• 한국문헌정보학회지
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• 제58권1호
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• pp.225-254
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• 2024

본 연구는 비대면 강의 수강 경험이 있는 학부생 및 대학원생을 대상으로 비대면 강의 플랫폼에 대한 인식과 만족도를 파악하고, 비대면 강의 플랫폼 설계 및 개발 과정에서 고려해야 할 이용성 요인이 강의 만족도에 미치는 영향 관계를 검증하는 데에 목적이 있다. 문헌조사를 통해 비대면 강의 플랫폼 개발 과정에서 고려해야 할 주요 요인을 정리하고 연구모형을 수립하였다. 이를 바탕으로 대학구성원을 대상으로 대학 비대면 강의 플랫폼 이용자 인터페이스 설계 원칙 충족도, 플랫폼 이용성, 플랫폼 만족도, 비대면 강의 만족도 등에 대한 인식을 묻는 설문조사를 수행하였다. 설문조사 응답 분석을 통하여 변수 간의 인과관계를 검증하고 모형화하였다. 학습자 유형 및 비대면 강의 방식 유형별로도 동일한 모형 적용이 가능함을 확인하였다. 본 연구는 학습자의 평가 결과를 토대로 플랫폼의 이용자 인터페이스 설계 원칙 충족도가 플랫폼 활용 비대면 강의 만족도에 영향을 미칠 수 있음을 검증하였다는 점에서 의의가 있다. 본 연구에서 제안한 연구모형이 향후 비대면 강의 환경 개선과 발전에 기여할 수 있을 것으로 기대한다.

• Archives of Plastic Surgery
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• 제50권2호
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• pp.200-209
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• 2023

Background As the coronavirus disease 2019 virus made its way throughout the world, there was a complete overhaul of our day-to-day personal and professional lives. All aspects of health care were affected including academics. During the pandemic, teaching opportunities for resident training were drastically reduced. Consequently, medical universities in many parts across the globe implemented online learning, in which students are taught remotely and via digital platforms. Given these developments, evaluating the existing mode of teaching via digital platforms as well as incorporation of new models is critical to improve and implement. Methods We reviewed different online learning platforms used to continue regular academic teaching of the plastic surgery residency curriculum. This study compares the four popular Web conferencing platforms used for online learning and evaluated their suitability for providing plastic surgery education. Results In this study with a response rate of 59.9%, we found a 64% agreement rate to online classes being more convenient than normal classroom teaching. Conclusion Zoom was the most user-friendly, with a simple and intuitive interface that was ideal for online instruction. With a better understanding of factors related to online teaching and learning, we will be able to deliver quality education in residency programs in the future.

• 한국임상약학회지
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• 제33권3호
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• pp.202-209
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• 2023

Background: With the emergent transition of online learning during the COVID-19 pandemic, the need for online/offline blended learning that can effectively be utilized in a team-based learning (TBL) course has emerged. Methods: We used the online metaverse platforms, Gather and Zoom, along with face-to-face teaching methods in a team-based Introductory Pharmacy Practice Experience (IPPE) course and examined students' learning satisfaction and achievement, as well as their preferences to the learning platforms. A survey questionnaire was distributed to the students after the IPPE course completion. All data were analyzed using Excel and SPSS. Results: Students had high levels of course satisfaction (4.61±0.57 out of 5) and achievement of course learning objectives (4.49±0.70 out of 5), and these were positively correlated with self-directed learning ability. While students believed that the face-to-face platform was the most effective method for many of the class activities, they responded that Gather was the most effective platform for team presentations. The majority of students (64.3%) indicated that blended learning was the most preferred method for a TBL course. Conclusion: Students in a blended TBL IPPE course had high satisfaction and achievements with the use of various online/offline platforms, and indicated that blended learning was the most preferred learning method. In the post-COVID-19 era, it is important to utilize the blended learning approach in a TBL setting that effectively applies online/offline platforms according to the learning contents and activities to maximize students' learning satisfaction and achievement.

• Journal of information and communication convergence engineering
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• 제18권4호
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• pp.239-244
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• 2020

This study analyzed the influencing factors of learning engagement and teaching presence in online programming practice classes. The subjects of this study were students enrolled in an industrial specialized high school, who practiced creating Arduino circuits and programming using a web-based virtual practice tool called Tinkercad. This research adopted a tool that can measure task value, learning flow, learning engagement, and teaching presence. Based on this analysis, learning flow had a mediating effect between task value and online learning engagement, as well as between task value and teaching presence. Increasing learning engagement in online classes requires sensitizing the learners about task value, using hands-on platforms available online, and expanding interaction with instructors to increase learning flow of students. Furthermore, using virtual hands-on tools in online programming classes is relevant in increasing learning engagement. Future research tasks include: confirming the effectiveness of online learning engagement and teaching presence through pre- and post-tests, and conducting research on various practical subjects.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.97-104
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• 2021

The authors provide characteristics of the open educational platforms, classification and quantitative analysis regarding the availability of IT courses, teaching language, thematic directions on the following platforms: Coursera, EdX, Udemy, MIT Open Course Ware, OpenLearn, Intuit, Prometheus, UoPeople, Open Learning Initiative, Open University of Maidan (OUM). The quantitative analysis results are structured and visualized by tables and diagrams. The authors propose to use open educational resources (teaching, learning or research materials that are in the public domain or released with an intellectual property license that allows free use, adaptation, and distribution) for organization of independent work; for organization of distance or correspondence training; for professional development of teachers; for possibility and expediency of author's methods dissemination in the development of their own courses and promoting them on open platforms. Post-project activities are considered in comparing the courses content of one thematic direction, as well as studying the experience of their attending on different platforms.

• 지능정보연구
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• 제29권2호
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• pp.285-301
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• 2023

비대면 교육의 중요성 및 필요에 따른 수요가 증가함에 따라 국내외 온라인 교육 오픈 플랫폼이 활성화되고 있다. 본 플랫폼은 대학 등 교육 전문기관과 달리 학습자의 자율성이 높은 특징을 가지며 이에 따라 개인화된 학습 도구를 지원하기 위한 학습 행동 데이터의 분석 연구가 중요시 되고 있다. 실제적인 학습 행동을 이해하고 패턴을 도출하기 위하여 프로세스 마이닝이 다수 활용되었지만 온라인 교육 플랫폼과 같이 자기 관리형(Self-regulated) 환경에서의 학습 로그를 기반한 사례는 부족하다. 또한, 대부분 프로세스 모델 도출 등의 모델 관점에서의 접근이며 분석 결과의 실제적인 적용을 위한 개별 패턴 및 인스턴스 관점에서의 방법 제시는 미흡하다. 본 연구에서는 온라인 교육 오픈 플랫폼 내 학습 패턴을 파악하기 위하여 프로세스 마이닝을 활용한 분석 방법을 제시한다. 학습 패턴을 다각도로 분석하기 위하여 모델, 패턴, 인스턴스 관점에서의 분석 방법을 제시하며, 프로세스 모델 발견, 적합도 검사, 군집화 기법, 예측 알고리즘 등 다양한 기법을 활용한다. 본 방법은 국내 오픈 교육 플랫폼 내 기계학습 관련 강좌의 학습 로그를 추출하여 분석하였다. 분석 결과 온라인 강의의 특성에 맞게 비구조화된 프로세스 모델을 도출할 수 있었으며 구체적으로 한 개의 표준 학습 패턴과 세 개의 이상 학습 패턴으로 세분화할 수 있었다. 또한, 인스턴스별 패턴 분류 예측 모델을 도출한 결과 전체 흐름 중 초기 30%의 흐름을 바탕으로 예측하였을 때 0.86의 분류 정확도를 보였다. 본 연구는 프로세스 마이닝을 활용하여 학습자의 패턴을 체계적으로 분석한다는 점에서 기여점을 가진다.

• Journal of East Asia Management
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• 제4권2호
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• Asia pacific journal of information systems
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• 제32권4호
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• pp.945-963
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• 2022

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.