Proceedings of the First AAAS Technology Education Research Conference

Cognitive Science: Implications for Technology Education

David Crismond
Georgia Institute of Technology

During the last day of the first AAAS Conference Technology Education Research, Fernando Cajas asked attendees to form small groups and sketch out a tentative research agenda for the field of technology education. This culminating activity seemed to me to be a litmus test for the effectiveness of the workshop generally. The need to catch a flight back to Georgia prevented me from sharing a transparency that I had brought from a talk on a similar theme that I had given at ITEA 99. In this piece of writing, I'll share that slide, unpack it a bit, and present some commentary that I would have liked to have offered during the December meeting.

Research Agenda for Cognitive Science
-- Don Norman [1980]

Education was then seen as the
"engineering branch of cognitive science"

•Belief Systems

The above slide was based on a talk that the late Ann Brown gave to the "Cognitive Science and Education" class that she and her husband, Joseph C. Campione, offered at the Harvard Graduate School of Education in the fall of 1997. Ann gave a bit of "cog-sci" history by speaking of a talk Don Norman gave in 1980 as part of a conference that sought to establish a research agenda for the then emerging field of cognitive science. She offered Norman's ideas as a reasonable list of topics that teachers and educators should keep in mind when creating curricula or designing learning environments for the classroom.

Norman, an electrical engineer and psychologist by training, was a founder of the Cognitive Science Society. Norman eventually left academia in 1993 to become an Apple Fellow, and write about design (e.g., in an essay in C.T. Mitchell's New Thinking in Design: Conversations on theory and practice, 1996, New York: Van Nostrand Reinhold) and how people interact with products (e.g., in The Design of Everyday Things).

The early 1980s was a time when a few budding cognitivists and AI (Artificial Intelligence) researchers who looked at such issues saw education as the "engineering branch of cognitive science." Norman's talk and resultant paper ("Twelve Issues for Cognitive Science," Perspectives on Cognition, 1981, edited by Don Norman, Norwood, NJ: Ablex Publ Corp) aimed to establish some boundaries and carve up the inchoate and ill-defined research/design space of that field into 12 bite-sized chunks.

Back then, Norman spoke of being dissatisfied with the status quo of psychology, including the information-procession and system models in vogue at the time. He was concerned with how the field of cognitive science was paying almost exclusive attention to topics involving pure cognition, and was ignoring topics that muddy the purer forms of social science research, including: environment, personal and cultural history, evolutionary and social contexts, and the emotions. From his vantage point back then, Norman spoke optimistically about how some of those in cognitive science were engaging in conversations about complex phenomena from different disciplines (from the neural to the psychological to the cultural), involving multiple levels of analysis.

The favorable reactions to his talk and paper were for him "a positive sign about the emergence of a new discipline." His recommendation was that "depth-first research" should continue to be done, but not in the rarified atmosphere of the experimental lab. He then presented a list of 12 topics that the field would do well to address in its future research:

1. Belief Systems 5. Interaction 9. Development
2. Consciousness 6. Language 10. Language
3. Development 7. Learning 11. Perception
4. Emotion 8. Memory 12. Thought

The driving force behind me taking the slide to the ITEA 99 and AAAS 99 meetings was an established product design practice of reviewing prior art and doing an informal product history before jumping into doing a product's redesign. I thought it might be inspiring, informative and helpful for members of the technology education community to look at the work of some bright people from a related field framing a research agenda. My hope was that reading such a list might stimulate technology education's lead visionaries, and also perhaps keep them from overlooking a key domain or two that might be critical to keep in mind, if not actively pursue, in the field's mission statements for the next five to ten years.

However, I strongly concurred with a point my colleague Janet Kolodner made during the conference—that the dominant research model technology education should be considering is one formulated by Ann Brown in her writing on "design experiments" (Journal of Learning Sciences, 2 (2), pp. 141–178). In that paper, Ann argues that learning scientists should do research that attempts "to engineer innovative educational environments and simultaneously conduct experimental studies of those innovations" (p. 141). Items in the above research agenda for cognitive science might not be appropriate, since the agenda in part aimed to do more foundation-building research on the nature of complex thought and cognition than technology education need do in the beginning of the 21st century.

What follows is a very brief summary of what Don Norman said about each of the 12 topics he proposed and their relationship to cognitive science. After each italicized summary, I have added my few thoughts on connections and intersections these topics seem to me to have with the field of technology education, for those who are forging a research-oriented pathway.

Belief Systems

A belief system for Norman involved the global view or "world knowledge" of a particular culture, which could influence pure logical thought. He thought that such topics could be studied best from an anthropological perspective that cognitive science seemed open to supporting.

One of the earlier claims from cognitive science was that the thought structures that were being investigated, the mental models and logical structures, were assumed to be shared by people around the world. The work of Richard Nisbett of the University of Michigan, and his colleagues, has shown some significant divides between what he codifies as prototypically Greek versus Chinese cultures of thought. The role of contradiction, analytic versus holistic perspectives, and the "illusion of control" are some key distinctions in thinking from western and eastern countries.

Some research has been done in the UK and Europe on the effects of attitudes and beliefs regarding technology on the efficacy of instruction, and has been reported at ITEA conferences and elsewhere. Gender-related differences towards doing technology-related tasks and understandings of mechanisms and how they work, continue to be reported and studied. People's evolving sensibilities, attitudes and beliefs towards technology will continue to be an issue of study through the new century as technologies evolve and increase their presence in our worlds.


For Norman, the rubric of consciousness included topics like self awareness, attention, how thinking is controlled, and intentions formed.

This topic may be one of the more theoretical titles that the technology education field need not develop as strongly as others may. Even so, given the breakdown of topics regarding consciousness that Norman provides, one can see that in-depth technology education research could involve these subtopics and range from eye-movement studies of designers use to show the patterns of attentional shift, to how people think about mechanisms when designing them. How designers think eventually will certainly become a topic of later brain-imaging studies, including PET scans and other technologies. NSF's reported plan for future research in learning and education includes brain research in one of the four quadrants of its ROLE program. ROLE expects to award $15M for projects in fiscal year 2001—but—because much foundation-building and groundwork need to be done first before higher-order activities like design, or understandings of mechanisms, or the cognition behind novices' and experts' performance in skillful behavior will probably be done years from now.


The adult is qualitatively much more than an oversized child with added experience. Norman indicated that cognitive science needed to address this issue that it had not been attending to previously.

ITEA's and AAAS's national standards for technology both contain recommendations for sequencing of skills, capacities and topics according to the developmental constraints of students. There is a crying need for good developmental studies of design and related technological capabilities. At what age in optimally supported tasks in known domains can children be self-reflective about their design moves? When are kids ready for real collaboration? When are a child's social-perspective-taking skills adequate for them to understand and anticipate users' needs that are different from their own? Does social-perspective taking improve when students do lots of design? Cognitive-developmental models, such as Fischer's skill theory (see Psychology Review, 87, pp. 477–531,) with a strong methodology already in place to help in studying such behavior, would be one good starting point for such research.


Don thought in 1981 that this was a sorely neglected yet central component of human cognition—this was an absence he noted throughout his talk and essay.

Norman called motivation the 13th category of cognitive science study, but did not include it in his list of 12 because it was derived from a number of the more primary categories, including emotion.

Certainly the fields of developmental and cognitive psychology have models and theories that include and give a central position to emotion. The role of human emotion in memory is critical, as Daniel Schacter and others who have studied trauma and recovered memories have written. The popular writings of Daniel Goleman (e.g., Emotional Intelligence) and Antonio Damasio (e.g., Descartes' Error: Emotion, Reason, and the Human Brain, The Feeling of What Happens: Body and Emotion in the Making of Consciousness) has been of interest to design educators (listen to Prof. Woodie Flower's presentation that refers to emotional intelligence in his talk to department heads at the ASME at I believe there is much to discover around the role of emotional intelligence in design, and current interest, both in creative insight and interpersonal interactions (see next category).


Humans act in a social context.

What are the strategies of novice-experts when doing collaborative design; how do groups share ideas online; what forms of exchanges do design and technology tasks take in the classroom?

Language and Perception

Both language and perception were already of central importance to the field of psychology when Norman did his work in 1980. Norman felt these topics did not need his voice added to the chorus of researchers already committed to questions related to language and perception.

There are many modalities of intelligences and commensurate languages that designers use—visual perception is linked to visual communication, and much design communication can be done spatially through simple gestures or by holding up physical objects and models to explain a subtle point in a design plan, as Harrison and Minneman have described. Some domains of technology involve little use of language. Don Schon in his Reflective Practitioner described cases where architects spoke in broken sentences, but communicated effectively through the wave of a hand, a few lines on a paper, and grunts. Don and Jeanne Bamberger at MIT both talk about designers maintaining conversations with materials and ideas.


Learning is a key topic that is "still eluding us" and involves knowledge accumulation, restructuring and re-tuning. Ditto on teaching.

One of the long-standing questions I've faced (and always revisit when I see NSF's Gerhard Salinger) is, "What do people learn when doing design?" Constructivist views of how understandings of technology and design get built need to be studied and articulated. All of the educational practice and curriculum development issues where technology education learning is the key outcome variable of focus are relevant in this category.


Describes how little is really known about memory at the time, and the descriptions of AI-styled brain mechanisms, the "address problem," and the holograph-as-memory metaphor show its age, as do the less anatomically accurate explanations. Still the importance of memory is clearly noted.

Limits of short-term memory are pretty well known, as the continued popularity of George Miller's "magic number 7" reveals. Also, Christopher Alexander's view of the dizzying complexity of the design problem space, and Simon's labeling it as "ill-defined" shows how taxing to memory complexities of design can be. So attempts to use graphic-rich statistical renderings of somewhat constrained design spaces that match our memory capacities, all can be of interest to those in the tech ed field and designers. More relevant to a research agenda may be longitudinal research: what understandings of science and technology last through one's formal education and beyond.


In his paper, Don focused mainly on motor control as expressed as speech and physical movements in his human-information-processing-system model. While fascinated with how the brain accomplishes expert motor control with little apparent conscious thought, his main point was that performance was a long neglected topic that was only then starting to receive appropriate attention.

Motor skills are certainly much more critical and important to the domain of technology education, more so than any other of the traditional subject disciplines taught in school is. Studies on acquiring hands-on skills at using a lathe, versus virtual training via the computer, have been done. Dr. Flowers during his ASME talk referred to research showing the efficacy of hands-on experience with purely mechanical systems where the difficult-to-teach torque-velocity relationships for motors is best learned through hands-on demonstrations rather than through other forms of instruction.


Don called skill a "combination of learning and performance", which might be localized in special areas of the brain. Studies that focus on expert-novices differences, and the key question of how people transit from one level to another, and the learning associated with the movement from novice to expert, fit here.

I have a hard time differentiating between Norman's “performance” and “skill” headings, but think that both titles belong on a short list of keywords for a key area of technology education's future research agenda. Performance assessment aims to identify capacities of skills that curricula aim to develop in students and workers. A topic of personal interest is that of exploring naive-novice differences in domains related to design or technology—which need to be understood both by technology education teachers and by curriculum designers more generally.


Cognitive science certainly is concerned with thought, but needs to look more at thought below conscious awareness. Can thought ever be divorced from specific contexts in which it is performed? Don wondered whether mental models might be closer to the elements from which thinking gets constructed than logical structures.

Even though much of the cognition in design and technological thinking is hidden from view—including a legion of half-remembered cases, tacit understandings, and hunches—improving methods of protocol analysis is still a worthwhile goal. Getting researchers to engage in such labor-intensive work provides them with opportunities to try to peek into the heads of people using and designing a product or device.

How valuable is this suggestion to make a connection between a 20-year-old effort at defining a research agenda of an emerging branch of psychology, and technology education's current efforts? Part of an answer to that question is how relevant and closely related the two fields are. Clearly, psychology's purview is broader, and looks at all sorts of human behavior, both interior thinking and external behavior, from the neuronal and micro-cognitional scale, to high-level symbol performances over a large spans of time.

Technology education's field of view is thankfully narrower, and does not seek, say, a fundamental law of learning, but more relevant expressions of how such a law manifests in the various technology education contexts. Also, Don Norman's list does not touch upon more institution-based issues of assessment and professional development. Still, I hope this paper provides yet another starting point to let others ponder which topics to explore more deeply, and which we might better pass over.