EXTERNAL REPRESENTATIONS FOR DATA DISTRIBUTIONS: IN SEARCH OF COGNITIVE FIT

Data distributions can be represented using different external representations, such as histograms and boxplots. Although the role of external representations has been extensively studied in mathematics, this is less the case in statistics. This study helps to fill this gap by systematically varying the representation that accompanies a task between participants, and assessing how university students use such representations in comparing aspects of data distributions. Following a cognitive fit approach, we searched for matches between items and representations. Depending on the item, some representations led to better achievement than other representations. However, due to the low overall accuracy rates and various difficulties that students displayed in interpreting these representations, we cannot make strong claims regarding matches between items and representations. First published May 2013 at Statistics Education Research Journal Archives

[1]  Chris J. Wild The Concept of Distribution. , 2006 .

[2]  C. KONOLD,et al.  STUDENTS ANALYZING DATA : RESEARCH OF CRITICAL BARRIERS , 1998 .

[3]  Theodosia Prodromou,et al.  THE ROLE OF CAUSALITY IN THE CO-ORDINATION OF TWO PERSPECTIVES ON DISTRIBUTION WITHIN A VIRTUAL SIMULATION , 2006 .

[4]  Robert C. delMas,et al.  Using assessment items to study students' difficulty reading and interpreting graphical representations of distributions , 2005 .

[5]  Felice S. Shore,et al.  Students' Misconceptions in Interpreting Center and Variability of Data Represented via Histograms and Stem-and-Leaf Plots , 2008 .

[6]  Iris Vessey,et al.  Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature* , 1991 .

[7]  Albert T. Corbett,et al.  The Resilience of Overgeneralization of Knowledge about Data Representations. , 2002 .

[8]  Barbara Tversky,et al.  Cognitive Principles of Graphic Displays , 1997 .

[9]  G. Goldin,et al.  Perspectives on representation in mathematical learning and problem solving , 2008 .

[10]  Christine M. Anderson-Cook The Challenge of Developing Statistical Literacy, Reasoning and Thinking , 2006 .

[11]  W. Schnotz Integrated Model of Text and Picture Comprehension , 2021, The Cambridge Handbook of Multimedia Learning.

[12]  B. Marx The Visual Display of Quantitative Information , 1985 .

[13]  B. Tversky,et al.  Searching imagined environments. , 1990 .

[14]  Lynn Arthur Steen,et al.  On the shoulders of giants: new approaches to numeracy , 1990 .

[15]  Dani Ben-Zvi,et al.  Junior high school students' construction of global views of data and data representations , 2001 .

[16]  Iris Vessey,et al.  The Role of Cognitive Fit in the Relationship Between Software Comprehension and Modification , 2006, MIS Q..

[17]  M. Malbrán The Cambridge Handbook of Multimedia Learning , 2007 .

[18]  A. Leavy Indexing Distributions of Data: Preservice Teachers' Notions of Representativeness , 2004 .

[19]  Stephen Michael Kosslyn,et al.  Graph Design for the Eye and Mind , 2006 .

[20]  Wolff‐Michael Roth,et al.  Mathematization of experience in a grade 8 open-inquiry environment: An introduction to the representational practices of science , 1994 .

[21]  Carl Lee,et al.  STUDENT UNDERSTANDING OF HISTOGRAMS: A STUMBLING STONE TO THE DEVELOPMENT OF INTUITIONS ABOUT VARIATION , 2002 .

[22]  J. Elen,et al.  Conceptualising, investigating and stimulating representational flexibility in mathematical problem solving and learning: a critical review , 2009 .

[23]  Charisse Griffith-Charles,et al.  Analysing the costs and benefits of 3D cadastres with reference to Trinidad and Tobago , 2013, Comput. Environ. Urban Syst..

[24]  M. Bannert,et al.  Construction and interference in learning from multiple representation , 2003 .

[25]  Jane Watson,et al.  The Beginning of Statistical Inference: Comparing two Data Sets , 1998 .

[26]  R. Lehrer,et al.  Technology and mathematics education , 2008 .

[27]  Dani Ben-Zvi,et al.  REASONING ABOUT VARIABILITY IN COMPARING DISTRIBUTIONS 4 , 2004 .

[28]  Traci Higgins,et al.  Data seen through different lenses , 2015 .

[29]  Steve Cohen,et al.  Identifying Impediments to Learning Probability and Statistics From an Assessment of Instructional Software , 1996 .

[30]  Naomi B. Robbins,et al.  Creating More Effective Graphs , 2004 .

[31]  Jiajie Zhang,et al.  A representational analysis of relational information displays , 1996, Int. J. Hum. Comput. Stud..

[32]  Arthur Bakker,et al.  LEARNING TO REASON ABOUT DISTRIBUTION , 2004 .

[33]  Maxine Pfannkuch,et al.  COMPARING BOX PLOT DISTRIBUTIONS: A TEACHER’S REASONING , 2006 .

[34]  D. Firth Bias reduction of maximum likelihood estimates , 1993 .

[35]  Alexander Pollatsek,et al.  Data Analysis as the Search for Signals in Noisy Processes. , 2002 .

[36]  Gary L. Brase Pictorial representations in statistical reasoning , 2009 .

[37]  Dani Ben-Zvi,et al.  Research on Statistical Literacy, Reasoning, and Thinking: Issues, Challenges, and Implications , 2004 .

[38]  A. Leavy USING DATA COMPARISON TO SUPPORT A FOCUS ON DISTRIBUTION: EXAMINING PRESERVICE TEACHERS’ UNDERSTANDINGS OF DISTRIBUTION WHEN ENGAGED IN STATISTICAL INQUIRY , 2006, STATISTICS EDUCATION RESEARCH JOURNAL.

[39]  Arthur Bakker,et al.  SHOULD YOUNG STUDENTS LEARN ABOUT BOX PLOTS , 2005 .

[40]  S. Ainsworth,et al.  External Representations for Learning , 2009 .