• Knowledge Management Research
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• v.22 no.4
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• pp.1-26
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• 2021

Measuring the intangible asset has been vigorously studied for its importance. Especially, the value of character in media industry is difficult to quantitatively evaluate in spite of the industry's rapid growth. Recently, the Social Network Analysis (i.e., SNA) has been actively applied to understand human usage patterns in a media field. By using SNA methodology, this study attempts to investigate how the character network characteristics of media works are linked to human search behaviors. Our analysis reveals the positive correlation and causality between character network centralities and character search data. This result implies that the character network can be used as a clue for the valuation of character assets.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.43-55
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• 2020

Recently, as the importance of experience data increases, there are many attempts to deal with experience data from a data science perspective. In the case of approaching as a collection method of a quantitative survey method that seeks to quantify numerically such as big data, it is difficult to interpret the value of experience in a wide range, and it is relatively expensive and time consuming, and personal information infringement There is a limit to the analysis due to the risk of However, since ethnography, a procedure for collecting experience data based on qualitative research, is mainly carried out in the natural real environment of future customers from the perspective of users, it is possible to confirm the nature that customers face with a small sample. In addition, it is also easy to interpret the relational dimension of the empirical data. Although the ethnography method of collecting experiential data is economical and efficient, it is important to reduce errors in the collection process because the lack of scientific procedures for the data collection process can be a problem. It is important to secure the validity of whether the correct measurement tool is used for ethnography-based experiential data collection and to secure the reliability of the use of a valid measurement tool and method by accurately selecting the measurement target. From this point of view, it is necessary to verify the reliability of the research method that clearly selects the measurement target and secures the validity for the development of the correct measurement method and tool for the collection of ethnography experience data. Therefore, in this study, a verification study was conducted on the data and methodology cases of the'I know you_AI' service that analyzes the customer experience of self-employed based on the ethnography method of collecting experience data..

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.17-31
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• 2023

An activity-based model requires detailed population information to model individual travel behavior in a disaggregated manner. The recent innovative approach developed deep generative models with novel regularization terms that improves fidelity and diversity for population synthesis. Since the method relies on measuring the distance between distribution boundaries of the sample data and the generated sample, it is crucial to obtain well-defined continuous representation from the discretized dataset. Therefore, we propose an improved entity embedding models to enhance the performance of the regularization terms, which indirectly supports the synthesis in terms of feasible and diverse populations. Our results show a 28.87% improvement in the F1 score compared to the baseline method.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.930-943
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• 2007

The purpose of this study was to investigate if students may actually experience scientific reasoning based on an epistemology of authentic science during authentic open inquiry. The samples were 86 10th graders in a science-high school in Seoul. The experimental group practiced authentic open inquiry and the control group practiced traditional school science inquiry in five weeks. Then, the questions students asked while performing inquiry tasks were analyzed. The frequency of the questions asked by students was almost same between two groups, however, the types of questions were different. The frequency of thinking questions in experimental group was higher than the control, and the difference was statistically significant (P<.01). Particularly, the frequency of expansive thinking questions and anomaly detection questions was much higher in experimental than the control group. Judging from the result, with the students from the experimental group asking questions reflecting on the epistemology of authentic science such as scientific methods, anomalous data, and uncertainty about reasoning, students may understand authentic science features during the activities of open authentic inquiry. The result from comparing questions according to the inquiry subject showed that more openness caused the higher frequency of anomaly detection questions and strategy questions, but that inductive thinking questions and analogical thinking questions were connected to inquiry subject rather than the openness of the inquiry.

• School Mathematics
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• v.14 no.3
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• pp.395-408
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• 2012

This study aims to examine the trends in research design and methods used in research on K-12 mathematics curriculum. By analyzing 124 peer-reviewed research articles published between 2000 and 2010, we concluded that more rigorous and various research design and methods should be conducted to improve educational research on curriculum. Although increasing scholarly attention has recently been given to systematic empirical studies about this topic, a large proportion of the studies examined in this study appeared to lack either a coherent conceptual framework or a systematic analytic tool or method. More effort needs to be made on improving the rigor of research in terms of research design and methods.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.17-17
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• 2017

구조방정식모델(Structural Equation Modeling: SEM)은 변수들 간의 인관관계 및 상관관계를 검증하기 위한 통계기법으로 사회학 및 심리학 분야에서 개발되었지만 현재는 경영학, 광고학, 교육학, 생물학, 체육학, 의학, 정치학 등 여러 학문분야에서 광범위하게 사용되고 있다. Amos는 기본적으로 그래픽(Amos graphics)과 베이직(Amos basic)을 제공하기 때문에 정확한 프로그램의 작성이나 행렬에 대한 지식이 없는 초보자들도 아이콘을 이용하여 복잡한 연구모델이나 다중집단분석모델을 분석할 수 있다. PLS(Partial Least Square)는 모형 추정과정에서 발생하는 잔차 또는 예측오차를 최소화하여 예측력을 극대화하기 위한 프로그램이며, 즉, PLS-SEM는 표본 수가 적고 자료가 정규분포를 보이지 않거나 조형지표 모델이거나 복잡한 연구모델 분석에 유용하다. 최근 빅데이터의 열풍으로 자료들을 분석을 위한 도구로 R이 실무 현장에서 인기를 끌고 있다. R은 통계 프로그래밍 언어이자 오픈 소프트웨어 환경으로 통계, 그래픽, 데이터마이닝 등의 다양하고 방대한 양의 패키지들을 지원한다. R에서 제공되는 패키지들이 오픈 소스이고 선형 및 비선형 모델링, 고전적인 통계분석, 시 계열 분석, 분류 및 군집분석 등의 다양한 통계 패키지들을 제공한다는 측면에서 R은 실무는 물론 학문적인 측면에서도, 특히 통계를 기반으로 실증분석을 수행하는 사회과학연구들에서 중요한 역할을 할 수 있을 것으로 기대된다.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.49-57
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• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.