• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.137-152
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• 2005

In this Paper, we Propose an efficient web-based video learning evaluation system that is tailored to individual student's characteristics through the use of user profile-based information filtering. As a means of giving video-based questions, keyframes are extracted based on the location, size, and color information, and question-making intervals are extracted by means of differences in gray-level histograms as well as time windows. In addition, through a combination of the category-based system and the keyword-based system, questions for examination are given in order to ensure efficient evaluation. Therefore, students can enhance school achievement by making up for weak areas while continuing to identify their areas of interest.

• Korean Journal of Adult Nursing
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• v.20 no.5
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• pp.778-790
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• 2008

Purpose: This study was conducted to develop Web-based multimedia contents for supporting student nurses' clinical practice on critical care, and to evaluate learners' responses. Methods: Based on the steps of Assessment, Design, Development, Implementation, & Evaluation(ADDIE) model, a total of 13 self-directed learning modules including live lectures and real video clips were developed through faculty collaboration of nine nursing colleges in Gwangju and Chonnam province. The finally developed multimedia contents were published on the Web of the learning management system at a local e-learning center. Results: The Web contents were evaluated after self-learning by 81 junior college nursing students who were encouraged to study it at their own pace during their two-week clinical practice at a medical or surgical intensive care unit of a university hospital and two hospitals. The knowledge (t = -27.66, p < .001) and self-evaluated clinical performance level(t = 7.54, p < .001) were significantly increased after learning of the Web contents and clinical practice, and satisfaction level that measured post-test only was 4.0 out of 5 point. Conclusion: The use of Web contents for critical care need to be extended as a complimentary material in a class room lecture or clinical practice of students to increase their self-learning ability and understandings of clinical knowledge and situation.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.69-76
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• 2008

The biggest strength of the Internet is to enable to access information without any limitation of time and space. As the Internet and IT technologies have been developed, various kinds of teaching ways in education field such as remote lectures, video lectures, and CAI(Computer Adapted Instruction) have emerged. In terms of education, evaluation can be a basic foundation to help teach students in the next learning stage according to each student's level. In addition, it is able to give the information of students'abilities and provides proper learning programs to teach students on a case-by-case basis. The purpose of the paper is to establish evaluation system on the WWW(World Wide Web) that can reflect learning activities part of students in their evaluation scores based on the two important learning theories, Behaviorism and constructivism, which are mainly used in evaluation procedures to judge learning ability of students. This system will give information about learners, and analyze the learning interest of learners. The proposed system enables teachers to evaluate learning ability of students through various kinds of information of learners, and to execute level-based education.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.193-207
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• 2021

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.