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The Current State of Instructional Technology

Peter Rich
Department of Educational Psychology and Instructional Technology, University of Georgia


“What do you do?”

“I’m an instructional technologist.”

“So, what’s that? Do you make technology for educational purposes?

How do we answer this question? The perception of the majority of those outside the field of instructional technology (IT) is that our focus is on technology rather than on learning (Seels & Richey, 1994). While technology is an important component of many IT research designs, most instructional technologists will report that they are more focused on how to maximize learning via the mediating factors of different tools. And, as the name indicates, we refer to these tools as “technology.”

Hooper and Rieber (1995), as stated in the previous chapter, draw a line between product and idea technologies. The layman’s perception of IT is that we research, design and develop product technologies. While these are an essential part of IT research, technological tools are merely the laboratory for researchers to experiment with different idea technologies to see what works and in what ways. Roy D. Pea pointed out the futility of concentrating IT research merely around tools as early as 1985: “We bet on the irrelevance of our work if we rely on off-the-shelf software and limit ourselves to describing what happens when it is introduced to the classroom” (p. 178). Seymour Papert (1987) distinguishes between the product-oriented and idea-oriented attitude in defense of his own product technology (LOGO) to promote his ideas (known as "microworlds"). He refers to the former as “technocentrism”, the tendency to allot egocentric centrality to a technical object (p.23). He recommends instead “computer criticism”, which, like literary criticism, is a constructive feedback that should center on the deeper conceptual make-up of a paper and not on its superficial grammatical merits.

Although it is easy to state that instructional technologists should focus on idea and not product technologies, it is not clear how to separate the two, or if we even should. The “great debate” within the field of technology was sparked between Richard Clarke (1984) and Robert Kozma (1991) and continues today, to an extent. Clarke held that past research in IT was too technocentric (to use Papert’s terminology). He argued that focusing on technology when studying learning outcomes is akin to attributing nutritional outcomes to the truck that delivers the groceries. Perhaps Papert put it more bluntly:

Does wood produce good houses? If I built a house out of wood and it fell down, would this show that wood does not produce good houses? Do hammers and saws produce good furniture? These betray themselves as technocentric questions by ignoring people and the elements only people can introduce: skill, design, aesthetics. (1987, p. 24)

Kozma rebutted with the idea that the affordances of certain product technologies lend or restrict themselves to specific idea technologies; the two are so intricately woven that the one cannot, or should not, be separated from the other. To do so would be to de-contextualize and therefore, change the nature of the questions one can ask. Therefore, it is impossible to separate the one from the other.

Regardless of one’s perspective, it is important to note that both product and idea technologies co-exist. IT researchers, though, will most likely report that their main focus is on learning outcomes and therefore on idea technologies. Product technologies, as important as they are, provide a means to an end and are not the end themselves. A perfunctory review of the articles in Educational Technology Research & Development in 2004—arguably the most competitive journal within IT—reveals a focus not on specific technologies, but rather on the conceptual questions that can be asked when employing particular theories, and the technologies used to test these assumptions. With this in mind, then, our present challenge is to examine how idea technologies come about.

Informing Theories

Idea technologies are informed by different theoretical perspectives, which are constantly developing, opposing, complementing and merging into one another. As behaviorist principles branched out into all fields of inquiry, researchers found they could not adequately explain all aspects of every domain from its perspective. Consequently, there was a “cognitive revolution” (Bruning, Schraw, Norby, & Ronning, 2004) that challenged the tendency of behaviorism to break everything into observable practices. Cognitivism instead focused on the mental processes that behaviorism claimed were explainable only by observable behavior. Many theories have arisen out of this perspective that presently encompass the bulk of research within IT.

The main outcome has been the development of constructivism, the notion that knowledge is not deposited into individuals like money in a bank (Friere), but rather that the individual constructs his or her own knowledge by connecting new information with prior experiences. Another key tenet of constructivism is that the learner is in control of the knowledge he or she receives. Constructivism has taken on its own identity within the field of education to the extent that most modern theoretical practices not directly attributable to behaviorism take some form of constructivism. This chapter will concern itself with these theories and how they are used within the field of instructional technology.

The term constructivism has become so widely used that simply stating that “theory X” adheres to constructivist principles actually reveals very little about that theory other than the two tenets described above. Most categories of constructivism can be broken into two camps—internally mediated constructivism and socially mediated constructivism (Brown et al., 2004). We will first present the internally mediated forms, followed by interactionist, or social, forms of constructivism [1].

Discovery Learning

“Discovery Learning”, introduced by Jerome Bruner (an educational psychologist), is an early constructivist approach that draws from Piaget’s work in developmental theory. Bruner proposed that, “children, as they grow, must acquire ways of representing the recurrent regularities in their environment” (Bruner, 1964, p. 13). Discovery Learning is a systematic way of discovering not what is out in the world, but what is in one’s own head and making connections to the aforementioned “recurrent regularities.” It is a deductive process that allows learners to project new knowledge from that which they already possess.

Initially, many educators took the term “discovery” as a phenomenon in and of itself and deemed anything that a child “discovered” as relevant. Bruner opposed this application of his theory stating that discovery is a systematic, iterative process of searching and finding. “Potshotters”—students who merely used “trial-and-error” approaches—lack connectivity and organization in their ability to gather information. It is also important to note that Discovery Learning does not indicate submersing a learner in an environment beyond his or her current state of knowledge. Rather, Bruner, consistent with developmental psychology, held that learners pass through three stages of understanding, moving from lower to higher order thinking representations (enactive » iconic » symbolic). Discovery learning differs from Piaget’s developmental theories in that these stages are not necessarily attached to any specific age progression. The educator’s role in Discovery learning, then, is to provide models that coincide with a learner’s current state of understanding and guide them in successfully constructing their own systematic learning.

One point on which both Piaget and Bruner agreed was the importance of creating disequilibrium, or cognitive conflict, in a learner. The use of such contradictions bring into sharp contrast what a student believes to be true with what s/he observes. As students confront these contradictions, they build new knowledge via discovery of why the contradiction occurred, constructing a progressively greater understanding of the world around them through these personal experiences.


Seymour Papert offered another developmental, yet more open-ended approach to constructivist learning. Microworlds, like discovery learning, propose that it is key to provide students the necessary resources to “build and refine their own knowledge in personal and meaningful ways” (Rieber 2004, p. 583). In contrast, however, microworlds do not presuppose that learners move from one true position to a more advanced true position. Rather, learners accept then later confront and dismiss many false notions.

The unorthodox theories of young children are not deficiencies or cognitive gaps, they serve as ways of flexing cognitive muscles, of developing and working through the necessary skills needed for more orthodox theorizing (Papert, 1980, p.133).

Micorworlds are subsets of reality that provide the tools whereby learners can experiment with and construct their own knowledge. Although there are boundaries set within these microworlds, they allow for active exploration “sufficiently rich for significant discovery” (Papert, 1980). Clements (1999) called microworlds the “playground for the mind." However, these “playgrounds” are domain-specific. They embrace the specific principles indicative of a certain curriculum.

It is important to note that microworlds are not simulations. Although simulations attempt to recreate a real world, they do not allow for the cognitive experimentation necessary to construct and test alternate theories. In a simulation, there is always a “right” answer, and this is rewarded while “wrong” answers are punished. Papert asserts that insistent dependence on the right answer stifles a learner’s ability and disposition to make and test hypotheses.

The original microworld developed by Papert was LOGO, a programming environment where children could experiment with mathematical principles to make a “turtle” act how they told it to. More modern microworlds include Geometer’s Sktechpad, a mathematical microworld that allows for the creation and manipulation of two and three dimensional shapes, and GenScope , an investigative tool for making and testing conjectures in order to discover genetic principles. For a more in depth discussion on microworlds and these concepts, see:

  • Rieber, L.P. (2004). Microworlds. In D. Jonassen (Ed.), Handbook of research for educational communications and technology (2nd ed.) (pp. 583-603). Mahwah, NJ: Lawrence Erlbaum Associates.

Anchored Instruction

Anchord instruction is a type of problem-based learning. John Bransford and the Cognition and Technology Group at Vanderbilt developed Anchored instruction from Dewey’s theme-based learning and Gragg’s (1940) case-based instruction. They argue that while case-based instruction provides a microcontext for studying phenomena, anchored instruction provides both micro and macro contexts. The former focus on a particular subset of a larger problem, while the latter “enable the exploration of a problem space for extended periods of time from many perspectives” (CTGV, 1990, p.3). Anchors are a specific topic around which all investigations center. For example, in the Young Sherlock Holmes Project (CGTV, 1991), a control group and an experimental anchored instruction group of middle school students both received excellent instruction with the goal of learning to write interesting stories. The control group was taught using a basal reading approach that takes examples of different stories to illustrate different points (e.g. protagonist, setting, plot development, etc.), whereas the anchored instruction group’s instruction was all anchored around The Young Sherlock Holmes (main anchor) and Oliver Twist (secondary, embedded, anchor). The CGTV reports that the anchored group delved into the writing tasks at a far greater level and consequently produced superior stories than the control group. Anchoring instruction led the group to research the time period (epoch) and identify specific facts relating to the anchors and anachronisms in the stories themselves.

Besides those already mentioned, there are two principle aspects of anchored instruction: they allow students to revisit already “taught” material time after time, and they include embedded teaching. These embedded experiences set anchored instruction apart from other internally constructivist methods. Instead of learning being a progression of discovery from one idea to another (whether true or false), anchored instruction immerses the learner in an authentic “every-day” environment rich with resources that may or may not be essential to solving the problem at hand, potentially providing “last step” information right from the very start. As students employ problem-solving strategies, instruction is embedded that helps them decide/confirm if they are on the right path and shows to them a piece of or path to a solution. The CGTV refers to this as “just-in-time teaching” (1993). True to constructivist principles, there are a myriad of “right” paths to any given solution.

Interactional (social) Constructivism

The idea of interacting variables is not new in education. It was discussed and introduced over 100 years ago as Dewey, Mead and others discussed the interactional nature of learning. Presently, though, most instructional technologists accredit social interactionist theories to L.S. Vygotsky, a soviet psychologist. Although Vygotsky’s works were originally written circa 1930, he died young and his works were not translated into English until 1978. His work, which had continued to develop under the auspices of Activity Theory in soviet circles, quickly worked its way into American educational practices. Whereas internal constructivism developed in contrast to behaviorism, social-interactionist theories shared much in common with already developed and researched constructivism; specifically, the focus on authentic, real-world contexts for learning. They differ in that the emphasis placed on interactionist theories is that knowledge is mediated through the use of tools. These tools can take the form of symbols or even people. Vygotsky presented the idea of the zone of proximal development (ZPD, see Figure 2.1).

Figure 2.1. This image depicts the Zone of Proximal Development described by Vygostky. It shows green, yellow and red regions. The green area is on the left and depicts things a person can do on their own without assistance. The yellow region is in the center and depicts the zone of development, the area of the graph where the person can only perform with assistance from a more knowledgeable other. The red zone is on the right and it depicts those things that a person cannot do even with assistance.

This is a zone in which the learner is extended beyond her own capabilities, but can reach the desired end through the aid of a more knowledgeable other (Vygotsky, 1987). The learner leaves the zone when he or she can perform the task without help or when no amount of intervention can lead to success. Learning is supposed to occur within this zone, emphasizing the need for interaction between the individual and a “more knowledgeable other”. There have been a number of theories that have either taken some aspect of Vygotsky’s ideas and those that continued to be developed through Soviet Activity Theorists (e.g. tools, community, etc.) or have merged them with matured internal constructivist notions. We present a few of the more dominant theories that are currently being explored within IT.

Situated Cognition

One might well point to an article by Brown, Collins and Duguid (1989) as situated cognition’s induction into IT (although researchers had begun experimenting with it prior to the article’s publication). Situated Cognition argues that conceptual knowledge cannot (and should not) be abstracted from its original activity. Consider a translator asked to translate a single word. She will inevitably ask for the sentence, or context, surrounding that word. Without this context, the translator can only guess at the desired meaning, resulting in an unsatisfactory definition. Giving the context situates the desired word. This is why automated translators are still not widely used—they directly translate word-for-word, using the first dictionary entry encountered. According to Brown, Collins and Duguid, all knowledge is like language.

Pivotal to this concept is the notion that concepts are like tools.

Tools share several significant features with knowledge: they can only be fully understood through use, and using them entails both changing the user’s view of the world and adopting the belief system of the culture in which they are used. (Brown, Collins, & Duguid, 1989).

Learning emerges out of situational use of these tools (Greeno, 1989). Lave and Wenger (1988) offer what has become one of the classic examples describing situated cognition in pointing to the example of a weight-watcher participant being presented with the problem of only being allowed 3/4 of the 2/3 portion of cottage cheese he had. Instead of working out the mathematic algorithm taught in school, he formed the 2/3 cup into a pile, split it into four “equal” parts and took three of the four quarters. This is much like the example of Brazilian children not performing well on addition problems in school, but adeptly doing so on the streets where they sold produce (Carraher, Carraher, & Schliemann, 1985).

The teacher’s task, then, is to situate learning in as real of a context as possible. Furthermore, as teachers and students work on a problem, learning occurs as they negotiate the answer between them (or between students, or whatever the group) (Greeno, 1998). As teachers engage learners in situated contexts, learners understand better the context and applicability of the concepts in question. Also important to note is that learning is a process of negotiation. As learners engage in situated contexts, the context itself relays information not possible to procure in any other form. In instructional technology this is best witnessed by the idea of rapid prototyping. In rapid prototyping, designers develop different iterations of the final product (Allen, 2003). Each iteration is a successive approximation of the final product. This product cannot reach its final state without the feedback of the interim products from each prior iteration.

Learning Communities and Legitimate Peripheral Participation

Cognitive apprenticeships are perhaps the central idea linking situated cognition and learning communities.

Cognitive apprenticeships should not be exactly the same as trade apprenticeships. Rather, there should be continual interaction between the two communities of practice such that the intern or apprentice is afforded opportunities to critically reflect on what he or she is learning. (Driscoll, 2000, p. 174)

Like cognitive apprenticeships, Learning communities bring novices and experts together to solve common, contextual problems. The difference is that there are more participants and less attention given to novices. Lave and Wenger propose the idea of Legitimate Peripheral Participation (LPP) as a sub-field of learning communities. From this perspective, a learner enters the fringes of a community and is slowly inducted as a participant. “The theory of [LPP] depicts learning and development primarily as a one-way movement from the periphery, occupied by novices, to the center, inhabited by experienced masters of the given practice” (Engeström & Miettinen, 1999). Much like a cognitive apprenticeship (Collins, Hawkins, & Carver, 1991), the tasks a learner participates in move from observational and simple to participatory and complex. This is not to say that novices do not participate, but simply that their participation is akin to that of a novice carpenter. They may be allowed to perform some of the more simple tasks under the direction of the expert. They will observe other tasks. As a learner moves toward the center of the community she assumes increasing responsibility for the community’s tasks.

Examples of LPP in IT are embodied in online learning (Gordin, Gomez, Pea & Fishman, 1996), adult education (Wenger, 1998), and business (Buffington, 2003). These environments emphasize both individual and collective learning in which members are working toward a unified, authentic goal (Bielaczyc and Collins, 1999). Learning communities foster automomy and identity in learners. Not only do learners own part of the process of making the product, each member lends him or herself to part of the process, thereby creating an individual and communal identity via personal contribution and group product.

Distributed Cognition

Distributed cognition breaks down the idea of ownership, or residence of knowledge, even further than situated cognition. Whereas situated cognition emphasizes activity, distributed cognition focuses on the tools and mediating factors. Knowledge does not reside in any one element, but rather across the continuum of participating elements and the configuration of these over and against each other. “Intelligence is accomplished rather than possessed” (Pea, 1993). This idea supports recent notions on resource-based learning environments (Hill & Hannafin, 2001). As artifacts work together, they create a synergetic whole (i.e., the product is greater than the sum of the parts). Particularly important from this perspective is not so much the product (although participants are working toward one common goal) but the communicative process of reaching that goal.

Distributed multimedia learning environments…serve to enrich the capabilities of participants in a communication to express what they are thinking about, to capture traces of that thought in new forms of representation, and to jointly work to create new artifacts. But more important, they enable us to see what we are building together, as participants in a learning conversation in which the transformative nature of communication is in the foreground, so that we can co-construct a new understanding or other learning product. (Pea, 1994, p. 206).

Distributed cognition differs from situated cognition in its preoccupation with cognitive aspects, effectively integrating the social with the cognitive. The advantage of such systems is that they “make possible the acquisition of knowledge that no single person, or a group of people without instruments, could possibly acquire” (Giere & Moffatt, 2003).

Conclusion: which theory is best?

There a myriad of theories informing our present ideas of instructional technology. Although some technologies lend themselves more readily to others, it should be clear that any one theory may be applied to a number of products. This is the beauty of experimentation. As you set about to study instructional technology you will need to find a focus in one of these (or perhaps another) theoretical approach. While practitioners may mix-and-match theories, researchers often choose one theory to experiment with. This creates a depth of understanding that allows researchers to fully comprehend the applicability, advantages and restrictions of that theory. Snellbecker (1999) re-emphasizes Hall & Lindzey’s approach that “the student should, once he has surveyed the available theories…adopt an intolerant and affectionate acceptance of a particular theoretical position without reservation…wallow in it, revel in it, absorb it, learn it thoroughly, and think that it is the best possible way” (1957). Although this may seem fanatic the intent is that, though espousing multiple beliefs, focusing on one gives the researcher depth of understanding and ground to stand on. It should be noted, however, that theories are theories, “they are tentative hypotheses, tried out to see whether they work, and all experimental corroboration is simply the result of tests undertaken in a critical spirit, in an attempt to find out where our theories err” (Popper, 1957). Thus, we should be open to debate and civil when confronting those inquiring about other theories. It is easy to assume a defensive stance of a single viewpoint, but to do so maliciously hurts scholarship more than it can ever help, dividing scholars whose opposing viewpoints could possibly otherwise result in deep, critical insights and original, more robust theories. Let us, then, as we consider our ideas, work together with those who think differently. The results will speak for themselves.


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1. Although there are a myriad of constructivist theories, we are only concerned with those that have been considerably used and experimented with in the field of instructional technology. There are many more versions of constructivism that do not appear here. For discussion on these, please refer to a href="">Emerging Perspectives on Learning, Teaching, and Technology</a>.

About the Author

At the writing of this chapter, Peter J. Rich had completed his first year of doctoral studies in the Instructional Technology program at the University of Georgia (UGA). Prior to his studies at UGA, Peter worked as a project consultant for the Provo School District and Brigham Young University (BYU). Peter received his bachelors in Spanish and TESOL at BYU.


APA Citation: Rich, P. (2007). The current state of Instructional Technology. In M. K. Barbour & M. Orey (Eds.), The Foundations of Instructional Technology. Retrieved <insert date>, from