Access the full text.
Sign up today, get DeepDyve free for 14 days.
Daniel Bodemer, R. Ploetzner, Inge Feuerlein, H. Spada (2004)
The Active Integration of Information during Learning with Dynamic and Interactive VisualisationsLearning and Instruction, 14
J. Schooler, S. Fiore, M. Brandimonte (1997)
At a Loss From Words: Verbal Overshadowing of Perceptual MemoriesPsychology of Learning and Motivation, 37
JW Moore, CL Stanitski (2015)
Chemistry: The molecular science
M. Rau (2017)
Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM LearningEducational Psychology Review, 29
D. Treagust, C. Tsui (2013)
Conclusion: Contributions of Multiple Representations to Biological EducationMultiple Representations in Biological Education, 7
MA Rau (2017)
10.1007/s11251-017-9403-7Instructional Science
A. diSessa, B. Sherin (2000)
Meta-representation: an introductionThe Journal of Mathematical Behavior, 19
(2000)
Principles and standards for school mathematics
Daniel Bodemer, R. Ploetzner, Katrin Bruchmüller, Sonja Häcker (2005)
Supporting learning with interactive multimedia through active integration of representationsInstructional Science, 33
P. Kellman, Christine Massey, Ji Son (2010)
Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and FluencyTopics in cognitive science, 2 2
E. Gibson (1969)
Principles of Perceptual Learning and Development
B Eilam (2013)
Multiple representations in biological education
M. Rau, V. Aleven, N. Rummel (2017)
Supporting Students in Making Sense of Connections and in Becoming Perceptually Fluent in Making Connections Among Multiple Graphical RepresentationsJournal of Educational Psychology, 109
RE Mayer (2009)
10.1017/CBO9780511811678
K. VanLehn (2011)
The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring SystemsEducational Psychologist, 46
A. diSessa (2004)
Metarepresentation: Native Competence and Targets for InstructionCognition and Instruction, 22
R. Noss, L. Healy, C. Hoyles (1997)
The Construction of Mathematical Meanings: Connecting the Visual with the SymbolicEducational Studies in Mathematics, 33
Paul O’Keefe, Susan Letourneau, B. Homer, Ruth Schwartz, J. Plass (2014)
Learning from multiple representations: An examination of fixation patterns in a science simulationComput. Hum. Behav., 35
MTH Chi (1994)
10.1016/0364-0213(94)90016-7Cognitive Science, 18
(2009)
Organic chemistry (5th ed.)
M. Rau, V. Aleven, N. Rummel (2015)
Successful learning with multiple graphical representations and self-explanation prompts.Journal of Educational Psychology, 107
(2016)
A framework for discipline-specific grounding of educational technologies with multiple visual representations
R. Kozma, E. Chin, J. Russell, Nancy Marx (2000)
The Roles of Representations and Tools in the Chemistry Laboratory and Their Implications for Chemistry LearningJournal of the Learning Sciences, 9
D. Gentner, A. Markman (1997)
Structure mapping in analogy and similarity.American Psychologist, 52
K Cramer (2001)
10.5951/MTMS.6.5.0310Mathematics Teaching in the Middle School, 6
J. Sweller, J. Merrienboer, F. Paas (1998)
Cognitive Architecture and Instructional DesignEducational Psychology Review, 10
Stephen Pape, M. Tchoshanov (2001)
The Role of Representation(s) in Developing Mathematical UnderstandingTheory Into Practice, 40
JH Larkin (1987)
10.1111/j.1551-6708.1987.tb00863.xCognitive Science: A Multidisciplinary Journal, 11
Robert Goldstone, L. Barsalou (1998)
Reuniting perception and conceptionCognition, 65
Catherine Chase, Jonathan Shemwell, Daniel Schwartz (2010)
Explaining across contrasting cases for deep understanding in science: an example using interactive simulations
A. Gegenfurtner, E. Lehtinen, R. Säljö (2011)
Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking Research in Professional DomainsEducational Psychology Review, 23
SR Hinze (2013)
10.1016/j.learninstruc.2012.12.002Learning and Instruction, 26
P. Kellman, Christine Massey (2013)
Perceptual Learning, Cognition, and ExpertisePsychology of Learning and Motivation, 58
R. Kozma, J. Russell (2005)
Students Becoming Chemists: Developing Representationl Competence
M. Chi, M. Bassok, Matthew Lewis, P. Reimann, R. Glaser (1989)
Self-Explonations: How Students Study and Use Examples in Learning to Solve ProblemsCogn. Sci., 13
J. Gilbert (2005)
Visualization: A Metacognitive Skill in Science and Science Education
W Schnotz (2005)
10.1017/CBO9780511816819.005
A. Nistal, W. Dooren, L. Verschaffel (2013)
Students’ reported justifications for their representational choices in linear function problems: an interview studyEducational Studies, 39
K Cramer (2001)
Using models to build an understanding of functionsMathematics Teaching in the Middle School, 6
P. Kellman, Christine Massey, Z. Roth, Timothy Burke, J. Zucker, Amanda Saw, Katherine Aguero, Joseph Wise (2008)
Perceptual Learning and the Technology of Expertise: Studies in Fraction Learning and Algebra., 16
(2017)
Making connections between multiple graphical representations of fractions: Conceptualunderstanding facilitates perceptual fluency
M. Rau (2015)
Enhancing undergraduate chemistry learning by helping students make connections among multiple graphical representationsChemistry Education Research and Practice, 16
Robert Byrge (2010)
Perceptual Learning for ESL
(2015)
Chemistry: The molecular science (5th ed.)
Cheryl Johnson, R. Mayer (2010)
Applying the self-explanation principle to multimedia learning in a computer-based game-like environmentComput. Hum. Behav., 26
A. Nistal, W. Dooren, L. Verschaffel (2014)
Improving students’ representational flexibility in linear-function problems: an interventionEducational Psychology, 34
M. Rau, V. Aleven, N. Rummel (2013)
Interleaved Practice in Multi-Dimensional Learning Tasks: Which Dimension Should We Interleave?.Learning and Instruction, 23
Shaaron Ainsworth, P. Bibby, D. Wood (2002)
Examining the Effects of Different Multiple Representational Systems in Learning Primary MathematicsJournal of the Learning Sciences, 11
(2006)
Test-enhanced learning: Taking memory tests improves longterm retention
MA Rau (2016)
10.1109/TLT.2016.2623303IEEE Transactions on Learning Technologies
HL Roediger III (2006)
10.1111/j.1467-9280.2006.01693.xPsychological Science, 17
T. Mueller (2006)
Learning to Think SpatiallyPhotogrammetric Engineering and Remote Sensing, 72
D Shanks (2005)
Handbook of cognition
M. Chi, N. Leeuw, M. Chiu, Christian LaVancher (1994)
Eliciting Self-Explanations Improves UnderstandingCogn. Sci., 18
C. Bowen (1990)
Representational Systems Used by Graduate Students while Problem Solving in Organic Synthesis.Journal of Research in Science Teaching, 27
(1987)
Towards a theory of symbol use in mathematics
M. Stieff, M. Hegarty, G. Deslongchamps (2011)
Identifying Representational Competence With Multi-Representational DisplaysCognition and Instruction, 29
Tina Seufert (2003)
Supporting Coherence Formation in Learning from Multiple RepresentationsLearning and Instruction, 13
Daniel Bodemer, U. Faust (2006)
External and mental referencing of multiple representationsComput. Hum. Behav., 22
R Kozma, J Russell (2005)
Visualization in science education
Joshua Gutwill, J. Frederiksen, B. White (1999)
Making Their Own Connections: Students' Understanding of Multiple Models in Basic ElectricityCognition and Instruction, 17
J Kaput (1987)
Problems of representations in the teaching and learning of mathematics
JK Gilbert (2008)
Visualization: Theory and practice in science education
R Kozma (2005)
10.1017/CBO9780511816819.027
(2010)
Effects of support for coherence formation in computer science education
Loretta Jones, K. Jordan, N. Stillings (2005)
Molecular visualization in chemistry education: The role of multidisciplinary collaborationChemistry Education Research and Practice, 6
Elsbeth Stern, Carmela Aprea, Hermann Ebner (2003)
Improving cross-content transfer in text processing by means of active graphical representationLearning and Instruction, 13
J. Merriënboer, R. Clark, M. Croock (2002)
Blueprints for complex learning: The 4C/ID-modelEducational Technology Research and Development, 50
D. Uttal, Nathaniel Meadow, Elizabeth Tipton, L. Hand, A. Alden, Christopher Warren, N. Newcombe (2013)
The malleability of spatial skills: a meta-analysis of training studies.Psychological bulletin, 139 2
K. Koedinger, Albert Corbett, C. Perfetti (2012)
The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student LearningCognitive science, 36 5
PJ Kellman (2008)
10.1075/p&c.16.2.07kelPragmatics and Cognition, 16
K. Taber (2014)
The significance of implicit knowledge for learning and teaching chemistryChemistry Education Research and Practice, 15
J. Airey, C. Linder (2009)
A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modesJournal of Research in Science Teaching, 46
(2005)
Multimedia learning of chemistry
Trisha Anderson, G. Bodner (2008)
What can we do about ‘Parker’? A case study of a good student who didn't ‘get’ organic chemistryChemistry Education Research and Practice, 9
Kirsten Berthold, A. Renkl (2009)
Instructional Aids to Support a Conceptual Understanding of Multiple Representations.Journal of Educational Psychology, 101
JK Gilbert, DF Treagust (2009)
Multiple representations in chemical education
J. Meij, T. Jong (2006)
Supporting students' learning with multiple representations in a dynamic simulation-based learning environmentLearning and Instruction, 16
CM Massey, PJ Kellman, Z Roth, T Burke (2011)
Developmental cognitive science goes to school
Paul Ayres (2015)
State‐of‐the‐Art Research into Multimedia Learning: A Commentary on Mayer's Handbook of Multimedia LearningApplied Cognitive Psychology, 29
C. Olde, T. Jong (2006)
Scaffolding learners in designing investigation assignments for a computer simulationJ. Comput. Assist. Learn., 22
K. Koedinger, Julie Booth, D. Klahr (2013)
Instructional Complexity and the Science to Constrain ItScience, 342
M Fahle (2002)
10.7551/mitpress/5295.001.0001
P. Kirschner, J. Sweller, R. Clark (2006)
Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based TeachingEducational Psychologist, 41
M. Chi, P. Feltovich, R. Glaser (1981)
Categorization and Representation of Physics Problems by Experts and NovicesCogn. Sci., 5
M. Stieff (2007)
Mental rotation and diagrammatic reasoning in scienceLearning and Instruction, 17
PJ Kellman, CM Massey (2013)
The psychology of learning and motivation
R. Mayer (2005)
The Cambridge handbook of multimedia learning, 1st Edition
R Frensch (2003)
10.1111/1467-8721.01213Current Directions in Psychological Science, 12
S. Ainsworth (2006)
DeFT: A Conceptual Framework for Considering Learning with Multiple Representations.Learning and Instruction, 16
VAWMM Aleven (2002)
10.1207/s15516709cog2602_1Cognitive Science, 26
P. Kellman, Patrick Garrigan (2009)
Perceptual learning and human expertise.Physics of life reviews, 6 2
J. Gilbert, D. Treagust (2009)
Towards a Coherent Model for Macro, Submicro and Symbolic Representations in Chemical Education
MA Rau, V Aleven, N Rummel (2017)
Making connections between multiple graphical representations of fractions: Conceptualunderstanding facilitates perceptual fluency, but not vice versaInstructional Science
Soniya Gadgil, Timothy Nokes-Malach, M. Chi (2012)
Effectiveness of holistic mental model confrontation in driving conceptual changeLearning and Instruction, 22
P. Cheng (1999)
Unlocking conceptual learning in mathematics and science with effective representational systemsComput. Educ., 33
JA Wise, T Kubose, N Chang, A Russell, PJ Kellman (2000)
Teaching and learning in a network world
W Schnotz (2005)
The cambridge handbook of multimedia learning
E. Gibson (2000)
Perceptual Learning in Development: Some Basic ConceptsEcological Psychology, 12
(2006)
Curriculum focal points for prekindergarten through grade 8 mathematics: A quest for coherence
J. Wertsch, S. Kazak (2011)
Saying More than You Know in Instructional Settings
J. Gilbert (2008)
Visualization: An Emergent Field of Practice and Enquiry in Science Education
W. Schnotz, M. Bannert (2003)
Construction and interference in learning from multiple representationLearning and Instruction, 13
S. Hinze, D. Rapp, Vickie Williamson, M. Shultz, G. Deslongchamps, Kenneth Williamson
Beyond ball-and-stick: Students ’ processing of novel STEM visualizations
Charalambos Charalambous, D. Pitta-Pantazi (2007)
Drawing on a Theoretical Model to Study Students’ Understandings of FractionsEducational Studies in Mathematics, 64
K. Ericsson (2014)
Perceptual and Memory Processes in the Acquisition of Expert Performance: The EPAM Model
R Goldstone (1997)
Perceptual learning
K. Ericsson (1996)
The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games
R. Mistretta, J. Porzio (2001)
"Principles and Standards for School Mathematics" in the Classroom.Teaching children mathematics, 7
M. Rau, V. Aleven, N. Rummel, Z. Pardos (2014)
How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods StudyInternational Journal of Artificial Intelligence in Education, 24
M. Stieff (2005)
Connected Chemistry—A Novel Modeling Environment for the Chemistry ClassroomJournal of Chemical Education, 82
Hsin-Kai Wu, P. Shah (2004)
Exploring visuospatial thinking in chemistry learningScience Education, 88
V. Aleven, K. Koedinger (2002)
An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive TutorCogn. Sci., 26
A. Cleeremans, Viktor Allakhverdov, Maria Kuvaldina (2019)
Implicit Learning
R. Atkinson, A. Renkl, M. Merrill (2003)
Transitioning From Studying Examples to Solving Problems: Effects of Self-Explanation Prompts and Fading Worked-Out Steps.Journal of Educational Psychology, 95
Tina Seufert, Roland Brünken (2006)
Cognitive load and the format of instructional aids for coherence formationApplied Cognitive Psychology, 20
H. Dreyfus, S. Dreyfus (1988)
Mind over Machine: The Power of Human Intuition and Expertise in the Era of the ComputerIEEE Expert, 2
D. Gentner (1983)
Structure-Mapping: A Theoretical Framework for AnalogyCogn. Sci., 7
S. Özgün-Koca (2008)
Ninth Grade Students Studying the Movement of Fish to Learn about Linear Relationships: The Use of Video-Based Analysis Software in Mathematics ClassroomsThe Mathematics Educator, 18
P. Lachenbruch (1989)
Statistical Power Analysis for the Behavioral Sciences (2nd ed.)Journal of the American Statistical Association, 84
Billie Eilam (2013)
Possible Constraints of Visualization in Biology: Challenges in Learning with Multiple Representations
Hildegard Urban-Woldron (2009)
Interactive Simulations for the Effective Learning of PhysicsThe Journal of Computers in Mathematics and Science Teaching, 28
J. Larkin, H. Simon (1987)
Why a Diagram is (Sometimes) Worth Ten Thousand WordsCogn. Sci., 11
To learn content knowledge in science, technology, engineering, and math domains, students need to make connections among visual representations. This article considers two kinds of connection-making skills: (1) sense-making skills that allow students to verbally explain mappings among representations and (2) perceptual fluency in connection making that allows students to fast and effortlessly use perceptual features to make connections among representations. These different connection-making skills are acquired via different types of learning processes. Therefore, they require different types of instructional support: sense-making activities and fluency-building activities. Because separate lines of research have focused either on sense-making skills or on perceptual fluency, we know little about how these connection-making skills interact when students learn domain knowledge. This article describes two experiments that address this question in the context of undergraduate chemistry learning. In Experiment 1, 95 students were randomly assigned to four conditions that varied whether or not students received sense-making activities and fluency-building activities. In Experiment 2, 101 students were randomly assigned to five conditions that varied whether or not and in which sequence students received sense-making and fluency-building activities. Results show advantages for sense-making and fluency-building activities compared to the control condition only for students with high prior chemistry knowledge. These findings provide new insights into potential boundary conditions for the effectiveness of different types of instructional activities that support students in making connections among multiple visual representations.
Instructional Science – Springer Journals
Published: Nov 3, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.