Conceptual Understanding of Causal Reasoning in Physics

College students often experience difficulties in solving physics problems. These difficulties largely result from a lack of conceptual understanding of the topic. The processes of conceptual learning reflect the nature of the causal reasoning process. Two major causal reasoning methods are the covariational and the mechanism‐based approaches. This study was to investigate the effects of different causal reasoning methods on facilitating students’ conceptual understanding of physics. 125 college students from an introduction physics class were assigned into covariational group, mechanism‐based group, and control group. The results show that the mechanism‐based group significantly outperformed the other two groups in solving conceptual problems. However, no significant difference was found in all three groups performance on solving computational problems. Speculation on the inconsistent performance of the mechanism‐based group in conceptual and computational problem solving is given. Detailed analyses of the results, findings, and educational implications are discussed

[1]  David W. Green,et al.  Explaining and envisaging an ecological phenomenon , 1997 .

[2]  I. Sigel,et al.  HANDBOOK OF CHILD PSYCHOLOGY , 2006 .

[3]  R. Ploetzner,et al.  Chapter 13 Multiple Mental Representations of Information in Physics Problem Solving , 1993 .

[4]  Robert A. Wilson,et al.  Explanation and Cognition , 2000 .

[5]  R. Driver,et al.  Students' use of the principle of energy conservation in problem situations , 1985 .

[6]  Amy M. Masnick,et al.  The Development of Causal Reasoning , 2007 .

[7]  Susan M. Land,et al.  Patterns of understanding with open-ended learning environments: A qualitative study , 1997 .

[8]  David N. Perkins,et al.  Models and Moves: Focusing on Dimensions of Causal Complexity To Achieve Deeper Scientific Understanding. , 2000 .

[9]  H. Kelley.  The processes of causal attribution. , 1973 .

[10]  Charles R. Fletcher,et al.  Using story‐based causal diagrams to analyze disagreements about complex events , 1995 .

[11]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[12]  P. Cheng.  From covariation to causation: A causal power theory. , 1997 .

[13]  J. Clement.  Students’ preconceptions in introductory mechanics , 1982 .

[14]  Richard Gunstone,et al.  Student understanding in mechanics: A large population survey , 1987 .

[15]  L. McDermott.  Research on conceptual understanding in mechanics , 1984 .

[16]  Richard A. Duschl,et al.  Philosophy of science, cognitive psychology, and educational theory and practice , 1992 .

[17]  F. Keil.  Concepts, Kinds, and Cognitive Development , 1989 .

[18]  Clark A. Chinn,et al.  Children's Responses to Anomalous Scientific Data: How Is Conceptual Change Impeded?. , 2002 .

[19]  Nick Hammond,et al.  HUMAN COGNITION , 1986 .

[20]  Mary B. Nakhleh,et al.  Are Our Students Conceptual Thinkers or Algorithmic Problem Solvers? Identifying Conceptual Students in General Chemistry , 1993 .

[21]  Susan Carey,et al.  On the origin of causal understanding. , 1995 .

[22]  R. Corrigan,et al.  Causal Understanding as a Developmental Primitive. , 1996 .

[23]  P. Thagard,et al.  Probabilistic Networks and Explanatory Coherence. , 1997 .

[24]  Joan K. Gallini,et al.  When Is an Illustration Worth Ten Thousand Words , 1990 .

[25]  Barbara Koslowski,et al.  When covariation is not enough: The role of causal mechanism, sampling method, and sample size in causal reasoning. , 1989 .

[26]  A. Premack,et al.  Causal cognition : a multidisciplinary debate , 1996 .

[27]  Xueli Zou,et al.  Multiple representations of work-energy processes , 2001 .

[28]  Anne Schlottmann,et al.  Perception Versus Knowledge of Cause and Effect in Children: When Seeing Is Believing , 2001 .

[29]  H. Wellman,et al.  Knowledge acquisition in foundational domains. , 1998 .

[30]  Ibrahim A. Halloun,et al.  The initial knowledge state of college physics students , 1985 .

[31]  David H. Jonassen,et al.  Designing Research-Based Instruction for Story Problems , 2003 .

[32]  Norman E. Wallen,et al.  How to Design and Evaluate Research in Education , 1990 .

[33]  B. Eylon,et al.  Potential difference and current in simple electric circuits: A study of students’ concepts , 1983 .

[34]  Hans Spada,et al.  Learning to Relate Qualitative and Quantitative Problem Representations in a Model-Based Setting for Collaborative Problem Solving , 1999 .

[35]  Paul Thagard,et al.  Explaining Disease: Correlations, Causes, and Mechanisms , 1998, Minds and Machines.

[36]  John Wills Lloyd,et al.  Designing High-Quality Research in Special Education , 2000 .

[37]  A. V. Heuvelen,et al.  Learning to think like a physicist: A review of research‐based instructional strategies , 1991 .

[38]  Ronald A. Howard,et al.  Influence Diagrams , 2005, Decis. Anal..

[39]  D. Medin,et al.  The role of covariation versus mechanism information in causal attribution , 1995, Cognition.

[41]  David P Maloney,et al.  Surveying students’ conceptual knowledge of electricity and magnetism , 2001 .

[42]  Ibrahim A. Halloun,et al.  Common sense concepts about motion , 1985 .

[43]  L. E. Klopfer,et al.  Factors influencing the learning of classical mechanics , 1980 .

[44]  Amy J. Phelps.  Teaching to Enhance Problem Solving: It's More than the Numbers , 1996 .

[45]  William F. Brewer,et al.  Explanation in Scientists and Children , 1998, Minds and Machines.

[46]  Harold F. O'Neil,et al.  Instructional strategies to help learners build relevant mental models in inferential statistics , 1992 .

[47]  Lucangeli,et al.  Cognitive and Metacognitive Abilities Involved in the Solution of Mathematical Word Problems: Validation of a Comprehensive Model. , 1998, Contemporary educational psychology.