Products of SRTL

SRTL 10:

Special issue in ZDM on “Innovations in Statistical Modelling to Connect Data, Chance and Context.” The guest editors of this Special issue are: Maxine Pfannkuch and Stephanie Budgett, Auckland University, New Zealand; and Dani Ben-Zvi, Haifa University, Israel.

Articles in this Special issue:

Innovations in statistical modeling to connect data, chance and context
Maxine Pfannkuch, Dani Ben-Zvi, Stephanie Budgett

Dot plots and hat plots: supporting young students emerging understandings of distribution, center and variability through modeling
Jill Fielding-Wells

Statistical modelling and repeatable structures: purpose, process and prediction
Katie Makar, Sue Allmond

Sixth grade students’ emerging practices of data modelling
Sibel Kazak, Dave Pratt, Rukiye Gökce

Statistical modeling to promote students’ aggregate reasoning with sample and sampling
Keren Aridor, Dani Ben-Zvi

The role of model comparison in young learners’ reasoning with statistical models and modeling
Michal Dvir, Dani Ben-Zvi

Developing a statistical modeling framework to characterize Year 7 students’ reasoning
Anne Patel, Maxine Pfannkuch

Students’ construction and use of statistical models: a socio-critical perspective
Lucía Zapata-Cardona

Middle school students’ reasoning about data and context through storytelling with repurposed local data
Michelle Hoda Wilkerson, Vasiliki Laina

Elementary preservice teachers’ reasoning about statistical modeling in a civic statistics context
Rolf Biehler, Daniel Frischemeier, Susanne Podworny

Every rose has its thorn: secondary teachers’ reasoning about statistical models
Nicola Justice, Andrew Zieffler, Michael D. Huberty, Robert delMas

Students’ use of narrative when constructing statistical models in TinkerPlots
Jennifer Noll, Kit Clement, Jason Dolor, Dana Kirin, Matthew Petersen

Modeling and linking the Poisson and exponential distributions
Stephanie Budgett, Maxine Pfannkuch

Statistics students’ identification of inferential model elements within contexts of their own invention
Matthew D. Beckman, Robert delMas

 

SRTL 9:

Special issue in Statistics Education Research Journal (SERJ) on “Statistical Reasoning about Models and Modelling in the Context of Informal Statistical Inference”. The guest editors of this Special issue are: Rolf Biehler, Daniel Frischemeier and Susanne Podworny (University of Paderborn, Germany)

Articles in this Special issue:

Computational Modelling and Children’s Expressions of Signal and Noise
Janet Ainley and Dave Pratt

The Co-Emergence of Aggregate and Modelling Reasoning
Keren Aridor and Dani Ben-Zvi

Modeling Signal-Noise Processes Supports Student Construction of a Hierarchical Image of Sample
Richard Lehrer

A Modeling Approach to the Development of Students’ Informal Inferential Reasoning
Helen M. Doerr, Robert delMas, and Katie Makar

Students’ Emergent Articulations of Statistical Models and Modeling in Making Informal Statistical Inferences
Hana Manor Braham and Dani Ben-Zvi

Students’ Emergent Modelling of Statistical Measures—A Case Study
Christian Büscher and Susanne Schnell

Promoting Modelling and Covariational Reasoning among Secondary School Students in the Context of Big Data
Einat Gil and Alison L. Gibbs

Modeling as a Core Component of Structuring Data
Clifford Konold, William Finzer, and Kosoom Kreetong

TinkerPlot™ Model Construction Approaches for Comparing Two Groups: Student Perspectives
Jennifer Noll and Dana Kirin

Elementary Preservice Teachers’ Reasoning about Modeling a “Family Factory” with Tinkerplots—A Pilot Study
Rolf Biehler, Daniel Frischemeier, and Susanne Podworny

Pre-Service Mathematics Teachers’ Use of Probability Models in Making Informal Inferences about a Chance Game
Sibel Kazak and Dave Pratt

An Analysis of Secondary Teachers’ Reasoning with Participatory Sensing Data
Robert Gould, Anna Bargagliotti, and Terri Johnson

 

SRTL 8:

Reasoning about Uncertainty: Learning and Teaching Informal Inferential Reasoning.
By A. Zieffler and E. Fry (Eds.). Catalyst Press.

Chapters in this book are:

Inferring to a Model: Using Inquiry-Based Argumentation to Challenge Young Children’s Expectations of Equally Likely Outcomes
Jill Fielding-Wells and Katie Makar

‘Howconfident are you?’ Supporting Young Students’ Reasoning about Uncertainty in Chance Games through Students’ Talk and Computer Simulations
Sibel Kazak

Students’ Articulations of Uncertainty in Informally Exploring Sampling Distributions
Hana Manor Braham and Dani Ben-Zvi

Experiment-to-Causation Inference: Understanding Causality in a Probabilistic Setting
Maxine Pfannkuch, Stephanie Budgett, and Pip Arnold

Preservice Teachers’ Reasoning about Uncertainty in the Context of Randomization Tests
Rolf Biehler, Daniel Frischemeier, and Susanne Podworny

Exploring Teachers’ Ideas of Uncertainty
Lucia Zapata-Cardona

 

 

SRTL 7:
Special issue in Educational Studies in Mathematics (March 2015, 88, 3):

Ben-Zvi, D., Bakker, A., & Makar, K. (2015). Learning to reason from samples. Educational Studies in Mathematics, 88(3), 291-303.

Konold, C, Higgins, T.,Russell, S. J.,Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88(3),305-325.

Garfield, J., Le, L., Zieffler, A., Ben-Zvi, D. (2015). Developing students’ reasoning about samples and sampling variability as a path to expert statistical thinkingEducational Studies in Mathematics, 88(3), 327-342.

Pfannkuch, M., Arnold, P., & Wild, C. J. (2015). What I see is not quite the way it really is: students’ emergent reasoning about sampling variability. Educational Studies in Mathematics, 88(3), 343-360.

Meletiou-Mavrotheris, M., Paparistodemou, E. (2015). Developing students’ reasoning about samples and sampling in the context of informal inferences. Educational Studies in Mathematics, 88(3), 385-404.

Ainley, J., Gould, R., & Pratt, D. (2015). Learning to reason from samples: commentary from the perspectives of task design and the emergence of “big data”. Educational Studies in Mathematics, 88(3), 405-412.

Noll, J., Hancock, S., (2015). Proper and paradigmatic metonymy as a lens for characterizing student conceptions of distributions and sampling. Educational Studies in Mathematics, 88(3), 361-383.

 

SRTL 6:
Special issue Mathematical Thinking and Learning 2011 Volume 13, Numbers 1-2
ISSN 1098-6065, http://www.informaworld.com/smpp/title~content=t775653685

EDITORIAL
The Role of Context in Developing Reasoning about Informal Statistical Inference
Katie Makar and Dani Ben-Zvi (Guest Editors)

EDITORIAL
The Role of Context in Developing Reasoning about Informal Statistical Inference
Katie Makar and Dani Ben-Zvi

ARTICLES
Lessons from Inferentialism for Statistics Education
Arthur Bakker and Jan Derry

The Role of Context in Developing Informal Statistical Inferential Reasoning:A Classroom Study
Maxine Pfannkuch

The Role of Context Expertise When Comparing Data
Cynthia Langrall, Steven Nisbet, Edward Mooney, and Sinchai Jansem

Conceptual Challenges in Coordinating Theoretical and Data-centered Estimates of Probability
Cliff Konold, Sandra Madden, Alexander Pollatsek, Maxine Pfannkuch,Chris Wild, Ilze Ziedins, William Finzer, Nicholas J. Horton, and Sibel Kazak

Explanations and Context in the Emergence of Students’ Informal Inferential Reasoning
Einat Gil and Dani Ben-Zvi

Statistically, Technologically, and Contextually Provocative Tasks: Supporting Teachers’ Informal Inferential Reasoning
Sandra R. Madden

Authentic Practices as Contexts for Learning to Draw Inferences Beyond Correlated Data
Adri Dierdorp, Arthur Bakker, Harrie Eijkelhof, and Jan van Maanen

The Reasoning Behind Informal Statistical Inference
Katie Makar, Arthur Bakker, and Dani Ben-Zvi

 

SRTL 5:
Special issue Statistics Education Research Journal, Volume 7 Number 2, November 2008
http://www.stat.auckland.ac.nz/~iase/publications.php?show=serjarchive

Introducing the Special Issue on Informal Inference
Dave Pratt and Janet Ainley (Guest Editors)

Reasoning about Informal Statistical Inference: One Statistician’s ViewInvited
Allan J. Rossman

Statistical Cognition: Towards Evidence-Based Practice in Statistics and Statistics Education
Ruth Beyth-Maron, Fiona Fidler, and Geoff Cumming

A Framework to Support Research on Informal Inferential Reasoning
Andrew Zieffler, Joan Garfield, Robert delMas, and Chris Reading

Exploring Beginning Inference with Novice Grade 7 Students
Jane M. Watson

Developing Young Students’ Informal Inference Skills in Data Analysis
Efi Paparistodemou and Maria Meletiou-Mavrotheris

Local and Global Thinking in Statistics Inference
Dave Pratt, Peter Johnston-Wilder, Janet Ainley, and John Mason

Statistical Inference at Work: Statistical Process Control as an Example
Arthur Bakker, Phillip Kent, Jan Derry, Richard Noss, and Celia Hoyles

 

SRTL 4:
Special issue Statistics Education Research Journal, Volume 5 Number 2, November 2006
http://www.stat.auckland.ac.nz/~iase/publications.php?show=serjarchive

Reasoning about distribution: A complex process
Guest Editors: Maxine Pfannkuch and Chris Reading

The Concept of Distribution
Chris Wild

Comparing Box Plot Distributions: A Teacher’s Reasoning
Maxine Pfannkuch

An Emerging Hierachy of Reasoning About Distribution: From a Variation Perspective
Chris Reading and Jackie Reid

The Role of Causality in the Co-ordination of Two Perspectives on Distribution Within a Virtual Simulation
Theodosia Prodromou and Dave Pratt

Using Data Comparisons to Support a Focus on Distribution: Examining Preservice Teachers’ Understandings of Distribution When Engaged in Statistsical Inquiry
Aisling Leavy

 

SRTL 3:
Special issue Statistics Education Research Journal, Volume 3 Number 2, November 2004
http://www.stat.auckland.ac.nz/~iase/publications.php?show=serjarchive

Research on Reasoning about Variability: A Forward
Dani Ben-Zvi and Joan Garfield (Guest Editors)

Variability: One Statistician’s View
Rob Gould (Invited)

Strategies for Managing Statistical Complexity with New Software Tools
James Hammerman and Andee Rubin

Reasoning about Variability in Comparing Distributions
Dani Ben-Zvi

Reasoning about Shape as a Pattern in Variability
Arthur Bakker

Student Description of Variation while Working with Weather Data
Chris Reading

 

SRTL 1+2:
Ben-Zvi, D. & Garfield, J. (Eds.). The challenge of developing statistical literacy, reasoning, and thinking. Dordrecht, the Netherlands: Kluwer Academic Publishers.
http://wikiet.edu.haifa.ac.il/images/f/f7/SRTL_book_front-matter.pdf