• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.472-479
• /
• 2010

The purpose of this paper is to investigates the determinants of learner intention to reuse in e-learning. Based on attitude and flow related studies, our paper proposes a theoretical model consisting of factors such as contents sufficiency, course feedback, self-directed learning, attitude, flow, and reuse intention. The survey method was used for this paper, and data from a total of 409 users in e-learning system were used for this analysis. To analyze the data, structural equation model was used. The results of this empirical study is summarized as follows. First, contents sufficiency has a positive effect on flow as well as attitude, and learner's self-directed learning has a positive effect on flow as well as attitude. Second, both attitude and flow have a positive effect on reuse intention. The findings have significant implications for determinant indicators of reuse intention in e-learning.

• Journal of Information Technology Applications and Management
• /
• v.28 no.1
• /
• pp.53-61
• /
• 2021

The year 2020 was a time when the coronavirus infections-19 (COVID-19) caused various changes in society. In particular, the fields that have been conducted face-to-face have been greatly confused by the transition to an online non-face-to-face method, and this is the case with the field of education. There are two main advantages of offline education. The first is that we can improve our understanding through communication with teachers, and the second is that we can develop social skills through interaction with friends. But as online classes progressed due to corona 19, interaction could not be achieved. As a result, the motivation for learning has been reduced due to difficulties in real-time feedback, and the participation rate has been significantly lowered, especially in lower grades, raising concerns about the learning gap that will occur after corona 19. However, there are some cases in which online classes were conducted as effectively as offline classes by utilizing various contents. What they have in common is the use of content. Teachers generally improved the quality of education by linking interesting sights and videos that enhance learning comprehension. The provided video conveys learning-related content into stories, enabling intuitive observation. Many students were already enjoying these videos through VOD (Video on Demand) such as TV and YouTube, they were able to connect their easy access to content and interest in learning. Appropriate use of video content has rather increased the learning effect and should continue after corona 19. Therefore, it is necessary to study methodologies that apply video content efficiently to education. This study looked at the steps that needed content application through the development of education programs, and observed its meaning. Students were curious about the content, motivated to learn and participated in learning on their own. Intuitive learning, conducted through appreciation, play and content production, provided an opportunity to learn on their own in everyday life.

• Journal of The Korean Association of Information Education
• /
• v.26 no.5
• /
• pp.353-360
• /
• 2022

The purpose of this study is to verify the effect of artificial intelligence storytelling education program designed to help learning artificial intelligence based on storytelling, the most important element of human intelligence, on learning flow. To this end, a 16-hour artificial intelligence education program was designed and developed, and applied over 8 weeks to 19 gifted students in 5th and 6th grades of elementary school. Artificial intelligence storytelling education program was developed in the form of teaching and learning course plans for each class and storybooks. Artificial intelligence storytelling education program application results showed significant improvements in average scores in all 9 sub-factors of learning flow, including combination of challenges and abilities, integration of behavior and consciousness, clear goal, concrete feedback, focus on task, sense of control, loss of self-consciousness, Distortion of the sense of time, and self-purpose experience. In other words, it was confirmed that artificial intelligence storytelling education program was effective in improving learning flow.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.4
• /
• pp.119-129
• /
• 2009

In The increasing use of computers and high-speed Internet network has greatly influenced education, causing a veering away from the typical and traditional way of delivering instruction. Specifically, the various kinds of Web-based multimedia technology, the interactive activities on the Internet, and satellite broadcasting technology are accelerating the emergence of a virtual-lectures-based educational model, which transcends time and space. Such virtual lectures make it possible for the entire teaching-learning process to be done in a virtual learning environment, thus giving rise to problem regarding learning guidance, feedback, and appraisal. In this paper, we propose a system for attendance appraisal for learner participation degree based virtual lecture, an appraisal element in virtual learning environments. This appraisal model can set the elements of virtual learning environments in such a way as to reflect in the attendance appraisal of the opened virtual learning environment information regarding the learner's participation in class. In addition, this model motivates the learners to actively participate in the virtual learning environment and to support instructors by accomplishing the activities that are needed for attendance appraisal.

• Journal of Software Engineering Society
• /
• v.27 no.1
• /
• pp.8-14
• /
• 2018

As cloud computing has emerged as a hot trend in the IT industry, various types of cloud services have emerged. In addition, cloud service broker (CSB) technology has emerged to alleviate the complexity of the process of selecting the desired service that user wants among the various cloud services. One of the key features of the CSB is to recommend the best cloud services to users. In general, CSB can use a method to evaluate a service by receiving feedback about a service from users in order to recommend a cloud service. However, since each user has different criteria for giving a rating, there is a problem that reliability of service evaluation can be low when the rating is only used. In this paper, a method is proposed to supplement evaluation of rating based service by applying machine learning based sentiment analysis to cloud service user's review. In addition, the CSB prototype is implemented based on proposed method. Further, the results of comparing the performance of various learning algorithms is proposed that can be used for sentiment analysis through experiments using actual cloud service review as learning data. The proposed service evaluation method complements the disadvantages of the existing rating-based service evaluation and can reflect the service quality in terms of user experience.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.135-149
• /
• 2014

This study proposed the ubiquitous learning system by finding the difficulties which students have in learning the course of science and analyzing the current learning applications that are used commercially. Through case studies, we found 4 problems. First, the request of long learning time by most of the existing video-based learning applications. Second, it is impossible to know their level of learning due to the lack of open learning contents. Third, it is difficult for learners to participate in interactive learning. Fourth, there are educational contents without considerations on the level of learners. To refine the difficulties due to these problems, we designed and implemented a new ubiquitous learning system which applies the small learning contents for short-term learning, open learning system and enhanced hierarchical learning contents. The system was implemented based on Android. It provides learners with useful science education. We conducted a questionnaire for third grade middle school students in order to show that the proposed system has a good educational effects. The questionnaire asks for the differences between the proposed ubiquitous learning system and the existing learning application. We concluded that the proposed system is better than the existing application by using t-test for improvement of learning effects using feedback with which students participate in interactive learning but not in unidirectional learning, and share the learning result.

• Journal of The Korean Association For Science Education
• /
• v.34 no.3
• /
• pp.273-283
• /
• 2014

The purpose of this study was to examine the potential of the use of the Classroom Response System (CRS), a kind of new ICT medium, in a quiz problem-solving oriented high school life science class. To find the usefulness of CRS as a teaching and learning strategy, the CRS group (n=34) sent prompt individual answers to the teachers' questions using the CRS terminal (Clicker), and the teacher then asked additional reasons of the individuals and gave personalized feedback. In the control group (n=35), the CRS was not used while the teacher asked overall questions and gave feedback in an undifferentiated way. As a result, the CRS increased students' interest and concentration during class, but there were no significant differences in study achievement between the two groups. However, there were significant differences between the medium-level groups when the two groups were divided into smaller ones based on their pre-scores. We suggest that, for effective use of the CRS for diagnostic and formative assessment, teachers should develop a teaching and learning strategy that can produce appropriate questions of various levels in advance, investigate the exact reasons for students' answers, and give customized feedback by individual as much as possible.

• Journal of Digital Contents Society
• /
• v.18 no.6
• /
• pp.1101-1107
• /
• 2017

This study explored the effect of ASR-based pronunciation instruction, using a mobile platform, on EFL learners' pronunciation development. Particularly, this quasi-experimental study focused on whether using mobile ASR, which provides voice-to-text feedback, can enhance the perception and production of target English consonants minimal pairs (V-B, R-L, and G-Z) of Korean EFL learners. Three intact classes of 117 Korean university students were assigned to three groups: a) ASR Group: ASR-based pronunciation instruction providing textual feedback by the mobile ASR; b) Conventional Group: conventional face-to-face pronunciation instruction providing individual oral feedback by the instructor; and the c) Hybrid Group: ASR-based pronunciation instruction plus conventional pronunciation instruction. The ANCOVA results showed that the adjusted mean score for pronunciation production post-test on the Hybrid instruction group (M=82.71, SD =3.3) was significantly higher than the Conventional group (M=62.6, SD =4.05) (p<.05).

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.47 no.4
• /
• pp.7-14
• /
• 2010

Stable-state behaviors of asynchronous sequential machines represented as finite state machines can be corrected by feedback control schemes. In this paper, we propose a state feedback control scheme for input/state asynchronous machines with uncertain transitions. The considered asynchronous machine is deterministic, but its state transition function is partially known due to model uncertainty or inner logic errors. The control objective is to compensate the behavior of the closed-loop system so that it matches a sub-behavior of a prescribed model despite uncertain transitions. Furthermore, during the execution of corrective action, the controller reflects the exact knowledge of transitions into the next step, i.e., the range of the behavior of the closed-loop system can be enlarged through learning. The design procedure for the proposed controller is described in a case study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.461-469
• /
• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.