Learning Opportunities Provided By Domain-Oriented Design Environments

Information overload, the advent of high-functionality systems, and a climate of rapid technological change have created new problems and challenges for education and training. New instructional approaches are needed to circumvent the difficult problems of coverage (i.e., trying to teach people everything that they may need to know in the future) and obsolescence (i.e., trying to predict what specific knowledge someone will need in the future). Learning on demand is a promising approach to address these problems for the following reasons: (1) it contextualizes learning by allowing it to be integrated into work rather than relegating it to a separate phase; (2) it lets learners see for themselves the usefulness of new knowledge for actual problem situations, thereby increasing the motivation for learning new skills and information; and (3) it makes new information relevant to the task at hand. In educational settings, learning is often restricted to the solution of well-defined problems; our research, in contrast, focuses on environments that support learning in the context of realistic, open-ended, ill-defined problems. In our environments, learners explore information spaces relevant to a self-chosen task at hand. Learning on demand provides learner-centered alternatives to teacher-centered tutoring systems and it augments open-ended, unsupported learning environments by providing advice, assistance, and guidance if needed in breakdown situations. Creating computational environments in support of learning on demand generates a large number of challenging problems. We have developed integrated, domain-oriented design environments supporting learning on demand that satisfy the following requirements: (1) they allow users to create new artifacts and understand existing ones, (2) they tailor instruction to serve the accomplishment of the task at hand, and (3) they do this without disrupting or otherwise interfering with the task.