Scientific Terminology

Feedback on the third edition of the Handbook of Research on Educational Communications and Technology (Spector, Merrill, van Merriënboer & Driscoll, 2008) suggests that there is a need to clarify basic scientific terminology, such as ‘theory’, ‘model’, ‘principle’ and ‘hypothesis’. A discussion at the 2009 AECT Session on “Building the Scientific Mind” led by Jan Visser suggests that additional terms also require clarification – namely, ‘perspective’, ‘approach’, ‘framework’, and ‘implication’. The editors of the 4th edition of the Handbook (Mike Spector, Dave Merrill, M. J. Bishop, and Jan Elen) are discussing having a chapter devoted to the use of these scientific terms in the context of instructional design and educational technology research. Meanwhile, I thought I might venture into this terrain myself to see where I might trip or falter.

These terms are the source of confusion and misunderstanding outside the domain of educational research as well. For example, the word ‘theory’ has an everyday, non-scientific use that is roughly equivalent to ‘supposition’. A person discussing why gasoline prices are high with another might say something like this: “My theory is that the oil companies are being greedy and manipulating prices to maximize profits.” That person could have used ‘supposition’ or ‘guess’ or ‘hunch’ or perhaps ‘belief’. In this case, the word ‘theory’ is used to refer to a particular claim. In science, ‘theory’ typically refers to a body of knowledge represented by a set of related claims. Moreover, a scientific theory typically has implications for what might happen in the future in addition to providing a basis for explanations of observed phenomena.

In short, the word ‘theory’ has a very different meaning when used in the context of scientific inquiry. In science, a theory is generally regarded as a set of well-established statements and principles that are used to explain groups of facts or a range of observed phenomena. The confusion about these two meanings of ‘theory’ is most evident in debates about the theory of evolution. Certain religious groups who advocate creationism use the word ‘theory’ in the informal, non-scientific sense when referring to the theory of evolution. Biologists and other scientists use the word ‘theory’ in the phrase ‘theory of evolution’ in the second sense. In effect, the two groups are talking about different things. It is worth adding that scientists are interested in explaining many observed facts, such as genetic changes in populations of organisms over successive generations and long periods of time. Moreover, two major processes comprise the modern theory of evolution – natural selection and genetic drift or mutation. Evolutionary biologists can explain a large number of observed facts and make predictions with regard to as yet unobserved phenomena. Creationists, on the other hand, are not trying to explain any particular set of facts. Rather, they are advocating a particular religious doctrine (or a specific claim) with regard to the origin of all things.

A further difference is that scientific claims, including scientific theories, are generally subject to refutation; that is to say that the scientist making a claim or defending a theory is, in principle, willing to be shown that the claim is wrong or the theory wrong-headed. The willingness and readiness to be wrong is what makes scientific progress possible (Popper, 1963, 1972). Kuhn (1962) and others argue that scientific theories are quite resistant to change and scientists are not nearly as willing as Popper suggests to embrace refutation of a long-held or well-established theory. In spite of such variations within the scientific community, I shall proceed with the scientific notion of ‘theory’ while acknowledging variations in interpretation.

In these notes, I am interested in the scientific use of ‘theory’ as it pertains to instructional design and educational technology research. Reigeluth (1983) notes that instructional design theory is primarily prescriptive in nature, rather than being descriptive in the way that learning theory is. For example, a cognitive theory of learning might involve a set of related claims about the role of mental models and schema in the development of expertise and understanding. Descriptive claim within a mental model theory of learning might be that (a) people construct internal representations to make sense of new or unexplained phenomena, and (b) these internal representations are created just when needed and are relatively transitory. Both claims are descriptive and could in principle shown to be wrong. An instructional design theory that builds on mental model theory might include, for example, these claims: (a) learners who do not have pre-existing experience with or knowledge in a particular area will progress more rapidly if provided an elaborated version of an expert’s mental model, and (b) learners with significant prior knowledge and experience will be inhibited or slowed down when presented an expert model in the course of designing a solution to a complex problem situation. These claims are prescriptive in the sense that they suggest how best to support learning, and, like descriptive claims, they could turn out to be wrong.

I want to work from the inside out – that is to say I want to consider a claim in educational technology research and then work backwards toward principles, models and theories that might be relevant.

A Claim: Attrition in first-year college calculus courses for non-mathematics majors is high because students do not see any relevance of calculus in their daily lives on in their careers. Note that I am not suggesting that this claim is true; in fact, it assumes facts not yet accepted – namely, that attrition in such courses is higher than in other courses. The instructional design claim that follows this claim would be something like this: First-year college calculus courses for non-mathematics majors will have a reduced attrition rate if issues of relevance are addressed early and throughout the course.

A Principle: What body of relevant instructional design knowledge might be relevant? What existing instructional design principles could be invoked to support the claim that devoting explicit time to issues of relevance will improve attrition? One might cite the first of Gagné’s (1985) nine events of instruction – namely, gain and maintain the attention of the learner in order to make learning effective. Citing relevance of what is to be learned might be one way to do this. Another principle that might be cited could be one of Merrill’s (2002) first principles of instruction – namely, help the learner integrate what has been learned into daily activities. Both principles suggest what can be done to help make learning activities more effective. As with other principles, either or both may turn out not to make a significant difference in terms of learning outcomes in particular contexts.

A Model: A model goes beyond principles and might be conceived of a bridge between a theory and a set of principles. A model can guide the articulation and instantiation of principles within the context of a particular theory. Both of the principles cited previously have associated models. Gagné (1985) presented nine events and subsequently articulated a model for implementing those events (Gagné, 1993). In that model, Gagné argued that the nine events did not need to occur in any particular order and they could often be grouped into three phases of instruction and treated together (set-up phase, primary presentation phase, resolution phase). Likewise, Merrill’s principle is one of five that include centering instruction around problems, activating prior knowledge (of individual or groups of learners), demonstrating new knowledge, applying the new knowledge to practical problems (whole tasks) with opportunities to practice with feedback, and helping learners integrate new knowledge in their daily lives or professional activities. In both cases, the individual principles are parts of a prescriptive model intended to guide the creation of effective learning. These sets of principles, with their interconnections and relationships one to another, comprise a model. At least that represents one kind of instructional model.

A Theory: The difference models and theories may be difficult to establish in the domain of instructional design and educational technology. For example, cognitive apprenticeship (Collins, 1991) is sometimes called an instructional design theory, sometimes an instructional design model, and sometimes an instructional design method. I am inclined to think of cognitive apprenticeship as another model comprised of a set of principles – that is to say that I view cognitive apprenticeship as more akin to Gagné’s nine events and Merrill’s first principles. A theory that might be associated with these three models (Gagné’s nine events, Merrill’s first principles, and Collins’ cognitive apprenticeship) might be situated theory (Lave & Wenger, 1990). Situated learning postulates, in one sense, that learning that is situated within a meaningful problem solving context, will be more effective than learning that is disassociated from meaningful problem solving contexts. This is a prescriptive theory. It is closely associated with and linked to a descriptive theory about learning – namely, the notion that people create internal representations in order to make sense of puzzling situations and new experiences. In other words, meaning is created or constructed in the context of specific situations. Meaning is context sensitive, in that sense. My general point is that a prescriptive instructional design theory could and probably should be motivated by a closely associated and established descriptive theory of learning.

Concluding Remarks
While these remarks may seem focused on individual words, my concern is not with particular words but, rather, with the thinking associated with scientific inquiry in instructional design and educational technology. My elaboration of these terms is probably naïve and perhaps wrongheaded on key points. I hope others will provide insights and improved representations. I do believe that we need to be careful in our use of scientific terminology. An instructional design theory should represent a set of well-established principles which can be used to generate prescriptions for designing effective learning support in a variety of circumstances. There may well exist many instructional design models (Andrews and Goodson, 1980), but one would expect there to be only a small number of instructional design theories. We need to take the scientific aspects of our instructional design and educational technologies activities seriously. I believe this because I believe that progress in instructional design and educational technology will depend on adopting principled, evidence-based approaches rather than relying on loosely held beliefs and advocating positions that are in vogue.

References
Andrews, D. H., & Goodson, L. A. (1980). A comparative analysis of models of instructional design. Journal of Instructional Development, 3(4), 2-16.

Collins, A. (1991). Cognitive apprenticeship and instructional technology. In L. Idol & B.F. Jones (Eds.), Educational values and cognitive instruction: Implication for reform (pp. 121-138). Hillsdale, NJ: Lawrence Erlbaum Associates.

Gagné, R. M. (1985). The conditions of learning (4th ed.) New York: Holt, Rinehart, & Winston.

Gagné, R. M. (1993). Computer-based instructional guidance. In J. M. Spector, M. C. Polson, & D. J. Muraida (Eds.), Automated instructional design: Concepts and issues (pp. 133-146). Englewood Cliffs, NJ: Educational Technology Publications.

Lave, J., & Wenger, E. (1990). Situated learning: Legitimate periperal participation. Cambridge, UK: Cambridge University Press.

Merrill, M. D. (2002). First principles of instruction. Educational Technology Research & Development, 50(3), 43-59.

Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.

Popper, K. (1963). Conjectures and refutations: The growth of scientific knowledge. London: Routledge.

Popper, K. (1972). Objective knowledge: An evolutionary approach. Oxford, UK: Clarendon Press.

Reigeluth, C. M. (Ed.) (1983). Instructional-design theories and models: An overview of their current status. Hillsdale, NJ: Lawrence Erlbaum Associates.

Spector, J. M., Merrill, M. D., van Merriënboer, J. J. G., & Driscoll, M. (Eds.) (2008). Handbook of research on educational communications and technology (3rd ed.). New York: Routledge.