Research Idea

The need for problem solving skills in our daily life for well-structured problems and ill-structured problems has never ceased. In well-structured problems (e.g., solutions to addition algorithm), all the elements necessary for a solution are knowable and known, and there is an effective procedure for solving it (Kitchener, 1983). In contrast to well-structured problems, ill-structured problems (e.g., methods to mitigate global warming) seldom have single, unequivocal solutions. Rather, they are emergent, complex, and multidimensional problems (Jonassen, 1997). Our daily life is filled with different types of novel problems. Among these novel problems, more ill-structured problems are encountered in everyday as well as professional practice (Jonassen, 2000). How to develop learners’ ill-structured problem-solving ability has attracted educators’ attention.

The primary goal of education is to promote effective problem-solving transfer, and to prepare students for solving problems that they have not previously encountered (Mayer & Wittrock, 1996). In order to successfully solve novel problems, it is of great value for educators to provide learning experiences that are useful beyond the specific conditions of initial learning (Lobato, 2006). Even though the goal in our education is to help learners apply knowledge learned in school into situations and problems encountered outside of school, many students experienced difficulty in fluently generating alternative solutions and flexibly transferring what they have learned in school to solve ill-structured problems (Jonassen, 1997; Nickerson, Perkins, & Smith, 1985). Since our educational system is inefficient at teaching in ways that promote transfer, many students further hamper their ability to advance in the workplace (Mestre, 2003).

Based on previous research, achieving significant transfer of learning has also proven to be a difficult task (Marini & Genereux, 1995; Detterman & Sternberg, 1993). To succeed on a novel transfer task, the learner must possess knowledge, strategies, abilities to recognize transfer situations, and dispositions. Due to the complexity of learning transfer, researchers in this field with different theoretical perspectives have tended to focus on different factors to facilitate transfer of learning. Thorndike and Woodworth (1901), proposing the identical elements theory of transfer, believed the higher the proportion of identical elements between the learned task and the novel task, the greater the likelihood of transfer from one task to the other. Judd (1939) argued that teachers must teach concepts of principles, common patterns, and relationships to facilitate students’ transfer.

The theory from Gestalt psychologists such as Katona (1940) emphasizes how transfer is influenced by the learner’s perception and understanding of tasks and their solutions. Another view of transfer – situated view – emphasizes the pivotal role of context in transfer. Situated view proponents believed what is transferred is not knowledge from task to task but patterns of participatory processes across situations (Greeno, 1997). They view social context, rather than individual learners or tasks, as the most important element of learning and transfer. Lave (1988) believed knowledge is constituted when that knowledge is applied in a novel situation. Greeno, Smith, and Moore (1999) proposed how learning to participate in an activity in one situation can influence one’s ability to participate in another activity in a different situation. According to another perspective, dispositional view of transfer from Bereiter in 1995, transfer is no longer thought of as skill training or strategy instruction, but as something more like character education. Among these different views of transfer, discussion on the factors to facilitate transfer of learning is an ongoing debate.

To enhance transfer of learning and improve problem-solving skills, researchers in this field has been creating instructional models. Some contemporary models of instruction include anchored instruction (Cognition and Technology Group at Vanderbilt, 1994), problem-based learning (Savery & Duffy, 1995), discovery learning (Bruner, 1967), and case-based learning (Schank, 1982). Among these instructional models, case-based learning is the method that aligns best with my belief. According to Kolodner (1993), in case-based reasoning, a learner remembers previous situations (cases) similar to the current one and uses them to help solve the new problem. Since cases are concrete, they are more engaging and more easily understood than abstract, domain-general principles (Gentner, Loewenstein, & Thompson, 2003; Kolodner, 1993; Pirolli & Anderson, 1985). Researchers from different strands have contrasting views on abstractions. The proponents of cognitive view argued that abstractions delete details and avoid contextual specificity, so abstract representations of knowledge can be applied across instances or situations and further help promote transfer (Bransford, Brown, & Cocking, 2000; Fuchs et al. 2003). On the other hand, situated theorists, like Lave & Wenger (1991), argued that learning is not to be identified with the structure or in gaining a discrete body of abstract knowledge, but takes place through legitimate peripheral participation in ongoing social practice. Also, knowledge is principally bound to situations (Gruber, Law, Mandl, & Renkl, 1996). Another point that supports my belief in case-based learning is using stories for sharing past experiences. Schank (1990) believed relating and listening to stories was the most important element in learning, because sharing of stories through our lives is so important. Therefore, by using case-based learning, novice problem solvers will gain experience vicariously from experts’ past experiences. For example, Lancaster and Koldner (1988) found that car mechanics frequently use their experiences and those of others when wrestling with new problems.

From what I have learned previously on different views of learning transfer and pros and cons for case-based learning, I understand that the issues in facilitating transfer of learning and improving problem-solving skills are complex and multidimensional. I will continue to focus on the relations between factors to facilitate learning transfer and components of case-based learning. The followings are primary research questions that will extend my understanding of these issue and guide me through this learning journey.

Research Questions

1. What are the factors that facilitate transfer of learning for professional development in the context of higher education?

2. What are implications of transfer of learning to the areas of instructional design, instructional technology, and case-based learning?

3. Many studies have demonstrated that case-based learning is useful to promote transfer. What is the efficacious way of presenting materials with multimedia to enhance learning transfer?

4. How can technology (e.g., computer and internet) as a cognitive tool help promote transfer of learning?

References

Bereiter, C. (1995). A Dispositional view of transfer. In: A. McKeough, J. Lupart, & A. Marini (Eds.), Teaching for transfer: Fostering generalization in learning. Hillsdale: Lawrence Erlbaum.

Bransford, J D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). Learning and transfer. In How people learn: Brain, mind, experience, and school (pp. 31-78). Washington, DC: National Academy Press.

Bruner, J. S. (1967). On knowing: Essays for the left hand. Cambridge, Mass: Harvard University Press.

Cognition and Technology Group at Vanderbilt. (1994). From visual word problems to learning communities: Changing conceptions of cognitive research. In K. McGilly (Eds.), Classroom lessons: Integrating cognitive theory and classroom practice. Cambridge MA: MIT Press.

Fuchs, L. S., Fuchs, D., Prentice, K., Burch, M., Hamlett, C. L., Owen, R., et al. (2003). Explicitly teaching for transfer: Effects on third-grade students’ mathematical problem solving. Journal of Educational Psychology, 95, 293-305.

Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95, 393-408.

Greeno, J. G., Smith, D. R., & Moore, J. L. (1993). Transfer of situated learning. In D. K. Detterman, & R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition and instruction. Norwood, Albex.

Greeno, J. G. (1997).Response: On claims that answer wrong questions. Educational Researcher, 26, 1, 5-17.

Gruber, H., Law, L., Mandl, H., & Renkl, A. (1996). Situated learning and transfer. In p. Reimann & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp. 168-188). Oxford, United Kingdom: Pergamon.

Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45, 65-94.

Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development,48, 63-85

Judd, C. H. (1939). Educational Psychology. New York: Houghton Mifflin.

Katona, G. (1940). Organizing and memorizing. New York: Columbia University Press.

Kitchener , K. S. (1983). Cognition, metacognition, and epistemic cognition. Human Development, 26, 222-232.

Lancaster, J. S., & Kolodner, J. L. (1988). Problem solving in a natural task as a function of experience. In Proceedings of the Ninth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.

Lave, J. (1988). Cognition in practice: Mind, mathematics, and culture in everyday life. Cambridge: Cambridge University Press.

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.

Koldner, J. (1993). Case-based reasoning. San Francisco, Calif.:Morgan Kaufmann.

Mayer, R. E., & Wittorck, M. C. (1996). Problem-solving transfer. In Alexander, P.A. & Winne, P.H. (Eds.), Handbook of Educational Psychology (pp. 47-62). New York: Macmillan.

Mestre, J. (2003). Transfer of learning: Issues and research agenda: Report of a workshop held at the National Science Foundation. Retrieved January 25, 2007, from http://www.nsf.gov/pubs/2003/nsf03212/nsf03212.pdf

Nickerson, R. S., Perkins, D. N., & Smith, E. E. (1985). The teaching of thinking. Hillsdale, NJ: Lawrence Erlbaum Associates.

Pirolli, P.L., & Anderson, J. R. (1985). The role of learning from examples in the acquisition of recursive programming skills. Canadian Journal of Psychology, 39, 240-272.

Savery, J., & Duffy, T. M. (1995). Problem based learning: An instructional model and its constructivist framework. In B. G. Wilson (Eds.). Constructivist learning environments: Case studies in instructional design. Englewood Cliffs, NJ: Educational Technology Publications.

Schank, R. C. (1990). Tell me a story: Narrative and intelligence. Evanston, IL: Northwestern University Press.

Schank, R. C. (1982). Dynamic memory: A theory of reminding and learning in computers and people. New York : Cambridge University Press

Thorndike, E. L., & Woodworth, R. S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247-261.

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