복합적 과제에서 전체과제계열화를 적용한 CTA(Cognitive-Task Analysis)가 전문가의 지식추출에 미치는 영향

초록

The purpose of this study is to explore how the knowledge elicitation result can vary according to the whole-task sequencing method provided to the expert when analyzing, through conducting CTA(Cognitive Task Analysis) interviews with experts, a complex task that involves an integration of factors such as knowledge, skills, and attitudes and with a coordination of partial functions. Further, through a comparison with traditional task analysis, this study aimed to examine how the content and type of elicited knowledge differ in quality, and how the errors and omissions arising from knowledge automation are different in quantitative terms. For this purpose, three experts were selected as subject of this study and the first interview was conducted in the traditional method of task analysis. Then, the second interview had applied what Van Merriënboer & Kirschner(2010) suggested; the three approaches of whole-task sequencing CTA in complex learning which includes simplifying conditions, emphasis manipulation, and knowledge progression. In order to investigate the qualitative difference the content and type of the task analysis results, the collected data were analyzed using a keyword frequency analysis and five-stage content analysis that involves transcription, reading, segmenting, coding, and keyword deduction. This study found that the three approaches of whole-task sequencing did not show any difference in extracting the most important constituent skills to perform the task. In particular, the knowledge progression was concluded to be the most effective method in both qualitative and quantitative terms, as it incorporates performance action, decision stages, and conceptual/procedural knowledge in different conditions through evoking the necessary problem-solving knowledge in the process of increasing complexities. Moreover, the whole-task sequencing CTA in complex learning, when compared to the traditional task analysis, was able to elicit practical knowledge in different situations that embodies the problem-solving actions, contents of decision making, conceptual knowledge, and success indicators. Also, it showed that there was less knowledge ommission as it elicited 2.1 time more of knowledge. Therefore, this study suggests that applying the whole-task sequencing method to the knowledge elicitation process of CTA has a great potential to derive diverse and practical knowledge needed in problem solving process.

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