The predictive power of intuitive rules: A critical analysis of the impact of `more A–more B' and `same A–same B'

In the international community of mathematics and science educators the intuitive rules theory developed by the Israeli researchers Tirosh and Stavy receives much attention. According to this theory, students' responses to a variety of mathematical and scientific tasks can be explained in terms of their application of some common intuitive rules. Two major intuitive rules are manifested in comparison tasks: ‘More A—more B’ and ‘Same A—same B’. In this paper, we address two important questions for which the existing literature on intuitive rules does not provide a convincing research-based answer: (1) are the reasoning processes of students who respond in line with a given intuitive rule actually affected by that rule or by essentially other misconceptions (leading to the same answer), and (2) are individual students consistent in their choice of one of the intuitive rules when confronted with different, conceptually unrelated tasks? A test consisting of five comparison problems from different mathematical subdomains was administered collectively to 172 Flemish students from Grades 10 to 12. An analysis of students' written calculations and justifications suggested that the students were considerably less affected by the intuitive rules than their multiple-choice answers actually suggested. Instead, essentially different misconceptions and errors were found. With respect to the issue of individual consistency, we found that students who made many errors did not answer systematically in line with one of the two intuitive rules.

[1]  P. Cobb,et al.  Sociomathematical Norms, Argumentation, and Autonomy in Mathematics. , 1996 .

[2]  B. Greer Modelling Reality in Mathematics Classrooms: The Case of Word Problems. , 1997 .

[3]  Lieven Verschaffel,et al.  The Effects of Different Problem Presentations and Formulations on the Illusion of Linearity in Secondary School Students , 2002 .

[4]  David N. Perkins,et al.  Patterns of Misunderstanding: An Integrative Model for Science, Math, and Programming , 1988 .

[5]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[6]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[7]  G. Lakoff,et al.  Where mathematics comes from : how the embodied mind brings mathematics into being , 2002 .

[8]  Gary G. Koch,et al.  Categorical Data Analysis Using The SAS1 System , 1995 .

[9]  I. Greca,et al.  Mental models, conceptual models, and modelling , 2000 .

[10]  Lieven Verschaffel,et al.  The Predominance of the Linear Model in Secondary School Students' Solutions of Word Problems Involving Length and Area of Similar Plane Figures , 1998 .

[11]  Ruth Stavy,et al.  How Students Mis/Understand Science and Mathematics: Intuitive Rules (Ways of Knowing in Science Series) , 2000 .

[12]  E. Hutchins Cognition in the wild , 1995 .

[13]  E. Fischbein,et al.  Intuition in science and mathematics , 1987 .

[14]  Rina Zazkis Intuitive rules in number theory: Example of ‘The more of A, the more of B’ rule implementation , 1999 .

[15]  Lieven Verschaffel,et al.  Improper use of linear reasoning: An in-depth study of the nature and the irresistibility of secondary school students' errors , 2002 .

[16]  Ruth Stavy,et al.  Intuitive Rules and Comparison Tasks , 1999 .

[17]  E. Fischbein,et al.  Intuitions and Schemata in Mathematical Reasoning , 1999 .

[18]  Ruth Stavy,et al.  Intuitive Rules: A way to Explain and Predict Students’ Reasoning , 1999 .

[19]  L. Verschaffel,et al.  Do realistic contexts and graphical representations always have a beneficial impact on students' performance? Negative evidence from a study on modelling non-linear geometry problems , 2003 .

[20]  G. Brousseau Theory of didactical situations in mathematics , 1997 .

[21]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[22]  M. Lecoutre Cognitive models and problem spaces in “purely random” situations , 1992 .

[23]  N. Schwarz Self-reports: How the questions shape the answers. , 1999 .