• Journal of Practical Engineering Education
• /
• v.14 no.2
• /
• pp.361-366
• /
• 2022

Recently, with the development of virtual and augmented reality technology, metaverse is emerging as a new paradigm that will lead the next-generation internet era, and social and economic activities are spreading around the game, entertainment, music, and content industries. Moreover, as non-face-to-face conversion accelerated after the outbreak of COVID-19, lifestyles and industrial sites are becoming untact and further rapidly becoming a metaverse. In particular, the application of metaverse to the education field is attracting attention because realistic classes using real-time voice conversations using avatars, 3D objects, and 360-degree images can increase immersion and overcome the limitations of distance education. This study examines the concept of metaverse and examines that education using metaverse can be an alternative that can increase the efficiency of education in the non-face-to-face era. In particular, it shows that it is effective in language education and suggests an actual metaverse-based Korea language education program.

• Journal of The Korean Association For Science Education
• /
• v.43 no.6
• /
• pp.563-572
• /
• 2023

This study aimed to analyze cases of online class design by pre-service science teachers to identify the teaching strategies employed for online classes. For this purpose, the real-time online class lesson plans of 12 pre-service science teachers, who had experienced education utilizing online teaching tools for a semester, were collected and analyzed. The pre-service science teachers considered all the elements that were essential in traditional face-to-face class designs, including prerequisites, statements of learning objectives, stimulating motivation, teaching and learning methods, wrapping up, teacher-student interaction, and assessment. They devised teaching strategies that could overcome the limitations of online teaching and were not feasible in face-to-face classes for each element. Additionally, they were considering new instructional strategies tailored to the online teaching environment, such as creating a conducive environment for using online teaching tools and strategies related to checking the online teaching environment. However, for statements of learning objectives, stimulating motivation, and wrapping up, most of the pre-service science teachers predominantly utilized teaching strategies from traditional face-to-face classes, especially those involving the presentation of visual materials through online tools. Student-centered approaches were rarely implemented in stimulating motivation or wrapping up. These findings imply that one semester of exposure to the utilization of online teaching tools may be insufficient in teacher education. Thus, there is a need for a continuous and expanded educational program on the utilization of online teaching tools as part of pre-service teacher education.

• Journal of Information Technology Services
• /
• v.21 no.3
• /
• pp.73-87
• /
• 2022

As the COVID-19 pandemic continues to prolong, non face-to-face, online classes has become the new normal in education. To examine the effect of course types and course delivery modes on student course satisfaction, the study analyzed survey data collected from 2,743 students enrolled in a 4-year university located in a metropolitan area. Basic Frequency analysis as well as keyword network analysis were used to analyze student survey data. The main results and implications of the study are as follows. First, the survey results indicated that students preferred asynchronous classes over synchronous online classes. This tendency was consistent regardless of student grades and majors as well as the course type. However, students majoring in more practice-oriented disciplines tend to prefer synchronous online classes and blended classes, and this tendency gets stronger with courses in major. Second, the keyword network analysis results further indicated that interactivity may play an important role in both synchronous and asynchronous online course satisfaction.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.176-188
• /
• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.525-534
• /
• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.4
• /
• pp.221-230
• /
• 2017

BL(Blended Learning) has been presented as a promising alternative learning approach. BL is defined as a learning approach that combines e-learning and face-to-face classroom learning. The adoption of BL in computer practice class is necessary due to the characteristics of computer practice class itself. This study proposes a research model that examines the determinants of learner's satisfaction of computer practice classes in BL environment. Considering the characteristics of computer practices classes contents and instructor interaction were identified as the determinants. The research model is tested using a questionnaire survey of 141 participants. Confirmatory factor analysis (CFA) was performed to test the reliability and validity of the measurements. The partial least squares (PLS) method was used to validate the measurement and hypotheses. The empirical findings shows that contents easiness and contents constructs are the primary determinants of instructor interaction in BL. Instructor interaction was also found to be related to the learner's satisfaction resulting in re-using. The findings provide insight into the planning and utilizing BL in computer practice classes to enhance learner's satisfaction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.609-611
• /
• 2022

LMS is software that automatically manages educational and learning activities, and is being used in most educational institutions these days when online education is increasing due to the spread of Corona. LMS covers a wide range of methods that are so versatile that they can be used in connection with offline classes as well as online classes. Therefore, it was confirmed that the software is useful not only for educational institutions but also for office workers who want to do online work such as telecommuting during the period of social distancing. This LMS function will be of great help to the development of the education market and online classes in the future.

• Journal of Information Technology Services
• /
• v.10 no.3
• /
• pp.61-76
• /
• 2011

The purpose of this paper was to design and implement effective learning model for internet ethics education, following the learning principle and procedure of PBL(Problem-Based Learning) which is one of the constructivism teaching-learning theories(, and to apply it). In this learning model, online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals and the main module for online learning run on Moodle, an open source LMS(Learning Management System). It is possible for learner to participate actively in creation of micro-contents and reorganize contents using various SNS(Social Network Service). The learner can achieve the learner-oriented learning and select micro-contents in order to reorganize the personalized learning contents to take advantage of SNS among learners. To examine educational effectiveness of the proposed learning model, an experimental study was conducted through the education content and method to the subjects of two classes in the second-grade of university located in OO city. 60 students(treatment group=30, control group=30) participated in the experiment. The result statistically verified that the proposed learning method is more effective in cultivating consciousness of internet ethics than the face-to-face PBL learning method. The results of this paper also showed that a lecture using blended learning is efficient in achieving learning performance and that learners responded positively(, which are indicating that the higher effectiveness of learning would be expected) by forming connectedness among learners using SNS. The results of this paper showed that a lecture using blended learning is effectiveness in achieving learning performance and that learners responded positively, which are indicating that the higher effectiveness of learning would be expected by forming connectedness among learners using SNS.

• International journal of advanced smart convergence
• /
• v.9 no.2
• /
• pp.203-211
• /
• 2020

This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

• Journal of Information Processing Systems
• /
• v.14 no.5
• /
• pp.1075-1086
• /
• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.