This is the second instalment of a 4 part series aimed at assisting an educator in designing a sequence of learning that drives towards the ultimate goal of knowledge transfer. The intro post is here.

Concrete to abstract to concrete  

Delivering new learning in concrete terms using concrete examples makes it easier for a student to encode the content. We assimilate or accommodate new information in the context of the patterns, mental models, examples, analogies and experiences that either already were or have been converted to the concrete in our brain, representations that have formed the basis of our schemata. Kolodner describes this reliance on prior learning as case-based scenarios1. The use of models help students to capture the mental patterns required for the knowledge to become understood. It could be suggested that this predisposition to preferring the concrete could lead one to theorise that the concrete represents understanding. However, a necessary process in the incremental development of a schema is the gradual introduction of abstractions, as they instigate the machinations of knowledge transfer.  

A lot of what we teach in higher education is abstract in nature, and necessarily so: ‘The goal of learning an abstract concept is not simply knowledge of one instantiation; it is the ability to transfer, or apply conceptual knowledge to a novel isomorphic situation.’2 However, this creates two issues for learners: abstract content is inherently harder to learn than concrete content, and the links between abstract content and real word applications can often seem distant at best.  

Why is it harder to learn through abstraction? 

It seems our brains are wired to think in concrete terms3. So, if it is harder to learn through abstraction, why not simply avoid it altogether and convert material to the concrete for our students? Well, it all has to do with the facilitation of knowledge transfer. Resnick and Omanson4 assert that ‘learning with concrete objects supports initial understanding of the instructed concept but does not support the transfer of that knowledge to novel but relevant contexts.’ Pashler et al5 concur and extend this: ‘Many experimental laboratory studies and a growing number of classroom based quasi-experiments have found that teaching students about key principles or concepts using only abstract or only concrete representations of those concepts leads to less flexible knowledge acquisition and use than teaching students to recognize and use those key principles across a range of different situations’.   

So how do we get the balance right? It appears that once we have set a foundation using concrete examples, turning to metaphor is the next step. Reece (2003)6 suggests that human analogical reasoning engaged through metaphor-based environments helps learners to incorporate new concepts into their existing mental schema. She advocates the use of metaphors believing them to be ‘especially appropriate when learners are introduced to new, abstract concepts’, and she cites Jonassen, (1981)7 who asserts that metaphor acts as a scaffolding ; that is, ‘a learner structures the to be-learned domain, the target, according to the relational structure of the concrete and more familiar domain, the source domain (see structure mapping theory in Gentner, 1983, 1989; Gentner & Markman, 1997).’ 

In designing effective metaphors, Carroll & Mack8 instruct that ‘Within pedagogical applications, an instructional metaphor source domain can be carefully structured so that it replicates the relational structure of the target domain’. It seems that this deliberate scaffolding of metaphor is consistent with the need to attend to the incremental building of a schema; the metaphor is designed to incrementally build abstraction by initially making connections to relational structures quite close, and then gradually making them more abstract. The student’s schema thereby develops by adding to the bank of patterns contained within.   

But a well-developed schema does not necessarily mean that transfer of learning to new contexts is automatic.  Gentner, Rattermann & Forbus (1993)9 report that ‘people often fail to access prior cases that would be useful, even when they can be shown to have retained the material in memory’. Transfer then, requires a lot more attention to design than just acquiring knowledge. One of the ways to achieve it is through analogous examples.  

That is the base of the next post.


  1. Case-Based Reasoning: Kolodner, Janet L; 24 April 2005, The Cambridge Handbook of the Learning Sciences,

2. Do Children Need Concrete Instantiations to Learn an Abstract Concept?
Jennifer A. Kaminski (, Vladimir M. Sloutsky (, Andrew F. Heckler ( – 

3. Lawson, A. E., Alkhoury, S., Benford, R., Clark, B. R., & Falconer, K. A. (2000). What kinds of scientific concepts exist? Concept construction and intellectual development in college biology. Journal of Research in Science Teaching, 37(9), 996-1018. 

4. Resnick, L.B., and Omanson, S.F. (1987). Learning to understand arithmetic. In R. Glaser (Ed.),  Advances in instructional psychology (Vol. 3, pp. 41-95). Hillsdale, NJ: Erlbaum.

5.  Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., and Metcalfe, J. (2007) Organizing Instruction and Study to Improve Stu. dent Learning (NCER 2007-2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from

6. Reece, D. 2003. Metaphor and content: An embodied paradigm for learning. Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Curriculum and Instruction (Instructional Technology). 

7. Jonassen, D. H. (1981, April 7). Content treatment interactions: a better design model. Paper presented at the Association for Educational Communication and Technology, Philadelphia, PA.

8. Carroll, J. M., & Mack, R. L. (1999). Metaphor, computing systems, and active learning. International Journal of Human-Computer Studies, 51, 385-403.

9. Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993). The roles of similarity in transfer: Separating retrievability and inferential soundness. Cognitive Psychology, 25, 524-575.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s