[quoting Hooper, 24 Feb 96] We are currently looking into the pro/cons of using CAI within our College. We have many questions that need to be answered, however I think we have to first evaluate at what level we want to get involved.
- do we buy canned material?
- if we produce our own material should it be produced centrally (Cookbook) or left to the individual faculty to produce at a less technical level?
- What software best suites our requirements?
There have been a number of reasonable responses to this query already, but I'd like to take a slightly different direction. In my opinion, the best advice so far is that which advises Doug and his colleagues to determine the needs, problems, and/or goals they hope to address with CAI (e.g., Terri Buckner's [25 Feb 96] note). Given that Doug intends to start with the "Electrical and Electronics" department, what needs, problems, and/or goals might be found there? One question that comes to my mind is "What kinds or levels of learning are perhaps more effectively or efficiently addressed by some form of CAI (broadly defined, as Martyn Wild suggests, to include virtually any form of interactive learning program, tool, or environment)?"
Kyllonen and Shute (1989) have proposed a taxonomy that represents the spectrum of learning skills with which contemporary cognitive psychologists are concerned. Their taxonomy begins with simple propositions (e.g., stating that Japan sells more electronic products than any other nation), proceeding through schema, rules, general rules, skills, general skills, automatic skills, and finally, mental models (e.g., analyzing the potential of a trade war between Japan and the United States based on an analysis of balance of trade trends and the political agendas of the parties in power in each country). The latter type of learning seems particularly important because mental models are the basis for generalizable problem-solving abilities (Halford, 1993; Rogers, Rutherford, & Bibby, 1992).
What learning skills are not being met by current pedagogical practices in the Electrical and Electronics department at Sir Sandford Fleming College? I have no background in Electrical Engineering (EE), but based on my experience with a different engineering department at another university, one of the major problems that may exist is that the EE students lack the ability to define and solve ill-defined problems. That is, although they may be capable of recalling and perhaps even applying simple propositions, schema, rules, and skills to common, previously encountered problems, their ability to apply higher order mental models of the theories and principles of EE to complex, novel problems may be limited.
If this is a problem in this situation, I'd refer Doug and his colleagues back to the original paper posted on ITForum last Spring by Dave Jonassen called Technology as cognitive tools: Learners as designers . Dave informed us that the people who learn the most from the systematic design of instructional materials are the designers themselves. Following the old adage that the surest way to learn about subject matter is to have to teach it, the process of designing and producing instructional materials enables designers to understand content much more deeply than the students whose thinking may be constrained and controlled by the very materials they are developing. It follows that empowering learners to design and produce their own knowledge representations can be a powerful learning experience. This was the secret behind the success Harel (1991) realized with the fourth graders who developed software to instruct third graders about fractions (which I described in my ITForum paper). It was the fourth graders who really learned fractions!
In his ITForum paper and a forthcoming book (Jonassen, in press), Dave maintains that the real power of computers to improve education will only be realized when technologies are used as cognitive tools rather than as tutors or repositories of information. What kinds of "cognitive tools" might be of use in an EE program? Examples of cognitive tools include (but are not necessarily limited to): databases, spreadsheets, semantic networks, expert systems, multimedia/hypermedia construction software, computer-based conferencing, collaborative knowledge construction environments, computer programming languages, and microworlds.
Rather than recommending that the EE faculty create CAI (a direction I would advise only if there is concern about "their" level of learning), I would suggest that the faculty explore opportunities to engage their students in solving difficult problems using some of the cognitive tools listed above. Just this morning on CNN, I saw a news story about a Canadian education program that seems to involve some elements of this approach. Teams of high school and college students compete to build robots capable of picking up balls and placing them in bins. For six weeks, they write proposals, find sponsors, draft models, build and test prototypes, and eventually bring a robot to the "arena" in which they compete with other robots to retrieve balls on an uneven surface. Although it was difficult to determine from the televised news story, it appeared that the students were using many of the cognitive tools listed above in their efforts to solve this problem.
On the other hand, referring back to the learning taxonomy defined by Kyllonen and Shute (1989), if the EE department is concerned about other levels of learning, for example, schema related to fundamental concepts in electricity, there may be some off-the-shelf software (such as Physic Simulations II: Electromagnetism Academic) that could be useful. At a slightly higher level of learning, a model building program such as Stella II might be an effective tool for helping students to construct schema and/or organize rules and skills into mental models. (Both of these programs are available from Intellimation; Fax in California, USA: 805/968-8899, as already recommended by Gail Fitzgerald.)
To sum up, I would first consider the level of learning you hope to accomplish, identify the pedagogy that is appropriate for that level (behaviorist, constructivist, or as Martyn Wild wisely counsels some blend of learning theories), and then select the tools (computer-based or otherwise) that can help you implement your pedagogical dimensions and attain your learning goals.
Halford, G. S. (1993). Children's understanding: The development of mental models. Hillsdale, NJ: Lawrence Erlbaum.
Harel, I. (Ed.). (1991). Children designers: Interdisciplinary constructions for learning and knowing mathematics in a computer-rich school. Norwood, NJ: Ablex Publishing.
Jonassen, D. H. (in press). Mindtools for schools. New York: Macmillan.
Jonassen, D. (1994). Technology as cognitive tools: Learners as designers [machine-readable data file]. Athens, GA: The University of Georgia, ITForum listserv [http://itech1.coe.uga.edu/faculty/gwilkinson/itforum/paper1/paper1.html].
Kyllonen, P. C., & Shute, V. J. (1989). A taxonomy of learning skills. In P. L. Ackerman, R. J. Sternberg, & R. Glaser (Eds.), Learning and individual differences: Advances in theory and research (pp. 117-163). New York: W. H. Freeman and Company.
Rogers, Y., Rutherford, A., & Bibby, P. A. (Eds.). (1992). Models in the mind: Theory, perspective and application. San Diego: Academic Press.