Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning
暂无分享,去创建一个
[1] Paul J. Feltovich,et al. Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..
[2] Lorelei Lingard,et al. ‘You see?’ Teaching and learning how to interpret visual cues during surgery , 2015, Medical Education.
[3] Tina Seufert. Supporting Coherence Formation in Learning from Multiple Representations , 2003 .
[4] Albert T. Corbett,et al. The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning , 2012, Cogn. Sci..
[5] James G. Greeno,et al. Learning in Activity , 2014 .
[6] G. Olympiou,et al. Blending Physical and Virtual Manipulatives: An Effort to Improve Students' Conceptual Understanding through Science Laboratory Experimentation , 2012 .
[7] Andreas Holzinger,et al. Dynamic Media in Computer Science Education; Content Complexity and Learning Performance: Is Less More? , 2008, J. Educ. Technol. Soc..
[8] Hans Spada,et al. Acquiring knowledge in science and mathematics : the use of multiple representations in technology based learning environments , 1998 .
[9] Canan Nakiboğlu,et al. INSTRUCTIONAL MISCONCEPTIONS OF TURKISH PROSPECTIVE CHEMISTRY TEACHERS ABOUT ATOMIC ORBITALS AND HYBRIDIZATION , 2003 .
[10] John Airey,et al. A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modes , 2009 .
[11] Rayne A. Sperling,et al. The effects of levels of elaboration on learners’ strategic processing of text , 2011 .
[12] Charalambos Y. Charalambous,et al. Drawing on a Theoretical Model to Study Students’ Understandings of Fractions , 2007 .
[13] Katharina Scheiter,et al. How Inspecting a Picture Affects Processing of Text in Multimedia Learning , 2013 .
[14] Konrad J. Schönborn,et al. Experts' Views on Translation Across Multiple External Representations in Acquiring Biological Knowledge About Ecology, Genetics, and Evolution , 2013 .
[15] A. Bandura. Social cognitive theory: an agentic perspective. , 1999, Annual review of psychology.
[16] Lieven Verschaffel,et al. Students’ reported justifications for their representational choices in linear function problems: an interview study , 2013 .
[17] D. Uttal,et al. The malleability of spatial skills: a meta-analysis of training studies. , 2013, Psychological bulletin.
[18] J. Roschelle. Learning by Collaborating: Convergent Conceptual Change , 1992 .
[19] Martina A. Rau,et al. Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry , 2015, Comput. Educ..
[20] S. Ainsworth. DeFT: A Conceptual Framework for Considering Learning with Multiple Representations. , 2006 .
[21] Richard E. Mayer,et al. The Cambridge Handbook of Multimedia Learning: Cognitive Theory of Multimedia Learning , 2005 .
[22] Y. Lincoln,et al. Scientific Research in Education , 2004 .
[23] Stacey Lowery Bretz,et al. Generating cognitive dissonance in student interviews through multiple representations , 2012 .
[24] N. Finkelstein,et al. Patterns of multiple representation use by experts and novices during physics problem solving , 2008 .
[25] P. Thompson,et al. Fractions and multiplicative reasoning , 2003 .
[26] Ruth Wylie,et al. The Self-Explanation Principle in Multimedia Learning , 2014 .
[27] Roger Azevedo,et al. The Cambridge Handbook of the Learning Sciences: Metacognition , 2014 .
[28] D. J. Gilmore,et al. Individual differences and strategy selection in reasoning , 1997 .
[29] Ruhama Even,et al. Subject matter knowledge for teaching and the case of functions , 1990 .
[30] Robert B. Kozma,et al. Students Becoming Chemists: Developing Representationl Competence , 2005 .
[31] K. Anders Ericsson. Perceptual and Memory Processes in the Acquisition of Expert Performance: The EPAM Model , 2014 .
[32] David F. Treagust,et al. Towards a Coherent Model for Macro, Submicro and Symbolic Representations in Chemical Education , 2009 .
[33] M. Just,et al. Constructing mental models of machines from text and diagrams. , 1993 .
[34] Roy D. Pea,et al. Distributed by Design: On the Promises and Pitfalls of Collaborative Learning with Multiple Representations , 2011 .
[35] Nicole M. McNeil,et al. Should you show me the money? Concrete objects both hurt and help performance on mathematics problems , 2009 .
[36] David W. Carraher,et al. The Transfer Dilemma , 2002 .
[37] Vincent Aleven,et al. Interleaved Practice in Multi-Dimensional Learning Tasks: Which Dimension Should We Interleave?. , 2013 .
[38] Ton de Jong,et al. The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment , 2011, J. Comput. Assist. Learn..
[39] Assaf Harel,et al. What is special about expertise? Visual expertise reveals the interactive nature of real-world object recognition , 2016, Neuropsychologia.
[40] A. Shusterman,et al. Teaching Chemistry with Electron Density Models , 1997 .
[41] S. Paris,et al. Children's Metacognition About Reading: issues in Definition, Measurement, and Instruction , 1987 .
[42] A. Northedge. Enabling Participation in Academic Discourse , 2003 .
[43] Beate Grawemeyer,et al. Evaluation of ERST - An External Representation Selection Tutor , 2006, Diagrams.
[44] Marios Papaevripidou,et al. Effects of experimenting with physical and virtual manipulatives on students' conceptual understanding in heat and temperature , 2008 .
[45] C. Hartshorne,et al. Collected Papers of Charles Sanders Peirce , 1935, Nature.
[46] David H. Uttal,et al. Comprehending and Learning from ‘Visualizations’: A Developmental Perspective , 2008 .
[47] Barbara Y. White,et al. Making Their Own Connections: Students' Understanding of Multiple Models in Basic Electricity , 1999 .
[48] Michelene T. H. Chi,et al. Self-Explanations: How Students Study and Use Examples in Learning To Solve Problems. Technical Report No. 9. , 1987 .
[49] Peter Gerjets,et al. Extending multimedia research: How do prerequisite knowledge and reading comprehension affect learning from text and pictures , 2014, Comput. Hum. Behav..
[50] Teaching the Concept of Unit in Measurement Interpretation of Rational Numbers , 2008 .
[51] Yuri Uesaka,et al. Active Comparison as a Means of Promoting the Development of Abstract Conditional Knowledge and Appropriate Choice of Diagrams in Math Word Problem Solving , 2006, Diagrams.
[52] M. Chi,et al. Eliciting Self‐Explanations Improves Understanding , 1994 .
[53] A. Gagatsis,et al. Representational Flexibility and Problem-Solving Ability in Fraction and Decimal Number Addition: A Structural Model , 2016 .
[54] E. Wiebe,et al. The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations , 2008 .
[55] Barbara Tversky,et al. Visualizing Thought , 2011, Top. Cogn. Sci..
[56] Trevor R Anderson,et al. The importance of visual literacy in the education of biochemists * , 2006, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.
[57] John K. Gilbert,et al. Visualization: A Metacognitive Skill in Science and Science Education , 2005 .
[58] P. Pintrich. A Motivational Science Perspective on the Role of Student Motivation in Learning and Teaching Contexts. , 2003 .
[59] R. Cox. Representation construction, externalised cognition and individual differences , 1999 .
[60] Jan Elen,et al. Understanding and Enhancing the Use of Multiple External Representations in Chemistry Education , 2012 .
[61] Michael L. Eastwood,et al. Fastest Fingers: A Molecule-Building Game for Teaching Organic Chemistry , 2013 .
[62] Jean McKendree,et al. The Role of Representation in Teaching and Learning Critical Thinking , 2002 .
[63] Miriam Reiner,et al. Visualization : theory and practice in science education , 2008 .
[64] Russell N. Carney,et al. Pictorial Illustrations Still Improve Students' Learning from Text , 2002 .
[65] Kathleen A. Cramer,et al. Using manipulative models to build number sense for addition of fractions: NCTM 2002 yearbook , 2002 .
[66] Rosária Justi,et al. Models and Modelling in Chemical Education , 2002 .
[67] Gayle Nicoll,et al. A report of undergraduates' bonding misconceptions , 2001 .
[68] Jay R. Campbell,et al. The Nation's Report Card: Reading, 2002. , 2003 .
[69] Kathleen A. Cramer,et al. Efficacy of Different Concrete Models for Teaching the Part-Whole Construct for Fractions , 2009 .
[70] Stephen J. Pape,et al. The Role of Representation(s) in Developing Mathematical Understanding , 2001 .
[71] David F. Feldon,et al. Five common but questionable principles ofmultimedia learning , 2005 .
[72] John Hattie,et al. Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement , 2008 .
[73] Helen Crompton,et al. Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic , 2012 .
[74] Slava Kalyuga,et al. Rethinking the Boundaries of Cognitive Load Theory in Complex Learning , 2016 .
[75] Brian J. Reiser,et al. The Cambridge Handbook of the Learning Sciences: Scaffolding , 2014 .
[76] R. Säljö,et al. Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking Research in Professional Domains , 2011 .
[77] Matthew Heinsen Egan,et al. Program visualization and explanation for novice C programmers , 2014, ACE.
[78] Melanie M. Cooper,et al. Development and validation of the implicit information from Lewis structures instrument (IILSI): do students connect structures with properties? , 2012 .
[79] L. Verschaffel,et al. Improving students’ representational flexibility in linear-function problems: an intervention , 2014 .
[80] Hope J. Hartman. Metacognition in Learning and Instruction , 2001 .
[81] Rayane F. Moreira. A Game for the Early and Rapid Assimilation of Organic Nomenclature , 2013 .
[82] R. Kozma,et al. Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena , 1997 .
[83] J. Shea. National Science Education Standards , 1995 .
[84] Sibel Kazak,et al. Saying More than You Know in Instructional Settings , 2011 .
[85] M. Stieff. Mental rotation and diagrammatic reasoning in science , 2007 .
[86] E. Gibson. Principles of Perceptual Learning and Development , 1969 .
[87] Keith S. Taber,et al. An alternative conceptual framework from chemistry education , 1998 .
[88] Albert T. Corbett,et al. The Resilience of Overgeneralization of Knowledge about Data Representations. , 2002 .
[89] Herbert A. Simon,et al. Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987 .
[90] Michal Yerushalmy,et al. Student perceptions of aspects of algebraic function using multiple representation software , 1991 .
[91] A. Renkl,et al. Instructional Aids to Support a Conceptual Understanding of Multiple Representations. , 2009 .
[92] Edward C. Rathmell,et al. Implementing the Standards. Number Representations and Relationships. , 1991 .
[93] H. Simon,et al. Perception in chess , 1973 .
[94] Irena Sajovic,et al. Action Research to Promote the Formation of Linkages by Chemistry Students Between the Macro, Submicro, and Symbolic Representational Levels , 2009 .
[95] Shaaron Ainsworth,et al. Examining the Effects of Different Multiple Representational Systems in Learning Primary Mathematics , 2002 .
[96] Wolfgang Schnotz,et al. Interactive and Non-Interactive Pictures in Multimedia Learning Environments: Effects on Learning Outcomes and Learning Efficiency , 2009 .
[97] Keith S. Taber,et al. Revisiting the chemistry triplet: drawing upon the nature of chemical knowledge and the psychology of learning to inform chemistry education , 2013 .
[98] Robert L. Goldstone,et al. The Transfer of Scientific Principles Using Concrete and Idealized Simulations , 2005, Journal of the Learning Sciences.
[99] Vincent Aleven,et al. Complementary Effects of Sense-Making and Fluency-Building Support for Connection Making: A Matter of Sequence? , 2013, AIED.
[100] Zachary A. Pardos,et al. How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study , 2013, International Journal of Artificial Intelligence in Education.
[101] J. Deloache. Dual representation and young children's use of scale models. , 2000, Child development.
[102] P. Shah,et al. Exploring visuospatial thinking in chemistry learning , 2004 .
[103] Philip J. Kellman,et al. Perceptual Learning and the Technology of Expertise: Studies in Fraction Learning and Algebra. , 2008 .
[104] Maria Caterina Tornatora,et al. An Eye-Tracking Study of Learning From Science Text With Concrete and Abstract Illustrations , 2013 .
[105] E. Gibson. Perceptual Learning in Development: Some Basic Concepts , 2000 .
[106] Etienne Wenger,et al. Situated Learning: Legitimate Peripheral Participation , 1991 .
[107] Ghislain Deslongchamps,et al. When do spatial abilities support student comprehension of STEM visualizations? , 2013, Cognitive Processing.
[108] Romina M. J. Proctor,et al. Integrating concrete and virtual materials in an elementary mathematics classroom: a case study of success with fractions , 2002 .
[109] Z. Zacharia,et al. Physical and Virtual Laboratories in Science and Engineering Education , 2013, Science.
[110] George M. Bodner,et al. Mental Models : The Role of Representations in Problem Solving in Chemistry PROCEEDINGS , 2002 .
[111] P. Donahue,et al. The Nation's Report Card[TM]: Reading, 2003. NCES 2005-453. , 2005 .
[112] Johnna J. Bolyard,et al. What Are Virtual Manipulatives , 2002 .
[113] Ruhama Even,et al. Factors involved in linking representations of functions , 1998 .
[114] L. Verschaffel,et al. What counts as a flexible representational choice? An evaluation of students’ representational choices to solve linear function problems , 2012 .
[115] P. Chandler,et al. Cognitive Load Theory and the Format of Instruction , 1991 .
[116] R. Mayer,et al. Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .
[117] John K. Gilbert,et al. Visualization: An Emergent Field of Practice and Enquiry in Science Education , 2008 .
[118] Martin Reisslein,et al. Teaching with Concrete and Abstract Visual Representations: Effects on Students' Problem Solving, Problem Representations, and Learning Perceptions. , 2011 .
[119] Judy S. DeLoache,et al. When a picture is not worth a thousand words: Young children's understanding of pictures and models , 1992 .
[120] Emily R. Fyfe,et al. “Concreteness fading” promotes transfer of mathematical knowledge , 2012 .
[121] Linda Jarvin,et al. When Theories Don't Add Up: Disentangling he Manipulatives Debate , 2007 .
[122] L. Verschaffel,et al. Representational flexibility in linear-function problems: a choice/no-choice study , 2010 .
[123] A. Northedge. Organizing Excursions Into Specialist Discourse Communities: A Sociocultural Account of University Teaching , 2008 .
[124] Thomas E. Kieren,et al. Rational and fractional numbers: From quotient fields to recursive understanding , 1993 .
[125] Billie Eilam,et al. Possible Constraints of Visualization in Biology: Challenges in Learning with Multiple Representations , 2013 .
[126] R. Kozma,et al. The Roles of Representations and Tools in the Chemistry Laboratory and Their Implications for Chemistry Learning , 2000 .
[127] P. Pintrich. Multiple Goals, Multiple Pathways: The Role of Goal Orientation in Learning and Achievement. , 2000 .
[128] James V. Wertsch. Properties of Mediated Action , 1997 .
[129] Mireille Betrancourt,et al. The Cambridge Handbook of Multimedia Learning: The Animation and Interactivity Principles in Multimedia Learning , 2005 .
[130] C. Hoyles,et al. The Construction of Mathematical Meanings: Connecting the Visual with the Symbolic , 1997 .
[131] Vincent Aleven,et al. Successful learning with multiple graphical representations and self-explanation prompts. , 2015 .
[132] Robert L. Goldstone,et al. Concreteness Fading in Mathematics and Science Instruction: a Systematic Review , 2014 .
[133] T. Mueller. Learning to Think Spatially , 2006 .
[134] V. Aleven,et al. Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments , 2014 .
[135] Vincent Aleven,et al. Sense Making Alone Doesn't Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations , 2012, ITS.
[136] Rosária Justi,et al. The Application of a ‘Model of Modelling’ to Illustrate the Importance of Metavisualisation in Respect of the Three Types of Representation , 2009 .
[137] Paul Cobb,et al. Guiding Inquiry-Based Math Learning , 2005 .
[138] M. Bannert,et al. Construction and interference in learning from multiple representation , 2003 .
[139] R. Mayer,et al. Eight Ways to Promote Generative Learning , 2016 .
[140] Joseph K. Torgesen,et al. Fraction Skills and Proportional Reasoning , 2007 .
[141] A. Rocke. Image and Reality: Kekulé, Kopp, and the Scientific Imagination , 2010 .
[142] A. Gagatsis,et al. The Effects of Different Modes of Representation on Mathematical Problem Solving. , 2004 .
[143] Phillip L. Ackerman,et al. Cognitive Ability and Non-Ability Trait Determinants of Expertise , 2003 .
[144] Susan E. Newman,et al. Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics. Technical Report No. 403. , 1987 .
[145] Ghislain Deslongchamps,et al. Beyond ball-and-stick: Students' processing of novel STEM visualizations , 2013 .
[146] Martha W. Alibali,et al. Modal Engagements in Pre-College Engineering: Tracking Math and Science Concepts Across Symbols, Sketches, Software, Silicone, and Wood , 2011 .
[147] John A. Hortin,et al. Identifying the Theoretical Foundations of Visual Literacy. , 1982 .
[148] Vicente A Talanquer,et al. Commonsense Chemistry: A Model for Understanding Students' Alternative Conceptions , 2006 .
[149] Ji Y. Son,et al. Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency , 2010, Top. Cogn. Sci..
[150] Billie Eilam,et al. Teaching, Learning, and Visual Literacy: The Dual Role of Visual Representation , 2012 .
[151] Jere Confrey,et al. The construction, refinement, and early validation of the equipartitioning learning trajectory , 2010, ICLS.
[152] Andrea A. diSessa,et al. Meta-representation: an introduction , 2000 .
[153] C. Furió,et al. Functional fixedness and functional reduction as common sense reasonings in chemical equilibrium and in geometry and polarity of molecules , 2000 .
[154] P. Kellman,et al. Perceptual Learning, Cognition, and Expertise , 2013 .
[155] D. Gentner. Structure‐Mapping: A Theoretical Framework for Analogy* , 1983 .
[156] D. Gentner,et al. Learning and Transfer: A General Role for Analogical Encoding , 2003 .
[157] A. Baddeley. Working memory: theories, models, and controversies. , 2012, Annual review of psychology.
[158] P. V. Meter,et al. The Promise and Practice of Learner-Generated Drawing: Literature Review and Synthesis , 2005 .
[159] Jennifer L. Chiu,et al. Evidence for effective uses of dynamic visualisations in science curriculum materials , 2015 .
[160] Robert L. Goldstone,et al. Learning to Bridge Between Perception and Cognition , 1997 .
[161] Daniel Bodemer,et al. External and mental referencing of multiple representations , 2006, Comput. Hum. Behav..
[162] Robert L. Goldstone,et al. Integrating Formal and Grounded Representations in Combinatorics Learning , 2013 .
[163] Bette Davidowitz,et al. Linking the macroscopic and sub-microscopic levels : diagrams , 2009 .
[164] Paul Brna,et al. Supporting the use of external representation in problem solving: the need for flexible learning environments , 1995 .
[165] Wolfgang Schnotz,et al. The Cambridge Handbook of Multimedia Learning: An Integrated Model of Text and Picture Comprehension , 2005 .
[166] S. Ainsworth. The Cambridge Handbook of Multimedia Learning: The Multiple Representation Principle in Multimedia Learning , 2014 .
[167] Adam Kraft,et al. What happens when representations fail to represent? Graduate students’ mental models of organic chemistry diagrams , 2010 .
[168] D. Gentner,et al. Structure mapping in analogy and similarity. , 1997 .
[169] Robert L. Goldstone,et al. Reuniting perception and conception , 1998, Cognition.
[170] A. Paivio. Mental Representations: A Dual Coding Approach , 1986 .
[171] P. Kellman,et al. Perceptual learning and human expertise. , 2009, Physics of life reviews.
[172] R. Keith Sawyer,et al. The Cambridge Handbook of the Learning Sciences: Analyzing Collaborative Discourse , 2005 .
[173] Lester C. Loschky,et al. How Does Visual Attention Differ Between Experts and Novices on Physics Problems , 2010 .
[174] Dor Abrahamson,et al. Understanding ratio and proportion as an example of the apprehending zone and conceptual-phase problem-solving models. , 2004 .
[175] M. Hegarty,et al. Individual Differences in Spatial Abilities , 2005 .
[176] Paul Cobb,et al. Cultural Tools and Mathematical Learning: A Case Study. , 1995 .
[177] David Pace,et al. Decoding the Disciplines: A Model for Helping Students Learn Disciplinary Ways of Thinking. , 2004 .
[178] Yash Patel,et al. Using Multiple Representations to Build Conceptual Understanding in Science and Mathematics , 2014 .
[179] Manjula D. Sharma,et al. Scientific representational fluency: Defining, diagnosing and developing , 2014 .
[180] Anna N. Rafferty,et al. Designing Instruction to Improve Lifelong Inquiry Learning , 2015 .
[181] Joan K. Gallini,et al. When Is an Illustration Worth Ten Thousand Words , 1990 .
[182] Patrik Pluchino,et al. Effects of Picture Labeling on Science Text Processing and Learning: Evidence From Eye Movements , 2013 .
[183] David F. Treagust,et al. Investigation of secondary school, undergraduate, and graduate learners' mental models of ionic bonding , 2003 .
[184] Abraham Arcavi,et al. The role of visual representations in the learning of mathematics , 2003 .
[185] Shaaron Ainsworth,et al. The Educational Value of Multiple-representations when Learning Complex Scientific Concepts , 2008 .
[186] C. W. Bowen,et al. Representational Systems Used by Graduate Students while Problem Solving in Organic Synthesis. , 1990 .