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
•Consciousness
•Development
•Emotion |
•Interaction
•Language
•Learning
•Memory |
•Perception
•Performance
•Skill
•Thought
|
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.
Consciousness
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.
Development
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.
Emotion
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 http://hitchcock.dlt.asu.edu/media2/cresmet/flowers/).
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).
Interaction
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
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.
Memory
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.
Performance
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.
Skill
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.
Thought
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.
