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경영학 전공자들의 IT 실습 교과목에 대한 인식: 기대가치이론을 중심으로
이준영 ( Junyeong Lee ),신세인 ( Sein Shin ) 한국실천공학교육학회 2021 실천공학교육논문지 Vol.13 No.1
This study attempted to explore the perception of business administration major students on course with IT practice based on expectancy-value theory, and suggested educational implications for improving course with IT practice for non-IT major students. Open-ended survey was conducted via online from 102 students who took course with IT practice, and response data was analyzed through qualitative content analysis. As a result, 4 main categories (perceived difficulty, expectation, value, cost) and 8 subcategories(unfamiliar terms, unfamiliar software, difficulty in mathematical concepts and thinking, low efficacy, intrinsic value, attainment value, utility value, long time required for learning) were revealed, and we provided educational suggestions that help to enhance IT practice learning for business administration major students (non-IT major students). This study has academic implication by empirically examining the perception of business administration major students based on expectancy value theory from the learner perspective, and also has practical implication via suggesting educational implications that could be applied to the substantive educational field based on the revealed students’ perceptions.
딥러닝 오픈소스 프레임워크의 사례연구를 통한 도입 전략 도출
최은주(Eunjoo Choi),이준영(Junyeong Lee),한인구(Ingoo Han) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.4
Many companies on information and communication technology make public their own developed AI technology, for example, Googles TensorFlow, Facebooks PyTorch, Microsofts CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framewo
개인의 흡수 역량이 프로세스 및 제품 혁신에 미치는 영향에 대한 연구
장재승 ( Jae Seung Jang ),이준영 ( Junyeong Lee ),곽찬희 ( Chanhee Kwak ),이희석 ( Heeseok Lee ) 한국지식경영학회 2016 지식경영연구 Vol.17 No.1
Absorptive capacity has been increasingly thought of as a potential source of innovation. From the knowledge management perspective, absorptive capacity is composed of a set of activities dealing with acquisition, assimilation, transformation, and exploitation of external and internal knowledge. This study investigates what relationship the absorptive capacity of individuals who have technical knowledge in the organization has with process innovation and product innovation. Mobile based survey was conducted from the employees working for the largest electronics manufacturer in Korea. The analyzed data was based on 156 responses from 199 participants. The analysis result shows that four phases of absorptive capacity such as acquisition, assimilation, transformation and exploitation have different effects on process innovation and product innovation, respectively. Specifically, transformation is found to be most critical in leading to innovation.
금융 특화 감정분석 모델과 딥러닝 시계열 예측 모델을 활용한 코스피 지수 예측
정가연(Gayeon Jung),이혁제(Hyeokje Lee),이준영(Junyeong Lee),이제혁(Jehyuk Lee) 대한산업공학회 2024 대한산업공학회지 Vol.50 No.4
This paper presents a methodology for predicting the KOSPI index using a news data-based sentiment analysis model and a deep learning-based time series prediction model. The closing price of the KOSPI index was used as a target variable, and macroeconomic indicators such as the gold price and market sentiment indicators such as sentiment scores were used as independent variables. We collected and preprocessed the KOSPI-related news data and used them in calculating the sentiment score by using the title or the summarized article. Subsequently, the KLUE-BERT model-based sentiment score by date and the KoFinBERT model-based sentiment score by date were extracted. LSTM, GRU, CNN-LSTM, and CNN-GRU were used as time series prediction models. As a result of conducting an experiment by combination of variables and models, the best performance was achieved when KLUE-BERT is applied on the summarized article and the CNN-GRU model were used.