Concept learning

The educational technology and digital learning wiki
Jump to navigation Jump to search


this entry mostly just contains quotations so far ...


Concept learning is one major learning type. While teaching simple concepts with clear instances is not that difficult, teaching concepts border cases is difficult, and teaching complex concepts remains a major challenge.

“Psychologists use the term concept formation, or concept learning, to refer to the development of the ability to respond to common features of categories of objects or events. Concepts are mental categories for objects, events, or ideas that have a common set of features” (Exploring Psychology retrieved, 17:17, 15 September 2006 (MEST))

Concept learning encompasses learning how to discriminate and categorize things (with critical attributes). It also involves recall of instances, integration of new examples and sub-categorization. Concept formation is not related to simple recall, it must be constructed.

A short history of models

Earlier models were based on behaviorist theory. “Stimulus-response association theory was proposed by Clark Hull (1920). He argued that we learn to associate a particular response (the concept) with a variety of stimuli that define the concept.”(Exploring Psychology)

Jerome Bruner formulated a concept formation theory that involved cognitive processes, i.e. hypothesis testing about a concept by making guesses about which attributes are essential for defining the concept. Concept attainment according to Bruner et al. 1967:233) is "the search for and listing of attributes that can be used to distinguish exemplars from nonexemplars of various categories" (Bruner, Goodnow, & Austin, 1967, p. 233). This model was later refined in several designs, e.g. in Taba's (1966) instructional design model or Joyce and Weil's (2000:143-160) "attaining concepts" model of teaching

Merrill & Tennison (1977), based on component display theory argued that concept formation focuses on attributes and examples. As instructional designers the goal of this model was to reduce overgeneralization, undergeneralization and misconception.

Eleanor Rosch (1978) suggested that the natural concepts in everyday life are learned through examples rather than abstract rules.

Anderson's Adaptive Control of Thought (ACT) theory suggests that long-term memory is an interconnect network of propositions (facts of concepts). Only a subset of interconnected propositions can be activated and more connected propositions are easier to retrieve. A concept that has many connections is elaborated.

“Tennyson & Cocchiarella (1986) suggest a model for concept teaching that has three stages: (1) establishing a connection in memory between the concept to be learned and existing knowledge, (2) improving the formation of concepts in terms of relations, and (3) facilitating the development of classification rules. This model acknowledges the declarative and procedural aspects of cognition.” (TIP -Concepts, retrieved, 17:17, 15 September 2006 (MEST))

“The prototype theory is described by Laurence and Margolis as follows: "Most concepts-including most lexical concepts-are complex representations whose structure encodes a statistical analysis of the properties their members tend to have" (p. 27). Concepts can be thought of, not as a list, but as a distribution of properties, some more central or typical than others. The prototype is an abstraction of the central properties and need not correspond to any example.” (Palmer, 2002: 600).

“Klausmeier (1974) suggests four levels of concept learning: (1) concrete - recall of critical attributes, (2) identity - recall of examples, (3) classification - generalizing to new examples, and (4) formalization - discriminating new instances.” (TIP - Concepts, retrieved, 17:17, 15 September 2006 (MEST))

“Typically in cognitive psychology, categorization is regarded as a process of determining what things belong together, and a category is a group or class of stimuli or events that so cohere. A concept is thought to be knowledge that facilitates the categorization process (e.g., Barsalou, 1991, 1992).” (Zentall et al, 2002:237).

Types of concepts

Not a concept

Simple recall or discrimation learning is not concept learning. E.g. in Gagné's framework of learning outcomes, two important categories don't refer to concepts:

  • Verbal information: reciting something from memory, e.g. recall a definition, tell a poem.
  • The first level of intellectual skills - discrimination : Recognizing that two classes of things differ, e.g. be able to identify objects, features, symbols, etc. as not being the same.

Concrete or perceptual concepts

From a behaviorist stance: The classes of stimuli that are united in perceptual concepts may be said, from a subject's perspective, to bear physical similarity to one another. (Zentall et al, 2002).

In Gagné's learning taxonomy, they are referred to as concrete concepts, since a learner can classify a thing according to its physical features. Examples would be a dog, a circle, a house, a cup of tea.

Defined, or relational and associated concepts

From a behaviorist stance: Relational concept learning makes use of more abstract properties of the stimuli. (Zentall et al, 2002).

From a behaviorist stance: In associative concept learning, the stimuli within classes bear no obvious physical similarity to one another, but rather cohere because of shared functional properties. (Zentall et al, 2002).

Gagés labels these as defined concepts, since abstract features are needed to identify (classify) such concepts. An example would be an "assigment" (in a pogramming language", a "political regime", oxidatation (in chemistry).

See also: concept maps.

Complex concepts

Complex concepts are constructs like schemas and scripts. Schemas can be described through lists of smaller concepts (features) and through associations of concepts. Scripts include actions one has to undertake (including variants). It often has been suggested that using computer simulations could help learning complex concepts (DiSessa, 2000; Dede et al., 1999; Squire 2004, de Jong & Joolingen, 1998). See also human information processing.

Concept teaching

For many behaviorist instructional designers, concept learning has to be carefully planned to go from simple concrete concepts to complex composite ones.

At a lower level, concept learning can be measured by learner's classification behavior. Beginners can only identify similar examples, wheras more advanced learners (for a given subject) are able to transfer classification to a very different set of stimuli. Learning of complex concepts involves more that discrimation and transfer and may engage problem-solving processes. E.g. what Gagné calls "defined concepts" (such as "symmetry" as opposed to "circle", or "transport" as opposed to "horse", or "going out for food" as opposed to "eating an apple") do require more processing.

Discrimination and identification of simple concepts

According to Alessi and Trollop (2001) a appropriate teaching design is to first teach relevant (essential) features, e.g. by stating a definition of the concept in terms of these features. Next simple instances are given and that contain all or many of the relevant features. E.g. for "mammal", we could show a horse, a dog and cow. At the same these example should not contain irrelevant or incidental features. In concept learning a concept all learners need to see examples and non-examples. Therefore in a next step noninstances are shown, e.g. a tree, a bird and a fish for the "mammal" concept. These examples contain few relevant feature and many irrelevant and incidental features. Finally, the same procedure has to be applied to difficult instances and non-instances, e.g. show dolphines and whales for instances and sharks for non-instances.

Typology of examples:

Examples Relevant (essential) features Incidental features Irrelevant features Examples
Clear instances All or many few or no few or no dog, cat, cow
Clear non-instances few or no few or no many little fish, bird
Difficult instances few many many dolphin, whale
Difficult non-instances many many few shark
(compared to dolphin)

A similar model is Hilda Taba's concept formation strategy, part of the Taba teaching strategy model model.

Concept learning and learning styles

Teaching may have to be adopted to learning styles. E.g. (Merril, 2002:3) suggests that holist learners tend to have a problem with undergeneralization, they need to see more divergent examples to promote generalization. Serialist learners tend to have a problem with overgeneralization, they need to see more matched example non-example pairs to facilitate their ability to discriminate among examples and non-examples. Both of these types of learners need examples and nonexamples as these are essential components of a concept instruction strategy. However, each type of learner requires a different emphasis in the relationships among these instances.

Multimedia presentations and animations

Teaching complex concepts is the issue with multimedia presentations. See multimedia presentation and multimedia animation

to be done

Constructivist approaches

Constructivist tend to be interested in teaching complex concepts. They also share the idea that knowledge must be applicable. De Jong and Joolingen (1998) in the context of their often quoted Scientific discovery learning with computer simulations of conceptual domains article, cite “hypertext environments (see e.g., Gall & Hannafin, 1994), concept mapping environments (see e.g., Novak & Wandersee, 1990), simulations (De Jong, 1991; Reigeluth & Schwartz, 1989), and modeling environments (e.g., diSessa & Abelson, 1986; Riley, 1990; Smith, 1986).”

See: computer simulation, SimQuest, Guided discovery learning

Using questions to learn defined concepts

“It would appear that higher-order questions, such as comprehension and analysis, support the learning of concepts more effectively than lower-order questions (Andre, 1979; Hamilton, 1985) and also demand greater attention from the learner (Halpain et al., 1985). Felker & Dapra (1975) and Watts & Anderson (1971) found that students' problem-solving abilities could be improved if the text that set out the principles was punctuated by questions requiring their application to novel examples. Possibly, this improvement is achieved by inducing the learner to consider the given concepts within new settings (Tennyson & Parks, 1980). It has also been shown that embedded questions involving novel examples help students identify more clearly their level of understanding (Glenberg et al., 1987), thus encouraging more selective and efficient revision of the text (Walczyk & Hall, 1989).” (Howard-Jones & Martin, 2002)

Conversational approaches

(to do, CSCL-related approaches)

Complex Hypertexts

Cognitive flexibility theory suggests that learners grasp the nature of complexity more readily by being presented with multiple representations of the same information in different contexts. "The remedy for learning deficiencies related to domain complexity and irregularity requires the inculcation of learning processes that afford greater cognitive flexibility: this includes the ability to represent knowledge from different conceptual and case perspectives and then, when the knowledge must later be used, the ability to construct from those different conceptual and case representations a knowledge ensemble tailored to the needs of the understanding or problem-solving situation at hand." (Spiro 1992). See cognitive flexibility hypertext.

Other studies, Britt (1996 and Rouet (1996) also conclude that hypertexts compared to linear text leads to better learning of complex concepts.

Problem-based learning

Teaching applicable complex concepts also is quite a challenge for education. Complex concepts such as the ones engaged in medical clinical diagnosis are difficult to teach since they also require problem solving skills and at some point it becomes difficult to distinguish concept learning from other higher-order learning types.

In medical, law and management schools one often applies a somewhat directed project-oriented learning strategy, such as problem-based learning or case-based learning. Both are based on the idea that a student is exposed to case and with which he has elaborate his concepts (apply, modify, acquire, ...).

Regarding the idea that mastery of complex concepts require authenticity of cases, see also approaches like anchored learning or cognitive apprenticeship that also focus very much on training of complex concepts.



(this is sort of random collection ....)

  • Alessi, Stephen. M. & Trollop, Stanley. R., (2001) Multimedia for Learning (3rd Edition), Pearson Allyn & Bacon, ISBN 0-205-27691-1. (This is probably the best overall introductory textbook for educational technology, but weak regarding in CMC, including e-learning).
  • Britt, M. A., Rouet, J.-F. et Perfetti, C. A. (1996). Using hypertext to study and reason about historical evidence. In J.-F. Rouet, J. J. Levonen, A. P. Dillon et R. J. Spiro (dir.), Hypertext and cognition (pp. 43-72). Mahwah, NJ: Lawrence Erlbaum.
  • Brooks L.R. Decentralized control of categorization: the role of prior processing episodes. In Concepts and Conceptual Development, U. Nasser, ed. Cambridge, England: Cambridge University Press, 1987, 141-174
  • Bruner, J. (1966). Toward a Theory of Instruction. Cambridge, MA: Harvard University Press.
  • Bruner, J., Goodnow, J. J., & Austin, G. A. (1967). A study of thinking. New York: Science Editions.
  • Chen-Chung Liu and Jia-Hsung Lee (2005) Prompting conceptual understanding with computer-mediated peer discourse and knowledge acquisition techniques. British Journal of Educational Technology 36:5, 821-837
  • de Jong, T. & van Joolingen, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2):179-201, 1998.
  • Dede, C., Salzman, M., Loftin, R. B., & Sprague, D. (1999). Multisensory immersion as a modeling environment for learning

complex scientific concepts. In W. Feurzeig & N. Roberts (Eds.), Modeling and Simulation in Science and Mathematics Education. New York: Springer Verlag.

  • diSessa. A. (1998). Changing minds. Cambridge: MIT Press
  • Durkin, M. C. (1993). Thinking through class discussion - the Hilda Taba approach. Lancaster, PA: Delmar Publishers.
  • Hall, R.H., Sidio-Hall, M.A., & Saling, C.B. (1995). Alternative materials for learning: Cognitive and affective outcomes of learning from knowledge maps. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
  • Howard-Jones P.A. & R.J. Martin (2002). The effect of questioning on concept learning within a hypertext system, Journal of Computer Assisted Learning, Volume 18 Page 10 - March 2002, doi:10.1046/j.0266-4909.2001.00203.x Abstract / PDF is (Access restricted).
  • Joyce, B., Weil, M., Calhoun, E. : Models of teaching, 6th edition, Allyn & Bacon, 2000. ISBN 0205389279. (This is on my essential reading list).
  • Liu, C. C. & Tsai, C. M. (2005). Peer assessment through web-based knowledge acquisition: tools to support conceptual awareness. Innovations in Education and Training International 42, 1, 45-61.
  • Margolis, E., & Laurence, S. (1999). Concepts: Core readings. Cambridge, MA: MIT Press.
  • Merrill, M.D. & Tennyson, R.D. (1977). Concept Teaching: An Instructional Design Guide. Englewood Cliffs, NJ: Educational Technology.
  • Merrill, M. D. (2002). Instructional strategies and learning styles: which takes precedence? In R. A. Reiser & J. V. Dempsey (Eds.), Trends and Issues in Instructional Technology. (pp. 99-106). Columbus, OH: Prentice Hall. PDF Preprint
  • Najjar, Lawrence, J. (1996). The Effects of Multimedia and Elaborative Encoding on Learning, Georgia Institute of Technology, Technical Report GIT-GVU-96-05. PDF
  • Norman G.R and Schmidt H.G. The psychological basis of problem-based learning: a review of the evidence. Acad Med 1992; 67(9):557-565.
  • Paivio, A. (1986). Mental Representations: A Dual Coding Approach. New York: Oxford University Press.
  • Rosch, E. & Lloyd, B. (1978). Cognition and Categorization. Hillsdale, NJ: Erlbaum.
  • Rouet, J.-F. (1999). Interactivité et compatibilité cognitive dans les systèmes hypermédias In Revue des sciences de l'éducation , Vol. XXV, n°1, (pp. 87-104).
  • Shaw, M. L. G. & Gaines, B. R. (1995). Comparing constructions through the web. Proceedings of CSCL: Computer Support for Collaborative Learning, 300-307.
  • Spoehr, K.T. (1994). Enhancing the acquisition of conceptual structures through hypermedia. In: Kate McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice. Cambridge, MA: MIT Press.
  • Taba, H. (1962). Curriculum development; theory and practice. New York,: Harcourt Brace & World.
  • Taba, H. (1967). Teacher's handbook for elementary social studies. Palo Alto, Calif.: Addison-Wesley.
  • Zentall Thomas R , Mark Galizio, and Thomas S Critchfied (2002), Categorization, concept learning, and behavior analysis: an introduction, J Experimental Analysis of Behavior, 78(3): 237-248., doi:10.1901/jeab.2002.78-237.
  • Palmer, David C., (2002) Psychological Essentialism: A Review Of E. Margolis And S. Laurence (Eds.), Concepts: Core Readings. J Exp Anal Behav. 2002 November; 78(3): 597-607. doi: 10.1901/jeab.2002.78-597. PDF
  • Rowe, Bobby L., Theoretical Contexts for Concept Learning in Art Education, Studies in Art Education, Vol. 16, No. 1. (1974 - 1975), pp. 18-25.
  • Schmidt, H.G. Foundation of problem-based learning: some explanatory notes. Medical Education 1993, 27, 422-432
  • Squire, Kurt; Mike Barnett, Jamillah M. Grant, and Thomas Higginbotham. 2004. Electromagnetism supercharged!: learning physics with digital simulation games. In Proceedings of the 6th international conference on Learning sciences (ICLS '04). International Society of the Learning Sciences 513-520.
  • Taba, Hilda (1966). Teaching Strategies and Cognitive Functioning in Elementary School Children. U.S. Department of Health, Education, and Welfare, Office of Education, Cooperative Research Project No. 2404. San Francisco: San Francisco State College.
  • Taba, H. (1967). Teacher's handbook for elementary social studies (Intro. ed.). Palo Alto, Calif.: Addison-Wesley.
  • Taba, H., Durkin, M. C., Fraenkel, J. R., & NcNaughton, A. H. (1971). A teacher's handbook to elementary social studies: An inductive approach (2nd ed.). Reading, MA: Addison-Wesley.
  • Vosniadou, S., & Brewer, W.F. (1987). Theories of knowledge restructuring in development. Review of Educational Research, 57, 51-67.