The chapters in Thinking With Data are based on presentations given at the 33rd Carnegie Symposium on Cognition. The Symposium was motivated by the confluence of three emerging trends: (1) the increasing need for people to think effectively with data at work, at school, and in everyday life, (2) the expanding technologies available to support people as they think with data, and (3) the growing scientific interest in understanding how people think with data.
What is thinking with data? It is the set of cognitive processes used to identify, integrate, and communicate the information present in complex numerical, categorical, and graphical data. This book offers a multidisciplinary presentation of recent research on the topic. Contributors represent a variety of disciplines: cognitive and developmental psychology; math, science, and statistics education; and decision science. The methods applied in various chapters similarly reflect a scientific diversity, including qualitative and quantitative analysis, experimentation and classroom observation, computational modeling, and neuroimaging. Throughout the book, research results are presented in a way that connects with both learning theory and instructional application.
The book is organized in three sections:
Part I focuses on the concepts of uncertainty and variation and on how people understand these ideas in a variety of contexts. Part II focuses on how people work with data to understand its structure and draw conclusions from data either in terms of formal statistical analyses or informal assessments of evidence. Part III focuses on how people learn from data and how they use data to make decisions in daily and professional life.
Contents: Preface. Part I:Reasoning About Uncertainty and Variation. A.
Masnick, D. Klahr, B. Morris, Separating Signal From Noise: Childrens
Understanding of Error and Variability in Experimental Outcomes. C. Schunn,
L. Saner, S. Kirschenbaum, J.G. Trafton, E.B. Littleton, Complex Visual Data
Analysis, Uncertainty, and Representation. S. Trickett, J.G. Trafton, L.
Saner, C. Schunn,I Dont Know Whats Going on There: The Use of Spatial
Transformations to Deal With and Resolve Uncertainty in Complex
Visualizations. B. delMas, Y. Liu, Students Conceptual Understanding of the
Standard Deviation. J. Garfield, B. delMas, B. Chance, Using Students
Informal Notions of Variability to Develop an Understanding of Formal
Measures of Variability. R. Lehrer, L. Schauble, Contrasting Emerging
Conceptions of Distribution in Contexts of Error and Natural Variation. G.
Leinhardt, J. Larreamendy-Joerns, Discussion of Part I: Variation in the
Meaning and Learning of Variation. Part II:Statistical Reasoning and Data
Analysis. K. Dunbar, J. Fugelsang, C. Stein, Do Naïve Theories Ever Go Away?
Using Brain and Behavior to Understand Changes in Concepts. P. Thompson, Y.
Liu, L. Saldanha, Intricacies of Statistical Inference and Teachers
Understandings of Them. K. McNeill, J. Krajcik, Middle School Students Use
of Appropriate and Inappropriate Evidence in Writing Scientific Explanations.
C. Konold, Designing a Data Analysis Tool for Learners. M. Lovet, N. Chang,
Data-Analysis Skills: What and How Are Students Learning? D.L. Schwartz, D.
Sears, J. Chang, Reconsidering Prior Knowledge. K. Koedinger, Discussion of
Part II: Statistical Reasoning and Data Analysis. Part III: Learning From and
Making Decisions With Data.D. Danks, Causal Learning From Observations and
Manipulations. P. Sedlmeier, Statistical Reasoning: Valid Intuitions Put to
Use. W.B. de Bruin, J. Downs, B. Fischhoff, Adolescents' Thinking About the
Risks of Sexual Behaviors. M. Burrage, M. Epstein, P. Shah, Discussion of
Part III: Learning From and Making Decisions About Data.
Lovett, Marsha C. ; Shah, Priti
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