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Developing English Language Teaching Materials Using a Text-driven Approach
박혜옥 중앙대학교 외국학연구소 2014 외국학연구 Vol.- No.27
This study explores a group of eighteen graduate school students' experiences with a text-driven approach during an English materials development class conducted in the spring semester of 2013 in Seoul. The research was designed to raise awareness of the effectiveness of the text-driven approach suggested by a number of researchers as an effective method of developing language teaching materials. Its emphasis rests on personal responses to texts by using various multidimensional mental representations such as visualization and inner speech. Unlike conventional reading activities, a text-driven approach does not check readers' comprehension by asking true- or-false types of questions. Instead, it provides various opportunities to experience the text with the reader's experiences and allows the readers to take advantage of all the knowledge they have. Despite its benefits for English second language learners, however, many Korean ELT practitioners are unaware of its very existence. The researcher, therefore, designed a seven-week project to introduce and to raise awareness of the approach to the graduate students who took the ELT materials development class. The participants of this study experienced the whole process of writing text-driven materials, from brainstorming topics for text writing to assessing materials developed by peers. Questionnaires, semi-structured interviews, and materials developed by the students were used to collect the data. The results of the questionnaires revealed that the students not only raised the awareness of the approach, but also recognized its differentiating features from conventional reading activities and its possible effectiveness as an ELT materials development method. The students' semi-structured interviews and the materials represented how much they had learnt about the approach from the project. Longitudinal classroom research where the text-driven material is taught was suggested as continuing research in order to localize the approach in the Korean context.
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텍스트마이닝을 활용한 빅데이터 기반의 디지털 트랜스포메이션 연구동향 파악
김민준 (사)한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.10
A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation. 빅데이터 기반의 디지털 트랜스포메이션은 데이터 및 데이터 관련 기술을 통해 기업의 성과 향상, 조직 변화, 사회 공헌 등의 목적 달성을 위해 수행하는 혁신적 프로세스를 의미한다. 성공적인 빅데이터 기반의 디지털 트랜스포메이션을 위해서는 관련 연구 현황, 주요 연구토픽, 주요 연구토픽 간의 관계를 이해하는 것이 필수적이다. 그러나 여러 연구들의 서로 다른 관점 및 이들 간 연계 가능성에 대해 이해하려는 노력은 아직 미진하다. 본 논문은 텍스트마이닝을 활용하여 관련 연구동향을 분석하고, 여러 연구의 다양한 관점을 통합적으로 이해하기 위한 기반 마련을 시도해보았다. Web of Science Core Collection에서 추출한 439편의 논문을 분석하여, 10개의 주요 연구토픽을 도출하였고, 이들 간의 관계를 분석하였다. 본 연구의 결과가 빅데이터 기반의 디지털 트랜스포메이션에 대한 통합적인 이해를 촉진하고, 성공을 위한 방향성 모색에 기여할 것으로 기대한다.
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김민준 한국품질경영학회 2023 품질경영학회지 Vol.51 No.3
Purpose: The acceleration of technological innovation, specifically Industry 4.0, has triggered the emergence of a quality management paradigm known as Quality 4.0. This study aims to provide a systematic overview of dispersed studies on Quality 4.0 across various disciplines and to stimulate further academic discussions and industrial transformations. Methods: Text mining and machine learning approaches are applied to learn and identify key research topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview of Quality 4.0. Results: 1) A total of 27 key research topics were identified based on the analysis of 1234 research papers related to Quality 4.0. 2) A relationship among the 27 key research topics was identified. 3) A multilevel framework consisting of technological enablers, business methods and strategies, goals, application industries of Quality 4.0 was developed. 4) The trends of key research topics was analyzed. Conclusion: The identification of 27 key research topics and the development of the Quality 4.0 framework contribute to a better understanding of Quality 4.0. This research lays the groundwork for future academic and industrial advancements in the field and encourages further discussions and transformations within the industry.
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