• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.65-75
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• 2006

With rapid developments in technology and communications' many teachers are increasingly exposed to a variety of e-Learning environments that they must develop new competencies and skills to be successful e-Learning teachers. The purpose of this training program, sponsored by HRD Korea (Human Resources Development Services of Korea), is to provide e-Learning teachers with meaningful opportunities for promoting quality e-Learning teaching. This program covers pedagogical issues as well as technical and practical aspects of the e-Learning environments. Before starting the program development the survey and the current e-Learning program assessments were conducted. The training program is divided into three modules as follows: 1) theoretical issues of e-Learning, 2) development of e-Learning contents, and 3) implementation of e-Learning environments. These three modules can be selectively reorganized in response to teacher requirements and demands. ln each module, there are five subtopics that include creative teaching and interaction strategies for promoting the effective e-Learning teaching. ln conclusion, teachers will gain greater understanding of teacher roles in e-Learning instruction, more flexibility in teaching jobs, increased confidence and knowledge to act as e-Learning facilitators through the completion of this training program.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• Journal of Engineering Education Research
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• v.13 no.6
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• pp.14-23
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• 2010

Whether the engineering department of universities employs ideal usage of e-learning or not needs to be investigated as many engineering departments diversify the use of the e-learning elements for educational purpose. Applying the teaching and learning methods and characteristics would lead to better strategies which are applied to development of contents and deployment of the e-learning courses. This study examines the characteristics and approaches of the usage of e-learning elements used by some instructors who use multimedia contents in offline teaching and learning environment. The results of this study shows that the e-learning elements assist the face-to-face course and the interactions are manifested in the classroom rather than in online setting. Lecture, hands-on-practice, simulation, and PBL(Problem-based learning) are turned out to be the major teaching and learning methods. This study signifies the need for use of various teaching and learning methods by the instructors and provision of PBL environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Journal of The Korean Association of Information Education
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• v.8 no.3
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• pp.307-317
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• 2004

In web-based education environment, it is necessary to provide individually adjusting feedback according to learner's characteristic. Despite this necessity, it is a current state that there are difficulties in deriving the variables of learners' characteristics and lack in developing the systematic strategies and practical tools for providing individually adjusting feedback. This study analyzed the learners' learning patterns, one of learner's characteristic variables regarded as important in web-based teaching and learning environment by employing Apriori algorithm, and also grouped the learners by learning pattern. Under this framework, the e-SRM feedback system was designed and developed to provide learning content, learning channel, and learning situation, etc. for individual learners. The proposed system in this study is expected to provide an optimal learning environment complying with learner's characteristic.

• 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 %.

• Safety and Health at Work
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• v.6 no.3
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• pp.211-217
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• 2015

Background: E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods: The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results: There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, "learning content" (${\beta}=0.344$, p < 0.001) affected the degree of learning satisfaction most significantly, followed by "motivation to learn" (${\beta}=0.293$, p < 0.001), "interactions with learners and instructors" (${\beta}=0.149$, p < 0.01), and "previous experience related to e-learning" (${\beta}=0.095$, p < 0.05). "Learning environment" had no significant influence on the degree of learning satisfaction. Conclusion: E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.524-535
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• 2011

For success in E-learning, support from a learning environment that enables learners to perform self-directed learning more effectively is assumed. However, most existing e-learning systems do not maximize the improvement in learners' self-regulated learning ability because they only partially accommodate factors that can facilitate self-directed learning. In this paper, a web-based e-learning system is designed and proposed that enables support of an enhanced self-directed learning environment by providing various learning methods, evaluation methods, learning content levels, and strategies for learning motivation in various conditions, and synthetically reflecting them. To validate the effectiveness of the proposed system, it was applied to the subject of data structures in a university course, and an online survey was conducted with the students. The results indicated that the proposed system can support a learning environment in which students can perform more effective self-directed learning, enhancing their learning ability.

• Journal of Gifted/Talented Education
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• v.24 no.5
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• pp.877-892
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• 2014

This Study was to examine whether high school students' autonomy support and beliefs of intelligence ability influence their self-regulated strategies. Of the 600 high school students surveyed from 3 high schools in two metropolitan cities, Korea, 478 completed and returned the questionnaires yielding a total response rate of 79.7%. Among the final sample consisted of 109 gifted students (22.8%), 190 high-achieving non-gifted students (39.7%), and low-achieving non-gifted students (37.4%). Measures of students' perceived autonomy support (i.e. from parents, teacher, peer), beliefs of intelligence ability (i.e. incremental, entity) and self-regulated strategies (i.e. managing environment and behavior, seeking and learning information, maladaptive regulatory behavior). Spearman's rho(${\rho}$) indicated that students' achieving level was positively associated with autonomy support (i.e. parents, teacher), beliefs of intelligence ability (i.e. incremental) and self-regulated strategies (i.e. managing environment and behavior, seeking and learning information). However, students' achieving level was negatively associated with beliefs of intelligence ability (i.e. entity) and self-regulated strategies (i.e. maladaptive regulatory behavior). Hierarchical multiple regression analyses showed that students' perceived autonomy support (i.e. from teacher) and beliefs of intelligence ability (i.e. incremental) were the crucial contributors for enhancing students' self-regulated strategies. Results are discussed in relation to theoretical implications and school settings.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.229-238
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• 2018

The purpose of this study is to investigate the mediating effect of learners' learning flow in the effect of presence on academic achievement in web-based e-learning. For this purpose, this study analyzed the influencing relationship between the each factor based on the structural model with the learning flow as a mediator variable. Based on existing theoretical studies, learning satisfaction and academic achievement, which represent learning outcomes, are set as dependent variables, and teaching presence, cognitive presence, and social presence are set as independent variables. Data collected from a total of 256 e-learning learners were used in the analysis of this study. According to the results of the analysis, teaching presence, cognitive presence, and social presence were found to have a significant effect on academic achievement when a learning flow is a mediator variable. Concretely, teaching presence, cognitive presence, and social presence have a positive effect on the learning flow, while learning flow has a positive effect on learning satisfaction. On the other hand, learning flow has a negative effect on academic achievement. As a result of verifying the mediating effect of learning flow on the relationship between presence, learning satisfaction, and academic achievement, there was meditating effect in the aggregate. This study implies that in order to increase the level of learning satisfaction and academic achievement, it is necessary to make the teaching-learning design in the provision of contents and materials for e-learning so that the learner can feel the presence. The results of this study can be used as a basic data for seeking support and promotion strategies for enhancement of future learning flow and presence.