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.


Symbolic learning & the grounding problem


Harnad, S. “The Symbol Grounding Problem.” Physica D 42 (1990). 335-346.


In a short paper, the authors attempt to define symbolism as a cognitive science but find that the theory fails due to the symbol grounding problem: that symbols are composed only of other symbols and thus self-referential.

They define 6 basic learning behaviors:

  1. discriminate
  2. manipulate
  3. identify
  4. describe
  5. produce descriptions
  6. respond to descriptions

which cognitive theory must explain. Examining the first and third behaviors, the authors propose a dual representation: iconic (symbol) and categorical (internal analog transforms). However, they admit one “prominent gap:” no mechanism to explain categorical  representations. The authors thus dismiss symbolism as a sole solution and turn to connectionism as a hybrid solution: “an intrisically dedicated symbol system…connected to nonsymbolic representations…via connectionist networks that extract the nonvariant features.”


The authors likely succeed for theorists but this was a little dense given my lack of background. I think I got the idea that a symbol (for example, a swastika) exists by itself and combined with “rules” (our prior learning and knowledge that the symbol has a recent association with the Nazi Party) to produce a composite symbol (loathing). I also took away that humans, especially in groups, are too complex to be semantically interpretable, and that connectionism (based not on symbols but on pattern activity in a multilayered network) may offer some answers.

The dual representation–iconic (symbol) and categorical (internal analog transforms)–seem to suggest a symbol paired with a real-world (our experience with/background on/knowledge of) event; however, the authors later define that as an interpretation. In addition, I’m not certain why the iconic representation is not symbolic as the authors state.

The conclusion makes sense (although this is classic Vygotsky–and connectionism seems like just another word for community): if a category is defined as a symbol (image) plus our experience with that symbol, I started to believe that all our knowledge is interconnected (within a single human) with past experiences–and agree that it may not be possible to model learning in a purely symbolic (i.e., no connection to the real world) fashion.

YAM (Yet Another Map)

Learning theory mapped to ID model

Learning theory mapped to ID model

Is connectivism a new learning theory?

Stephen Downes and George Siemens are active bloggers in education. Over the past two years, they have proposed a new theory of learning, connectivism, based on their vision of how the availability of ubiquitous networks have changed the nature of learning. An article by Kop and Hill in the October issue (Volume 9, Number 3) of IRRODL (International Review of Research in Open and Distance Learning) considers whether connectivism qualifies as a theory.

On the surface, the argument from Downes and Siemens “feels” intuitively right:

  • since the power law applies to (computers attached to) the Internet, doubling the number of users quadruples the number of connections; therefore, connections are a critical component of knowledge construction;
  • since the rate of change of information is accelerating, the rate of change of our knowledge must accelerate, a feat which can only be accomplished through a power law network rather than our personal cognitive structures

However, a theory must provide more than a feeling. The article states that an emerging theory must be based on scientific research; even a developmental theory must meet certain criteria: describe changes within behavior, describe changes among behaviors, and explain the development that has been described.

Using connectivism to describe changes within learning theory, Siemens argues that:

  • objectivism is realized in behaviorism where knowledge is acquired through experience
  • pragmatism is realized in cognitivism where knowledge is negotiated between reflection and experience
  • interpretivism is realized in constructivism where knowledge is situated within a community
  • distributed knowledge  (from Downes) is realized in connectivism where knowledge is the set of networked connections

The author analyzes this argument and concludes that previous work by Vygotsky, Papert, and Clark already account for the changes connectivism attempts to claim as its own. In addition, Siemens’ argument seems circular: acknowledgement of knowledge as a set of connections (distributed knowledge) is required as a foundation for the theory of connectivism where knowledge is the set of networked connections. And in fact, some implications of the theory sound ludicrous:

  • there is no such thing as building knowledge;
  • our activities and experience form a set of connections, and those connections are knowledge;
  • the learning is the network.

The authors conclude that connectivism fits a pedagogical level rather than a theoretical level. “People still learn in the same way,” but connectivist explanations and solutions can help us deal with the onslaught of information and the enabling power of networked communication.


I have to admit the behaviorism article made my hair hurt. I read some paragraphs 3 or 4 times before I remotely understood them. But let me add the fault is probably mine, rather than the author’s. The end result of the reading and re-reading is that I have more sympathy for the behaviorist position; prior to the article, I was squarely in the cognitive camp. What helped my change in heart was a practical distinction: behaviorists view learning as an action; cognitive psychologists view learning as an indication of the presence of a personal mind (or a group mind for constructivists). I appreciate the solidity of the behavioral approach when I have to prove my learning designs produce results. The end of the article clearly summed up what behaviorism rejects:

  • Structuralism (separates consciousness into elements: mind’s eye)
  • Operationalism (attempts to change unmeasurable behaviors to measurable ones by stating they are determined by measurable operations: anger = loudness of voice)
  • Logical positivism (ignores consciousness and feelings)

A key to my understanding behaviorism better was the distinction between Pavlov and Skinner. Methodological behaviorism (respondent learning) says that all behaviors are caused by a stimulus. However, selectionist behaviorism (operant conditioning) says that the cause of behavior a is the consequence of behavior b not the stimulus that preceded behavior a. That distinction incorporated several surprising (to me) principles:

  • selectionist behaviorism accepts public and private behavior (although the latter is hard to measure/observe);
  • selectionist behaviorism gives credence to the environmental history of the learner (socio/cultural influences); and
  • selectionist behaviorism accepts the social constructivist view that meaning is created though social interaction among people (but NOT between people and a group “mind”).

In a behaviorist view, the role of learner is to learn and thus to adapt his or her behavior. Learning itself is defined as a change in behavior due to experience,  which is governed by (1) discrimination (responding differently to different stimuli) and (2) generalization (responding the same to similar stimuli).

Several behavioral techniques seem key to the ID process:

  • Keeping causes and consequences contiguous (close in time);
  • Making clear the contingency (explicit dependence)  between causes and consequences (while acknowledging that these contingencies vary from person to person depending on the individual’s history);
  • Building a gradual elaboration of complex patterns of behavior (demonstrated through the transfer of behavior from simpler to more complex patterns);
  • Maintaining changes through reinforcement  upon successful achievement of each stage (using different schedules: continuous/fixed/variable; shaping; conjunctive/tandem chaining);
  • Providing a matrix of specific consequences: positive/negative and reinforcing/punishing; and
  • Providing feedback with assessment (giving answers not just the score) because measurements show increased learning as a result.

The results from practical applications of behaviorism were impressive, from PSI’s  emphasis on student control and proctors to Bloom’s focus on looping  back upon failure to Precision’s focus on rate rather than percentage correct. From a personal view, I appreciated the perspective that social learning is behavioral change based on group consequences and that problem-solving is behavioral change based on trial and error. The only significant disagreement I noted in the article was the implication that the primary benefit of distance learning was the affordability of computer graded assessments.