learning models remains challenging yet essential for preparing preservice and in-service educators to empower learners with the data literacy skills needed to engage in authentic scientific inquiry and problem-solving now and to better prepare them for post-secondary education. One of the overarching contributions of this study, absent from the field, is new knowledge on how to help science educators develop a variety of data-rich practices and advance their data fluency by demonstrating the connections among DRP, DF, and PD. Simply put, these studies address the “how to” of integrating data to support science content learning and data skills in learners, rather than merely relying on the principles that identify them.