Visualization in Science Education http://www.fishpond.co.nz/Books/Visualization-Science-Education-John-K-Gilbert-Edited-by/9781402036125

Models and Modeling in Science Education

By John K. Gilbert (Edited by)

Table of Contents

Introduction-John K. Gilbert.- Section A: The significance of visualization in science education: 1. Visualization: A metacognitive skill in science and science education-John K. Gilbert. 2. Prolegomenon to scientific visualization-Barbara Tversky. 3. Mental models: Theoretical issues for visualizations in science education-David Rapp. 4. A model of molecular visualization-Michael Briggs, George Bodner. 5. Leveraging technology and cognitive theory on visualization to promote students' learning-Janice D. Gobert.- Section B: Developing the skills of visualization: 6. Teaching and learning with three-dimensional representations-Mike Stieff, Robert Bateman, David Uttal. 7. Students becoming chemists: Developing representational competence-Robert Kozma, Joel Russell. 8. Imagery in physics: From physicists' practice to naive students' learning-Galit Botzer and Miriam Reiner. 9. Imagery in science learning in students and experts-John Clement, Aletta Zietsman, and James Monaghan.- Section C: Integrating visualization into curricula in the sciences: 10. Learning electromagnetism with visualizations and active learning-Yehudit Judy Dori, John Belcher. 11. Visualizing the science of genomics-Kathy Takayama. 12. Visualization in undergraduate geology courses-Stephen J. Reynolds, Julia K. Johnson, Michael D. Piburn, Debra E. Leedy, Joshua A. Coyan, Melanie M. Busch.- Section D: Assessing the development of visualization skills: 13. Evaluating the educational value of molecular structure representations.-Vesna Ferk Savec, Margareta Vrtacnik, John K. Gilbert. 14. Assessing the learning from multi-media packages in chemical education-Joel Russell, Robert Kozma.- Endpiece.- Future research and development on visualization in science education-John K. Gilbert.

Reviews

International Journal of Science Education

Vol. 30, No. 15, 15 December 2008, pp. 2091a "2096

ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/08/152091a "06

DOI: 10.1080/09500690802065940

BOOK REVIEW

T1I0B2T0Jjrna090aSaoty.50myeEo1a080rlk0sDo@n-0h 8r0raR0_ r0ht6a&e2Aei/b9noe0v _c3dRnF09is3e ar85a(eF0wlapm0. r6ntJra0i7oiacfnn6rdi6ucs.t9a0r)i0ne/s.1s8sag4.l0i m6n2o40f -6S55c29i8e49n0 c(eo nEldinuec)ation Visualization in Science Education

John K. Gilbert (Ed.), 2005

Dordrecht, The Netherlands: Springer

346 pp., a, --49.95 (hbk)

ISBN 978-1-4020-3612-5

Visualisation is an area that has fascinated scientists and science educators alike, yet

it has proved problematic for research and study (Mathewson, 1999). It is only in

the past 10 years that science educators have had some success in understanding and

tackling the questions related to visualisation and its role in learning. Research in

this area has been eclectic in nature, often spurred by the entry of new visualisation

technologies into the classroom, and drawing on theoretical frameworks and analytical

tools developed by cognitive scientists as well as historians of science and science

educators. The studies have so far remained scattered over a range of disciplines and

several interdisciplinary journals and books. The present volume does an exemplary

service in bringing together the research in this new and emerging field, placing it

firmly on the radar of science educationists.

In science education, the closely related area of models and modelling has been of

interest for some time now. Visualization in Science Education is in fact the first in a

series of volumeson a ~Models and Modelling in Science Educationa (TM) edited by John

Gilbert and published by Springer. Several articles in this volume examine in detail

the relationship between a ~modelsa (TM) and a ~visualizationa (TM) in science education.

The book is organised into four sections that recall a classic sequence in education:

a ~The Significance of Visualization in Science Educationa (TM), a ~Developing the Skills

of Visualizationa (TM), a ~Integrating Visualization into Curricula in the Sciencesa (TM), and

a ~Assessing the Development of Visualization Skillsa (TM). John Gilberta (TM)s introductory

chapter brings out the relationship between models, both a ~in the worlda (TM) and a ~in the

minda (TM), and visualisations, which also could be both external and internal. Gilbert

sees visualisation as a metacognitive skill, involving the monitoring and control of an

image being learnt, knowing how to rehearse and retain it in memory, retrieving the

appropriate image when necessary, and, finally, amending and transforming the

image according to the reasoning demanded by the task at hand. This chapter gives

several examples to bring out the role of visualisation in student learning and in

classroom practice.

Chapter 2 by Barbara Tversky looks at the many ways in which external depictions

convey information. Tversky is a psychologist who has researched visualisation

in relatively complex domains. She therefore easily moves beyond the common

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2092 Book Review

psychological paradigm of visuals as percepts, to consider visuals that could be

related with mental models. Herexamples are drawn from route maps, mechanical

diagrams, and animations used in education. Tversky suggests some cognitive

design principles for effective visualisations, both static diagrams and animations.

In Chapter 3 David Rapp draws on work in cognitive and educational psychology

to outline the characteristics of mental models. He looks at the evidence for mental

models coming from the domains of text comprehension, logical reasoning, and

understanding of mechanical systems. Rapp then goes on to examine some qualities

of educational situations that influence learning with mental models. Identifying

a ~cognitive engagementa (TM) and a ~interactivitya (TM) as two supporting factors, Rapp points out

the mixed evidence for effectiveness of a ~multimedia learninga (TM). Thus visualisations,

used here in the sense of a ~novel visual presentations of dataa (TM), are shown to be not

consistently helpful in learning, or in building mental models.

In Chapter 4 Michael Briggs and George Bodner use a phenomenographic

approach to propose a theoretical model of molecular visualisation. Drawing on data

from an exploratory study with college undergraduates the authors describe the role

of visualisation in understanding molecular structures, arguing that this process

leads to the construction of a mental model. Briggs and Bodner see visualisation as

an operation that brings about a one-to-one correspondence between a mental

representation and its referent, serving therefore as the dynamic component of

model-based reasoning.

Chapter 5 by Janice Gobert focuses on external visualisations and their role in

supporting learning. Gobertreviews the literature on the processing of textual and

graphic information in both static and dynamic form, finding that expertsa (TM) use of

visualisations is highly sensitive to domain and task contexts. Although Gobert holds

that mental visualisations are not tractable to empirical research, she does use the

framework of model-based teaching and learning to examine studentsa (TM) mental

models as they work in a technology-supported environment. She describes two

projects developed to enhance studentsa (TM) model-based reasoning: a ~Making Thinking

Visiblea (TM) and a ~Modeling across the Curriculuma (TM). a ~Making Thinking Visiblea (TM) used

WISE, a web-based science learning environment that allowed students to access

real-time data (related to plate tectonics) through the Web and also to interact with

peers from geographically distinct locations. In a ~Modeling across the Curriculuma (TM),

Pedagogicaa"[ was used to track studentsa (TM) interactions with models (from genetics,

classical mechanics, and chemistry) and to gain an index of their reasoning and

modelling skills. Studentsa (TM) domain knowledge as well as their understanding of the

nature of modelling was found to be enhanced.

Section B, consisting of four chapters, is concerned with ways of developing the

skills of visualisation. Chapter 6 by Mike Steiff, Robert Bateman, and David Uttal

critically examines the role of computer-based visualisation tools in the science classroom.

The authors review both content-specific tools and general modelling environments.

They note that research in the effectiveness of these tools has suffered

from limitations of design and occasional mixed results, while both research and

development of visualisation strategies have lacked a clear theoretical perspective on

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Book Review 2093

why the particular tools are likely or unlikely to help learning. Steiff et al. offer some

cognitively grounded principles for the design of effective visualisation tools in

chemistry, investigation of their role and efficacy, and development of suitable pedagogies

for their use. Visualisation tools should support spatial cognition by helping

students comprehend spatial relationships as well as manipulate molecules to solve a

given problem.

In Chapter 7 Robert Kozma and Joel Russell review the research related to developing

representational competence in students of chemistry. They consider the

chemical curriculum in terms of two important goals: studentsa (TM) acquisition of chemical

concepts and principles, and their participation in the investigative practices of

chemistrya "a ~students becoming chemistsa (TM). These goals pertain to cognitive or learning

theory and to situative theory respectively. Beginning with the latter, the authors

look at the everyday practices of chemists during scientific investigations and

compare them with those of students, showing that competence in using visual

representations is a feature distinguishing the two practices. They then review the

literature on learning theories applied to multimedia learning and consider their

implications for investigative work, particularly in defining representational practices

in chemistry. The chapter concludes with an extensive review of research pertaining

to chemical visualisation technologies of two major kinds: molecular modelling, and

computer simulations and animations of dynamic chemical processes.

Chapter 8, by Galit Botzer and Miriam Reiner, recalls the practice of physics in

history, focusing on the specific case of electromagnetic theory. Mental models and

visual imagery are believed to have played a major role in the work of Galileo,

Newton, Faraday, Maxwell, and Einstein. Botzer and Reiner begin with a scheme of

classification derived by Arthur Miller from the history of physics, in which modes

of representation are seen as sensory based, pure imaginary, or formalism based.

They look at case studies of ninth-grade students collaboratively exploring magnetic

phenomena, and find that the historically derived classification works well with

studentsa "when nuanced with cognitive considerations like projections of former

experiences to explain a new situation, and transformations of mental images.

Implications for physics learning are suggested in terms of conceptual understanding,

communication and tools for research and evaluation.

In Chapter 9, John Clement, Aletta Zietsman, and James Monaghan take on the

challenge of studying mental imagery in science learning. They review three prior

studies in elementary mechanics with the aim to develop observable indicators for the

presence ofimager