A Data Visualization Experience for Preservice STEM Teachers

The College Of New Jersey, Ewing NJ

Investigators

Abstract

The majority of teacher preparation programs do not explicitly teach data literacy skills or how to teach those skills. As a result, in-service teachers often do not feel confident with their own data competencies, and do not integrate data skills and practices into their teaching. Yet the presence of data and data visualizations in society continues to grow. Schools should be training students in how to collect, analyze, visualize, and interpret this data. This project intends to provide higher education faculty with the tools to teach their students (preservice teachers) how to integrate data skills into their future K-8 classrooms. The overarching goal is to create a more data literate society, starting with the youngest learners by setting their teachers up for success to teach them these critical data skills. This project will support higher education faculty in guided efforts to implement data literacy skills into existing STEM education methods courses, thereby providing K-8 preservice teachers the skills and self-efficacy to work with data and integrate data literacy into their future classrooms. Four cohorts of five faculty members each will attend a two-day Faculty Training Institute (FTI) in which they are trained to integrate the five session Curriculum Supplement Initiative (CSI) into their courses. Following the training, faculty will implement the content during an existing education methods course, providing preservice teachers the opportunity to collect, analyze, synthesize, and visualize data. The project has five objectives: (1) scale implementation of the CSI materials to 20 new-to-the-program faculty across the United States over 3 years, (2) examine the development of exploratory data and question-asking skills of preservice K-8 STEM teachers (at least 400 in total), (3) examine preservice teachers’ self-efficacy in teaching with STEM data, (4) refine and publish CSI materials aimed at increasing preservice K-8 STEM teachers' exploratory data skills and self-efficacy with the larger community, and (5) broaden the reach of the approach by supporting faculty to share the project with wider audiences through broader impact initiatives. To achieve each of the above objectives, the project team plans to: (1) recruit a geographically and demographically diverse set of faculty teaching education methods courses through STEM education professional organizations, (2) assess preservice teachers’ data literacy skills at the beginning and end of the semester in which the CSI is implemented, (3) assess preservice teachers’ data self efficacy at the beginning and end of the semester in which the CSI is implemented, (4) complete two evidence-based revision iterations of the CSI materials, then make the CSI available for public use, and (5) provide support for faculty who have implemented the CSI to attend conferences and design professional development sessions to share with the broader community. This project intends to refine and disseminate a model for training preservice K-8 teachers to work with and use STEM data in their future classes, and contribute to theory by investigating how preservice teachers’ skills about asking questions related to data changes over time. The project has the potential to fundamentally change the way teacher education programs train preservice teachers to work with data, which can in turn improve the quality of data education for the nation’s youngest learners, resulting in a more data literate society. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. Partial funding for this project is from the Robert Noyce Teacher Scholarship program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.