The Design of Disciplinarily-Integrated Games as Multirepresentational Systems

Disciplinarily-integrated games represent a generalizable genre and template for designing games to support science learning with a focus on bridging across formal and phenomenological representations of core science relationships (Clark, Sengupta, Brady, Martinez-Garza, and Killingsworth, 2015; Clark, Sengupta, & Virk, 2016; Sengupta & Clark, 2016). By definition, disciplinarily-integrated games (DIGs) are therefore multirepresentational systems with the affordances and challenges associated with that medium. The current paper analyzes the DIG structure through the focal parameters framed by the DeFT framework (Ainsworth, 2006) to synthesize effective design considerations for DIGs in terms of the specific design and intended functions of the representations themselves as well as the overarching environment and activity structures. The authors leverage the literatures on embodied cognition, adaptive scaffolding, representations in science education, and learning from dynamic visualizations to address the challenges, tradeoffs, and questions highlighted by the framework. They apply these research-derived design considerations to an existing DIG (SURGE Symbolic) and to hypothetical examples of other DIGs in other domains to explore generalizability of the design considerations and the genre. KeywoRDS Ainsworth, Cognitive Flexibility, DeFT, Design Thinking, DIGs, Multiple Representations, Representational Bridges, Science Educational Technologies

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

[2]  Hans Spada,et al.  The Active Integration of Information during Learning with Dynamic and Interactive Visualisations , 2004 .

[3]  John B. Black,et al.  Incorporating haptic feedback in simulation for learning physics , 2011, Comput. Educ..

[4]  L. Schauble,et al.  Symbolic communication in mathematics and science: Co-constituting inscription and thought. , 2002 .

[5]  J. Gee Social Linguistics and Literacies : Ideology in Discourses , 2008 .

[6]  B. Rittle-Johnson,et al.  Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. , 2007 .

[7]  R. Mayer,et al.  When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? , 2001 .

[8]  Richard Lehrer,et al.  What Kind of Explanation is a Model , 2010 .

[9]  T. Jong,et al.  Supporting students' learning with multiple representations in a dynamic simulation-based learning environment , 2006 .

[10]  Scotty D. Craig,et al.  Animated Pedagogical Agents in Multimedia Educational Environments: Effects of Agent Properties, Picture Features, and Redundancy , 2002 .

[11]  Slava Kalyuga,et al.  Managing split-attention and redundancy in multimedia instruction , 1999 .

[12]  Richard Catrambone,et al.  Aiding subgoal learning: Effects on transfer. , 1995 .

[13]  Douglas B. Clark,et al.  Disciplinary integration of digital games for science learning , 2015, International Journal of STEM Education.

[14]  Shih-Chieh Huang Grounded Learning Experience: Helping Students Learn Physics through Visuo-Haptic Priming and Instruction , 2013 .

[15]  Mary Hegarty,et al.  Constructing and Revising Mental Models of a Mechanical System: The role of domain knowledge in understanding external visualizations , 2004 .

[16]  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..

[17]  Daniel L. Schwartz,et al.  Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. , 2011 .

[18]  Kurt VanLehn,et al.  Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies , 2011, User Modeling and User-Adapted Interaction.

[19]  R. Stevens,et al.  Making Space: A Comparison of Mathematical Work in School and Professional Design Practices , 1994 .

[20]  Susan Jang,et al.  Embodied Cognition and Virtual Reality in Learning to Visualize Anatomy , 2010 .

[21]  Kristian Kiili,et al.  On Educational Game Design: Building Blocks of Flow Experience , 2005 .

[22]  Richard Lowe,et al.  Learning with Animation: Research Implications for Design , 2007 .

[23]  Lena A. E. Tibell,et al.  Do haptic representations help complex molecular learning , 2011 .

[24]  Heather Brasell,et al.  The effect of real‐time laboratory graphing on learning graphic representations of distance and velocity , 1987 .

[25]  M. Hegarty Dynamic visualizations and learning: getting to the difficult questions , 2004 .

[26]  S. Ainsworth,et al.  Multiple Forms of Dynamic Representation. , 2004 .

[27]  Béatrice S. Hasler,et al.  Learner Control, Cognitive Load and Instructional Animation , 2007 .

[28]  T. Gog,et al.  A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning , 2010 .

[29]  Douglas B. Clark,et al.  Integrating self-explanation functionality into a complex game environment: Keeping gaming in motion , 2014, Comput. Educ..

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

[31]  B. Rittle-Johnson,et al.  Developing procedural flexibility: are novices prepared to learn from comparing procedures? , 2012, The British journal of educational psychology.

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

[33]  Paul Cobb,et al.  Symbolizing, Modeling, and Instructional Design , 2000 .

[34]  M. Hegarty,et al.  Representational Translation With Concrete Models in Organic Chemistry , 2012 .

[35]  Michael Barnett,et al.  Electromagnetism Supercharged! Learning Physics with Digital Simulation Games , 2004, ICLS.

[36]  Mary Hegarty,et al.  The Roles of Mental Animations and External Animations in Understanding Mechanical Systems , 2003 .

[37]  Shih-Chieh Huang,et al.  The Effects of LEGO Robotics and Embodiment in Elementary Science Learning , 2011, CogSci.

[38]  Richard E. Mayer,et al.  Principles for Managing Essential Processing in Multimedia Learning: Segmenting, Pre-training, and Modality Principles , 2005 .

[39]  R. Mayer,et al.  Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity , 1999 .

[40]  Daniel L. Schwartz,et al.  Chapter 3: Rethinking Transfer: A Simple Proposal With Multiple Implications , 1999 .

[41]  Loretta L. Jones,et al.  The role of multiple representations in the understanding of ideal gas problems , 2011 .

[42]  Richard Lowe,et al.  Animation and learning: selective processing of information in dynamic graphics , 2003 .

[43]  Allan Collins A Study of Expert Theory Formation: The Role of Different Model Types and Domain Frameworks , 2011 .

[44]  A. Collins,et al.  Epistemic Fluency and Constructivist Learning Environments. , 1995 .

[45]  N. Nersessian The Cognitive Basis of Science: The cognitive basis of model-based reasoning in science , 2002 .

[46]  Noel Enyedy Inventing Mapping: Creating Cultural Forms to Solve Collective Problems , 2005 .

[47]  Shih-Chieh Huang,et al.  Learning Classic Mechanics with Embodied Cognition , 2011 .

[48]  J. Clement,et al.  Misconceptions in Graphing , 2011 .

[49]  F. Paas,et al.  Attention Cueing as a Means to Enhance Learning from an Animation , 2007 .

[50]  Yvonne Kammerer,et al.  The effects of realism in learning with dynamic visualizations , 2009 .

[51]  M. P. Jacob Habgood,et al.  Motivating Children to Learn Effectively: Exploring the Value of Intrinsic Integration in Educational Games , 2011 .

[52]  Douglas B. Clark,et al.  Disciplinarily-Integrated Games: Generalizing Across Domains and Model Types , 2016 .

[53]  Robert K. Atkinson,et al.  Animated pedagogical agents: does their degree of embodiment impact learning from static or animated worked examples? , 2007 .

[54]  David N. Rapp,et al.  Models and modeling in science learning , 2011 .

[55]  Chung-Yuan Hsu,et al.  Facilitating Third Graders’ Acquisition of Scientific Concepts through Digital Game-Based Learning: The Effects of Self-Explanation Principles , 2012 .

[56]  John B. Black,et al.  Direct-manipulation animation: incorporating the haptic channel in the learning process to support middle school students in science learning and mental model acquisition , 2006 .

[57]  J. Mokros,et al.  The impact of microcomputer‐based labs on children's ability to interpret graphs , 1987 .

[58]  Ton de Jong,et al.  Learning with Multiple Representations: Supporting students’ translation between representations in a simulation-based learning environment , 2003 .

[59]  R. Giere Explaining Science: A Cognitive Approach , 1991 .

[60]  Herbert A. Simon,et al.  Why a diagram is (sometimes) worth 10, 000 word , 1987 .

[61]  Marcia C. Linn,et al.  Can generating representations enhance learning with dynamic visualizations , 2011 .

[62]  Paul Ginns Meta-Analysis of the Modality Effect. , 2005 .

[63]  Rolf Ploetzner,et al.  The construction and coordination of complementary problem representations in physics , 1995 .

[64]  L. Schauble,et al.  Cultivating Model-Based Reasoning in Science Education , 2005 .

[65]  Pratim Sengupta,et al.  Playing Modeling Games in the Science Classroom: The Case for Disciplinary Integration , 2016, 1607.05094.

[66]  F. Reif,et al.  Teaching scientific thinking skills: Students and computers coaching each other , 1999 .

[67]  P. Chandler,et al.  The Role of Visual Indicators in Dual Sensory Mode Instruction , 1997 .

[68]  R. Mayer,et al.  Interactive Multimodal Learning Environments , 2007 .

[69]  L. Barsalou Grounded cognition. , 2008, Annual review of psychology.

[70]  Stephen J. Payne,et al.  Internalizing and the Use Specificity of Device Knowledge , 1993, Hum. Comput. Interact..

[71]  B. White ThinkerTools: Causal Models, Conceptual Change, and Science Education , 1993 .

[72]  Mireille Betrancourt,et al.  The Cambridge Handbook of Multimedia Learning: The Animation and Interactivity Principles in Multimedia Learning , 2005 .

[73]  Ruth Wylie,et al.  The Self-Explanation Principle in Multimedia Learning , 2014 .

[74]  S. Ainsworth The Cambridge Handbook of Multimedia Learning: The Multiple Representation Principle in Multimedia Learning , 2014 .

[75]  A. Collins,et al.  Epistemic forms and Epistemic Games: Structures and Strategies to Guide Inquiry , 1993 .

[76]  Alan Bell,et al.  The Interpretation of Graphs Representing Situations. , 1981 .

[77]  Brian C. Nelson,et al.  SURGE’s Evolution Deeper into Formal Representations: The Siren’s Call of Popular Game-Play Mechanics , 2016 .

[78]  K. VanLehn The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .

[79]  Jeremy Roschelle,et al.  The SimCalc Vision and Contributions , 2013 .

[80]  J. B. Black An Embodied/Grounded Cognition Perspective on Educational Technology , 2010 .

[81]  Cheryl I. Johnson,et al.  Adding Instructional Features That Promote Learning in a Game-Like Environment , 2010 .

[82]  Sten R. Ludvigsen,et al.  Student sensemaking with science diagrams in a computer-based setting , 2013, Int. J. Comput. Support. Collab. Learn..

[83]  Richard E. Mayer,et al.  Signaling as a Cognitive Guide in Multimedia Learning , 2001 .

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

[85]  Roy D. Pea,et al.  Addressing the Challenges of Inquiry-Based Learning Through Technology and Curriculum Design , 1999 .

[86]  Richard E. Mayer,et al.  Multimedia learning in an interactive self-explaining environment: What works in the design of agent , 2003 .

[87]  Raymond W. Kulhavy,et al.  Organized spatial displays and texts : Effects of presentation order and display type on learning outcomes , 1997 .

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

[89]  Raymond W. Kulhavy,et al.  Learning from Cartograms: The Effects of Region Familiarity , 1996 .

[90]  Jiajie Zhang,et al.  A representational analysis of relational information displays , 1996, Int. J. Hum. Comput. Stud..

[91]  Timo Lainema,et al.  Foundation for Measuring Engagement in Educational Games , 2008 .

[92]  J. Gee Good video games and good learning , 2007 .

[93]  Alexander Renkl,et al.  Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations , 2009 .

[94]  J. Gee Social Linguistics And Literacies: Ideology in Discourse , 1996 .

[95]  Paul Chandler,et al.  Levels of Expertise and Instructional Design , 1998, Hum. Factors.

[96]  Claude Janvier,et al.  A Study of the Interpretation of Trends in Multiple Curve Graphs of Ecological Situations. , 1992 .

[97]  Seungoh Paek,et al.  The Impact of Multimodal Virtual Manipulatives on Young Children's Mathematics Learning. , 2012 .

[98]  Mike Stieff,et al.  Identifying Representational Competence With Multi-Representational Displays , 2011 .

[99]  David Wood,et al.  There is more than one way to solve a problem: Evaluating a learning environment that supports the development of children's multiplication skills , 1998 .

[100]  Tina Seufert Supporting Coherence Formation in Learning from Multiple Representations , 2003 .

[101]  Richard Lehrer,et al.  Images of learning, images of progress , 2009 .

[102]  Gaea Leinhardt,et al.  Functions, Graphs, and Graphing: Tasks, Learning, and Teaching , 1990 .

[103]  Sophie Pfeifer,et al.  Taking Science To School Learning And Teaching Science In Grades K 8 , 2016 .

[104]  Sara Jones,et al.  Diagram Representation: A Comparison of Animated and Static Formats , 1999 .

[105]  R. Lehrer,et al.  Technology and mathematics education , 2008 .

[106]  Andrew Pickering,et al.  The mangle of practice : time, agency, and science , 1997 .

[107]  Michelene T. H. Chi,et al.  The Cambridge Handbook of Multimedia Learning: The Self-Explanation Principle in Multimedia Learning , 2005 .

[108]  Douglas B. Clark,et al.  Digital Games, Design, and Learning , 2016, Review of educational research.

[109]  A. Lu Video Games and Learning: Teaching and Participatory Culture in the Digital Age , 2013 .