The ontologies of complexity and learning about complex systems

This paper discusses a study of students learning core conceptual perspectives from recent scientific research on complexity using a hypermedia learning environment in which different types of scaffolding were provided. Three comparison groups used a hypermedia system with agent-based models and scaffolds for problem-based learning activities that varied in terms of the types of text based scaffolds that were provided related to a set of complex systems concepts. Although significant declarative knowledge gains were found for the main experimental treatment in which the students received the most scaffolding, there were no significant differences amongst the three groups in terms of the more cognitively demanding performance on problem solving tasks. However, it was found across all groups that the students who enriched their ontologies about how complex systems function performed at a significantly higher level on transfer problem solving tasks in the posttest. It is proposed that the combination of interactive representational scaffolds associated with NetLogo agent-based models in complex systems cases and problem solving scaffolding allowed participants to abstract ontological dimensions about how systems of this type function that, in turn, was associated with the higher performance on the problem solving transfer tasks. Theoretical and design implications for learning about complex systems are discussed.

[1]  Dirk Helbing,et al.  Agent-Based Modeling , 2012 .

[2]  Mitchel Resnick,et al.  Turtles, termites, and traffic jams - explorations in massively parallel microworlds , 1994 .

[3]  Elizabeth S. Charles,et al.  Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change , 2004 .

[4]  Sigmar-Olaf Tergan,et al.  Conceptual and Methodological Shortcomings in Hypertext/Hypermedia Design and Research , 1997 .

[5]  Michael J. Jacobson,et al.  A design framework for educational hypermedia systems: theory, research, and learning emerging scientific conceptual perspectives , 2008 .

[6]  D. Gentner,et al.  Learning and Transfer: A General Role for Analogical Encoding , 2003 .

[7]  John L. Casti,et al.  Complexification: Explaining a Paradoxical World Through the Science of Surprise , 1994 .

[8]  G. Lakoff,et al.  Women, Fire, and Dangerous Things: What Categories Reveal about the Mind , 1988 .

[9]  Stella Vosniadou,et al.  Mental Models of the Day/Night Cycle , 1994, Cogn. Sci..

[10]  Gary Boyd,et al.  An ontological approach to conceptual change: the role that complex systems thinking may play in providing the explanatory framework needed for studying contemporary sciences , 2003 .

[11]  Naomi Miyake,et al.  Explorations of Scaffolding in Complex Classroom Systems , 2004, The Journal of the Learning Sciences.

[12]  MICHAEL J. JACOBSON,et al.  Problem solving, cognition, and complex systems: Differences between experts and novices , 2001, Complex..

[13]  Nora H. Sabelli,et al.  Complexity, Technology, Science, and Education , 2006 .

[14]  M. Gell-Mann A Theory of Everything. (Book Reviews: The Quark and the Jaguar. Adventures in the Simple and the Complex.) , 1994 .

[15]  Michael J. Jacobson,et al.  Hypertext Learning Environments, Cognitive Flexibility, and the Transfer of Complex Knowledge: an Empirical Investigation Center for the Study of Reading Center for the Study of Reading Hypertext Learning Environments, Cognitive Flexibility, and the Transfer of Complex Knowledge: an Empirical Invest , 2007 .

[16]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[17]  John H. Holland,et al.  Hidden Order: How Adaptation Builds Complexity , 1995 .

[18]  Michael J. Jacobson,et al.  Learning with hypertext learning environments: theory, design, and research , 1995 .

[19]  H. Van Dyke Parunak,et al.  Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide , 1998, MABS.

[20]  R. Azevedo,et al.  Scaffolding Self-regulated Learning and Metacognition – Implications for the Design of Computer-based Scaffolds , 2005 .

[21]  Robert L. Goldstone The Complex Systems See-Change in Education , 2006 .

[22]  Michelene T. H. Chi,et al.  Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust , 2005 .

[23]  Andrea A. diSessa,et al.  The Cambridge Handbook of the Learning Sciences: A History of Conceptual Change Research , 2005 .

[24]  Susan E. Newman,et al.  Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics. Technical Report No. 403. , 1987 .

[25]  M. Resnick,et al.  Diving into Complexity: Developing Probabilistic Decentralized Thinking through Role-Playing Activities. , 1998 .

[26]  M. Resnick,et al.  Thinking in Levels: A Dynamic Systems Approach to Making Sense of the World , 1999 .

[27]  J. Bruner,et al.  The role of tutoring in problem solving. , 1976, Journal of child psychology and psychiatry, and allied disciplines.

[28]  R. Sawyer The Cambridge Handbook of the Learning Sciences: Introduction , 2014 .

[29]  H. Schweingruber,et al.  TAKING SCIENCE TO SCHOOL: LEARNING AND TEACHING SCIENCE IN GRADES K-8 , 2007 .

[30]  Manu Kapur,et al.  A further study of productive failure in mathematical problem solving: unpacking the design components , 2011 .

[31]  M. Chi,et al.  From things to processes: A theory of conceptual change for learning science concepts , 1994 .

[32]  Kate Thompson The value of multiple representations for learning about complex systems , 2008, ICLS.

[33]  U. Wilensky,et al.  Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences , 2006 .

[34]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[35]  S. Kauffman At Home in the Universe: The Search for the Laws of Self-Organization and Complexity , 1995 .

[36]  Robert L. Goldstone,et al.  Please Scroll down for Article Journal of the Learning Sciences Promoting Transfer by Grounding Complex Systems Principles , 2022 .

[37]  Manu Kapur Productive failure in mathematical problem solving , 2010 .

[38]  James A. Levin,et al.  Teachers' Conceptions of the Internet and the World Wide Web: A Representational Toolkit as a Model of Expertise , 1999 .

[39]  U. Wilensky,et al.  Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach , 2006 .

[40]  Cindy E. Hmelo-Silver,et al.  Fish Swim, Rocks Sit, and Lungs Breathe: Expert-Novice Understanding of Complex Systems , 2007 .

[41]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[42]  Manu Kapur Productive Failure , 2006, ICLS.

[43]  Michael J. Jacobson,et al.  The Design of Hypermedia Tools for Learning: Fostering Conceptual Change and Transfer of Complex Scientific Knowledge , 2000 .