Learning Genetics from Dragons: From Computer-Based Manipulatives to Hypermodels

This chapter addresses an issue central to the design of educational technology: the extent to which one should explicitly guide the student as opposed to simply creating an open-ended tool for discovery and experimentation. The basis for the discussion is the experience of the lead author, described in an earlier publication, regarding the use of a program called GenScope. GenScope offered students a multilevel model of genetics, ranging from DNA to populations, wherein manipulations made at any one level could affect other levels, much as a change in one cell of a spreadsheet may cause a “ripple effect” on other cells down the line. In common with other general-purpose computer models, GenScope embodied no specific educational agenda. The chapter describes a more recent program, BioLogica, which augments the functionality of GenScope by monitoring and logging students’ actions, providing online, context-sensitive scaffolding, and situating student activities within a context of real-world examples. We present results from a 5-year program of research conducted with BioLogica in high school biology classes throughout the United States.

[1]  Esther R. Steinberg,et al.  Review of Student Control in Computer-Assisted Instruction , 1977 .

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

[3]  Robert Kozma,et al.  Innovations in Science and Mathematics Education: Advanced Designs for Technologies of Learning. , 2000 .

[4]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[5]  Peter Gärdenfors,et al.  Getting the story straight: The role of narrative in teaching and learning with interactive media , 2005 .

[6]  Lawrence B. Flick,et al.  Scientific inquiry and nature of science : implications for teaching, learning, and teacher education , 2004 .

[7]  Paul Horwitz.  Designing Computer Models That Teach , 1999 .

[8]  Daniel T. Hickey,et al.  Design Experimentation with Multiple Perspectives: The GenScope Assessment Project , 2003 .

[9]  Janice D. Gobert,et al.  Leveraging Technology and Cognitive Tehory on Visualization to Promote Students’ Science , 2005 .

[10]  Norman G. Lederman.  Syntax Of Nature Of Science Within Inquiry And Science Instruction , 2006 .

[11]  Janice D. Gobert,et al.  Introduction to model-based teaching and learning in science education , 2000 .

[12]  John K. Gilbert,et al.  Visualization in science education , 2005 .

[13]  Chris Dede,et al.  Model-Based Teaching and Learning with BioLogica™: What Do They Learn? How Do They Learn? How Do We Know? , 2004 .

[14]  Alyson Klein,et al.  No Child Left Behind on the Campaign Trail. , 2006 .

[15]  Mable B. Kinzie,et al.  Learner Control and Achievement in Science Computer-Assisted Instruction. , 1988 .

[16]  Paul Horwitz,et al.  Teaching science at multiple space time scales , 1996, CACM.

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

[18]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[19]  Mark W. Aulls.  The contributions of co-occurring forms of classroom discourse and academic activities to curriculum events and instruction. , 2002 .

[20]  A. Magoon.  Constructivist Approaches in Educational Research , 1977 .

[21]  Richard E. Clark,et al.  Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching , 2006 .

[22]  Patrick Suppes,et al.  Computer-assisted instruction: an overview of operations and problems , 1968, IFIP Congress.

[23]  Marilyn Bohl,et al.  Information processing , 1971 .

[24]  Donald M. Blais.  Constructivism: A Theoretical Revolution in Teaching. , 1988 .

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

[26]  Jos Beishuizen,et al.  Determinants of discovery learning in a complex simulation learning environment , 2005 .

[27]  John J. Clement,et al.  Learning via model construction and criticism: protocol evidence on sources of creativity in science , 1988 .

[28]  Paul Horwitz,et al.  Looking inside the black box: assessing model-based learning and inquiry in BioLogicaTM , 2010, Int. J. Learn. Technol..

[29]  Rosária Justi,et al.  Science teachers' knowledge about and attitudes towards the use of models and modelling in learning science , 2002 .

[30]  Heinz Neber,et al.  Classroom of the Future: Orchestrating Collaborative Spaces , 2011 .

[31]  Ann C. H. Kindfield,et al.  Integrating Curriculum, Instruction, Assessment, and Evaluation in a Technology-Supported Genetics Learning Environment , 2003 .

[32]  Paul Horwitz,et al.  Implementation and Evaluation of the GenScope Learning Environment: Issues, Solutions, and Results , 1998 .

[33]  Jack L. Rosenfeld.  Information processing 74 : proceedings of IFIP Congress 74 , 1974 .

[34]  Paul Horwitz,et al.  Advancing Educational Theory by Enhancing Practice in a Technology-Supported Genetics Learning Environment. , 1999 .

[35]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .