Christopher D. Sessums

Toward a Theory of Discontent: What can learning theory contribute to education?

A theory is as much an assumption, a basis for belief, as it is an explanation, an ontology, or an understanding of things. Typically, theories evolve out of the language of experience. Out of such language, a grammar and syntax are invented/uncovered/supplied that underpin our observations.

It is natural that we have so many theories. Some prove more valuable than others, some have historicity, while others serve as temporary bookmarks saving our place while we focus on other aspects. Theories are reminders of where we are, where we left off, where we need to return.

Educational theories range in depth and complexity and are as abundant as snowflakes. So what are we to make of them? Which ones are most pertinent? Are all theories relative or contextual? Do we need a grand unifying theory of learning? Or will hundreds or thousands do?

Dennett (1978) frames theories on three levels:

the physical level  -- as in neurological postulates of learning; the design level  -- as in psychological speculation; and the intentional level -- where philosophical/humanistic schema reside to include scientifically testable theories.

Bereiter (1990) suggests that a real learning theory, that is, one distinct from bridging theories that link beliefs to practice, is necessary as it would (hopefully) provide an explanation appropriate for instructional theories.

Bereiter (1990) states: “An educational learning theory may have a bearing on cultural change, but mainly needs to explain the enculturation of the individual” (p. 607). He goes on to layout a framework that serves as a means for guiding further inquiry:

What should the boundaries of a learning theory be? What should a learning theory try to explain? What are its building blocks?

So what is it we wish we understood but do not?

Bereiter ultimately wrestles with a question that is quite important in terms of explaining learning: How do people acquire knowledge that is more complex than what they already have? In other words, how do we learn things that are difficult to learn?

An educational learning theory should provide us with a systematic account of the conditions implicated in learning difficulty, and it should be able to tell us how we overcome such learning difficulties.

In 1978, Case published a developmentally based theory where the learners ability to control for complexity took center stage. This explanation was not a learning theory but instead provided a good instructional ontology to assist others in supporting students in the creation of knowledge. The question of how students experience, adapt to, and overcome learning difficulties remained un-clarified.

Functionally, others have examined the notion of expertise in overcoming learning difficulties, yet these theories can only provide an operational explanation and generally ignore issues associated with the process of learning (e.g., How do you get to Carnegie Hall?)

George Siemens offers connectivism-–an external form of connectionism--that provides an explanation of how we build and leverage knowledge based on our external networks of both human and digital connections. If I understand Siemens correctly, the proposed unit for his educational theory is the contextual module known as the node -- the atomic unit on which networks are constructed. Each node is a network itself of both internal and external networks that responds to and interact with external stimuli.

The question is, does connectivism move us beyond the functional aspects of learning and explain how we acquire knowledge that is more complex than we already have? What does connectivism offer that other theories of learning have not?

In his OCC 2007 presentation Stephen Downes proposes a theory of learning based on practicing and reflecting what’s been modeled and demonstrated. Again, Downes puts forward a functional theory based on the development of procedural knowledge and skills, declarative knowledge, pattern recognition, goal structures, etc. that can be developed and elaborated. Like Siemens, Downes’ model recognizes the numerous cognitive structures affected by external phenomena and stimuli that shape learning yet resists an explanation of how we overcome learning difficulties.

Task learning and personal knowledge construction

Perhaps it is important to consider the difference between task learning and personal knowledge construction. Task learning is what we acquire from schooling. Out of this process, we develop what Bereiter calls a schoolwork module, that is, a schema by which we process incoming information presented in the context of school. Beyond this model, the intentional learning module is one that is not necessarily cultivated in most school settings. This module pushes us to struggle with complexity; it recognizes familiar patterns as well as unfamiliar ones. Once this module/learner enters unfamiliar territory, the module “kicks in” and allows the learner to engage in active problem solving using a range of resources and networks in order to construct “the more complex knowledge that is foreshadowed by the premises…. The educational problem is how to foster development of an intentional learning module in students who are not so fortunate in their circumstances that it develops spontaneously” (my emphasis -- Bereiter, 1990, p. 617).

This where the work of Siemens, Downes, and others seems to be directed: how do we design educational settings or social contexts for learning in such a way that they encourage and develop intentional learning.

Toward a functional theory of discontent

As for my own thoughts on learning theory, I feel we need a functional theory of discontent -- a theory that allows our beliefs and ideas to be challenged by what others think.

Cognitive dissonance offers a schema that starts us in the right direction: Holding two conflicting or contradictory beliefs gives us drive, it starts the engines running so that we begin to reconcile these two beliefs into something that serves us in our desire for closure. Yet it is this drive, this desire itself, that creates calamity because closure is never certain (nor will it ever be).

This leads us to a certain level of uncertainty and insecurity. Uncertainty means a lack of knowledge. It is an epistemic category linked to the question “what do we know?” On the other hand, insecurity means need for control. It is a pragmatic category linked to the question “what shall we do?” Researchers like Harald Mieg (2001) and others define an occupation to be of low uncertainty if there is sufficient formalized knowledge, e.g., engineering or basic health problems. An occupation is said to be of high insecurity if there is a discussion of precautions that needs to be taken, e.g., foreign policy or basic health care issues.

So where does teaching and learning fit in this model?

There are certain occupations where the public accepts insecurity as relatively high but sees uncertainty as relatively low. In this case, solutions are offered by professionals who carry enough social capital to serve us in a relatively purposive way. Teaching seems to be an aoccupation somewhere inbetween the uncertainty/insecurity domain, thus the publics‘ general ignorance and acceptance of the issues portrayed in the media.

Within this uncertainty/insecurity framework, if we want to place education on a political agenda, then it is necessary to increase public concern, i.e., increase perceived insecurity. On the downside, if we want to achieve a scientific outlook on education, then we have to promise to reduce uncertainty by establishing standards that are quantifiable, e.g., high stakes testing.

Toward an instructional theory of discontent

On the one hand, as educators and learners we need to be rewarded for being confused. We’ve spent years listening to others only to determine whether we agree with them or not. We need an instructional theory that states: “we must listen to people who think differently than we do.”

Since we live in and with complexity, we need an instructional theory that says, if we want to understand this complexity, then we must spend more time not knowing.

Part of our identity is defined by what we believe. We need an instructional theory of curiosity. We do not need to rid ourselves of what we believe, but we do need to be curious about what other people believe. Our survival may depend on it.

Oftentimes, we are unable to figure things out on our own; consequently, we need others to verify or support what we believe, what we see and think. We don’t need to agree, and as such, we need an instructional theory that says as much.

So what if you don’t like this theory? Sometimes we get comfortable listening to our selves and people with similar tastes. Listening to other people leads to uncertainty and potentially change -- thus change serves as a by-product of listening.

Ultimately, change starts with confusion. And great ideas appear in that wonderful abyss of not knowing. If we can learn to discard fear and enter the abyss, we can be greatly rewarded. This is where creativity is born.

I applaud the work of theorists who are dissatisfied with the way we see things. Their passions ultimately push them to re-arrange the deck so as to provide us with new points of view, new ways to consider what we do, how we think, and where we need to go. While an instructional theory of discontent may not be the best way to frame my argument, it provides us a means to continue the debate what teaching and learning needs to look like now and in the future.

References:

Bereiter, C. (1990). Aspects of an educational learning theory. Review of Educational Research 60(4): 603-624.

Case, R. (1978). Piaget and beyond: Toward a developmentally-based theory and technology of instruction. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 1, pp. 167-228). Hillsdale, NJ: Lawrence Erlbaum Associates.

Dennett, D.C. (1978). Brainstorms. Montgomery, VT: Bradford Books,

Mieg, H. (2001) The social psychology of expertise: Case studies in research, professional domains, and expert roles. Mahwah, NJ: Lawrence Erlbaum.