William Finzer

Professional Title
Senior Scientist
About Me (Bio)
Bill is PI of the DRK-12 CODAP project. Prior to that he led the Data Games project and the Fathom Software development team at KCP Technologies. His experience includes software development, curriculum development, research into programming tools, teacher professional development, classroom teaching, and research on learning statistics. He has been principal investigator of several NSF/SBIR and ED/SBIR funded projects, most recently Data Games, a collaboration with Cliff Konold at UMass Amherst.

Bill's experience as a software developer began in 1978 at San Francisco State University working with Diane Resek to create curriculum and computer environments for Statistics without Fear, Computers without Fear, and Computers in the Classroom. With Resek, he co-authored the Mirrors on the Mind software series and was co-PI of the Math Worlds project and the Computer Curriculum Cadre project. His research with Laura Gould at the Xerox Palo Alto Research Center culminated in the development of a Smalltalk-based authoring environment called Programming by Rehearsal. At the Lawrence Hall of Science in Berkeley, he led the team that developed DataRelator, an early hypertext relational database. Bill’s current interests center on developing software and curriculum to prepare students and teachers to make intelligent use of the data deluging our society.

This project addresses a critical need to help middle school teachers learn to incorporate data science in their teaching. It uses an open-source platform called the Common Online Data Analysis Platform (CODAP) as a tool for teachers to learn about data science and develop resources for students’ learning. The project team will develop a framework for teachers’ knowledge of data science teaching and learning. Insights from the project will help develop effective practices for teaching data science and understanding how students learn data science.

Concord Consortium

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?"

KCP Technologies, Inc.

The Data Games project has developed software and curriculum materials in which data generated by students playing computer games form the raw material for mathematics classroom activities. Students play a short video game, analyze the game data, develop improved strategies, and test their strategies in another round of the game.

Gulf of Maine Research Institute (GMRI)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

University of Florida (UF)

LOCUS (Levels of Conceptual Understanding in Statistics) is an NSF Funded DRK12 project (NSF#118618) focused on developing assessments of statistical understanding. These assessments will measure students’ understanding across levels of development as identified in the Guidelines for Assessment and Instruction in Statistics Education (GAISE). The intent of these assessments is to provide teachers and researchers with a valid and reliable assessment of conceptual understanding in statistics consistent with the Common Core State Standards (CCSS).

Education Development Center, Inc. (EDC)

This project is developing and studying high school curriculum modules that integrate social justice topics with statistical data investigations to promote skills and interest in data science among underrepresented groups in STEM.