From Agents to Continuous Change via Aesthetics: Learning Mechanics with Visual Agent-based Computational Modeling

Novice learners find motion as a continuous process of change challenging to understand. In this paper, we present a pedagogical approach based on agent-based, visual programming to address this issue. Integrating agent-based programming, in particular, Logo programming, with curricular science has been shown to be challenging in previous research on educational computing. We present a new Logo-based visual programming language—ViMAP—and, a sequence of learning activities involving programming and modeling, designed specifically to support seamless integration between programming and learning kinematics. We describe relevant affordances of the ViMAP environment that supports such seamless integration. We then present ViMAP-MoMo, a curricular unit designed in ViMAP for modeling kinematics, for a wide range of students (elementary—high school). Finally, we describe in detail a sequence of learning activities in three phases, discuss the underlying rationale for each phase, and where relevant, report results in the form of observational data from two studies.

[1]  David Hammer,et al.  Dynaturtle Revisited: Learning Physics Through Collaborative Design of a Computer Model , 1993, Interact. Learn. Environ..

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

[3]  B. Latour.  Pandora's Hope: Essays on the Reality of Science Studies , 1999 .

[4]  Harold Abelson,et al.  Boxer: a reconstructible computational medium , 1986, CACM.

[5]  Michael Eisenberg,et al.  Pervasive Fabrication: Making Construction Ubiquitous in Education , 2008 .

[6]  R. Giere.  Cognitive Models of Science , 1992 .

[7]  Judith Wechsler,et al.  On Aesthetics in Science , 1980 .

[8]  Lillian C. McDermott,et al.  Student difficulties in connecting graphs and physics: Examples from kinematics , 1987 .

[9]  Richard Lehrer,et al.  Designing to develop disciplinary dispositions: modeling natural systems. , 2009, The American psychologist.

[10]  Uri Wilensky,et al.  NetLogo: A simple environment for modeling complexity , 2014 .

[11]  Andra A. DiSessa.  Inventing Graphing: Meta­ Representational Expertise in Children , 1991 .

[12]  Pratim Sengupta,et al.  Learning kinematics in elementary grades using agent-based computational modeling: a visual programming-based approach , 2012, IDC '12.

[13]  J. Piaget,et al.  Logic and Psychology , 1953 .

[14]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[15]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[16]  Ibrahim A. Halloun,et al.  The initial knowledge state of college physics students , 1985 .

[17]  Andrew Elby,et al.  What students' learning of representations tells us about constructivism , 2000 .

[18]  U. Wilensky.  Abstract Meditations on the Concrete and Concrete Implications for Mathematics Education , 1991 .

[19]  Bruce L. Sherin,et al.  How students invent representations of motion: A genetic account , 2000 .

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

[21]  P. Johnson-Laird.  Mental models , 1989 .

[22]  P. Ross.  TURTLE GEOMETRY The Computer as a Medium for Exploring Mathematics , 1987 .

[23]  Andrea A. diSessa.  An Overview of Boxer. , 1991 .

[24]  Andrea A. diSessa,et al.  A Principled Design for an Integrated Computational Environment , 1985, Hum. Comput. Interact..

[25]  Mark Guzdial,et al.  Software-Realized Scaffolding to Facilitate Programming for Science Learning , 1994, Interact. Learn. Environ..

[26]  Dewey I. Dykstra,et al.  Conceptual development about motion and force in elementary and middle school students , 2009 .

[27]  M. McCloskey.  Naive Theories of Motion. , 1982 .

[28]  Steven L. Tanimoto,et al.  VIVA: A visual language for image processing , 1990, J. Vis. Lang. Comput..