Technology as Media: A Learner Centered Perspective
James A. Levin
Bertram C. Bruce
University of Illinois
Paper presented at the 2001 AERA Meeting, Seattle WA
In Y. Zhao (Ed.), What Teachers Should Know about Technology? Perspectives and Practices. Information Age Press, (pp. 45-51), 2003.
The balance between learning and doing is impacted by new technologies for learning. In this paper, we explore a framework for expertise that emphasizes the power of multiple coordinated representations. We use a learner-centered taxonomy of technology uses for learning as a framework for systematically developing powerful environments for learning. This taxonomy can help us determine the most appropriate technologies for learning, given the educational goals and the available educational resources.
It may be somewhat heretical to raise this question with educators, but why do people learn? Why should people learn? To make this a plausible question, let us enumerate some of the costs of learning.
Costs of learning
There are at least six costs of learning.
Benefits of learning
Now that we have seen the costs of learning, we can of course enumerate the benefits. Learning is necessary for meaningful participation in a complex world, especially one in which human activity is defined by dynamic sociohistorical systems of meaning. It also allows individuals, groups, and societies to adapt to changing contexts much faster and much more effectively than other mechanisms (evolution, for example). Learning is the only way that individuals and groups of individuals can deal with rapid change.
Balance between learning and doing
If we had a society in which nobody learned anything, there would be no way for that society and the individuals in it to adapt to rapid changes in the environment. On the other extreme, if we had a society in which individuals always learned and never performed, then the society would not produce the necessities of life and would collapse. So we can see that there is a need for a balance between learning and doing, in which the costs of learning are outweighed by the benefits of learning.
That balance can be impacted by the media available for learning. As the media change, then the costs of learning can change and the benefits of learning also can change. Let us look at a range of media, and examine in some depth the ways that they impact learners.
A Representational ToolKit model for expertise
What does it mean to have learned something? Over the past twenty years, there have been a series of studies of what distinguishes experts in a domain from novices (Chi, Feltovich, & Glaser, 1981; Chi, Glaser, & Farr, 1988; Larkin, McDermott, Simon, & Simon, 1980). In a number of different domains, the findings are consistentexperts have multiple ways of thinking about the domain, while novices have only one or a few ways. Experts have many different representations of the knowledge domain, they can switch from one representation to another, and they have the meta-knowledge that allows them to know which representation to choose for which task and which representation to switch to while solving the task.
When an expert accomplishes a task smoothly, he or she uses this meta-knowledge to select an appropriate initial representation for the task, typically a global, qualitative representation. The expert proceeds until meta-knowledge indicates the value of switching to another representation, often more detailed and sometimes more quantitative (depending on the domain). Finally, the expert completes the task, after perhaps more switches of representations.
When an expert encounters a problem, he or she uses this meta-knowledge to switch to a different representation that may allow the expert to overcome the problem. If not, then the expert switches to yet another representation, until either the problem is solved or the expert has applied all representations.
One model for thinking about expertise in this way is the Representational Toolkit model (Levin, Stuve, & Jacobson, 1999). An expert craftsman has a range of different tools, and the craftsman has the skills to use each tool and the knowledge of when to use each tool in a sequence to accomplish a task. Similarly, an expert in a knowledge domain has a range of different knowledge representations, and the meta-knowledge of which to use at different times. Debates about which is the "best" representation of a knowledge domain, in this model, may be similar to arguments about which is the "best" tool, a hammer or a saw.
Implications for the design of learning environments
What are the implications of this way of thinking about expertise for designing learning environments? One is that learning environments should be designed to help learners acquire multiple representations of the domain area. A second implication is that learning environments should help learners acquire skill at switching between one representation and another. In other words, the representations should be coordinated. A third implication is that learning environments should be help learners acquire meta-knowledge about these representations, including the meta-knowledge of when to use each representation and when to stop using it and instead switch to another.
One of the strengths of new computer-based technologies is that they can display multiple presentations of knowledge in a domain to the learner at the same time, in a way that is coordinated. In other way, an action by the learner on one representation is automatically reflect in changes in all the other presentations. These multiple coordinated presentations can then support the acquisition of multiple coordinated representations. But how can we systematically think about multiple presentations, given the diversity of knowledge domains?
A taxonomy of uses of technologies for learning
Bruce & Levin (1997; 2003) proposed a taxonomy of uses of technologies for learning based on the natural impulses of a child proposed by John Dewey (1943): inquiry, communication, construction, and expression. In this taxonomy, the diversity of uses of technologies for learning is captured by these four different media for learning, based on the goals of the learner. Could this same framework help us to systematically think about the diversity of knowledge domains and therefore help us to construct learning environments that support the development of expertise? To explore this, let us examine each of the four categories to see.
Media for inquiry
Technologies can be used as media for learning through inquiry. The inquiry-based learning framework focuses on this particular set of uses. Innovative learning approaches in science, mathematics, and engineering have concentrated on this set of uses (Bruce & Levin, 1997).
Inquiry can be used as the basis of a presentation mode. One example is the Inquiry Page (Bruce, 2001; Bruce & Bishop, 2001) http://inquiry.uiuc.edu/ , a Web-based portal of resources focusing on inquiry learning. Many other presentations are inquiry-oriented, especially those designed for science, either for learning or doing. The best examples of these science inquiry environments are the "workbench" web sites, for example, the Biology Workbench http://biology.ncsa.uiuc.edu/ and the Biology Student Workbench http://bioweb.ncsa.uiuc.edu/educwb/ . These were explicitly created to facilitate inquiry processes, including the search of multiple databases and the analysis of retrieved datasets.
Media for communication
Technologies can also be used as media for learning through communication. Teaching, for example, is a specialized form of communication, and many of the existing and new technologies have served to support learning through teaching. There are also other communicative uses that can support learning through communication with other learners or through communication with others outside the current educational system. Many of the innovative approaches to the use of new technologies for learning in the language arts, for example, have focused on media for communication (Bruce & Levin, 2003).
Many innovative uses of technologies for learning have been communication presentations. Most of the "course package" web systems (WebCT, Blackboard, WebBoard) explicitly present a communicative model. There are conferences, homework drop-boxes, announcement sections, test sections, lectures and lecture note, which are all forms of communication. Collaborative learning, especially learning mediated through new technologies, is another cutting-edge approach to learning.
Media for construction
Technologies can be used as media for learning through construction. The current constructivist approaches emphasize knowledge construction. In fact, the new "constructionist" approach explicitly focuses on the construction of external artifacts as important for learning (Kafai & Resnick, 1996). Even for older problem-based learning and project-based learning approaches, construction (either individually or jointly) plays a major role in learning.
Several uses of innovative technologies for learning have taken a "construction set" presentation mode. In this mode, the user is presented with a set of "parts", and constructs entities by selecting parts and combining them to create some new computational object. For example, the whole range of simulation building educational environments function as construction sets, with the person learning through building, whether building a city, a world, an physics experiment, or a ant colony. Once built, these constructions then "run", and the person learns by observing the results of their particular construction.
Media for expression
Technologies can be used as media for learning through expression. This is a fairly frequent use of new technologies in the language arts (Bruce & Levin, 2001; 2003). Much of the focus of theories of writing and other creative arts deals with the development of one's own voice (Graves, 1983), which is a focus on self-expression.
One of the first tools developed for modern graphic interfaces was a series of painting and drawing programs (PaintPot from Xerox PARC; MacPaint from Apple). Computer-based photo editing, music editing, and video editing applications are example of new technologies oriented toward expression.
Evaluation of the taxonomy
We have applied it in several learning domains, including NSF-funded science education projects and language arts software. When we looked at educational software, we saw patterns across curricular areas.
NSF Science Education Projects
Sunburst Language Arts Software
Table 1: Examples of Software Organized by the Taxonomy
Learning technologies in science tended to emphasize inquiry over expression, while those in language arts tended to emphasize communication and expression over inquiry. One issue that this comparison raises is whether there might not be new uses of technologies in the language arts that focus on inquiry and construction, and whether there might not be new uses of technology in science that focus on expression and construction.
Multiple coordinated literacies
The taxonomy presented here is a step toward a framework for thinking about learning, especially when considering the different technological media available to support learning. In this multiple coordinated representations framework for learning, the taxonomy presented here points to an important dimension along which representations of technology uses vary, the "impulses" of the learner. Expertise in the domain of appropriate uses of different media in education would involve an understanding of these different representations and skill at switching among them.
In order to help learners acquire expertise, we need to help them acquire multiple representations of a domain and the meta-knowledge about those representations to choose a productive representation and to switch to a more productive representation during the process of performing a cognitive task or solving a problem. The taxonomy presented here helps teachers and learners select representations and media appropriate for a given goal and a given set of resources, and to switch to a different representation when that is appropriate.
The skills and knowledge associated with each representation constitutes a form of literacy. The taxonomy presented here points to the need for multiple coordinated literacies, as a crucial element of expertise in learning and teaching (Alvermann, 2002; Bruce, 1998; Reinking, McKenna, Labbo, & Kieffer, 1997). The particular point of view represented by this taxonomy focuses on the learner rather than on the technology. The multiple literacies described by the taxonomy are then based on learner impulses, and the expertise characterized by the coordination of these literacies thus characterizes a learner-centered expertise in learning and teaching.
By examining both the costs and benefits of learning, we can see the impact that new technologies for learning have on the learner, the learning environment, and the larger society. The learner-based taxonomy of technology uses for learning when combined with emerging theories of the nature of expertise provide a basis for systematically designing more powerful contexts for learning.
Alvermann, D. (Ed.) (2002). Adolescents and literacies in a digital age. New York: Peter Lang.
Bruce, B. C. (2001). The Inquiry Page: A collaboratory for curricular innovation. Learning Technology, 3(1),
Bruce, B. C., & Bishop, A. P. (2001). Using the web to support inquiry-based language learning. In Y. S. Chuang & P. L. Wang (eds.), Proceedings of ROCMELIA 2001, the Fifth International Conference on Multimedia Language Instruction. Taipei: Crane.
Bruce, B., & Levin, J. (2003). Roles for new technologies in language arts: Inquiry, communication, construction, and expression. In J. Jenson, J. Flood, D. Lapp, & J. Squire (Eds.), The handbook for research on teaching the language arts. Macmillan.
Bruce, B. C., & Levin, J. A. (1997). Educational technology: Media for inquiry, communication, construction, and expression. Journal of Educational Computing Research, 17(1), 79-102.
Bruce, B. C. (1998, September). New literacies. Journal of Adolescent and Adult Literacy, 42(1), 46-49.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
Chi, M. T. H., Glaser, R., & Farr, M. J. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.
Dewey, J. (1943). The child and the curriculum: The school and society. Chicago: University of Chicago Press.
Graves, D. H. (1983). Writing : teachers and children at work. Portsmouth, NH: Heinemann Educational Books.
Kafai, Y. B., & Resnick, M. (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. Mahwah, NJ: Lawrence Erlbaum Associates.
Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
Levin, J., Levin, S. R., & Waddoups, G. (1999). Multiplicity in learning and teaching: A framework for developing innovative online education. Journal of Research on Computing in Education, 32(2), 256-269.
Levin, J. A., Stuve, M. J., & Jacobson, M. J. (1999). Teachers' conceptions of the Internet and the World Wide Web: A representational toolkit as a model of expertise. Journal of Educational Computing Research, 21(1), 1-23.
Reinking, D., McKenna, M., Labbo, L., & Kieffer, R. (Eds.) (1997). Handbook of literacy and technology: Transformations in a post-typographic world (pp. 269-281). Hillsdale, NJ: Lawrence Erlbaum Associates.