• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.11
• /
• pp.339-344
• /
• 2020

During COVID-19, education industry is organizing the concept of 'Edutech', which has evolved one step further from the existing e-Learning, by introducing various intelligent information technologues based on the core technology of the 4th industrial revolution and spreading it through diverse contents. Meanwhile, each industries are creating new industries by applying new technology to existing businesses and ask for needs of cultivating human resources who understand the existing traditional ICT technology and industrial business which can solve a newly rising problems. However, it is difficult to build contents for cultivating such human resources with the existing e-learning of transferring knowledge by one-way or some two-way commnication system which has established some interactive conversational system. Accordingly, this study conducted a research on a cooperative e-learning system that enables educators to communicate with learners in real time and allows problem-solving education based on the existing two-way communication system. As a result, frame for contents and prototype was developedp and artially applied to the actual class and conducted an efficiency analysis, which resulted in the validation of being applied to the actual class as a simulation-based cooperative content.

• Journal of The Korean Association of Information Education
• /
• v.20 no.1
• /
• pp.29-38
• /
• 2016

This study selected and utilized the 'Naver post' as an e-book production tool to be used for learning. The production of such SNS format e-books aimed at founding out its effect on learning attitude and academic achievement by stimulating interest and confidence in the learning of the students. To accomplish such an aim, the study selected 50 students from two classes in the fourth grade of a public elementary school. One class of 25 students went through a social studies lesson that applied SNS type e-book production activities, and the other class of 25 students underwent a regular social studies lesson as the comparative group. The major results of the study's analysis is SNS type e-book production did not significantly improve academic achievement in social studies, but SNS type e-book production significantly improved the learning attitude during social studies.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.11a
• /
• pp.239-242
• /
• 2003

본 논문에서는 학습자의 요구와 무관한 학습 컨텐츠 제공의 문제점을 알아보고, 학습자의 요구에 부합되는 강의 컨텐츠제공을 위한 방법을 제안한다. 기존의 LMS(Learning Management System)는 시간적, 공간적 제약을 받지 않고 교육이 가능하다는 온라인 교육의 장점에도 불구하고 학습자의 요구와 수준에 무관하게 학습과 관련한 컨텐츠들이 획일적으로 구성됨으로써, 학습자의 요구를 만족시키지 못하고있다. 이에 본 논문에서는 학습에 필요한 강의 컨텐츠 생성 시 효율성과 재사용성을 높인 SCORM을 기반으로 하여 학습 컨텐츠를 생성하고, 평가된 학습자의 학습 수준을 통해서, 학습자의 수준에 맞는 강의 컨텐츠를 제공하는 학습자 수준별 학습 시스템(Learner Level e-Learning System)을 구현하였다. 이를 통해 컨텐츠 저작자는 학습 객체의 재사용이 가능하며, 학습자는 요구에 충족하는 강의를 수강하는 것이 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1773-1793
• /
• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Korea Multimedia Society
• /
• v.20 no.11
• /
• pp.1793-1802
• /
• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• International Journal of Advanced Culture Technology
• /
• v.10 no.1
• /
• pp.1-9
• /
• 2022

The notion of outcome-based educational paradigm and its adaptability for higher education has become a recent growing and quite stirring trend. In the year 2017-18, this educational philosophy has been embraced by some of the higher educational institutions in Pakistan as well. This research attempts to investigate OBE and non-OBE systems in the context of students learning outcomes and academic attainment levels in engineering education in Pakistan. The study has been conducted on undergraduate students of MUET, Jamshoro, Sindh Pakistan. The students of the software engineering department are taken as the sample. Student cohorts are formed i.e., OBE and non-OBE (traditional/teacher-centered approach) cohorts. The summative assessments of semester exams are used for data analysis descriptive statistics and independent samples t-test is performed to set up the group statistic. The findings of this study show that, in terms of students' performance, the OBE system outperforms the traditional system and this transition in engineering institutions might be beneficial in the future.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.191-202
• /
• 2021

This research was conducted with the aim to appraise the level of satisfaction of students and faculty members with the services of the Deanship of e-Learning and Distance Education at Umm Al-Qura University. In addition, it investigated any differences arising between the evaluation of students and faculty members for these services owing to their gender..To achieve these goals, a descriptive analysis methodology was used in this research. The sample comprised 1357 students (704 male and 653 female) and 372 faculty members (208 male and 164 female) from Umm Al-Qura University in the academic year 2020-2021. To collect the requisite data, the study participants were asked to complete a 5-point Likert scale questionnaire, and the validity and reliability of the data were then assessed. The findings revealed the existence of a high level of satisfaction of students and faculty members with the services of Deanship of e-Learning and Distance Education at Umm Al-Qura University. There are no statistically significant differences between the mean scores of students (male/female) at Umm Al-Qura University in evaluating the said services. Furthermore, there are no statistically significant differences between the mean scores of faculty members (male/female) at Umm Al-Qura University in evaluating these. There exist statistically significant differences between the mean scores of faculty members and students in the evaluation of the services of the Deanship for the benefit of faculty members.

• KIPS Transactions on Software and Data Engineering
• /
• v.13 no.1
• /
• pp.28-34
• /
• 2024

Since e-learning is conducted based on the learner's autonomy, motivation to continuously participate is crucial for success in e-learning. As the number of adult learners participating in lifelong education increases, it is necessary to study learner participation and the motivating factors. Drawing upon the Expectancy-Value Theory and Self-Regulated Learning Theory, this study analyzed the influence of motivational factors (value, costs, cognitive regulation, and scheduling) on learner participation. An e-learning program was implemented on MoodleCloud, and learners completed a survey before going through the program. Regression analysis was conducted using the survey response data along with the participation score, calculated using the log data. The results of the analysis demonstrated that value and scheduling significantly influenced learner participation, with gender differences found in value. This means that as adult learners perceive higher value in the e-learning program and possess better scheduling skills, they are more likely to participate. These findings can be utilized in developing teaching and learning strategies for both learners and instructors, ultimately helping to prevent dropout in e-learning.

• Journal of Korea Multimedia Society
• /
• v.21 no.2
• /
• pp.188-198
• /
• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.