Building a Connection between Experimental and Theoretical Aspects of Probability

This paper addresses a question identified by Graham Jones: what are the connections made by students in the middle years of schooling between classical and frequentist orientations to probability? It does so based on two extended lessons with a class of Grade 5/6 students and in-depth interviews with eight students from the class. The Model 1 version of the software TinkerPlots was used in both settings to simulate increasingly large samples of random events. The aim was to document the students' understanding of probability on a continuum from experimental to theoretical, including consideration of the interaction of manipulatives, the simulator, and the law of large numbers. A cognitive developmental model was used to assess students' understanding and recommendations are made for classroom interventions.

[1]  Manfred Borovcnik,et al.  Empirical Research in Understanding Probability , 1991 .

[2]  Naresh K. Malhotra,et al.  Marketing Research: An Applied Orientation , 1993 .

[3]  Dor Abrahamson,et al.  THE ODDS OF UNDERSTANDING THE LAW OF LARGE NUMBERS: A DESIGN FOR GROUNDING INTUITIVE PROBABILITY IN COMBINATORIAL ANALYSIS , 2006 .

[4]  J. Biggs,et al.  Multimodal Learning and the Quality of Intelligent Behavior , 1991 .

[5]  Clifford Konold HANDLING COMPLEXITY IN THE DESIGN OF EDUCATIONAL SOFTWARE TOOLS , 2006 .

[6]  Susanne Prediger Do You Want Me to Do It with Probability or with My Normal Thinking? Horizontal and Vertical Views on the Formation of Stochastic Conceptions , 2008 .

[7]  Dor Abrahamson,et al.  Learning axes and bridging tools in a technology-based design for statistics , 2007, Int. J. Comput. Math. Learn..

[8]  Hollylynne Stohl,et al.  Developing notions of inference using probability simulation tools , 2002 .

[9]  Cliff Konold,et al.  Reconnecting Data and Chance , 2008 .

[10]  Lynn Arthur Steen Every Teacher Is a Teacher of Mathematics. , 2007 .

[11]  Jane Watson,et al.  Fairness of dice: a longitudinal study of students' beliefs and strategies for making judgements , 2003 .

[12]  Tina Blythe,et al.  The Teaching for Understanding Guide , 1997 .

[13]  U. Wilensky Abstract Meditations on the Concrete and Concrete Implications for Mathematics Education , 1991 .

[14]  Robin Rider,et al.  DIFFERENCES IN STUDENTS' USE OF COMPUTER SIMULATION TOOLS AND REASONING ABOUT EMPIRICAL DATA AND THEORETICAL DISTRIBUTIONS , 2006 .

[15]  Jane Watson,et al.  The development of chance measurement , 1997 .

[16]  Dor Abrahamson,et al.  There Once Was a 9-Block …- A Middle-School Design for Probability and Statistics , 2006 .

[17]  Richard Noss,et al.  The Microevolution of Mathematical Knowledge: The Case of Randomness , 2002 .

[18]  D. Clements ‘Concrete’ Manipulatives, Concrete Ideas , 2000 .

[19]  Julie Sarama,et al.  Using Computers for Algebraic Thinking , 1998 .

[20]  J. Piaget The construction of reality in the child , 1954 .

[21]  Tim Erickson USING SIMULATION TO LEARN ABOUT INFERENCE , 2006 .

[22]  Kevin F. Collis,et al.  Evaluating the Quality of Learning: The SOLO Taxonomy , 1977 .

[23]  Kari Augustine Mystery Three, Find It, and Treasure Hunt , 2006 .

[24]  Cliff Konold,et al.  Understanding Distributions by Modeling Them , 2007, Int. J. Comput. Math. Learn..

[25]  Jamie D. Mills,et al.  Using Computer Simulation Methods to Teach Statistics: A Review of the Literature , 2002 .

[26]  Maria Meletiou-Mavrotheris,et al.  DEVELOPING YOUNG STUDENTS’ INFORMAL INFERENCE SKILLS IN DATA ANALYSIS , 2008 .

[27]  M. Stein,et al.  Manipulatives: One Piece of the Puzzle. , 2001 .

[28]  S. Krantz,et al.  A NEW KIND OF INSTRUCTIONAL MATHEMATICS LABORATORY , 2006 .