Cognitive Flexibility Theory (CFT)

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Last Update

  • Updated.jpg 24 June 2017


See also ADDIE model | Behaviourism | Constructivism | Instructional design models | Teaching library users

" ...cognitive flexibility is the ability to spontaneously restructure one's knowledge, in many ways, in adaptive response to radically changing situational demands...this is a function of both the way knowledge is represented (along multiple rather single conceptual dimensions) and the processes that operate on those mental representations (processes of schema assembly rather than intact schema retrieval)..." — Spiro & Jehng, 1990

Cognitive flexibility theory is a conceptual model for designing learning environments based on cognitive learning theory. As such, it focuses on the nature of learning in complex and unstructured domains of knowledge. CFT is used in learning environments that support interactive technologies and emphasizes advanced knowledge acquisition. CFT allows for "flexible reassembly of preexisting knowledge to adaptively fit to the needs of a new situation." (Spiro et al, 1991). CFT embodies fundamental concepts of both cognitivism and constructivism; its emphasis is on presentation of information from multiple perspectives and the use of case studies to present diverse examples. Founded by Spiro with Feltovich and Coulson, CFT is intended to be applied to new approaches of learning in the design of hypermedia. Their research includes knowledge acquisition in complex domains, hypermedia learning environments, multimedia case-based methods in education, biomedical cognition, and constructive processes in comprehension and recall. CFT research deals with trying to find out ways to avoid over simplifying instruction while designing platforms.

Spiro & Jehng (1990, p. 165) state: "By cognitive flexibility, we mean the ability to spontaneously restructure one's knowledge, in many ways, in adaptive response to radically changing situational demands...This is a function of both the way knowledge is represented (e.g., along multiple rather single conceptual dimensions) and the processes that operate on those mental representations (e.g., processes of schema assembly rather than intact schema retrieval)."

Cognitive flexibility theory

The theory aims to foster learners' ability to spontaneously reconstruct their knowledge to adopt different situational demands (Spiro & Jehng, 1990). The means to achieve cognitive flexibility is to manipulate knowledge representation, and the processes that operate those representations. The major principles of doing this are:

  • Reflecting knowledge complexity to provide opportunities for learners to establish interconnections of concepts and principles
  • CFT avoids presenting problems as simple, linear sequences of decision-making
  • Providing multiple representations of content: students should access content at different times and contexts for different purposes and perspectives
  • Multiple thematic organization of content, multiple perspectives of content help learners to construct multiple representations of content
  • A variety of cases can be used to illustrate different themes and perspectives and to support a variety of contexts for learning
  • Supporting context-dependent knowledge: knowledge cannot be oversimplified
  • Oversimplification isolates knowledge from its context, segments knowledge into discrete components and represents interrelationships of components into a single unifying dimension. It is essential to provide contextual variability for different multiple knowledge representations and multiple interconnectedness of knowledge components (Sprio, Feltovich, Jacobson & Coulson, 1991).
  • In addition, the use of CFT should seek to reduce cognitive loading. Online course designers suggest that the amount of text on a computer screen should never exceed 150 words; alternatively, text can be presented in small meaningful chunks.

Examples or cases where CFT is used

  • Jonassen et al describe the use of CFT to the design of a hypertext program on transfusion medicine. The program presents different complex clinical cases which students can use to diagnose and treat patients using various sources of information (including advice from experts). The learning environment is ill-defined and complex, presents multiple perspectives and emphasizes construction of knowledge. Feltovich et al found that medical students had some difficulties in transferring knowledge that was previously learned in one context, such as in medical school, to new situations such as problem-solving in the clinic. They argued that students’ deficiencies in learning (i.e., inert knowledge) at this advanced stage was as a result of oversimplification (reductive bias) of complexity.
  • Cunningham describes a method of teacher education, which involves case-based learning that encourages the pre-service teacher to begin thinking like an expert teacher. Cunningham states that a case-study approach to teacher education “bridges the gap between theory and practice” (p. 1) by getting students actively involved in analyzing and interpreting scenarios that they can revisit numerous times. Instead of presenting tidy scenarios with only one right answer, the proposed instruction provides more authentic, “messy”, complex problems that, upon analysis, present multiple solutions.

Criss-crossing knowledge landscapes

Spiro and Jehng (1990) used Wittgnestein's (1953) "criss-crossed landscape" as an analogy to explain the complexity of advanced knowledge acquisition:

"...because the complexity of a single region (issues, example, case) in a landscape would not be fully graspable in any single context, its full multifacetedness would be brought out by rearranging the sequence of sketch presentations in the album so that the region would be revisited from a variety of vantage points, each perspective highlighting aspects of the region inn a somewhat different way than the other perspectives."

As learners criss-cross knowledge in different directions (the main instructional metaphor of CFT from Wittgenstein) a revisiting of ideas and concepts is possible. The result is knowledge representations whose strength is determined not by single conceptual threads running across the domain but rather from overlapping conceptual “fibers”.


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