Thursday, February 23, 2012

Learning Concepts (Part II)

Imagery is a cognitive phenomena of long-standing, first studied by Wilhelm Wundt at the turn of the century. From a theoretical perspective, imagery is a critical issue in terms of memory structures and processes (e.g., Shepard & Cooper, 1982). Theories that postulate a propositional basis for memory (e.g., ACT ) have difficulty accounting for imagery. A number of imagery researchers have developed their own theories of memory that focus on the visual components of imagery. Paivio has proposed a dual coding theory that suggests that verbal and nonverbal information is processed separately. Kosslyn (1980) has proposed a two-stage model of imagery that involves a surface representation generated in working memory from a deep representation in long-term memory. Piaget & Inhelder (1971) discuss the role of imagery in cognitive development.
From a practical point of view, imagery has been shown to facilitate recall in many studies. It also appears to play a major role in problem-solving and creativity. For example, there are many anecdotes of imagery in scientific discovery (Miller, 1984). Imagery also appears to help sensory-motor skills by allowing mental rehearsal of a task or activity. However, it is clear from theories of intelligence (e.g., Guilford ) that people differ in their ability to create visual images.
Bower, J. (1972). Mental imagery and associative learning. In L.
Gregg (ed.), Cognition in Learning and Memory. New York: Wiley.
Kosslyn, S. (1980). Image and Mind. Cambridge, MA: Harvard University Press.
Miller, A. (1984). Imagery in Scientific Thought. Boston: Birkhauser.
Richardson, A. (1969). Mental Imagery. New York: Springer.
Piaget, J. & Inhelder, B. (1971). Mental Imagery and the Child. New York: Basic Books.
Sheehan, P. (1972). The Function and Nature of Imagery. New York: Academic Press.
Shepard, R. & Cooper, L. (1982). Mental Images and Their Transformations. Cambridge, MA: MIT Press.

Learning Strategies

Learning strategies refer to methods that students use to learn. This ranges from techniques for improved memory to better studying or test-taking strategies. For example, the method of loci is a classic memory improvement technique; it involves making associations between facts to be remembered and particular locations. In order to remember something, you simply visualize places and the associated facts.
Some learning strategies involve changes to the design of instruction. For example, the use of questions before, during or after instruction has been shown to increase the degree of learning (see Ausubel). Methods that attempt to increase the degree of learning that occurs have been called "mathemagenic" (Ropthkopf, 1970).
A typical study skill program is SQ3R which suggests 5 steps: (1) survey the material to be learned, (2) develop questions about the material, (3) read the material, (4) recall the key ideas, and (5) review the material.
Research on metacognition may be relevant to the study of learning strategies in so far as they are both concerned with control processes. A number of learning theories emphasize the importance of learning strategies including: double loop learning ( Argyris ), conversation theory (Pask), and lateral thinking ( DeBono ). Weinstein (1991) discusses learning strategies in the context of social interaction, an important aspect of Situated Learning Theory.
H.F. O'Neil (1978). Learning strategies. New York: Academic Press.
H.F. O'Neil & C. Spielberger (1979). Cognitive and Affective Learning Strategies. New York: Academic Press.
Rothkopf, E. (1970). The concept of mathemagenic behavior. Review of Educational Research, 40, 325-336.
Schmeck, R.R. (1986). Learning Styles and Learning Strategies. NY: Plenum.
Weinstein, C.E., Goetz, E.T., & Alexander, P.A. (1986). Learning and Study Strategies. NY: Academic Press.
Weinstein, C.S. (1991). The classroom as a social context for learning. Annual Review of Psychology, (42), 493-525.


A fundamental change in thinking about the nature of instruction was initiated in 1963 when John B. Carroll argued for the idea of mastery learning. Mastery learning suggests that the focus of instruction should be the time required for different students to learn the same material. This contrasts with the classic model (based upon theories of intelligence ) in which all students are given the same amount of time to learn and the focus is on differences in ability. Indeed, Carroll (1989) argues that aptitute is primarily a measure of time required to learn.
The idea of mastery learning amounts to a radical shift in responsibility for teachers; the blame for a student's failure rests with the instruction not a lack of ability on the part of the student. In a mastery learning environment, the challenge becomes providing enough time and employing instructional strategies so that all students can achieve the same level of learning (Levine, 1985; Bloom, 1981).
The key elements in matery learning are: (1) clearly specifying what is to be learned and how it will be evaluated, (2) allowing students to learn at their own pace, (3) assessing student progress and providing appropriate feedback or remediation, and (4) testing that final learning critierion has been achieved.
Mastery learning has been widely applied in schools and training settings, and research shows that it can improve instructional effectiveness (e.g., Block, Efthim & Burns, 1989; Slavin, 1987). On the other hand, there are some theoretical and practical weaknesses including the fact that people do differ in ability and tend to reach different levels of achievement (see Cox & Dunn, 1979). Furthermore, mastery learning programs tend to require considerable amounts of time and effort to implement which most teachers and schools are not prepared to expend.
The mastery learning model is closely aligned with the use of instructional objectives and the systematic design of instructional programs (see Gagne, Merrill). The Criterion Referenced Instruction (CRI) model of Mager is an attempt to implement the mastery learning model. In addition, the theoretical framework of Skinner with its emphasis on individualized learning and the importance of feedback (i .e., reinforcement) is also relevant to mastery learning.
Block, J. H. (1971). Mastery Learning: Theory and Practice. New York: Holt, Rinehart & Winston.
Block, J. H., Efthim, H. E., & Burns, R.B. (1989). Building Effective Mastery Learning Schools. New York: Longman.
Bloom, B.S. (1981). All Our Children Learning. New York: McGraw-Hill.
Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 723-733.
Carroll, J.B. (1989). The Carroll model: A 25 year retrospective and prospective view. Educational Researcher, 18(1), 26-31.
Cox, W.F. & Dunn, T. G. (1979). Mastery learning: A psychological trap? Educational Pyschologist, 14, 24-29.
Levine, D. (1985). Improving Student Achievement Through Mastery Learning Programs. San Francisco: Jossey-Bass.
Slavin, R.E. (1987). Mastery learning reconsidered. Review of Educational Research, 57(2), 175-214.


Memory is one of the most important concepts in learning; if things are not remembered, no learning can take place. Futhermore, memory has served as a battleground for opposing theories and paradigms of learning (e.g., Adams, 1967; Ashcraft, 1989; Bartlett, 1932; Klatzky, 1980; Loftus & Loftus, 1976; Tulving & Donaldson, 1972). Some of the major issues include recall versus recognition, the nature of forgetting (i.e., interference versus decay), the structure of memory, and intentional versus incidental learning.
According to the early behaviorist theories (e.g., Thorndike, Guthrie, Hull), remembering was a function of S-R pairings which acquired strength due to contiguity or reinforcement. Stimulus sampling theory explained many memory phenomenon on the basis of statistical outcomes. On the other hand, cognitive theories (e.g., Tolman) insisted that meaning (i.e., semantic factors) played an important role in remembering. In particular, Miller suggested that information was organized into "chunks" according to some commonality. The idea that memory is always an active reconstruction of existing knowledge was championed by Bruner and is found in the theories of Ausubel and Schank.
Some theories of memory have concerned themselves with the nature of the processing. Paivio suggests a dual coding scheme for verbal and visual information. Craik & Lockhart proposed that information can be processed to different levels of understanding. Rumelhart & Norman describe three modes of memory (accretion, structuring and tuning) to account for different kinds of learning.
Other theories have focused on the representation of information in memory. ACT assumes three types of structures: declarative, procedural, and working memory. Merrill proposes two forms: associative and algorithmic. On the other hand, Soar postulates that all information is stored in procedural form. Kintsch (1974) suggests that memory is propositional in nature and it is the relationship among propositions that gives rise to meaning.
Many theories of instruction do not make assumptions about the nature of memory but do specify how information should be organized for optimal learning. For example, Pask outlines the development of entailment structures and Reigeluth discusses elaboration networks.
Individual differences in memory abilities are discussed by Eysenck (1977) and Guilford and represent an important aspect of intelligence.
Adam s, J. (1967). Human Memory. New York: McGraw-Hill.
Ashcraft, M. (1989). Human Memory and Cognition. Glenview, IL: Scott Foresman.
Bartlett, F.C. (1932). Remembering: An Experimental and Social Study. Cambridge: Cambridge University Press.
Eyse nck, M. (1977). Human Memory: Theory, Research and Individual Differences. Oxford: Pergamon Press.
Kintsch, W. (1974). The Representation of Meaning in Memory. Hillsdale, NJ: Erlbaum.
Klatzky, R.L. (1980). Human Memory: Structures and Processes (2 nd Edition). San Francisco: Freeman.
Loftus, G. & Loftus, E. (1976). Human Memory: The Processing of Information. Hillsdale, NJ: Erlbaum.
Tulving,E. & Donaldson, W. (1972). Organization of Memory. New York: Academic Press.

Mental Models
Mental models are representations of reality that people use to understand specific phenomena. Norman (in Gentner & Stevens, 1983) describes them as follows: "In interacting with the environment, with others, and with the artifacts of technology, people form internal, mental models of themselves and of the things with which they are interacting. These models provide predictive and explanatory power for understanding the interaction."
Mental models are consistent with theories that postulate internal representations in thinking processes (e.g., Tolman , GOMS , GPS ). Johnson-Laird (1983) proposes mental models as the basic structure of cognition: "It is now plausible to suppose that mental models play a central and unifying role in representing objects, states of affairs, sequences of events, the way the world is, and the social and psychological actions of daily life." (p397)
Holland et al. (1986) suggest that mental models are the basis for all reasoning processes: "Models are best understood as assemblages of synchronic and diachronic rules organized into default hierarchies and clustered into categories. The rules comprising the model act in accord with the principle of limited parallelism, both competing and supporting one another." (p343) Schumacher & Czerwinski (1992) describe the role of mental models in acquiring expertise in a task domain.
Some of the characteristics of mental models are:
  • They are incomplete and constantly evolving
  • They are usually not accurate representations of a phenomenon; they typically contain errors and contradictions
  • They are parsimonious and provide simplified explanations of complex phenomena
  • They often contain measures of uncertainty about their validity that allow them to used even if incorrect
  • They can be represented by sets of condition-action rules.
The study of mental models has involved the detailed analysis of small knowledge domains (e.g., motion, ocean navigation, electricity, calculators) and the development of computer representations (see Gentner & Stevens, 1983). For example, DeKleer & Brown (1981) describe how the mental model of a doorbell is formed and how the model is useful in solving problems for mechanical devices. Kieras & Bovair (1984) discuss the role of mental models in understanding electronics. Mental models have been applied extensively in the domain of troubleshooting (e.g., White & Frederiksen, 1985).
One interesting application of mental models to psychology is the Personal Construct Theory of George Kelley (1955). While the primary thrust of Kelly's work was therapy rather than education, it has seen much broader applications (see [Thanks to Richard Breen for bringing this to my attention]
For an exploration of the relationship between mental models, systems theory, and cyberspace culture, see "A house of horizions and perspectives" by Heiner Benking and James Rose.
Collins, A., & Gentner, D. (1987). How people construct mental models. In D. Holland & N. Quinn (eds.), Cultural Models in Thought and Language. Cambridge: Cambridge University Press.
deKleer, J. & Brown, J.S. (1981). Mental models of physical mechanisms and their acquisition. In J.R. Anderson (ed.), Cognitive Skills and their Acquistion. Hillsdale, NJ: Erlbaum.
Gentner, D. & Stevens, A.(1983). Mental Models. Hillsdale, NJ: Erlbaum.
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R. (1986). Induction: Processes of Inference, Learning and Discovery. Cambridge, MA: MIT Press.
Johnson-Laird, P. (1983). Mental Models. Cambridge, MA: Harvard University Press.
Kelly, G. (1995). Principles of Personal Construct Psychology. Norton.
Kieras, D. & Bovair, S. (1984). The role of mental models in learning to operate a device. Cognitive Science, 8, 255-273.
Schumacher, R. & Czerwinski, M. (1992). Mental models and the acquisition of expert knowledge. In R. Hoffman (ed.), The psychology of expertise. New York: Springer-Verlag.
White, B. & Frederiksen, J. (1985). Qualitative models and intelligent learning environments. In R. Lawler & M. Yazdani (Eds.), Artifical Intelligence and Education. Norwood, NJ: Ablex.


Metacognition is the process of thinking about thinking. Flavell (1976) describes it as follows: "Metacognition refers to one's knowledge concerning one's own cognitive processes or anything related to them, e.g., the learning-relevant properties of information or data. For example, I am engaging in metacognition if I notice that I am having more trouble learning A than B; if it strikes me that I should double check C before accepting it as fact." (p 232).
Flavell argued that metacognition explains why children of different ages deal with learning tasks in different ways, i.e., they have developed new strategies for thinking. Research studies (see Duell, 1986) seem to confirm this conclusion; as children get older they demonstrate more awareness of their thinking processes.
Metacognition has to do with the active monitoring and regulation of cognitive processes. It represents the "executive control" system that many cognitive theorists have included in their theories (e.g., Miller, Newell & Simon, Schoenfeld). Metacognitive processes are central to planning, problem-solving, evaluation and many aspects of language learning.
Metacognition is relevant to work on cognitive styles and learning strategies in so far as the individual has some awareness of their thinking or learning processes. The work of Piaget is also relevant to research on metacognition since it deals with the development of cognition in children.
For further discussion of Metacognition, see or
Brown, A. (1978). Knowing when, where and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in Instructional Psychology. Hillsdale, NJ<: Erlbaum Assoc.
Duell, O.K. (1986). Metacognitive skills. In G. Phye & T. Andre (Eds.), Cognitive Classroom Learning. Orlando, FL<: Academic Press.
Flavell, J. (1976). Metacognitive aspects of problem-solving. In L.
Resnick (Ed.), The Nature of Intelligence. Hillsdale, NJ: Erlbaum Assoc.
Forrest-Pressly, D., MacKinnon, G., & Waller, T. (1985). Metacognition, Cognition, and Human Performance. Orlando: Academic Press.
Garner, R. (1987). Metacognition and Reading Comprehension. Norwood, NJ: Ablex.


Motivation is a piviotal concept in most theories of learning. It is closely related to arousal, attention, anxiety, and feedback/reinforcement. For example, a person needs to be motivated enough to pay attention while learning; anxiety can decrease our motivation to learn. Receiving a reward or feedback for an action usually increases the likelihood that the action will be repreated. Weiner (1990) points out that behavioral theories tended to focus on extrinsic motivation (i.e., rewards) while cognitive theories deal with intrinsic motivation (i.e., goals) .
In most forms of behaviorial theory, motivation was strictly a function of primary drives such as hunger, sex, sleep, or comfort. According to Hull's drive reduction theory, learning reduces drives and therefore motivation is essential to learning. The degree of the learning achieved can be manipulated by the strength of the drive and its underlying motivation. In Tolman's theory of purposive behaviorism, primary drives create internal states (i.e., wants or needs) that serve as secondary drives and represent instrinsic motivation.
In cognitive theory, motivation serves to create intentions and goal-seeking acts (see Ames & Ames<, 1989). One well-developed area of research highly relevant to learning is achievement motivation (e.g., Atkinson & Raynor, 1974; Weiner). Motivation to achieve is a function of the individual's desire for success, the expectancy of success, and the incentives provided. Studies show that in general people prefer tasks of intermediate difficulty. In addition, students with a high need to achieve, obtain better grades in courses which they perceive as highly relevant to their career goals. On the other hand, according to Rogers, all individuals have a drive to self-actualize and this motivates learning.
Malone (1981) presented a theoretical framework for instrinsic motivation in the context of designing computer games for instruction. Malone argues that instrinsic motivation is created by three qualities: challenge, fantasy, and curosity. Challenge depends upon activities that involve uncertain outcomes due to variable levels, hidden information or randomness. Fantasy should depend upon skills required for the instruction. Curiosity can be aroused when learners believe their knowledge structures are incomplete, inconsistent, or unparsimonious. According to Malone, instrinsically motivating activities provide learners with a broad range of challenge, concrete feedback, and clear-cut criteria for performance.
Keller (1983) presents an instructional design model for motivation that is based upon a number of other theories. His model suggests a design strategy that encompasses four components of motivation: arousing interest, creating relevance, developing an expectancy of success, and producing satisfaction through intrinsic/extrinsic rewards.
The Choice Theory of William Glasser is also relevant to the motivation aspects of learning (see )
For descriptions of other theories of motivation, see
For suggestions about how to apply motivation to teaching, see
Ames<, C. & Ames, R. (1989). Research in Motivation in Education, Vol 3. San Diego<: Academic Press.
Atkinson, J. & Raynor, O. (1974). Motivation and Achievement. Washington<: Winston.
Keller, J. (1983). Motivational design of instruction. In C. Riegeluth (ed.), Instructional Design Theories and Models. Hillsdale, NJ<: Erlbaum.
Malone, T. (1981). Towards a theory of instrinsically motivating instruction. Cognitive Science, 4, 333-369.
McClelland, D. (1985). Human Motivation. Glenview, IL<: Scott, Foresman.
Weiner, B. (1990). History of motivational research in education. Journal of Educational Psychology, 82(4), 616-622.

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