• 유통과학연구
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• 제22권5호
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• pp.81-91
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• 2024

Purpose: Edutech, which is the application of information and communication technology to education, is being introduced in various ways across all levels, from primary and secondary education to lifelong education. The purpose of this study was to present metadata about Edutech tools to provide the method for providing various Edutech tools. Research design, data, and methodology: To achieve the research purpose, the necessary elements for the metadata of Edutech tools were first derived based on a literature review. A focus group interview (FGI) with experts was conducted to gather opinions on the developed metadata, further validating its appropriateness. Results: The metadata area consisted of "Basic Information", "Product Information", and "Utilization Information". The "Basic Information" section had 9 items, "Product Information" had 8 items, and "Utilization Information" was presented with 4 items. Conclusions: This study proposed metadata for Edutech tools, which can be utilized to develop distribution system to proliferate and harness various Edutech tools in the educational setting. For the future establishment of an Edutech tool distribution system based on this metadata, it's imperative to operate a credible platform to ensure a stable distribution framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• 한국항행학회논문지
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• 제24권3호
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• pp.238-244
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• 2020

스마트 시대를 살아가는 요즘 스마트폰을 전 국민이 사용하는 상황으로 봐도 무방할 정도로 빠른 발전과 보급이 진행되었으며, 스마트폰 사용으로 국내 미디어 콘텐츠 이용을 위한 필수 매체로 자리 잡아 성별, 연령, 지역 등에 상관없이 수많은 사람이 다양한 콘텐츠를 이용하고 있다. 최근 다양한 매체를 통해 온라인에서 학습을 위한 동영상 콘텐츠 소비가 이루어지고 있으며, 이는 학습자들이 온라인에서 동영상 콘텐츠를 활용하여 학습에 활용한다는 것을 알 수 있다. 기존 연구에서는 콘텐츠 유형에 따른 만족도 연구를 진행하였으며, 학습 콘텐츠 자체를 평가하여 학습자들에게 제공하는 방법에 대한 연구가 이루어 지지 않아 개선 방안이 필요하다고 판단하였다. 본 논문에서는 학습을 위한 동영상 콘텐츠 제공 방식 동향과 양질의 학습 콘텐츠를 제공하기 위한 개선 방안으로 학습 콘텐츠 자체의 평가와 리뷰를 통해 시스템을 제안하려고 한다.

• 유통과학연구
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• 제18권11호
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• pp.15-22
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• 2020

Purpose: A marketing strategy for private institutions requires marketers to leverage consumer behaviors and educational psychologies when advertising and promoting product portfolios. Indeed, understanding consumers can make marketers more effective, and the purpose of this research is to tackle private institutions' education marketing by combining marketing theories and learning theories. Research design, data and methodology: The content analysis used in this study will be suitable because there exists numerous prior studies regarding marketing strategy and educational theories. Therefore, the current author could obtain and collect adequate textual facts from much of the literature review. Results: Marketing strategies that are mixed with educational theories increase consumer enrolment due to perceived usefulness, and this implies that an adequate marketing model could help improve sustainability and income as a result of enrollment in private educational institutions. The research also identified that marketing is connected to psychology and that marketers can exploit educational and psychological theories to increase successful enrolment in private educational institutions. Conclusions: Most importantly, the target market for private educational institutions is diverse, and institutions can use direct marketing to appeal to specific audiences. Also, the research implies that diversification strategies can increase enrolment if marketers exploit behavioral learning theories in the marketing process.

• 유통과학연구
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• 제11권6호
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• pp.51-58
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• 2013

Purpose - Nowadays, more than ever before, fierce competition, deep market segmentation, short product life cycles, and intensifying customer needs are putting increasing pressure on franchise's organizations to satisfy their customers by creating market-oriented relationships with and enhancing their market knowledge of them. One way that this might be achieved is by establishing deep ties (i.e., job commitment and job satisfaction) with their employees. Therefore, the purpose of this study is to examine how two important constructs of franchises' strategic efforts, LO (learning orientation) and RMO (relationship marketing orientation), affect job satisfaction, given the mediating role of job commitment. A franchise system comprises a set of contractual arrangements by which mutual obligations are performed. An organizational learning goal motivates employees to improve their abilities and master the tasks they perform. Relationship marketing, in addition, is to identify, establish, maintain, and enhance relationships with customers and other stakeholders to ensure that the objectives of all parties are met and this is done through the mutual exchange of promises. In a relationship marketing orientation, then, a firm creates, maintains, and enhances a strong relationship with its customers by sustaining long-term ties. This study was designed to examine the evolution of various theoretical approaches to franchise systems in order to determine whether theories about firms have significantly affected the franchise system. To this end, the authors developed a structural model consisting of several constructs. Previous studies have suggested that franchises' learning and relationship marketing orientations are important occupational immersion dimensions driving job satisfaction. Research design, data, methodology - We empirically tested a process of how the learning orientation and the relationship marketing orientation influence job commitment and job satisfaction using survey data drawn from 150 responding franchisees who were interviewed about their individual tendencies. Results - The results of this study provide empirical evidence that learning orientation, relationship marketing orientation, and job commitment all influence franchisees' job satisfaction. The results of this study indicate that, first, learning orientation had a significant effect on job satisfaction; second, relationship marketing orientation was positively related to job commitment; third, job commitment had a significant effect on job satisfaction. We also found that relationship marketing orientation and job satisfaction were mediated by job commitment. Conclusions - The findings of this study confirm the importance of learning orientation and relationship marketing orientation in maintaining a positive marketing relationship between franchiser and franchisee from to the perspective of the market. This indicates that franchiser support such as educational programs provided by the franchiser will help franchisees attain higher business management achievement and satisfaction. Moreover, a positive relationship between franchisees and consumers can be maintained through tie effects. Our findings also suggest that learning orientation plays a critical role in job satisfaction within the franchise system.

• 연구윤리
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• 제4권2호
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• pp.1-5
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• 2023

Purpose: There are already hundreds of millions of users of the Metaverse platform, and within a few years, it is expected to develop into a stage for new economic activities with huge industrial ripple effects due to the size of users. The purpose of this study is to derive considerations for the development of metaverse ethical guidelines. Research design, data, and methodology: The concept of the metaverse was examined through various opinions of industry and experts on the metaverse, and literature related to metaverse ethics was analyzed in the Korean journal database. Results: Six issues were identified through the existing research. (1) Establishing a unified definition of metaverse (2) Necessity of establishing ethical principles considering the operator (3) Personal information protection and privacy (4) Expression in a virtual environment (5) Copyright and intellectual property rights of creations (6) Virtual economy and fairness of trade. Conclusions: Metaverse ethics will be developed and implemented in a form and method different from the real world, but basically, continuous discussions on ethical rationality are needed in the process. In addition, since the ethical judgment in the metaverse environment accompanies cultural differences and epochal changes, it is necessary to focus on metaverse ethics cases.

• Journal of Information Science Theory and Practice
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• 제7권3호
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• pp.6-20
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• 2019

Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.

• 유통과학연구
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• 제13권6호
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• pp.105-118
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• 2015

Purpose - Currently, strengthening small and medium-sized enterprises (SME) in terms of competitiveness is a key economic issue. However, the problem is that many SMEs lack the internal competence required to cope with a rapidly changing market structure. Such problems can act as an obstacle to economic development, yet most SMEs in Korea are dealing with this problem today. A company's source of competitive advantage is changing from quantity to quality, facility to knowledge, and hardwork to creativity. Under such circumstances, a company should place learning and sharing of knowledge and continuously creating new knowledge as its priority. This study aims to identify the effect of a chief executive officer's (CEO) compassion and forgiveness - positive factors in organizational emotion - on learning organization activities and organizational performance, through a theoretical comparison. Research design, data, and methodology - For this study, SMEs based in Daejeon and Chungcheong area were selected. To secure credibility of the data, the subjects were selected among those who have been working at the business for six months or longer. The survey was conducted for 30 days from March 5, 2015 to April 5, 2015. Both offline and online surveys were conducted. Fifty companies were chosen and 700 questionnaires were distributed, with 506 used for analysis. Fifty subject companies (25 from Daejeon, 10 from Chungnam, 10 from Chungbuk, and five from Sejong) were selected and the objective, target, and survey content were explained to a manager at each company either face-to-face or on the phone. Of the total of 700 questionnaires distributed via mail or e-mail, 78.6% or 550 copies were returned. Excluding 44 insufficient questionnaires, the remainder, 506 questionnaires, were used for analysis. Results - This study analyzed how the CEO's compassion and forgiveness affects learning organization activities and organizational performance. First, compassion of the CEO at the SMEs directly affected the learning organization activities and indirectly affected the organizational performance. Second, forgiveness of the CEO at the SMEs did not affect the learning organization activities and organizational performance directly or indirectly. Conclusions - The study conclusions are as follows. First, CEO compassionate behavior at the SMEs was a significant variable that directly and indirectly affected learning organization activities and organizational performance. Therefore, the CEO of an SME can create a positive organizational atmosphere through compassionate behaviors in the organization. Second, the forgiving behavior of the CEO did not have direct or indirect effects on learning organization activities and organizational performance. However, the reason for a CEO to continue his or her forgiving behavior is because it strengthens employee resilience, commitment, and self-efficacy to protect the organization from negative influences such as layoffs, risks, and wrongdoings. The action of forgiveness does not have direct or indirect effects. However, the CEO shall continue such behavior to strengthen members' physiological resilience, commitment, and self - effectiveness, and to protect the organization from risks including layoff and external negative factors.

• 한국농림기상학회지
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• 제25권4호
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• pp.311-325
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• 2023

본 연구는 가리왕산, 유명산에 자생하는 주요 밀원수종인 음나무, 벚나무류, 피나무류 및 쪽동백나무를 대상으로 머신러닝기법(i.e., MaxEnt)을 활용하여 서식지 적합성 분석을 수행하였다. 분석 결과, 모형의 예측정확도인 AUC 값은 대부분의 밀원수종이 0.7 이상이었으며, 주요 환경 변수에 대한 반응 곡선 결과 주요 밀원수종의 서식지 적합성에 영향을 미치는 환경적 요인은 고도, 연평균 강수량, 연평균 기온으로 나타났다. 이는 고도 구배에 따른 기후 인자가 주요 밀원수종의 분포 패턴 설명에 있어 주요한 환경 변수임을 의미한다. 본 연구는 우리나라 밀원수림의 조성 및 관리 전략수립 뿐만 아니라 밀원수종의 분포도 제작 시 근거 자료로써 기여할 수 있을 것이다. 향후 다양한 층위에서의 꿀과 화분의 생산량 증대 및 꿀의 주년 생산을 위해서는 생물학적 변수를 반영한 다양한 지역 단위의 추가적인 데이터 수집 및 분석이 필요할 것이다.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.