복합적 과제에서 전체과제계열화를 적용한 CTA(Cognitive-Task Analysis)가 전문가의 지식추출에 미치는 영향
The CTA(Cognitive Task Anaysis) applied with whole-task sequencing: the effect on expert knowledge elicitation in complex task
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참고문헌
키워드
초록
본 연구는 지식․기능․태도 요소들의 통합과 부분기능들의 협응을 통해 이루어지는 복합적 과제의 과제분석을 진행할 때, 내용전문가와의 면담을 통한 CTA(Cognitive Task Analysis)를 실시하여, 전문가에게 제공하는 전체과제 계열화 방식에 따라 지식 추출 결과가 어떻게 달라지는지를 알아보는 것을 목적으로 하였다. 또한, 전통적 과제분석과 비교를 통해, 추출된 지식의 내용과 유형이 어떻게 다르며, 지식의 자동화로 인해 발생하는 오류나 누락의 양적인 측면은 어떻게 다른지를 살펴보고자 하였다. 이를 위해 3명의 전문가를 대상으로 Van Merriënboer & Kirschner(2010)가 제시한 복합적 학습(Complex learning)의 전체과제 계열화의 세가지 방식인 조건단순화하기(simplying conditions)와 강조부분변화시키기(emphasis manipulation), 지식수준진전시키기(knowledge progression)를 적용한 CTA면담과 전통적 과제분석 면담을 실시하였다. 추출된 결과물의 내용과 유형을 알아보기 위해 키워드 빈도 분석 및 전사, 읽기, 세그멘팅, 코딩, 주제어 도출의 5단계 내용분석을 실시하였다. 연구 결과, 세가지 전체과제 계열화방식은 해당 과제를 수행하는데 가장 중요한 부분 과제를 공통적으로 도출하여 차이를 나타내지 않았으며, 지식수준진전시키기 방법은 쉬운 조건의 난이도부터 점차 어려운 조건으로 이동해가면서 필요한 문제해결 지식을 이끌어냄으로써 조건별로 수행 활동 및 의사결정 단계, 개념과 절차적 지식이 모두 포함되어 질적, 양적 측면에서 가장 효과적인 계열화 방법임을 알 수 있었다. 또한, 복합적 학습의 전체과제 계열화방식을 적용한 CTA는 전통적 과제분석에 비해 다양한 상황에 따른 문제해결 행동과 의사결정 내용 및 개념적 지식, 성공수행 조건 등을 담은 실제적 지식들이 두드러지게 추출되었으며, 2.1배 많은 양의 지식을 추출하여 지식의 누락이 적음을 알 수 있었다. 따라서, 본 연구는 CTA면담을 통한 지식 추출과정에 전체과제 계열화를 적용하는 것은 문제해결과정에서 필요한 다양하고 실제적인 지식 도출을 가능하게 해 준다는 것을 시사하고 있다.
Abstract
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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