• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.47-58
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• 2019

This paper is brought to analyze an impact of coaching activities over entrepreneur with respect to entrepreneurs' personal characteristics on the performance of Startups focusing on entrepreneur's awareness conversion effect. In particular, the situation of falling focus of serious startup failure on uncoachable obstinate entrepreneur among the several causes of startup failures, this paper define the research mission of reducing this risk by inducing coaching types of cooperations between senior entrepreneur having serial successful startup experiences and beginning entrepreneur. This paper carry out literature reviews chiefly on startup coaching researches rather than mentoring studies because coaching stressing on self-oriented problem defining and solving by entrepreneur actively tend to be more effective than mentoring relying on mentor's guideline passively as solving startup's main upcoming problems. This paper implement four related researches. First, the impact analysis of entrepreneur's personal characteristics on coaching activities, second, influences of entrepreneurs environmental factors on them, Third, impact of coaching activities on entrepreneur's awareness conversion effect, Fourth, how strongly this conversion effect contributing on the performance of startup. As results of empirical research, first, this paper prove that entrepreneur's environmental factors impact positively on coaching activities over entrepreneur although failing on validating on significant impact of entrepreneur's personal characteristics on coaching activities. Second, this paper prove that coaching activities positively influence on entrepreneur's awareness conversion, hence ultimately positively on the performance of startups. As conclusion, this paper validate that coaching activities over entrepreneur through inducing on the conversion of entrepreneur's awareness lead entrepreneur to setting the bar high and accountable planning of business milestone. This paper contribute on delivering policy implication that government initiatin action-oriented training program for entrepreneurs should accept the module of coaching session to produce more effective results.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.595-609
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• 2020

The purpose of this study is to analyze the experiences of teachers who participated in the development of online science class videos in the context of covid-19, their perception of online science class, and the characteristics of the online science class content developed by teachers. A survey and interviews were conducted with ten elementary school teachers who made online science class videos themselves. Also the characteristics of the online science class were investigated by analyzing the online science class video produced by the participants. As a result, participants in the study recognized the lack of production time, difficulty in filming and editing, concerns over misconceptions, the problem of solving copyrights for existing materials, and the burden of external disclosure. Although it was a teacher who had experience producing online science class video contents, no research participants actively answered the merits of online science class. On the other hand, the study participants cited that the shortcomings of online science classes were that students had fewer opportunities for inquiry and lack of communication or interaction. In particular, these shortcomings were thought to have a great influence on the quality of online science classes, especially in making inquiry classes difficult. Some teachers took a negative view that online science classes could not completely replace face-to-face classes. However, if multiple teachers are presented with supplementary teaching activities that complement the content-based online teaching method, the method of combining online science classes and face-to-face classes is not. Through the analysis of the contents of the online science class, the introduction and arrangement steps of the online science class were similar to the process of the face-to-face science class, but the inquiry step and the conceptual explanation step showed a big difference from the face-to-face science class.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• The Korean Society of Law and Medicine
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• v.23 no.3
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• pp.139-207
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• 2022

As various state restrictions on individual freedom were imposed during the COVID-19 pandemic, concerns have been raised that excessive infringements on fundamental rights were indiscriminately permitted based on the public interest of preventing infectious diseases. Therefore, the question of how to set acceptable limits of liberty restrictions on individuals has emerged. However, since the phenomenon of infections spreading to the population is only predicted statistically, how to deal with the risk of the infected individual as a subject of legal analysis has become a problem. In the absence of a theoretical framework of legal analysis of risk, the risk of infected individuals during the pandemic was not analyzed strictly, and proportionality review of infection prevention measures was often only an abstract comparison of the importance of public interest and individual rights. Therefore, this research aims to conduct a theoretical review on how risk can be conceptualized legally in a public health crisis, and to develop a theoretical framework for proportionality review of the risk of liberty-limiting measures during a pandemic. Chapter 2 analyzes the legal philosophical concepts of risk, which are the basis for liberty restrictions during a public health crisis, and applies and extends them to the pandemic. Chapter 3 reviews previous studies related to liberty restriction measures in the context of the COVID-19 pandemic, and points out they have a limitation that specific criteria for the proportionality review of public health measures in the pandemic have not been presented. Accordingly, Chapter 3 specifies the methodological framework for proportionality review, referring to the theoretical discussion on risks in Chapter 2. Chapter 4 reviews the legitimacy of gathering restriction orders, applying the theoretical discussion in Chapter 2 and the criteria for proportionality review established in Chapter 3. In particular, Section 4 examines logic of proportionality review in judicial precedents over the ban on gathering restrictions implemented in the COVID-19 pandemic. In analyzing the precedents, the logic of proportionality review in each case is critically reviewed and reconstructed based on the theoretical framework presented in this research.

• Korean Journal of School Psychology
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• v.17 no.2
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• pp.165-180
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• 2020

As a tiered system of supports, School-Wide Positive Behavior Support (SWPBS) is an evidence-based practice in the educational system of Korea. An important aspect of SWPBS is the ongoing progress monitoring and evaluation of implementation fidelity. This study aimed to develop and validate the Korean Tier 3 School-Wide Positive Behavior Support Implementation Fidelity Checklist (KT3-FC). The preliminary KT3-FC consisted of a 37-item, 6-factor checklist. In the first phase of the study, 10 experts reported that the range of content validity of the KT3-FC was adequate. In the second phase of the study, 185 teachers (52 men and 133 women) who implemented SWPBS completed the KT3-FC, Individualized Supports Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and PBS Effectiveness Scale. An exploratory factor analysis resulted in a 5-factor structure, with 20 items, instead of 37 items, consisting of: (a) progress monitoring and evaluation of the individualized supports, (b) provision of supports by aligning and integrating mental health and SWPBS, (c) crisis management planning, (d) problem behavior assessment, and (e) establishment of individualized support team. The internal consistency of the KT3-FC was good (full scale α = .950, sub-factor α = .888 ~ .954). In addition, the KT3-FC showed good convergent validity, having statistically significant correlations with the Individualized Support Questionnaire, School Climate Questionnaire, School Discipline Practice Scale, and the PBS Effectiveness Scale. Finally, the confirmatory factor analysis showed that the 5-factor model of the KT3-FC had some good model fits, indicating that the newly developed fidelity measure could be a reliable and valid tool to assess the implementation of Tier 3 supports in Korean schools. Accordingly, the KT3-FC could contribute to implement SWPBS as an evidence-based behavioral intervention for Korean students.

• Journal of Life Science
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• v.33 no.1
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• pp.56-63
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• 2023

In this study, two cases of food poisoning caused by Salmonella that occurred in Gyeongsangnam-do in September 2021 are reported. One of the outbreaks occurred in a school and the other in a company. The molecular epidemiological characteristics of the isolated strains in the two outbreaks were analyzed. In the case of the school outbreak, 29 (4.9%) of 588 individuals experienced diarrhea and abdominal pain. As a result of a test of 36 individuals (patients, n=29; cook workers, n=7), Salmonella enterica serotype Enteritidis was detected in 17 (47.2%) patients, suggesting this serotype was the principal cause. Meanwhile, Salmonella spp. were not detected in 35 food and environmental samples. In the company outbreak, 87 (3.0%) of 2,900 individuals who had intaked from the same source experienced diarrhea, abdominal pain, and fever. In a test of 50 individuals (patients, n=40; cook workers, n=10), S. Enteritidis was detected in 28 patients (56.0%). Also, Vibrio cholerae (NAG) was detected in four patients with S. Enteritidis, and V. cholerae (NAG) only was detected in one patient. Salmonella spp. were not detected in 118 preserved foods, but S. Enteritidis was detected in one eaten food (toast) delivered in group by the company. Through PFGE genetic homology analysis of the isolated strains, all S. Enteritidis detected in patients and consumed foods were the same type. It seems that these S. Enteritidis isolates were the same type as detected in a previous school outbreak and in patients of group food poisoning in other regions, leading to an enhanced problem of food poisoning and epidemiology. Our analytic results can provide data for epidemiological management and food poisoning prevention based on molecular characteristics.

• Journal of the International Relations & Interdisciplinary Education
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• v.3 no.1
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• pp.51-75
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• 2023

Relying on disciplinary competitions to enhance college students' innovation and entrepreneurship is one of the specific paths to explore the reform of innovation and entrepreneurship education in colleges and universities. This paper conducts an empirical study on the practice of disciplinary competitions among Chinese university students, the problems of innovation and entrepreneurship education in Chinese universities and the impact of disciplinary competitions on innovation and entrepreneurship among Chinese university students, using university students in Chinese universities as the respondents. The data collected online and offline were analysed using SPSS26 statistical software. The results of the analysis show that Chinese university students show a high level of interest in innovation and entrepreneurship competitions and that there are some differences in the level of interest in innovation and entrepreneurship competitions among university students of different academic levels. More than half of Chinese university students have participated in innovation and entrepreneurship competitions and the initiative of participating in innovation and entrepreneurship competitions varies by grade. The biggest problem facing innovation and entrepreneurship education in schools is the lack of professional innovation and entrepreneurship teachers, followed by the lack of guidance on innovation and entrepreneurship-related policies, and the unreasonable reward system, which makes teachers and students less motivated to innovate and entrepreneurship. Through one-dimensional linear regression analysis, it is found that the degree of attention to innovation and entrepreneurship among college students affects college students' entrepreneurial awareness and entrepreneurial practice; the degree of initiative of college students' innovation and entrepreneurship competition affects college students' entrepreneurial effect; and the degree of initiative of college students' innovation and entrepreneurship competition affects college students' entrepreneurial practice.

• Journal of Korean Philosophical Society
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• v.152
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• pp.59-79
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• 2019

The main purpose of this paper is to critically examine the intractable problems of Peirce's notion of 'symbol' as a higher and perfect mode of sign, and present a more appropriate account of the higher status of symbol from an experientialist perspective. Peirce distinguished between icon, index, and symbol, and suggested symbol to be a higher mode of sign, in that it additionally requires "interpretation." Within Peirce's picture, the matter of interpretation is to be explained in terms of "interpretant," while icon or index are not. However, Peirce's conception of "interpretant" itself remains fraught with intractable opacities, thereby leaving the nature of symbol in a misty conundrum. Drawing largely on the experientialist account of the nature and structure of symbolic experience, I try to explicate the complexity of symbol in terms of "the symbolic mapping." According to experientialism, our experience consists of two levels, i.e., physical and symbolic. Physical experience can be extended to symbolic level largely by means of "symbolic mapping," and yet is strongly constrained by physical experience. Symbolic mapping is the way in which we map part of certain physical experience onto some other area, thereby understanding the other area in terms of the mapped part of the physical experience. According to this account, all the signs, icon, index, and symbol a la Peirce, are constructed by way of symbolic mapping. While icon and index are constructed by mapping physical level experience onto some signifier(i.e. Peirce's "representamen"), symbol is constructed by mapping abstract level experience onto some signifier. Considering the experientialist account that abstract level of experience is constructed by way of symbolic mapping of physical level of experience, the symbolic mapping of abstract level of experience onto some other area is a secondary one. Thus, symbol, being constructed by way of secondary or more times mapping, becomes a higher level sign. This analysis is based on the idea that explaining the nature of sign is a matter of explaining that symbolic experience, leaving behind Peirce's realist conception of sign as a matter of an event or state of affairs out there. In conclusion, I suggest that this analysis will open up new possibilities for a more appropriate account of the nature of signs, beyond Peirce's complicated riddles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.35-50
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• 2022

After the introduction of the central bus lane system, bus traffic was prioritized. This resulted in improved trust from bus users. However, the low capacity at the central bus stop reduces traffic speed and punctuality. In addition, physical constraints are inevitable because the construction of central bus lanes and bus stops considers the city's road geometry. Therefore, this study attempted to optimize the effective green time of the traffic signal system at the entrance and exit of the central bus stop to remedy its insufficient operational capacity. The Transit Capacity and Quality of Service Manual and Korea Highway Capacity Manual were used as the analysis methodologies. The number of stop areas for central bus stops to be built was determined by excluding variable physical factors, and field survey data collected from nine randomly selected central bus stops currently installed in Seoul were used. A scenario analysis was conducted on the central bus stops with insufficient capacity by adjusting the effective green time, and the capacity of the central bus stop was set as the dependent variable. According to the results, 26.7 percent of the central bus stops with insufficient capacity can solve the problem of insufficient capacity. Therefore, the results of this study can be verified by improving the operation level, and it can be effective even if the number of central bus stops calculated by engineering is not guaranteed during the planning stage of the central bus stop. As the number of central bus stops is expected to increase further as the number of central bus stops increases, it is necessary to improve the number of central bus stops. Therefore, it is hoped that the results presented in this study will be used as basic data for the improvement plan at the operational level before introducing the physical improvement plan.