Structural Learning Theory Research Paper Starter

Structural Learning Theory

Developed in the late 1960s by University of Pennsylvania professor Joseph Scandura, structural learning theory came onto the scene just as the fields of psychology and education were transitioning from a behaviorist to a cognitive orientation. This article outlines the ways in which structural learning theory adopts a cognitive orientation to learning, as well as some of the elements it shares with behaviorist theory. The basic tenets of the theory are outlined, with a discussion of the ways in which it has been applied. Although structural learning theory hasn't generated a significant amount of discussion in the academic literature, it has had a substantial impact in the fields of instructional design and software development.

Keywords Behaviorism; Deterministic; Inputs; Operations; Outputs; Probabilistic; Response; Rules; Scandura, Joseph; Stimulus

Educational Theory: Structural Learning Theory


In the late 1960s, Joseph Scandura, a professor at the University of Pennsylvania, invited his colleagues to a conference to discuss the nature of human learning. As a result of that conference, Scandura developed what has become known as structural learning theory. At that time, he later wrote, "we were not interested in reporting finished research, but, rather, we were unsatisfied with the kind of research being done on complex human learning and wanted to see what we could do about improving it" (Scandura, 1973, p. vii). Whether or not Scandura and his colleagues improved research on human learning is up for debate; however, it's important to understand the ways in which they believed they were extending the conversation.

Scandura marketed his theory as the first to approach the understanding of human learning from a deterministic point of view as opposed to a probabilistic one. In other words, Scandura believed it possible to identify the conditions under which human behavior could be predicted and controlled with near certainty. He opens his book, Structural Learning: Theory and Research, with the following statement:

In spite of the diversity which presently exists in behavioral theorizing, reference to probabilistic notions is all-pervasive. Even support at the .05 significance level is often enough to elicit whoops of glee from most cognitive theorists. Given this milieu, it is not too surprising that…no one seem to have seriously pursued the possibility that deterministic theorizing about complex human learning may actually be easier than stochastic theorizing. And yet, this is precisely what in my own work I have found to be the case (Scandura, 1973, p. 1).

What allowed Scandura to suggest that human behavior could be predicted and controlled was his belief that theory building should occur under idealized conditions. That is, Scandura eliminated the variables that might reduce certainty and predictability in human learning – mainly, our limited capacities for information processing and memory – and devised a theory to explain how learning might occur were our resources unlimited. "One of the major reasons why we have been relatively unsuccessful in devising adequate theories of complex learning and behavior, I believe, is because we have tended to tackle the problem as a whole. With a few exceptions, the possible value of ignoring the effect of memory in theorizing about…behavior has not been taken seriously by most psychologists" (Scandura, 1973, p. 171). Scandura was not suggesting, however, that memory should be excluded from theorizing altogether – although he strongly believed that memory was of minimal consequence in many real-life learning tasks – but rather that it should be considered only after theorizing at an idealized level.

Move from Behaviorism to Cognitivism

At the time Scandura was developing his theory, the fields of education and psychology were experiencing a transition. Prior to the 1970s, behaviorism reigned supreme; researchers equated learning with changes in behavior brought about by the environment, and ignored almost entirely the internal experience of individuals. Some denied the existence of the mind altogether, often referring to it as 'the black box.' Critics began realizing, however, that behaviorist theories – with their emphasis on the stimulus and response – were limited in their ability to explain more complex learning such as language acquisition. As a result, the cognitive revolution began, and attention was turned toward the very construct behaviorists had ignored – the human mind. Scandura's theory reflects the tension of this transition, simultaneously rejecting and upholding certain behaviorist tenets.

Like many of his cognitivist peers, Scandura rejected the notion that all learning could be explained by simple associations between stimulus and response. Clearly positioning himself in the cognitive camp, Scandura argued that human learning is the result of rules, not associations. "The to-be-proposed theory," he wrote, "is clearly of the information processing variety; learning is the result of internal operations and not of contingencies between overt stimuli and responses. Information processing theorists generally view learning as a problem solving process and mine is no exception" (Scandura, 1973, p. 7). The notion that all human behavior is rule-governed is central to Scandura's theory.

Scandura's theory also reflected a second new trajectory in his profession. Behaviorists, and other theorists before them, had been in search of a 'holy grail' of sorts – a single theory or law that could explain all human learning. B.F. Skinner, one of the most well-known behaviorists, for example, suggested all learning could be explained according to the principles of operant conditioning. Cognitivists, however, were increasingly recognizing the futility of such a search, emphasizing the different processes – memory, perception, attention, and motivation, to name just a few – that confound human learning. Scandura too made this discovery, even suggesting his theory surpassed other cognitive theories in this respect. He believed that in most present-day information processing theories the questions asked were: how do subjects remember x? How do subjects learn y? Or, solve problem z? It is assumed that there is a unique answer to such questions. Scandura's theory renders such a question meaningless, positing that there are many different possible ways in which any particular subject might perform (Scandura, 1973, p. 7).

In an important way, structural learning theory maintains an alliance of sorts with behaviorists. Scandura believes, for example, that educators and researchers must focus on that which can be observed – in other words, behavior. "Behavior is the only thing that instructional scientists can observe. It is impossible to know all or exactly what any person does or does not know that causes him to behave as he does. Instructional scientists do not have license or means to look inside" (Scandura, 2001, p. 2). Thus, structural learning theory combines what many view as diametrically opposed elements – behaviorism and cognition. Scandura (2001) himself describes it as a cognitive theory, but one with "methods for operationally defining human knowledge in terms of behavior" (p. 3).

Description of the Theory

Scandura (2001), like many of his colleagues, was attempting to answer four basic questions:

• What does it mean to know something, and how can that be represented behaviorally?

• How do learners use and acquire knowledge?

• How does one determine what an individual does and does not know?

• How does knowledge change as learners interact with the environment?

What Does It Mean to Know Something?

In structural learning theory, knowledge must be demonstrated through performance, and all performance (or human behavior) is rule-governed. As he writes in 2001, "the competence required to successfully perform tasks…is represented in structural learning theory in terms of a finite set of higher and lower order rules" (Scandura, 2001, p. 2). What then, is a rule? Rules are conceptualized as ordered triplets (D, O, R), where D refers to the determining properties of a stimuli, and O to the operation which occurs to derive a particular response, referred to as R. Scandura uses the addition algorithm as a concrete example, where a set of whole numbers serves as the stimulus (D), the addition operation represents O, and a different set of whole numbers (sums) serves as the response. Because rules can be devised to account for all behavior, there are many different...

(The entire section is 3810 words.)