PROCEEDINGS of the HUMAN FACTORS SOCIETY 33rd ANNUAL MEETING-1989 PERSPECTIVES ON COGNITIVE TASK-ANALYSIS: THE STATE OF THE STATE OF THE ART

This study presents a critical analysis of the state of current technologies, methods, and tools used in cognitive task-analysis, Methods for cognitive task-analysis, derived from methods used in cognitive science, are relatively new and have not been systematized. Current methodologies demand considerable time and expertise to conduct properly and often yield data which is difficult to readily translate into practical application. This paper examines these problems and proposes some directions for future research and training program development. Recent advances in cognitive science indicate that cognitive factors (such as problem-solving, decision-making, attention allocation, and memory organization),need to be seriously considered in training design. This has led to the development of new procedures for task analysis, which incorporate into the task analysis a consideration of the cognitive skills, operations, and processes required to perform the task at various skill levels. By examining the change in knowledge structures and mental processes as trainees progress from a novice to expert, cognitive task-analysis aids in the understanding of how a skill is learned and how learning might be expedited or improved. This contrasts with the traditional approach which simply specifies training objectives, materials, and the required behavioral criteria and knowledge needed to meet performance objectives. Traditional-Versus Cognitive-Task Analvsis Cognitive task analysis differs from traditional methods, such as Instructional Systems Development (ISD), in a number of ways. The traditional, behavioral approach to task analysis identifies the skills and knowledge required for each task component, but without any concept of overall knowledge organization. The cognitive approach, however, analyzes knowledge base organization for the whole job, looking at the interrelations among concepts. Both approaches gather data from subject-matter experts; however, the cognitive approach recognizes the importance of skill development and the need to collect data from novices as well. The cognitive approach also recognizes that with regard to SMEs, their conscious knowledge of certain aspects of the task no longer exists, so their explanations for their behavior may not match their actual processing mechanisms. This requires using indirect, experimental procedures to assess expert knowledge. Another difference is that the emphasis of the cognitive approach is on the determination of mental models used in task performance; nothing analogous is contained in traditional methods. The criteria for task segmentation are also different. Traditional task analysis segments the job into behaviorally distinct tasks. Cognitive task analysis decomposes the job into skill -based tasks and consistent task components whichmay or may not be behaviorally or temporally distinct (Ryder, Redding, & Beckschi, 1987). Another issue important in cognitive task analysis is the identification of those component skills which students can learn to perform "automatically", that is, without conscious effort (Fisk & Schneider, 1981). One requirement for the development of "automaticity" is that the task component consistently places the same behavioral demands upon the learner (Fisk & Schneider, 1981). Empirical methods have been developed for identifying automatic components (see Fisk & Schneider, 1981; Knerr, et al., 1985); however, methods for precisely identifying "consistently mapped" components are still being developed. Automaticity is a good example of the value of the cognitive approach to task analysis, as behavioral task segmentation would not segment components for training based upon such considerations. The State of the State of the Art Recently, methods derived from cognitive science have been used to conduct cognitive task analysis in operational settings (e.g., Knerr, et al., 1985; Lesgold, et al., 1986; Rasmussen, 1986). Although these advances appear to be very promising, there are enormous practical problems with the current methods. The current state of the art is very ad hoc; there is no systematized methodology which a technician could use. Problems with data collection, for example, include the necessity for technicians to be thoroughly trained in cognitive science and statistics, as well as the need to collect data from a large number of people who represent different proficiency levels. Furthermore, it is often difficult to apply the findings from a cognitive task analysis to instructional design, because the data produced is often too complex for easy translation into application or is difficult to precisely interpret. The solution to these problems may be threefold. One solution is the development and use of simpler data-collection and