Conceptual representations for transfer: A case study tracing back and looking forward

Main Article Content

Suparna Sinha
Steven Gray
Cindy E. Hmelo-Silver
Rebecca Jordan
Catherine Eberbach
Ashok Goel
Spencer Rugaber Rugaber

Abstract

A primary goal of instruction is to prepare learners to transfer their knowledge and skills to new contexts, but how far this transfer goes is an open question.  In the research reported here, we seek to explain a case of transfer through examining the processes by which a conceptual representation used to reason about complex systems was transferred from one natural system (an aquarium ecosystem) to another natural system (human cells and body systems). In this case study, a teacher was motivated to generalize her understanding of the Structure, Behaviour, and Function (SBF) conceptual representation to modify her classroom instruction and teaching materials for another system. This case of transfer was unexpected and required that we trace back through the video and artefacts collected over several years of this teacher enacting a technology-rich classroom unit organized around this conceptual representation. We provide evidence of transfer using three data sources: (1) artefacts that the teacher created (2) in-depth semi-structured interview data with the teacher about how her understanding of the representation changed over time and (3) video data over multiple years, covering units on the aquatic ecosystem and the new system that the teacher applied the SBF representation to, the cell and body. Borrowing from interactive ethnography, we traced backward from where the teacher showed transfer to understand how she got there. The use of the actor-oriented transfer and preparation for future learning perspectives provided lenses for understanding transfer. Results of this study suggest that identifying similarities under the lens of SBF and using it as a conceptual tool are some primary factors that may have supported transfer.

Article Details

How to Cite
Sinha, S., Gray, S., Hmelo-Silver, C. E., Jordan, R., Eberbach, C., Goel, A., & Rugaber, S. R. (2013). Conceptual representations for transfer: A case study tracing back and looking forward. Frontline Learning Research, 1(1), 3-23. https://doi.org/10.14786/flr.v1i1.14
Section
Articles

References

Bechtel, W., & Abrahamson, A. (2005). Explanation: A mechanist alternative. Studies in the History and Philosophy of Biological and Biomedical Sciences, 36, 421-441.

Bransford, J. D., Vye. N„ Kinzer, C, & Risko, V. (1990). Teaching thinking and content knowledge: Toward an integrated approach. In B. F. Jones & L. Idol (Eds.), Dimensions of thinking and cognitive instruction: Implications for educational reform. Hillsdale, NJ: Erlbaum.

Bransford, J. D. & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of research in education, 24, 61-100.

Bridges, S., Botelho, M., Green, J. L., & Chau, A. C. M. (2012). Multimodality in Problem-Based Learning
(PBL): An Interactional Ethnography. In S. Bridges, C. McGrath & T. L. Whitehill (Eds.), Problem-Based Learning in Clinical Education (pp. 99-120). Dordrecht Netherlands: Springer.

Castanheira, M. L., Green, J. L., & Yeager, E. (2009). Investigating inclusive practices: An interactional ethnographic approach. In K. Kumpalainen, C. E. Hmelo-Silver & M. César (Eds.), Investigating classroom interaction: Methodologies in action (pp. 145-178). Rotterdam: Sense Publishers.

Cobb, P., & Bowers, J. S. (1999). Cognitive and situated learning perspectives in theory and practice. Educational Researcher, 28, 4-15.

Darling-Hammond, L., & McLaughlin, M. W. (1995). Policies that support professional development in an era of reform. Phi Delta Kappan, 76, 597-604.

Feltovich, P. J., Coulson, R. L., & Spiro, R. J. (2001). Learners’ (mis)understanding of important and difficult concepts. In K.D. Forbus & P. J. Feltovich (Eds.), Smart machines in education: The coming revolution in educational technology (pp. 349–375). Menlo Park, CA: AAAI/MIT Press.
Greeno, J.G. (1997). Response: On claims that answer the wrong questions. Educational Researcher, 26, 5-17.

Goel, A. K., Gomez de Silva Garza, A., Grué, N., Murdock, J. W., Recker, M. M., & Govinderaj, T. (1996). Towards designing learning environments -I: Exploring how devices work. In C. Fraisson, G. Gauthier & A. Lesgold (Eds.), Intelligent Tutoring Systems: Lecture notes in computer science. NY: Springer.

Grotzer, T.A., & Bell-Basca, B. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding? Journal of Biological Education, 38, 16-28.

Hmelo-Silver, C. E., Jordan, R., Honwad, S., Eberbach, C., Sinha, S., Goel, A., Rugaber, S., & Joyner, D. (2011). Foregrounding behaviors and functions to promote ecosystem understanding. Proceedings of Hawaii International Conference on Education (pp. 2005-2013). Honolulu HI: HICE.

Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16, 307-331.

Hmelo-Silver, C. E., Liu, L., & Jordan, R. (2009). Visual Representation of a Multidimensional Coding Scheme for Understanding Technology-Mediated Learning about Complex Natural Systems. Research and Practice in Technology Enhanced Learning Environments, 4, 253-280

Hogan, K., & Fisherkeller, J. (1996). Representing students’ thinking about nutrient cycling in ecosystems: Bidimensional coding of a complex topic. Journal of Research in Science Teaching, 33, 941– 970.

Hogan, K. (2000). Exploring a process view of students' knowledge about the nature of science. Science Education, 84, 51-70.

Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15, 11-34.

Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4, 39-103.

Konkola, R., Tuomi-Grohn, T., Lambert, P., & Ludvigsen, S. (2007). Promoting learning and transfer between school and workplace. Journal of Education and Work, 20, 211-238.
Leach, J., Driver, R., Scott, P. & Wood-Robinson, C.: 1996, ‘Children’s Ideas about Ecology 3: Ideas about the Cycling of Matter found in Children aged 5–16. International Journal of Science Education, 18, 129-142.

Liu, L., & Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 9, 1023-1040.

Lobato, J., Ellis, A. B., & Munoz, R. (2003). How Focusing Phenomena in the Instructional Environment Support Individual Students Generalizations. Mathematical Thinking and Learning, 5, 1-36.

Lobato, J. (2004). Abstraction, situativity, and the “actor-oriented transfer” perspective. In J. Lobato (Chair), Rethinking abstraction and de contextualization in relationship to the “transfer dilemma.” Symposium conducted at the annual meeting of the AERA, San Diego, CA.

Lobato, J. (2006). Alternative Perspectives on the Transfer of Learning: History, Issues, and Challenges for Future Research. The Journal of the Learning Sciences, 15, 431-449.
Machamer, P., Darden, D., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1-25.

Moore, J. L., & Schwartz, D. L. (1998). On learning the relationship between quantitative properties and symbolic representations. In Proceedings of the International Conference of the Learning Sciences (pp. 209-214). Mahwah, NJ: Erlbaum.

Moreno, R., (2004). Decreasing Cognitive Load for Novice Students: Effects of Explanatory versus Corrective Feedback in Discovery-Based Multimedia. Instructional Science, 32, 99-113.

National Research Council. (2012). A framework for K-12 science education practices, crosscutting concepts, and core ideas. Washington, DC.

Okita, S., & Schwartz, D. L. (in press ). Learning by teaching human pupils and teachable agents: The importance of recursive feedback Journal of the Learning Sciences.

Powell, A. B., Francisco, J., & Maher, C. A. (2003). An analytical model for studying the development of learners' mathematical ideas and reasoning using videotape data. Journal of Mathematical Behaviour, 22, 405-435.

Reiner, M., & Eilam, B. (2001). Conceptual classroom environment: A system view of learning. International Journal of Science Education, 23, 551-568.

Sabelli, N. (2006). Complexity, technology, science, and education. Journal of the Learning Sciences, 15, 5-10.
Stake, R. E. (1998). Case studies. In N. K. Denzin & Y. S. Lincoln (Eds.), Strategies of qualitative inquiry (pp. 86-109). Thousand Oaks CA: Sage.

Tan, J., & Biswas, G., (2006). The Role of Feedback in Preparation for Future Learning: A Case Study in Learning by Teaching Environments. Intelligent Tutoring Systems, 2, 370-381.

Van Oers, B. (1998). From Context to Contextualizing. Learning and Instruction, 8, 473-488.

Vattam, S., Goel, A., Rugaber, S., Hmelo-Silver, C., Jordan, R., Gray, S., & Sinha, S. (2011) Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models. Educational Technology & Society, 14, 66-81.

Wilensky, U. & Reisman, K. (2006). Thinking like a wolf, a sheep or firefly: Learning biology through constructing and testing computational theories – an embodied modelling approach. Cognition and Instruction, 24, 171-209.

Yin, R. K. (2009). Case study research: Design and methods (Fourth ed.). Thousand Oaks CA: Sage.