• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.124-132
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• 2022

Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.

• Journal of Korean Academy of Dental Administration
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• v.6 no.1
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• pp.43-47
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• 2018

Online data can be explored for topics browsed by an unspecified population to detect professional information demands more quickly. The purpose of this study was to collect and analyze online questionnaires in order to find information required by dental hygienists. We analyzed the frequency of posting words after isolating nouns from questions of the Korean Dental Hygienists Association homepage's Q & A section, the Naver Knowledge-iN service, and a dental hygienists' online meeting site in Naver. We found that queries of the Korean Dental Hygienists Association's homepage were concentrated on education renewal and license notification. The queries about dental hygienists in the Naver Knowledge-iN service used words related to job or career choice, and the queries of the dental hygienist-affiliated site had many words related to dental practice, dental work, and turnover. This study showed that the information needs of unspecified dental hygienists varied depending on the online environment such as homepage, blog, and information service.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.267-272
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• 2022

Online exams are not limited by time and space. It has the advantage that it does not require a separate exam site for examinees, and there is no time and cost required to move to the exam site. However, the online exam has the disadvantage that various cheating is possible because the exam is conducted in an individual environment. In addition, there is a difficulty in detecting cheating due to the lack of exam supervision methods. In addition, since the exam process and result data exist only as digital data, it is inconvenient to check directly on the server where the exam result is stored in order to check whether the exam result is forged or not. If the data related to the exam is maliciously changed, the authenticity cannot be verified. In this study, we tried to increase the reliability of the online exam by developing a blockchain-based online exam cheating detection system that stores exam progress-related data in the blockchain to detect cheating. Through the experiment, it was confirmed that forgery and falsification are detected as a result of the exam.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.401-407
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• 2024

The present research intends to examine how online learners' digital literacy skills affects their learning flow and academic self-efficacy in the universities. The data were collected from a group of 228 students taking online course at the four-year university in Cheonan. The collected data were analyzed with the SPSS 29.0 program: descriptive statistics, correlation analysis and regression analysis. As a result of this study, the digital literacy skills of university students taking cyber lectures were found to have a statistically significant positive correlation with learning flow in online classes and academic self-efficacy. Through this study, it is hoped to provide an opportunity to explore ways to improve learning flow and academic self-efficacy as the problems of non-face-to-face classes. Therefore, in order to ensure the quality of online classes, it is hoped to expand educational opportunities such as various research and programs that can improve digital literacy skills.

• The Journal of Bigdata
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• v.8 no.2
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• pp.15-25
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• 2023

Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.113-120
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• 2021

As a result, the interaction between personal motivation and class type is not significant. On the other hand, the interaction between social motivation and class type is significant and overall online class increases engagement with educational issues. In particular, the group with low social motivation showed greater change than the group with high social motivation, so online education seems to be more effective in the group with low social motivation. It means that by stimulating students' social motivation rather than personal motivation, the effectiveness of online education can be enhanced, and it can lead to education outcomes - behavioral changes and attitudes of learners. In order to revitalize social motivation in the intensely personal space of online, it is necessary to activate social communication methods such as SNS, and development of interpersonal issues and learning materials would be more efficient. In order to derive more specific results, it is necessary to measure the level of prior knowledge and involvement of the participants in class, and to comprehensively investigate and analyze the state of learners before and after class through more variables. Finally, in order to increase the reliability of the research results, it is necessary to clearly prove it through the establishment of a structural model.

• Proceedings of KHEA Conference
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• 1997.07a
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• pp.74-74
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• 1997

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.171-178
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• 2020

Many countries are establishing and operating blended learning that combines the advantages of online and offline education. However, online education lecture-based Mooc courses have a very low level, with a graduation rate of less than 5-10%. Therefore, in order to increase the graduation rate of students taking online Mooc distance education lectures that anyone can easily take lectures anytime, anywhere on the web-based basis, it is necessary to introduce automatic analysis of students' understanding level of lectures and an automatic academic warning system. Moreover, in order to enter an advanced education country, it is necessary to develop an automatic judgment SW for wrong answer rate, automatic summary SW for lectures, and automatic analysis SW education for lecture-based weak subjects based on mixed learning levels. In order to improve this problem, in this paper, we proposed and simulated an automatic summarization system for lecture contents, an automatic warning system for incorrect answers, and an automatic judgment algorithm for weak subjects.

• Journal of the Korean Society of Costume
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• v.51 no.7
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• pp.49-62
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• 2001

The purposes of this study were to find out the catalogue and online-order apparel shoppers' impulsive purchase orientation and impulsive purchase stimuli. Self-administered questionnaires were distributed to women over than 20 years, and collected from middle to the end of November in 2000. Frequencies, percentages, and mean were calculated. One-way ANOVA, chi-square test, factor analysis, and cluster analysis were used, and Duncan's Multiple Range test was followed. 1. Factors of impulsive purchase orientation were relax from negative moods, design property, inducement from neighbors, taste congruence, price property of apparel, positive moods, and loose-control, and were segmented into the low impulsive purchaser, the reasonable purchaser, the fulfilled with positive moods, and the high impulsive purchaser. The factors of impulsive purchase stimuli were apparel property, consumer service, sales promotion on the point of sales, and low price. 2. The low impulsive Purchaser was affected little by impulsive purchase stimuli, spent a little money on apparel, and the married with high education level were the most. The reasonable purchaser was affected by sales promotion on the point of sales or low price, spent a little money on apparel, and students or house-wives were the most The fulfilled with positive moods was affected by low price, and students or career women with high education level were the most, but spent less money on apparel. The high impulsive purchaser was affected by various impulsive purchase stimuli, the young unmarried with high education level were the most, and spent more money on apparel. 3. The younger, the unmarried, students or career women, and shoppers with higher income or apparel expenditure showed a higher impulsive purchase tendency for relax from negative mood, design property, for inducement from neighbor, taste congruence, and positive moods. 4. The older, the married, house wives, and shoppers with higher apparel expenditure were stimulated by apparel property or consumer services.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.57-63
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• 2009

With an exponential increase in electronic commerce (e-commerce), marketers are attempting to gain a competitive advantage by emphasizing service quality and post interaction service aspects, which leads to customer satisfaction or behavioral consequence. Particularly for apparel, service quality is one of the key determinants in encouraging customer e-loyalty, and hence the success of apparel retailing in the context of electronic commerce. Therefore, this study explores e-service quality (E-SERVQUAL) factors and their unique effects on e-loyalty for apparel online shopping based on Parasuraman et al' s (2005) framework. Specific objectives of this study are to identify underlying dimension of E-SERVQUAL, and analyze a structural model for examining the effect of E-SERVQUAL on e-loyalty for online apparel shopping. For the theoretical framework of service quality in the context of online shopping, literatures on traditional and electronic service quality factors were comparatively reviewed, and two aspects of core and recovery services were identified. This study hypothesized that E-SERVQUAL has an effect on e-loyalty; customer satisfaction has a positive effect on e-service loyalty for apparel online shopping; and customer satisfaction mediates in the effect of E-SERVQUAL on e-loyalty for apparel online shopping. A self-administered questionnaire was developed based on literatures. A total of 252 usable questionnaires were obtained from online consumers who had purchase experience with online shopping for apparel products and reside in standard metropolitan areas, in the United States. Factor analysis (e.g., exploratory, confirmatory) was conducted to assess the validity and reliability and the structural equation model including measurement and structural models was estimated via LISREL 8.8 program. Findings showed that the E-SERVQUAL of shopping websites for apparel consisted of five factors: Compensation, Fulfillment, Efficiency, System Availability, and Responsiveness. This supports Parasuraman (2005)'s E-S-QUAL encompassing two aspects of core service (e.g., fulfillment, efficiency, system availability) and recovery related service (e.g., compensation, responsiveness) in the context of apparel shopping online. In the structural equation model, there are five exogenous latent variables for e-SERVQUAL factors; and two endogenous latent variables (e.g., customer satisfaction, e-loyalty). For the measurement model, the factor loadings for each respective construct were statistically significant and were greater than .60 and internal consistency reliabilities ranged from .85 to .88. In the estimated structural model of the e-SERVEQUAL factors, the system availability was found to have direct and positive effect on e-loyalty, whereas efficiency had a negative effect on e-loyalty for apparel online shopping. However, fulfillment was not a significant predictor for explaining consequences of E-SERVQUAL for apparel online shopping. This finding implies that perceived service quality of system available was likely to increase customer satisfaction for apparel online shopping. However, it was not supported that e-loyalty was determined by service quality, because service quality has an indirect effect on e-loyalty (i.e., repurchase intention) by mediating effect of value or satisfaction in the context of online shopping for apparel. In addition, both compensation and responsiveness were found to have a significant impact on customer satisfaction, which influenced e-loyalty for apparel online shopping. Thus, there was significant indirect effect of compensation and responsiveness on e-loyalty. This suggests that the recovery-specific service factors play an important role in maximizing customer satisfaction levels and then maintaining customer loyalty to the online shopping site for apparel. The findings have both managerial and research implications. Fashion marketers can establish long-term relationship with their customers based on continuously measuring customer perceptions for recovery-related service quality, such as quick responses to problem and returns, and compensation for customers' problem after their purchases. In order to maintain e-loyalty, recovery services play an important role in the first choice websites for consumers to purchase clothing. Given that online consumers may shop anywhere, a marketing strategy for improving competitive advantages is to provide better service quality, maximize satisfaction, and turn to creating customers' e-loyalty for apparel online shopping. From a researcher's perspective, there are some limitations of this research that should be considered when interpreting its findings. For future research, findings provide a basis for the further study of this important topic along both theoretical and empirical dimensions. Based on the findings, more comprehensive models for predicting E-SERVQUAL's consequences can be developed and tested. For global fashion marketing, this study can expand to a cross-cultural approach into e-service quality for apparel by including multinational samples.