Skip to main content
SpringerLink
Log in

Enhancing transfer by learning generalized domain knowledge structures

  • Published:
European Journal of Psychology of Education Aims and scope Submit manuscript

Abstract

Transferring one’s knowledge in new situations is usually associated with cognitively demanding processes. The paper explores an approach to facilitating transfer of knowledge by explicitly instructing learners in medium-level generalized but yet domain-connected knowledge structures that are applicable to a broader range of tasks in the domain and could be essential in managing the cognitive load associated with transfer. The paper includes a theoretical analysis of the potential role of the generalized domain knowledge in transfer and an experimental study designed to investigate the effectiveness of explicit instruction in a generalized domain knowledge structure (function–process–structure schema) in technical areas. Forty-nine undergraduate university students with low or no prior knowledge in the domain participated in the randomised 2 (schema-based vs. non-schema-based instruction) × 2 (general-to-specific vs. specific-to-general knowledge sequences) experiment investigating the effects of these two factors on posttest transfer performance and subjective ratings of learning difficulty (interpreted as indicators of cognitive load). The results indicated a significant (p < 0.05) main effect of schema-based instruction; a possible trend (p < 0.1) favouring general-to-specific instructional sequence for posttest test performance; and a significant interaction between the two factors for ratings of difficulty. The paper concludes that (a) transfer within a domain could be facilitated by explicitly instructing learners in generalized domain schemas; (b) general-to-specific approach could possibly be used as a preferred instructional sequence for enhancing transfer; and (c) cognitive load perspective could add some valid arguments to explain the role of generalized domain knowledge in transfer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Baddeley, A. (1986). Working memory. New York: Oxford University Press.

    Google Scholar 

  • Barnett, S., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128, 612–637.

    Article  Google Scholar 

  • Bassok, M., & Holyoak, K. J. (1993). Pragmatic knowledge and conceptual structure: determinants of transfer between quantitative domains. In D. K. Detterman & R. J. Sternberg (Eds.), Transfer on trial: intelligence, cognition, and instruction (pp. 68–98). Norwood, NJ: Ablex.

    Google Scholar 

  • Bhatta, S. L., & Goel, A. (1997). Learning generic mechanisms for innovative strategies in adaptive design. The Journal of the Learning Sciences, 6(4), 367–396.

    Article  Google Scholar 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: mind, brain, experience, and school. Washington, DC: National Academy Press.

  • Bransford, J. D., & Schwartz, D. H. (1999). Rethinking transfer: a simple proposal with multiple implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of research in education, vol. 24 (pp. 61–100). Washington, DC: AERA.

    Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chi, M., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7–75). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Cohen, J. (1973). Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educational and Psychological Measurement, 33, 107–112.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Cowan, N. (2001). The magical number 4 in short-term memory: a reconsideration of mental storage capacity. The Behavioral and Brain Sciences, 24, 87–185.

    Article  Google Scholar 

  • Custers, E. J. F. M., Boshuizen, H. P. A., & Schmidt, H. G. (1998). The role of illness scripts in the development of medical diagnostic expertise: results from an interview study. Cognition and Instruction, 16, 367–398.

    Article  Google Scholar 

  • De Groot, A. (1965). Thought and choice in chess. The Hague, the Netherlands: Mouton.

    Google Scholar 

  • Detterman, D. K., & Sternberg, R. J. (Eds.). (1993). Transfer on trial: intelligence, cognition, and instruction. Norwood, NJ: Ablex.

    Google Scholar 

  • Duncan, R. G. (2007). The role of domain-specific knowledge in generative reasoning about complicated multileveled phenomena. Cognition and Instruction, 25, 271–336.

    Article  Google Scholar 

  • Evans, J. (2011). Dual-process theories of reasoning: contemporary issues and developmental applications. Developmental Review, 31, 86–102.

    Article  Google Scholar 

  • Feltovich, P. J., & Barrows, H. S. (1984). Issues of generality in medical problem solving. In H. G. Schmidt & M. L. De Volder (Eds.), Tutorials in problem-based learning (pp. 128–142). Assen, the Netherlands: Van Gorcum.

    Google Scholar 

  • Geary, D. (2008). An evolutionarily informed education science. Educational Psychologist, 43, 179–195.

    Article  Google Scholar 

  • Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, l–38.

    Google Scholar 

  • Gobet, F., & Simon, H. A. (1998). Expert chess memory: revisiting the chunking hypothesis. Memory, 6, 225–255.

    Article  Google Scholar 

  • Goldstone, R. L., & Sakamoto, Y. (2003). The transfer of abstract principles governing complex adaptive systems. Cognitive Psychology, 46, 414–466.

    Article  Google Scholar 

  • Gruber, T. R., & Russell, D. M. (1996). Generative design rationale: beyond the record and replay paradigm. In T. P. Moran & J. M. Carroll (Eds.), Design rationale: concepts, techniques, and use (pp. 323–349). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Hinds, P., Patterson, M., & Pfeffer, J. (2001). Bothered by abstraction: the effect of expertise on knowledge transfer and subsequent novice performance. Journal of Applied Psychology, 86, 1232–1243.

    Article  Google Scholar 

  • Hmelo, C. E., Holton, D., & Kolodner, J. L. (2000). Designing to learn about complex systems. The Journal of the Learning Sciences, 9, 247–298.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.

    Article  Google Scholar 

  • Holyoak, K. J., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory & Cognition, 15, 332–340.

    Article  Google Scholar 

  • Inagaki, K., & Miyake, N. (2007). Perspective on the research of Giyoo Hatano. Human Development, 50, 7–15.

    Article  Google Scholar 

  • Kalyuga, S. (1984). On the content of polytechnical knowledge. Soviet Pedagogics, 6, 44–48 (in Russian).

    Google Scholar 

  • Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.

    Google Scholar 

  • Kalyuga, S., Renkl, A., & Paas, F. (2010). Facilitating flexible problem solving: a cognitive load perspective. Educational Psychology Review, 22, 175–186.

    Google Scholar 

  • Kalyuga, S., & Hanham, J. (2011). Instructing in generalized knowledge structures to develop flexible problem solving skills. Computers in Human Behavior, 27, 63–68.

    Google Scholar 

  • Kalyuga, S., Rikers, R., & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24, 313–337.

    Google Scholar 

  • Karpov, Y. V., & Bransford, J. D. (1995). L. S. Vygotsky and the doctrine of empirical and theoretical learning. Educational Psychologist, 30, 61–66.

    Article  Google Scholar 

  • Lobato, J. (2006). Transfer strand: alternative perspectives on the transfer of learning: history, issues, and challenges for future research. The Journal of the Learning Sciences, 15, 431–449.

    Article  Google Scholar 

  • McKeough, A., Lupart, J., & Marini, A. (Eds.). (1995). Teaching for transfer: fostering generalization in learning. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Moseley, D., Baumfield, V., Elliott, J., Higgins, S., Miller, J., Newton, D. P., et al. (2005). Frameworks for thinking: a handbook for teaching and learning. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Ngu, B. H., & Yeung, A. S. (2012). Fostering analogical transfer: the multiple components approach to algebra word problem solving in a chemistry context. Contemporary Educational Psychology, 37, 14–32.

    Article  Google Scholar 

  • Nokes, T., & Ohlsson, S. (2005). Comparing multiple paths to mastery: what is learned? Cognitive Science, 29, 769–796.

    Article  Google Scholar 

  • Paas, F., Tuovinen, J., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.

    Article  Google Scholar 

  • Patel, V. L., & Groen, G. J. (1991). Developmental accounts of the transition from medical student to doctor: some problems and suggestions. Medical Education, 25, 527–535.

    Article  Google Scholar 

  • Polya, G. (1945). How to solve it. Princeton: Princeton University Press.

    Google Scholar 

  • Rikers, R. M. J. P., Schmidt, H. G., & Boshuizen, H. P. A. (2000). Knowledge encapsulation and the intermediate effect. Contemporary Educational Psychology, 25, 150–166.

    Article  Google Scholar 

  • Rikers, R. M. J. P., Schmidt, H. G., & Boshuizen, H. P. A. (2002). On the constraints of encapsulated knowledge: clinical case representations by medical experts and subexperts. Cognition and Instruction, 20, 27–45.

    Article  Google Scholar 

  • Schalk, L., Saalbach, H., & Stern, E. (2011). Designing learning materials to foster transfer of principles. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 300–305). Austin, TX: Cognitive Science Society.

    Google Scholar 

  • Schoenfeld, A. H. (1985). Mathematical problem solving. Orlando, FL: Academic.

    Google Scholar 

  • Schoenfeld, A. H. (1999). Looking toward the 21st century: challenges of educational theory and practice. Educational Researcher, 28, 4–14.

    Article  Google Scholar 

  • Schraagen, J. M. (1993). How experts solve a novel problem in experimental design. Cognitive Science, 17, 285–309.

    Article  Google Scholar 

  • Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12, 257–285.

    Article  Google Scholar 

  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.

    Book  Google Scholar 

  • Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189–208.

    Article  Google Scholar 

  • Tuomi-Grцhn, T., & Engestrцm, Y. (Eds.). (2003). Between school and work: new perspectives on transfer and boundary-crossing. New York: Pergamon.

    Google Scholar 

  • Van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: recent developments and future directions. Educational Psychology Review, 17, 147–177.

    Article  Google Scholar 

  • Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., et al. (2011). Understanding complex natural systems by articulating structure–behavior–function models. Educational Technology & Society, 14(1), 66–81.

    Google Scholar 

  • Walczak, S., & Fishwick, P. (1997). A quantitative analysis of pattern production and its relationship to expert performance. Journal of Experimental & Theoretical Artificial Intelligence, 9, 83–101.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Education, University of New South Wales, Sydney, NSW, 2052, Australia

    Slava Kalyuga

Authors
  1. Slava Kalyuga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Slava Kalyuga.

Additional information

Dr Slava Kalyuga. School of Education, University of New South Wales, Sydney, NSW 2052, Australia. Tel: +61-2-93851985, Fax: +61-2-93851946. E-mail: s.kalyuga@unsw.edu.au; Web site: http://education.arts.unsw.edu.au/

Current themes of research:

Research in cognitive load theory. multimedia learning. diagnostic assessment.

Most relevant publications in the field of Psychology of Education:

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory (250 pp.). New York: Springer.

Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23, 1–19.

Kalyuga, S., Renkl, A., & Paas, F. (2010). Facilitating flexible problem solving: A cognitive load perspective. Educational Psychology Review, 22, 175–186.

Kalyuga, S. (2008). When less is more in cognitive diagnosis: A rapid online method for diagnosing learner task-specific expertise. Journal of Educational Psychology, 100, 603–612.

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.

Kalyuga, S. (2009). Managing cognitive load in adaptive multimedia learning (312 pp.). New York: Information Science Reference.

Appendix

Appendix

Any technical device could be described by its function (purpose), the processes that allow achieving this function, and the structure (components) that are used to implement the processes.

Any air conditioner has the function to lower (cool) the temperature of the air.

This is achieved by the process of absorbing heat from the air. This process is implemented using the following major component of its structure: a special substance (a refrigerant).

A common air conditioner used in residential buildings has this function: to take in warm air, cool this air down, and then return this cool air to the room.

This is achieved by this process: refrigerant (in a cold liquid form) absorbs heat from the air and transforms into a vapourised gas. The absorption of heat has the effect of cooling the air. Once the air is cooled within the air conditioner, it is recirculated back into the room via a fan.

This process is implemented by these components of the structure: a refrigerant contained within a set of coils and a fan which circulates the air through the coils.

A typical room air conditioner has the function of cooling the temperature of the air by continuously transforming the warm vapourised refrigerant back into a cool liquid form.

This is achieved by the following process: the warm vapourised refrigerant is compressed to become liquid and then is pumped through the coils at the back of the air conditioner. In the process, the refrigerant gives off its heat to the outside air and transforms back into a cold liquid form. The pressure is then removed from the liquid refrigerant to allow its vapourisation by absorbing heat from the room air.

This process is implemented using the following structure: compressor to pressurise the vapourised refrigerant and move it through the system; hot coils for the warm refrigerant to give up its heat; outer fan to transfer heat from the hot coils to the air outside; expansion valve to remove the pressure from the liquid refrigerant entering the cooling coils; cooling coils with the refrigerant in cold liquid form to absorb heat from the room air; inner fan to blow the room air over the cooling coils and back into the room.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kalyuga, S. Enhancing transfer by learning generalized domain knowledge structures. Eur J Psychol Educ 28, 1477–1493 (2013). https://doi.org/10.1007/s10212-013-0176-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10212-013-0176-3

Keywords

Search

Navigation