Rediscovering Learning: Acquiring Expertise in Real World Problem Solving Tasks

摘要

The importance of continuous learning in high-tech work settings is beingrediscovered as industry and the military services react to external forces such as increasingly complex and rapidly changing equipment systems as well as highly competitive product service markets. Competitiveness in turn dictates a leaner, downsized workforce for the private sector, and diminished defense spending has resulted in dramatic losses of personnel in the Armed Forces. Those who remain are expected to do more, and yet, performance demands routinely override training opportunities. Moreover on the job training that follows either the traditional master apprentice behavioral model or relies heavily on didactic instruction is typically impractical or ineffective. An alternative learning oriented approach that accelerates skill acquisition in high-tech jobs is described here. With this approach cognitive performance models provide both the input to instruction and the desired criterion performance to be attained. The instructional medium is an intelligent tutoring system. A knowledge elicitation approach called the PARI cognitive task analysis methodology is described, along with the cognitive models of performance yielded by this analysis. The performance models in turn inform a coached apprenticeship practice environment embodied in an intelligent computer tutor. The system was recently evaluated in a controlled experiment at three geographically separated Air Force workcenters. Results reveal that the experimental group significantly accelerated their acquisition of problem solving skills when compared to a matched control group; moreover, their newly acquired troubleshooting skills generalized to a novel equipment system.