Matching Jobs, People, and Instructional Content: An Innovative Application of a Latent Semantic Analysis-Based Technology

摘要

New latent semantic analysis (LSA)-based agent software helps to identify required job knowledge, determine which members of the workforce have the knowledge, pinpoint needed retraining content, and maximize training and retraining efficiency. The LSA-based technology extracts semantic information about people, occupations, and task-experience contained in a natural-text databases. The various kinds of information are all represented in the same way in a common semantic space. As a result, the system can match or compare any of these objects with any one or more of the others. To demonstrate and evaluate the system, we analyzed tasks and personnel in three Air Force occupations. We measured the similarity of each airman to each task and estimated how well each airman could replace another. We also demonstrated the potential to match knowledge sub-components needed for new systems with ones contained in training materials and with those possessed by individual airmen. It appears that LSA can successfully characterize tasks, occupations, and personnel and measure the overlap in content between instructional courses covering the full range of tasks performed in many different occupations. Such analyses may suggest where training for different occupations might be combined, where training is lacking, and identify components that may not be needed at all. In some instances it may suggest ways in which occupations might be reorganized to increase training efficiency, improve division of labor efficiencies, or redefine specialties to produce personnel capable of a wider set of tasks and easier reassignment.