Identifying Representational Competence With Multi-Representational Displays

Increasingly, multi-representational educational technologies are being deployed in science classrooms to support science learning and the development of representational competence. Several studies have indicated that students experience significant challenges working with these multi-representational displays and prefer to use only one representation while problem solving. Here, we examine the use of one such display, a multi-representational molecular mechanics animation, by organic chemistry undergraduates in a problem-solving interview. Using both protocol analysis and eye fixation data, our analysis indicates that students rely mainly on two visual–spatial representations in the display and do not make use of two accompanying mathematical representations. Moreover, we explore how eye fixation data complement verbal protocols by providing information about how students allocate their attention to different locations of a multi-representational display with and without concurrent verbal utterances. Our analysis indicates that verbal protocols and eye movement data are highly correlated, suggesting that eye fixations and verbalizations reflect similar cognitive processes in such studies.

[1]  A. diSessa.  Metarepresentation: Native Competence and Targets for Instruction , 2004 .

[2]  Paul Brna,et al.  Supporting the use of external representation in problem solving: the need for flexible learning environments , 1995 .

[3]  Mary Hegarty,et al.  The Role of Gestures in Mental Animation , 2005, Spatial Cogn. Comput..

[4]  M. Just,et al.  Constructing mental models of machines from text and diagrams. , 1993 .

[5]  S. Ainsworth.  DeFT: A Conceptual Framework for Considering Learning with Multiple Representations. , 2006 .

[6]  K. Rayner.  Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[7]  Freeman J. Dyson,et al.  The same and not the same , 1995 .

[8]  Shaaron Ainsworth,et al.  Examining the Effects of Different Multiple Representational Systems in Learning Primary Mathematics , 2002 .

[9]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[10]  Priti Shah,et al.  A Model of the Perceptual and Conceptual Processes in Graph Comprehension , 1998 .

[11]  M. Just,et al.  Eye fixations and cognitive processes , 1976, Cognitive Psychology.

[12]  Philip Smith.  Roger Bakeman John M. Gottman , 1987, Animal Behaviour.

[13]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

[14]  R. Kozma,et al.  The Roles of Representations and Tools in the Chemistry Laboratory and Their Implications for Chemistry Learning , 2000 .

[15]  Richard Lowe,et al.  Interrogation of a dynamic visualization during learning , 2004 .

[16]  Joseph Krajcik,et al.  Promoting Conceptual Understanding of Chemical Representations: Students' Use of a Visualization Tool in the Classroom , 2000 .

[17]  R. Kozma,et al.  Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena , 1997 .

[18]  Robert B. Kozma,et al.  Use of Simultaneous-Synchronized Macroscopic, Microscopic, and Symbolic Representations To Enhance the Teaching and Learning of Chemical Concepts , 1997 .

[19]  M. Hegarty.  Mental animation: inferring motion from static displays of mechanical systems. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[20]  E. Wiebe,et al.  The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations , 2008 .

[21]  Joseph Krajcik,et al.  Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom , 2001 .

[22]  Roald Hoffmann,et al.  Representation in Chemistry , 1989 .

[23]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[24]  Detlev Leutner,et al.  How instruction guides attention in multimedia learning , 2004 .

[25]  K. Emmorey,et al.  Gesture, Thought, and Spatial Language , 2001 .

[26]  Martha W. Alibali,et al.  Gesture in Spatial Cognition: Expressing, Communicating, and Thinking About Spatial Information , 2005, Spatial Cogn. Comput..

[27]  Harvey S. Smallman,et al.  Naive Realism: Misplaced Faith in Realistic Displays , 2005 .

[28]  Arlene A. Russell.  Symposium on Systemic Reform in Chemistry , 1997 .

[29]  Shaaron Ainsworth,et al.  The Interaction between Informational and Computational Properties of External Representations on Problem-Solving and Learning , 2003 .

[30]  Zenzi M. Griffin,et al.  Why Look? Reasons for Eye Movements Related to Language Production. , 2004 .

[31]  T. Gog,et al.  Uncovering Expertise-Related Differences in Troubleshooting Performance: Combining Eye Movement and Concurrent Verbal Protocol Data , 2005 .

[32]  Eric N. Wiebe,et al.  Visual Representations of DNA Replication: Middle Grades Students’ Perceptions and Interpretations , 2005 .

[33]  Shaaron Ainsworth,et al.  The functions of multiple representations , 1999, Comput. Educ..

[34]  Peter C.-H. Cheng,et al.  Modeling the Effect of Task and Graphical Representation on Response Latency in a Graph Reading Task , 2003, Hum. Factors.

[35]  Mike Stieff,et al.  Connected Chemistry—Incorporating Interactive Simulations into the Chemistry Classroom , 2003 .

[36]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[37]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[38]  Fernanda Ferreira,et al.  Scene Perception for Psycholinguists. , 2004 .

[39]  David J. Gilmore,et al.  Comprehension and Recall of Miniature Programs , 1984, Int. J. Man Mach. Stud..

[40]  U. Neisser.  VISUAL SEARCH. , 1964, Scientific American.

[41]  Andrea A. diSessa,et al.  Meta-representation: an introduction , 2000 .

[42]  Arthur C. Graesser,et al.  Question asking and eye tracking during cognitive disequilibrium: Comprehending illustrated texts on devices when the devices break down , 2005, Memory & cognition.

[43]  Mike Stieff,et al.  Connected Chemistry—A Novel Modeling Environment for the Chemistry Classroom , 2005 .

[44]  Joseph Krajcik,et al.  A Scaffolding Design Framework for Software to Support Science Inquiry , 2004, The Journal of the Learning Sciences.

[45]  Jennifer L. Chiu,et al.  Teaching and Assessing Knowledge Integration in Science , 2006, Science.

[46]  D. E. Irwin.  Fixation location and fixation duration as indices of cognitive processing , 2004 .

[47]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

[48]  Mike Stieff,et al.  Increasing Representational Fluency with Visualization Tools , 2006, ICLS.