CASE STUDIES OF FIFTH-GRADE STUDENT MODELING IN SCIENCE THROUGH PROGRAMMING: COMPARISON OF MODELING PRACTICES AND CONVERSATIONS

Title of dissertation: CASE STUDIES OF FIFTH-GRADE STUDENT MODELING IN SCIENCE THROUGH PROGRAMMING: COMPARISON OF MODELING PRACTICES AND CONVERSATIONS Loucas Louca, Doctor of Philosophy, 2004 Dissertation directed by: Associate Professor David Hammer. Department of Physics and Department of Curriculum & Instruction. This is a descriptive case study investigating the use of two computer-based programming environments (CPEs), MicroWorlds (MW) and Stagecast Creator (SC), as modeling tools for collaborative fifth grade science learning. In this study I investigated how CPEs might support fifth grade student work and inquiry in science. There is a longstanding awareness of the need to help students learn about models and modeling in science, and CPEs are promising tools for this. A computer program can be a model of a physical system, and modeling through programming may make the process more tangible: Programming involves making decisions and assumptions; the code is used to express ideas; running the program shows the implications of those ideas. In this study I have analyzed and compared students’ activities and conversations in two after-school clubs, one working with MW and the other with SC. The findings confirm the promise of CPEs as tools for teaching practices of modeling and science, and they suggest advantages and disadvantages to that purpose of particular aspects of CPE designs. MW is an open-ended, textual CPE that uses procedural programming. MW students focused on breaking down phenomena into small programmable pieces, which is useful for scientific modeling. Developing their programs, the students focused on writing, testing and debugging code, which are also useful for scientific modeling. SC is a non-linear, object-oriented CPE that uses visual program language. SC students saw their work as creating games. They were focused on the overall story which they then translated it into SC rules, which was in conflict with SC’s object-oriented interface. However, telling the story of individual causal agents was useful for scientific modeling. Programming in SC was easier, whereas reading code in MW was more tangible. The latter helped MW students to use the code as the representation of the phenomenon rather than merely as a tool for creating a simulation. The analyses also pointed to three emerging “frames” that describe student’s work focus, based on their goals, strategies, and criteria for success. Emerging “frames” are the programming, the visualization, and the modeling frame. One way to understand the respective advantages and disadvantages of the two CPEs is with respect to which frames they engendered in students. CASE STUDIES OF FIFTH-GRADE STUDENT MODELING IN SCIENCE THROUGH PROGRAMMING: COMPARISON OF MODELING PRACTICES AND CONVERSATIONS

[1]  Kathleen E. Metz Reassessment of Developmental Constraints on Children’s Science Instruction , 1995 .

[2]  L. Schauble,et al.  Building Functional Models: Designing an Elbow , 1997 .

[3]  D. Penner Cognition, Computers, and Synthetic Science: Building Knowledge and Meaning Through Modeling , 2000 .

[4]  J. Frederiksen,et al.  Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students , 1998 .

[5]  Jagjit Kaur Singh Cognitive Effects of Programming in Logo: A Review of Literature and Synthesis of Strategies for Research , 1992 .

[6]  J. Sinclair,et al.  Towards an Analysis of Discourse: The English Used by Teachers and Pupils , 1975 .

[7]  B. Inhelder,et al.  If you want to get ahead, get a theory , 1975, Cognition.

[8]  E. Markman,et al.  Categories and induction in young children , 1986, Cognition.

[9]  Jean Underwood,et al.  Collaboration and discourse while programming the KidSim microworld simulation , 1996, Comput. Educ..

[10]  D. Kuhn Children and adults as intuitive scientists. , 1989, Psychological review.

[11]  Wouter R. van Joolingen,et al.  The effect of representations on communication and product during collaborative modeling , 2002, CSCL.

[12]  N. Hoffart Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory , 2000 .

[13]  Richard Lehrer,et al.  From Physical Models to Biomechanics: A Design-Based Modeling Approach. , 1998 .

[14]  Horst Schecker The Didactic Potential of Computer Aided Modeling for Physics Education , 1993 .

[15]  Genrikh Golin,et al.  Structure of Scientific Knowledge and Curriculum Design , 1997 .

[16]  Philip Bell,et al.  The knowledge integration environment: theory and design , 1995, CSCL.

[17]  James D. Kiper,et al.  Criteria for Evaluation of Visual Programming Languages , 1997, J. Vis. Lang. Comput..

[18]  Edward L. Smith,et al.  Student Use of Narrative and Paradigmatic Forms of Talk in Elementary Science Conversations. , 2002 .

[19]  Richard E. Mayer,et al.  Children's Naive Conceptions and Confusions About Logo Graphics Commands. , 1987 .

[20]  Larry Tesler,et al.  Novice Programming Comes of Age , 2001, Your Wish is My Command.

[21]  M. Pressley,et al.  Discourse Patterns and Collaborative Scientific Reasoning in Peer and Teacher-Guided Discussions , 1999 .

[22]  E. V. Zee,et al.  Analysis of a student-generated inquiry discussion , 2000 .

[23]  S. Papert The children's machine: rethinking school in the age of the computer , 1993 .

[24]  C. Mutch Qualitative Research for Education: An Introduction to Theory and Methods [Book Review] , 2006 .

[25]  J. Creswell Qualitative inquiry and research design: choosing among five traditions. , 1998 .

[26]  David F. Treagust,et al.  Modelling in Science Lessons: Are There Better Ways to Learn With Models? , 1998 .

[27]  Andrea A. diSessa,et al.  Unlearning Aristotelian Physics: A Study of Knowledge-Based Learning , 1982, Cogn. Sci..

[28]  J. Roschelle Learning by Collaborating: Convergent Conceptual Change , 1992 .

[29]  Ken Kahn,et al.  Helping children learn hard things: computer programming with familiar objects and activities , 1998 .

[30]  P. Johnson-Laird,et al.  Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness , 1985 .

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

[32]  Ala Samarapungavan,et al.  Children's judgments in theory choice tasks: Scientific rationality in childhood , 1992, Cognition.

[33]  David Canfield Smith,et al.  Making programming easier for children , 1996, INTR.

[34]  Cyndi Rader,et al.  Degrees of comprehension: children's understanding of a visual programming environment , 1997, CHI.

[35]  Patrick W Thompson A Piagetian Approach to Transformation Geometry via Microworlds. , 1985 .

[36]  Carol L. Smith,et al.  Understanding models and their use in science: Conceptions of middle and high school students and experts , 1991 .

[37]  P. Medawar Pluto's Republic , 1982 .

[38]  Robert Glaser,et al.  Model–based analysis and reasoning in science: The MARS curriculum , 1995 .

[39]  Neil Mercer,et al.  Common Knowledge: The Development of Understanding in the Classroom , 1987 .

[40]  Richard Lehrer,et al.  Reflective Teaching of Logo , 1999 .

[41]  Gurminder Singh,et al.  Components of the visual computer: A review of relevant technologies , 1992, The Visual Computer.

[42]  A. Collins National Science Education Standards: A Political Document. , 1998 .

[43]  Emily H. van Zee,et al.  Reflective discourse: developing shared understandings in a physics classroom , 1997 .

[44]  Richard A. Duschl,et al.  Restructuring Science Education: The Importance of Theories and Their Development , 1990 .

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

[46]  Loucas T. Louca,et al.  Epistemological Resources: Applying a New Epistemological Framework to Science Instruction , 2004 .

[47]  Karen Gallas,et al.  Talking their way into science : hearing children's questions and theories, responding with curricula , 1995 .

[48]  Mitchel Resnick,et al.  Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo , 2001 .

[49]  Andrew Elby,et al.  Tapping Epistemological Resources for Learning Physics , 2003 .

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

[51]  David Hestenes,et al.  A modeling method for high school physics instruction , 1995 .

[52]  B. Bederson,et al.  Children as our technology design partners , 1998 .

[53]  David Hammer,et al.  Students' Collaborative use of Computer-Based Programming Tools in Science , 2003, CSCL.

[54]  David Hammer,et al.  Epistemological Beliefs in Introductory Physics , 1994 .

[55]  Edward F. Redish,et al.  Student programming in the introductory physics course: M.U.P.P.E.T. , 1993 .

[56]  D. Ball With an Eye on the Mathematical Horizon: Dilemmas of Teaching Elementary School Mathematics , 1993, The Elementary School Journal.

[57]  David Canfield Smith,et al.  KidSim: end user programming of simulations , 1995, CHI '95.

[58]  S. Toulmin The uses of argument , 1960 .

[59]  Gregory J. Kelly,et al.  Students’ reasoning about electricity: combining performance assessments with argumentation analysis , 1998 .

[60]  Lieven Verschaffel,et al.  A Logo-Based Tool-Kit and Computer Coach to Support the Development of General Thinking Skills , 1993 .

[61]  R. Driver,et al.  A Constructivist Approach to Curriculum Development in Science , 1986 .

[62]  Emrah Orhun Learning Problem Solving Through Computer Programming , 1993 .

[63]  Ann L. Brown,et al.  Domain-Specific Principles Affect Learning and Transfer in Children , 1990, Cogn. Sci..

[64]  Emily H. van Zee,et al.  Student and teacher questioning during conversations about science , 2001 .

[65]  Roy D. Pea,et al.  Logo and development of thinking skills , 1987 .