A Design Heuristic for Analyzing and Interpreting Data

Abstract

Students reasoning with data in an authentic science environment had the opportunity to learn about the process of science and the world around them while developing skills to analyze and interpret self-collected and secondhand data. Our results show that nearly 50% of the treatment group responses were accurate when describing the reason for measuring water parameters, compared with 26% in the traditional lab group. When pre- and post-survey scores were compared, students in the treatment group outperformed students in the traditional group on four items: making claims about water pollution based on data; understanding water pollution in the Hudson River; understanding the relationship between temperature, pH, and salinity values; and feeling prepared to justify their reasoning on water pollution. Our evidence points to greater engagement by the treatment group and stronger descriptions about their claims, evidence, and reasoning around measuring water parameters and potential water pollution problems.

Additional information

Notes on contributors

Sandra Swenson

Sandra Swenson ([email protected]) is a lecturer and curriculum designer

Yi He

Yi He ([email protected]) is a professor of chemistry, both in the Department of Sciences at John Jay College of Criminal Justice at the City University of New York

Heather Boyd

Heather Boyd ([email protected]) is a program evaluator

Kate Schowe Good

Kate Schowe Good ([email protected]) managed the environmental and natural science teaching labs at John Jay College and is currently a tree conservation research assistant at the Morton Arboretum.