Guiding Inquiry-Based Math Learning

In this chapter, we present a case study of a classroom approach whose development was inspired by learning sciences research. We conducted a sequence of classroom design experiments in urban seventh and eighth grade classrooms (see Barab, this volume, and Confrey, this volume, for discussions of design experiment methodology) focused on teaching and learning statistical data analysis. During this process, we formulated, tested, and revised specific conjectures about both the process of students' learning in the domain of statistics, and ways to scaffold that learning. The primary products of these design experiments were two sequences of instructional activities: one that focused on the analysis of univariate data and one on bivariate data, and three computer-based data analysis tools that were used in both. In this chapter, we restrict our focus to the first of the two classroom design experiments – the one focused on the analysis of univariate data. We start by critically examining the traditional instructional goals of middle school math. Learning sciences research rejects a conception of knowledge as consisting of facts and procedures to be memorized; unfortunately, this traditional style of instruction still predominates in most mathematics classrooms. As part of this discussion, we briefly summarize recent developments in both the use of statistics in wider society and in statistics as a discipline. We then describe the initial assessments of students' statistical reasoning, and contrast them with the concluding assessments.

[1]  Wolff-Michael Roth,et al.  Where is the Context in Contextual Word Problems?: Mathematical Practices and Products in Grade 8 Students' Answers to Story Problems. , 1996 .

[2]  B. Latour Science in Action , 1987 .

[3]  Uri Wilensky,et al.  What is Normal Anyway? Therapy for Epistemological Anxiety , 1997 .

[4]  G. Wiggins,et al.  Understanding by Design , 1998 .

[5]  Roland W. Scholz,et al.  Didactics of mathematics as a scientific discipline , 2002 .

[6]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[7]  Luciano Meira,et al.  Making Sense of Instructional Devices: The Emergence of Transparency in Mathematical Activity. , 1998 .

[8]  L. Delpit The Silenced Dialogue: Power and Pedagogy in Educating Other People's Children , 1988 .

[9]  Paul Cobb,et al.  Learning About Statistical Covariation , 2003 .

[10]  Johan F. Hoorn,et al.  Distributed cognition , 2005, Cognition, Technology & Work.

[11]  J. Banks,et al.  Equity pedagogy: An essential component of multicultural education , 1995 .

[12]  Celia Hoyles,et al.  Touching epistemologies: meanings of average and variation in nursing practice , 1999 .

[13]  Paul Cobb,et al.  Individual and Collective Mathematical Development: The Case of Statistical Data Analysis. , 1999 .

[14]  D. S. Moore,et al.  Mathematics, statistics, and teaching , 1997 .

[15]  James J. Kaput,et al.  Authentic Inquiry With Data: Critical Barriers to Classroom Implementation , 1992 .

[16]  K.P.E. Gravemeijer,et al.  Developing realistic mathematics education , 1994 .

[17]  Paul Cobb,et al.  An analysis of students’ initial statistical understandings: developing a conjectured learning trajectory , 2002 .

[18]  Kay McClain,et al.  Teacher's and Students' Understanding: The Role of Tools and Inscriptions in Supporting Effective Communication. , 2002 .

[19]  Paul Cobb,et al.  Supporting Students' Ability to Reason about Data , 2001 .

[20]  Richard Lehrer,et al.  Exploring Children's Data Modeling , 1996 .

[21]  Joan Garfield,et al.  The challenge of developing statistical literacy, reasoning and thinking , 2004 .