Case-Based Reasoning

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Contents

Introduction

See also ADDIE model | Behaviourism | Cognitivism | Instructional design models | Problem-based learning | Teaching library users

Case-based reasoning is a cognitivist learning theory that argues we draw on our previous experiences to make sense of the world (and our place within it). The assumption is that, as cases are introduced and indexed cognitively in the human brain (where other experiences are "indexed"), reminders are sent to us and the potential for generalization occurs (Kolodner & Kolodner, 1987). Compared to expert systems, and storing past experiences as a set of rules, CBR seeks to store past experiences as instances of problem-solving (Kolodner, 1992; Maher, Balachandran, & Zhang, 1995). Further, CBR aims to generate solutions based on previously-solved problems and the principle that people solve new problems by remembering similar episodes and situations.

According to Schank (1990), human memory is story-based, and human beings typically index knowledge as stories (and even stories as knowledge). This is particularly true in medicine which is based on patient stories or histories. Medical students and residents (i.e., problem-solvers) are reminded of their past experiences with patients, and use those experiences to guide themselves through new experiences. Kolodner defines a case as "a contextualized piece of knowledge representing an experience that teach a lesson fundamental to achieving the goal of the reasoner." In CBR, problem-solving is a process of remembered problem-solving steps which may be adapted to fit new circumstances. Here, too, adapted solutions are indexed as new cases are moved into learner memory banks for future use.

Studies on the nature of expertise show that experts differ from novices in the amount of knowledge they store and its organization and accessibility (Chase & Simon, 1973; Chi, Feltovich, & Glaser, 1981; Gobbo & Chi, 1986). A critical factor here is the ability to deal with new situations by recalling and reusing experience. It is thought to be valuable to use CBR to teach novices, who do not have a lot of problem-solving experience, by presenting stories of others in problem-solving contexts.

Analogies in case-based reasoning

In order to assist learning from prior problem-solving episodes, the problem-solver should be able to execute the basic process of retrieving, adapting and storing stories. Montazemi & Gupta (1997) elaborate on the process between adapting and storing, i.e., evaluating the adapted solution and predicting success or failure of the solution. In tandem with Leake's concept of analogy (1996), a process model of CBR might be proposed.

The proposed process emphasizes the performance of reasoning through analogy, and the feedback of evaluation to learn lessons about success or failure. Leake says that case-base reasoning is fundamentally analogical. Didierjean and Cauzinille-Marmeche (1998) say that there are two processes that support reasoning through analogy: one is to use abstract knowledge, the other is to use case-based reasoning. Psychological experiments show that human reasoners do use both processes of reasoning by analogy simultaneously (Goldin-Meadow et al., 1993; Didierjean & Canzinille-Marmeche, 1998).

In case-based reasoning, the degree of relevance of the retrieved case to the new situation is crucial. Research into CBR focuses on the issue of indexing to form correspondence between the new experience and the previous. An index allows a reminder to be sent as patterns are recognized and where cases seem relevant. Kolodner makes clear the importance of this, and defines a case as "a contextualized piece of knowledge representing an experience that teaches a lesson fundamental to achieving the goal of the reasoner". The key for learners is to remember how to apply the previous case knowledge, and how much information is applicable so that the situations can be resolved even when they appear, at first glance, to be rather different.

Benefits of cases

  • To retrieve in memory the experience closest to the new situation
  • To locate a solution based on experience and adapted to meet demands of new situation
  • To evaluating use of solutions
  • To storing problem-solving as part of memory system

Case-based reasoning originated in the field of artificial intelligence. As such, it promotes higher-ordered thinking through cases. Support for teaching with cases comes from the research in cognitive science. Situated cognition emphasizes the importance of context. Learning should therefore be situated in practitioners' culture (Brown, Collins, & Duguid). Cases that consist of actors, goals and a sequence of events (Kolodner & Guzia, 2000) are useful as instructional tools because they involve learners in analyzing authentic, real-life problems.

This model derives its theoretical support from memory organization and reminding in cognitive science (Shank, 1982; 1990). It uses prior experiences in the process of problem solving. As a learning model, it shows that the acquisition of expertise is an accumulation of experiences and a cognitive indexing of experiences for retrieval (Koschmann et al, 1997). The power of reasoning through access to old cases provides instructional practices. Kolodner, Hmelo, and Narayanan (1996) suggest incorporating CBR with PBL to enhance learning; they suggest that a library of cases should provide a variety of problem-solving experiences to augment memory and enhance learners' problem solving and reasoning skills. The theoretical support for learning in CBR comes from the ability of reasoning the old problem solving to navigate new ones.

References

  • Brown JS, Collins A, Duguid P. Situated cognition and the culture of learning. Educational Researcher. 1989;18(1):32-42.
  • Chase WG, Simon HA. Perception in chess. Cognitive Psychology. 1973;4:55-81.
  • Chi MTH, Feltovich PJ, Glaser R. Categorization and representation of physics problems by experts and novices. Cognitive Science. 1982;5:121-152.
  • Chi MTH, Lewis MW, Glaser R. Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science. 1989;13:145-182.
  • Didierjean A, Cauzinille-Marmeche E. Reasoning by analogy: is it schema-mediated or case-based? Eur J Psych Ed. 1998;8(3):385-98.
  • Domeshek E, Kolodner J. Using the point of large cases. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 1993;7(2):87-96.
  • Edelson DC. Learning from stories: indexing and reminding in a Socratic case-based teaching system. Northwestern University, 1993.
  • Gick ML, Holyoak KJ. Analogical problem solving. Cognitive Psych. 1980;12, 306-355.
  • Gobbo C, Chi MTH. How knowledge is structured and used by expert and novice children. Cognitive Development. 1986;1:221-237.
  • Goldin-Meadow S, Nusbaum H, Garber P, Chruch RB. Transition in learning: Evidence for simultaneously activated strategies. J Exp Psych: Human Percep Perform. 19(1):92-107.
  • Hammond KJ. Case-based planning: a framework for planning from experience. Cognitive Science. 1990;14:385-443.
  • Kolodner JL. An introduction to case-based reasoning. Artificial Intelligence Review. 1992;6(3):3-34.
  • Kolodner JL, Guzdial M. Theory and practice of case-based learning aids. In: Theoretical foundations of learning environments. Mahwah, NJ: Lawrence Erlbaum Associates, 2000.
  • Kolodner JL, Kolodner RM. Using experience in clinical problem solving: Introduction and framework. IEEE Transactions on Systems, Man and Cybernetics. 1987;17(3):420-431.
  • Koshmann T, Collins A, Klein G, Holyoak K, Kolodner J. The role of cases in learning. Annual Conference of the Cognitive Science Society, 1997.
  • Krasne S, Stevens CD, Wilkerson L. Improving medical literature sourcing by first-year medical students in problem-based learning: outcomes of early interventions. Acad Med. 2014 May 13.
  • Leake DB. CBR in context: The present and future.
  • Maher ML, Balachandran MB, Zhang DM. Case-based reasoning in design. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Montazemi AR, Gupta KM. A framework for retrieval in case-based reasoning systems, Annals of Operations Research, 72, 51-73.
  • Shank RC. Dynamic memory: A theory of learning in people and computers. Cambridge: Cambridge University Press, 1982.
  • Shank RC. Tell ma a story: narrative and intelligence. Evanston, IL: Northwestern University Process, 1990.
  • Shank RC, Berman TR, Macpherson KA. Learning by doing. In: Instructional Design Theories and Models. Mahwah, NJ: Lawrence Erlbaum Associates.
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