• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.211-217
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• 2011

Recent IT (information technology) environmental changes, such as emerging social network services or increasing user participation in multimedia environment, have made it necessary for e-learning systems to undergo changes in various ways. Metadata is an agreement for interoperability between different systems. The standardization of metadata for e-learning system has been driven by some domestic and international organizations, but applying diverse environmental changes into the design of e-learning metadata is in dire need. In this paper, we present a methodology for the analysis and design of modeling e-learning metadata and elicit the design requirements, on the basis of the metadata standard KEM 3.0, about the elements that are expected to be needed in future e-learning systems. Based on the requirements from the analysis, we present the three-layer model for classifying the requirements by the importance of metadata elements per Kana Model. An intelligent e-learning system is to be developed based on the proposed modeling design, which we hope to influence the development of an international standard in the future.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.149-176
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• 2009

In the recent e-learning environment, avatars are often used to help learners get familiar with the contents, which is ultimately to motivate them to study more. Therefore, it is important to investigate whether avatars have actually the desirable effect on users of e-learning materials. Surprisingly, however, no extensive study has been conducted on this crucial issue Accordingly, main objectives this study are summarized as follows. First, we need to gain better understanding of how much learners' trust towards avatars (termed as "avatar trust") is transferred to learners' trust towards e-learning contents (termed as "contents trust"). Second, we need to investigate how much learners' personal relationships with avatars as well as learning behaviors change depending on avatar types (attractive vs. professional) and contents complexity (easy vs. difficult). As described in the study objectives, in order for us to analyze empirical data more systematically, we classified avatar types into two: "attractive" and "professional;" the contents are categorized as either "easy" or "difficult." Therefore, it is essential for this study to build a prototype e-learning website on which our research purpose can be realized and tested effectively with proper avatar types and e-learning contents. For this purpose, we built a prototype e-learning website, in which avatars are invited from currently working avatar instructors used in real-world e-learning websites, and e-learning contents are adapted from real-world contents about Java programming topic, which have been proved to have shown high quality and reliability. Our research method includes questionnaire survey by inviting a number of valid respondents comprised of office workers who are believed to have high demands for the e-learning contents as well as those who have previous experience with avatar instructors. Respondents were given one of the four e-learning experiment conditions (2 avatar types x 2 contents types) on a random basis. Each experimental e-learning condition is framed to have the same quality but different avatar type and content complexity. Then the respondents are asked to fill out the survey form which has questions about avatar trust, contents trust, personal relationships with avatar, and learning behavior, among others. Regarding the constructs used in research model, we based them rigorously on previous studies. For example, we used six constructs such as behavior to give information (BGI), behavior to obtain information (BOI), need for inclusion wanted, need for control wanted, contents trust, and avatar trust. To measure them, 7-Likert scales were used in the questionnaire. E-learning performance was measured indirectly through two constructs such as BGI and BOI. Six constructs used in the research model were adopted and revised from the FIRO-B model suggested by Schutz. Empirical results are as follows: First, professional avatars are more effective for difficult contents, while attractive avatars were not as effective for easy contents. Second, our study results ascertained that avatar trust transfers to contents trust regardless of avatar types and contents complexity.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.325-333
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• 2019

This study was conducted to investigate the factors affecting satisfaction and dissatisfaction of e-learning learner students using Kano model and Timko's customer satisfaction coefficient. The results of the analysis showed that firstly, the students are highly satisfied when it is convenient to learn visually and audibly, when the students can ask questions at any time, and when the professor was interested in the students. Second, the rational criteria and accurate evaluation of grades and assignments were confirmed as factors that should be satisfied. Third, unlike the results of the basic study that the students use e-learning due to the convenience of learning time and learning space and the ease of learning process, it is no longer an attractive factor to use e-learning. The results of this study suggest that it is possible to present effective directions for the development of e-learning education and strategic application of each factor classified by the two-dimensional recognition method.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1834-1847
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• 2016

Main objectives of the this study are: to develop a model of "Flipped Leaning" that is designed to enhance self-directed learning, learning motivation and self-control, and to verify its effectiveness-in higher education. The verification process initially concentrated on the feasibility study of the model with a thorough literature review and case analyses; then, its general and practical applicability were tested with a field study. As a result, first, the CHANGE Class Model, specifically designed for effective and efficient "Flipped Learning", was developed. It is thus named for the stages that the learning process takes place in the model-i.e., (1) Check ${\rightarrow}$ (2) Ask ${\rightarrow}$ (3) Notice ${\rightarrow}$ (4) Group presentation ${\rightarrow}$ (5) Evaluation, and it emphasizes the dynamic, questions centered (i.e. back and forth between the students and the instructor as well as between the students) learning process. Second, the Model was instrumental in enhancing self-directed learning, learning motivation and self-control; thus, as a result, it significantly improved the effectiveness, the level of concentration and the attractiveness of the learning process. The value of this study lies in pointing to a clear plan to allow a student in higher learning to set-up a self-directed learning plan, to be able to control it while being continuously motivated to complete it.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.92-98
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• 2006

The purpose of this paper is to study of the attitude of e-learning on the effectiveness of e-learning and the antecedents of e-learning. For this study, samples were collected in the school where e-learning was students of cyber lecture. Structural equation model was used to analyze the data. The result of this empirical study is summarized as followings. First, The attitude of e-learning has a positive effect on the e-learning effectiveness. Second, ease of use of learning management system, learning motivation, information quality of learning contents has positive effect on the attitude of e-learning, but usefulness of learning management system has no effect on the attitude of e-learning. Therefore, the former consists of learning management system, learning contents and information quality that are provided by e-learning and the latter means learning motivation.

• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.100-108
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• 2006

The purpose of this paper is to study of the attitude of e-learning on the effectiveness of e-learning and the antecedents of e-learning. For this study, samples were collected from students who had attended a cyber lecture at university. Structural equation model was used to analyze the data. The result of this empirical study is summarized as followings. First, the attitude of e-learning has a positive effect on the e-learning effectiveness. Second, ease of use of learning management system, learning motivation, information quality of learning contents has positive effect on the attitude of e-learning, but usefulness of learning management system has no effect on the attitude of e-learning. Therefore, the former consists of learning management system, learning contents and information quality that are provided by e-learning and the latter means learning motivation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.208-220
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• 2015

This study analyze Online class's current condition in Smart era. And suggest better operation model based on Internet Architecture. This study focuses the condition of e-learning operation model in University online class. Especially, 'Time Check Idea' that using for attendance on e-learning class has some side effects. So this study would applied 'Qualitative Check Idea Concept' on e-learning class. Question Based System, QBS is example model. QBS is leading a Learner's participation in e-class by Making Quiz. These quizs are shared with other students and refer to studing contents. Practically operating Qualitative Concept model QBS on university e-class, we can seek for the effectiveness of Qualitative e-learning model QBS.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.77-88
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• 2022

In response to the significant COVID-19 outbreak, countries have enforced the use of E-learning systems as an alternative to traditional learning; to contain the virus and minimize the infection rate while maintaining the continuity of the learning experience. However, the effective adoption of E-learning systems requires a well-understanding of critical factors, especially in times of crisis. In this regard, this study intends to assess the success of the E-learning system adoption by Higher Education Institutions (HEIs) during the crisis of COVID-19 by utilizing the Information Systems Success (ISS) model. This study's adopted model consists of nine interdependent dimensions, namely: Technical System Quality, Information Quality, Service Quality, Learner Quality, Perceived Satisfaction, Perceived Usefulness, System Use, Intention to Use, and System Success. An electronic survey was distributed among higher education students from different universities in Saudi Arabia to explore each model's dimension. Structural Equation Modeling (SEM) has been applied via SmartPLS software to test the causal relationships between dimensions. This study's main results revealed that students' Service Quality, Learner Quality, and the Intention to Use by students are essential drives for E-learning System Use during the Covid-19 pandemic. Meanwhile, the Intention to Use the system is significantly influenced by Perceived Satisfaction and Perceived Usefulness dimensions. Further, Perceived Satisfaction, Perceived Usefulness, and System Use are interdependent, and all three have a significant positive impact on E-learning System Success.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.83-94
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• 2013

The e-Learning is one of best ways to generate the substitution effect for classroom learning, and robust and rational distribution structure for e-Learning industry is the key issue for successful educational performance of e-Learning, however the recent e-Learning market has a distribution status quite different from rational structure. This paper focuses on issues of e-Learning distribution status and alternatives for policy making. In order to make this study successful, we discuss about concepts and scopes of e-Learning distribution and various types of distribution structure by business models. We conducted an interview survey for business individual experts for distribution modelling. Based on the result of the survey, this paper describes issues of distribution structure and suggests alternatives for policy making in the Korea e-Learning market.