Cognition: Revisited (Part I)

Winn provides not only a comprehensive overview of cognitive theory but also a thorough integration of constructivist contributions, producing a revised cognitivism which seems both compelling and common sensical. The only issue I had with the article is the length and semantic density; the flip side is that I think I’ll be referring to this article for years to come.

Winn starts by covering the basic issues in cognitive theory:

  • information is represented by internal symbols which map to the  outside world through translations
  • the internal and external worlds are separated physically as well as phenomenologically
  • the separation applies to timing (toss and catch) as well as location
  • the internal representations are idiosyncratic and thus only partially accurate

Four dissatisfactions with this essentially computational view of learning are:

  1. cognitive activity is prompted by environmental changes that are not represented  internally
  2. cognitive activity is not separate from context and is embedded in an environment
  3. the learner and the environment are coupled not separate, although the learner’s history of environmental adaptation is more important than the environment itself (which suggests quantum entanglement)
  4. knowledge value is personal; while anything a learner constructs is of value personally, the community assigns permanent value

Since all 4 dissatisfactions involve the influence of environment, Winn then outlines 4 new approaches to cognition that incorporate environmental aspects:

  • System theory: interactions between learner and environment are complex, mutual, dynamic, and often nonlinear
  • Biological: learning is an adaptation to environment
  • Neuroscience: cognition changes as a result of interaction with the environment, and learning causes physical changes to the central nervous system (interestingly, this branch suggests that the complex behaviors that led to cognitivism may actually be a chain of behavioral S-R events)
  • Neural networks: networks represent information through the way in which the nodes are connected and changes in these connections are the “processes by which learning takes place.”

After tracing the early evolution of Gestalt to behavioral psychology, Winn succinctly differentiates behavioral and cognitive theories: “cognitive psychology is concerned with meaning, while behavioral psychology is not.” And while I may appreciate the freedom of cognitivisim, the real impetus for my belief in this approach lies in the research showing that the parts of the brain that are active when learners report a mental image are the same parts that are active when the learner views an image. Finally, some science behind the idea of imagaic memory. The section concludes with a discussion of levels of cognitive theory: some mechanisms can be explained in biological terms; those which cannot, can be explained in more abstract metaphors for what is taking place.

Cognition: Refined (Part II)

Winn continues by covering different views of schema:

  1. Memory structures: built over time as a result of interaction with the environment and colored by encoding and recall
  2. Abstractions: placeholders to be “instantiated by recall” (schema are general; images are specific)
  3. Dynamic structures: constantly changing through assimilation (bottom-up matching followed by top-down testing) or accommodation (schema tuning or schema creation through analogy)
  4. Context: activation to set context (ambiguous information is interpreted differently, depending on the context brought by the learner).

He then applies schema to three practical applications in educational technology:

  1. Creating materials which are isomorphic to the schema
    • As images
      • because pictures are encoded as images, OR
      • because pictures impose a structure and propositions about this structure are encoded
    • As structures – making the spatial structure reflect the semantic structure; the interesting research is that artificial visual boundaries override spatial proximity in recall tests
  2. Helping learners create structural schema (information mapping)
  3. Using schema to represent information in a computer (AI)

Winn then briefly discusses mental models which are broader than schema because models also specify causal actions–how changes in one part of the model affect other parts.

Cognition: Revised (Part III)

Winn then begins to integrate constructivism. He details 5 stages as novices become experts in a community of practice:

  1. Novices – learn set facts and rules
  2. Advanced beginners – develop a larger context for those facts
  3. Competent – situations begin to overwhelm the learner who must develop decision-making strategies
  4. Proficient – automatic decision-making
  5. Expert – understanding without objective evidence

The implications for ID are helpful:

  • Start with facts
  • Introduce situational knowledge at the advanced beginner stage
  • Don’t expect proficient learners to articulate solutions

Winn continues by elaborating on key concepts from tacit knowledge construction:

  • automaticity frees up cognitive resources, is created by overlearning, and is a process whereby declarative knowledge becomes procedural
  • experts solve problems by pattern recognition, not by breaking information into parts; they are faster not because of improved searching but because matching is faster than searching
  • mental representations depend on concurrent interactions with the environment, the knowable aspect of which actually changes as learners come to understand it

Winn next applies information processing theory to cognition and outlines key findings and views:

  • Information in long-term memory is not a direct representation of short-term memory but rather is an abstract schema.
  • Cognition is driven as much by what we know as by the information, suggesting that designers must activate relevant schema by guiding top-down processing.
  • Bottom-up processes are unconscious and thus unaddressable, although how our perceptual systems process information determine how our cognitive systems will process it.
  • Cognition is a process of symbo0l manipulation, leading to the use of pictures for identification and of drawings for structure and function.
  • Reasoning applies rules to encoded information which manipulate that information to reveal solutions; “the information is encoded as a production system which operates by testing whether the conditions of rules are true or not.”

Winn concludes this section by considering knowledge construction through conceptual change; in a circular fashion, “what we know directs how we seek information, how we seek information determines what information we get,” and the information we get affects what we know. The most applied section lays out a 3-step strategy:

  1. Challenge reality, elicit misperception (gain attention)
  2. Stimulate recall and connect/integrate with prior knowledge (the new knowledge must fit or it will be discarded)
  3. Transfer the new knowledge to solving a new problem

The concepts are embodiment (we use gestures to physically communicate) and embededness (we are part of the environment and thus we influence it) are added to produce the claim that learning is solely an adaptation to the environment, a claim that makes sense from the importance of environment but also overly simplistic.

In the final section, Winn applies his revised view of cognitivism to instrucitonal design. He points out three potential problems:

  1. Theories gain value by generality, but designs by specificity
  2. Designers seldom know the subject domain which determines the design
  3. Designs are environmentally-specific and difficult to translate

However, all is not lost. Winn points to a future where learning environments are adaptive in real time and thus where design is situated in the same context as the learning. By integrating design with instruction, Winn sees three solutions:

  1. Movement to a nonlinear design process where objectives change as often as strategies
  2. Design tools are embedded in instructional environments so that the tools change as they are used
  3. Development of interactive problem-solving simulations.

PBL is the goal of educational games and offers the most effective solution.

Cognitive Apprenticeship

While this article provides useful definitions and distinctions among implementations of cognitive apprenticeship, the emphasis on a survey of articles provides with rare exception neither practical advice nor theoretical support. The initial definition of cognitive apprenticeship (“learning that occurs as experts and novices interact socially while focused on completing a task”) is instructive but needlessly includes the social component; all human-human interaction is social. However, the Lave/Wenger concept of legitimate peripheral participation (“a process in which newcomers enter on the periphery and gradually move toward toward full participation) was significant as it accurately describes workplace learning in my experience.

The discussion of scaffolding, fading, intersubjectivity (negotiated shared understanding), modeling, mentoring, and coaching were redundant with previous readings. However, there were practical nuggets:

  • modeling is more efficient than trial and error
  • mentors and coaches help tacit knowledge become explicit
  • coaching focuses on a specific goal
  • expert outlines reduce cognitive load
  • discovery alone is insufficient to ensure learning will take place
  • individuals expect others to share their understanding (myopic as this may seem)

In particular, the productive mentoring practices (structure, regular meetings, and mentor training) point the way to effective support design. And though the explanation of ZPD was also familar, the description of activities based on ZPD as just within a learners’ current ability level (the ZPD is just beyond) is eerily reminiscent of video games which aim to create gameplay levels which are barely doable.

The Perfect Theory

Snelbecker also tackles design theories but not by contrasting them with learning theories like Reigeluth; instead, Snelbecker contrasts design theories from theoretical and practitioner points of view. The former is designed not to yield rules of practice but to help practitioners “design conditions that facilitate learning.” As such, theories, while closely related to practice (as opposed to learning theories), are indirect.

Theories, designed by Snelbecker’s knowledge producers, are expected to provide definitive answers by knowledge users (instructors and designers); however, because theorists exercise caution in drawing conclusions, theories seldom satisfy users who need immediate answers. I particularly appreciated the perspective that theorists view their work as progress reports designed to help users “consider the merits of alternative approaches.”

The irony is that while theorists do not want practitioners to consider their work as final answers, these same producers adopt dogmatic stances regarding their personal theory. Snelbecker’s solution involves posing three questions to the theorists:

  1. Is any theory perfect?
  2. Does any theory include everything?
  3. Should any theory be the only theory?

After answering, “No” to each of these questions, Snelbecker concludes by recommending that theories identify the added value they provide to our understanding of how instruction can be designed.

ID Theory

Reigeluth’s own contribution to the volume he edited (Instructional Design Theories and Models: A New Paradigm of Instructional Theory, Vol. 2. 1983. NJ: Erlbaum. pp. 5-29) is immense. He draws a clear distinction between design (prescriptive, decision-oriented) theory and descriptive (predictive, conclusion-oriented) theory. Learning theory is descriptive; design theory shows us how to accomplish our goals and includes three primary characteristics:

  • goals
  • methods
  • situations

The idea that design theory is probabilistic is equally true of descriptive theory. The final distinction made the most sense:

descriptive theory concerns validity, while design theory concerns preferability

A learning situation has both conditions and outcomes. The  conditions (the 4 he listed actually seem like 3) generally map to outcomes.


  1. What is to be learned
  2. The learner
  3. The environment and constraints


  1. Effectiveness (of the learning)
  2. Appeal (to the learner)
  3. Efficiency (of the delivery environment)

In addition, the methods of design theories are componential (with different parts, kinds, and criteria) although the individual components cannot be simply “added” to increase the probability of learning success.

Reigeluth then argues for a new model of learning based on the transition from an industrial to an information society. His vision of the current educational paradigm as based on mass production and standardization as befits an industrial (factory) approach was clearly articulated; his argument that we must move to mass customization was equally compelling, and I especially appreciated the analogy that the factory model was designed for sorting not for learning. However, his vision for a path to change was less helpful. While I buy the argument that people learn at different rates and that if time is constant, achievement must vary, the sole alternative (allowing time to vary) does not necessarily follow; achievement may also vary with the quality of the instruction (and by quality, I mean the broadest sense of the word: instruction matched perfectly to the learner’s needs at the moment the learning is delivered).

The distinction between basic and variable methods seemed somewhat artificial; variable methods that are proved become basic methods. At the same time, the alternative methods chart offers practical advice even though several of the methods are not discrete but compositions of other methods. The most challenging topic was reserved for the end: the argument that, “the only viable way to make decisions about instructional strategies that meshes with cognitive theory” is to do so during instruction, a proposition that implies an adaptive system.

Conditions-based Theory

Ragan, Tillman J. and Smith, Patricia L. (2004) “Conditions Theory and Models for Designing Instruction.” In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology. Mahwah, NJ: Lawrence Erlbaum Associates. 623-649.

The incredible value of this article is in so clearly summing up Gagnés work and relating it to other theories: learning outcomes vary across contents, contexts, and learners; distinctive cognitive processing demands can be supported by different methods, strategies, and conditions. An early discussion of the background for Gagné noted that instructional design effectiveness varied among rote learning, skill learning and problem solving; however, the authors express doubt in a hierarchy of skills (and report that aside from the intellectual category, Gagné later moved away from his original taxonomic chain).

Rule-using (intellectual) skills are stored in hierarchical structures: verbal learning stored as propositional networks or schemata; rules stored as “if…then” productions; problem-solving are interconnections of schemata and productions. Gagné differentiated problem-solving (and concept formation) from other types in that PBL does not include any portion of the solution in the problem itself. One of Gagné’s contributions was to tie external events or instruction to internal events of learning; the latter he suggested were most impacted by prior knowledge; manner of long-term encoding; and requirement for retrieval and transfer to new problems.

The article’s coverage of multiple additions to Gagne’s work was equally clear. Merrill’s Component Display Theory categorizes learning objectives as a performance level (remember, use, or find) and a content type (fact, concept, principle, or procedure). Five operations (based 4 memory structures: associative, episodic, image, and algorithmic) can be conducted on subject matter:

  1. identity (facts)
  2. inclusion (concepts)
  3. intersection (concepts)
  4. order (procedures)
  5. cause (principles)

Reigeluth’s Elaboration Theory proposes three structures: conceptual (parts, kinds, matrices); procedural (order, decision); and theoretical (descriptive, prescriptive). Smith and Ragan argue that a middle ground exists between instruction-supplied and learner-initiated events. Tennyson describes three storage processes (declarative, procedural, and conceptual) and three retrieval processes (differentiating, integrating, and creating). Declarative knowledge is stored as associative networks or schemata; procedural knowledge is related to intellectual skills; and contextual (is this the same as conceptual?) knowledge is related to problem-solving. Finally, Ellen Gagné contributes the idea that declarative knowledge can be represented by propositions, images, linear orderings, or schemata (composed of the first three, while procedural knowledge is represented as a production system.

After agreeing with the fundamental premises (of conditions-based theory, the article demonstrate the lack of a proven link between learning categories and external conditions:

  • competition may be superior when a task is simple, but cooperative goal structures are more effective in problem-solving
  • explicit organization affected achievement although sequence modification did not
  • punishment is more effective than reward for discrimination learning
  • introductory students benefited more from direct guidance while advanced students performed better with more opportunities for autonomy
  • novice learners need more explicit learning guidance in employing cognitive strategies

The call for additional research is apropos although the need for additional knowledge of the relationship between internal learner conditions and subsequent learning is complicated by the difficulty in determining those internal (private) conditions.