• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.462-470
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• 2009

As the learning space has expanded, the distance education has become a recent scholarship in teaching-learning method, and also a great type of media, technologies and strategies to support distance education are attracting a fair amount of attention. However in order to manage a distance education system, it is necessary to be endowed user with technical ability and operational expenses. On the other hand, although a web-based system that makes simple may cut cost, it is difficult to analyze learner's behaviors. Therefore, in this paper, we developed a interactive distance system based on individualized questioning, which relies upon learner's knowledge state and applies a efficient individualized learning method. Additionally, this study is instrument to reduce users' technical ability and operational expenses.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• Journal of the Korean Society of School Health
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• v.36 no.2
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• pp.21-30
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• 2023

Purpose: The study aimed to identify factors that affect college students' learning immersion in non-face-to-face remote classes. Methods: During COVID-19, a survey was conducted on 140 college students who were taking non-face-to-face remote courses at universities located in Seoul, Gyeonggi-do, and Chungcheong-do, Korea. Data were analyzed using the Pearson correlation coefficients, Independent t-test, ANOVA, and Hierarchial stepwise multiple regression with SPSS (Windows version 27.0). Results: In the study, the most important variable influencing learning immersion was the student's self-efficacy, followed by instructor presence, class participation, lecture satisfaction, and credits. Conclusion: Instructors who teach major courses at college need to develop and apply ways to enhance learners' self-efficacy and class content that can boost learners' motivation in order to maximize learners' learning immersion. In order to facilitate learners' access to online media and maintain their interest in remote classes, passionate efforts need to be made by active instructors.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.96-109
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• 2011

Recently, the geographic information system(GIS) is generally used in various fields with the development of information and communication technology, with expansion of its applications and utilization scope. Especially, utilizing GIS is expected to have positive effects on the geography learning and more helpful for the geographic information observation compared to the picture or 2D based media. The effective visualization of complex geographic data does not only take realization of its visual information but also increases the human ability in analysis and understanding to use the geographic information. In this paper, we examine a method to develop the geography learning contents based on the technology with augmented reality and GIS, and then we have a case study for various kinds of visualization techniques and examples to use in geography learning situation. Moreover, we introduce an example of the manufacturing process from the existing GIS data to augmented reality based geography learning system. From the above, we show that the usefulness of our method is applicable for effective visualization of the three-dimensional geographic information in the geography learning environment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.23-25
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• 2021

This paper describes an approach of composing music with multiple AI composers. This approach enriches more the creativity space of artificial intelligence music composition than using only one composer. This paper presents a simple example with 2 different deep learning composers working together for composing one music. For the experiment, the two composers adopt the same deep learning architecture of an LSTM model trained with different data. The output of a composer is a sequence of notes. Each composer alternatively appends its output to the resulting music which is input to both the composers. Experiments compare different music generated by the proposed multiple composer approach with the traditional one composer approach.

• Korean Journal of Childcare and Education
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• v.17 no.3
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• pp.65-87
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• 2021

Objective: This study aimed to examine the effects of maternal monitoring, shared activities with children, maternal education-oriented behavior, and allowing children to own smart-phones on smart media usage patterns based on smart-phone usage time and purposes among elementary school children. Methods: The participants were 1,315 second-grade elementary school children from the 9th wave of PSKC. Latent profile analysis and the three-step estimation approach were used to examine the determinants of the latent profile and the effects of maternal parenting on the profile. Results: Four latent profiles were identified: 'High-level usage & Entertaining oriented,' 'Moderate-level usage & Social/entertaining oriented,' 'Moderate-level usage & Learning oriented,' and 'Low-level usage.' Additionally, results showed that each profile can be predicted by maternal monitoring, education-oriented behavior, and permitting children to own smart-phones. Conclusion/Implications: Our outcomes suggested that it would be necessary to understand the smart media usage patterns of elementary school children, considering both the amount of time spent with smart media and purposes of uses. Further, it is helpful for mothers to monitor children's daily activities, support their educational activities, and take the role of gatekeeper for smart media as a way of appropriate guidance for their children's use of smart media.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3171-3191
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• 2016

Due to growing demand on wireless data traffic, a large number of different types of base stations (BSs) have been installed. However, space-time dependent wireless data traffic densities can result in a significant number of idle BSs, which implies the waste of power resources. To deal with this problem, we propose an active state control algorithm based on semi-Markov decision process (SMDP) for a heterogeneous network. A MDP in discrete time domain is formulated from continuous domain with some approximation. Suboptimal on-line learning algorithm with a random policy is proposed to solve the problem. We explicitly include coverage constraint so that active cells can provide the same signal to noise ratio (SNR) coverage with a targeted outage rate. Simulation results verify that the proposed algorithm properly controls the active state depending on traffic densities without increasing the number of handovers excessively while providing average user perceived rate (UPR) in a more power efficient way than a conventional algorithm.

• Cartoon and Animation Studies
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• s.48
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• pp.271-310
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• 2017

This paper examines the composition and the content of media art which is an art education subject in a national curriculum of Australia; and discusses implications for Korean education curriculums. Media covered by Media Art subject in Australia are the multi types of general media including TV, movie, video, newspaper, radio, video game, the internet, and mobile media; and their contents. The purpose of ACARA's media art education curriculum is to improve creative use, knowledge, understanding, and technology of communication techniques for multiple purposes and the audiences. Through the Media Art subject, both the students and the community are able to participate in the actual communications with the rich culture surrounding them and to develop the knowledge and understanding of the 5 core concepts of language, technology, system, audience and re-creation while testing the culture. The implication of this study is as the following. ACARA's media art education curriculum has been developed as an independent educational program and has a special significance within Australian education curriculums. Although ACARA's media art education curriculum is formed as an independent subject, it is suggested within the curriculum to instruct in close connection with other subjects upon execution. Its organization and elaborateness in curriculum composition are very effective in terms of the teacher's teaching-learning design and as well as the evaluation. This seems to show a good model of leading media literacy curriculum. ACARA's media art education curriculum can be a great reference in introducing media literacy to Korean national education curriculums.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.25-45
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• 2016

This study investigates how high school students with low academic achievement seek and use information. Participants were seven US students in an American Literature and Composition course of the $11^{th}$ grade Remedial Education Program who completed a class project that required comprehensive information seeking and use. Data were collected through comprehensive observation and individual interviews with each student, the teacher, and two library media specialists. Additionally, we gathered and analyzed the instructions the teacher and the two library media specialists provided and all documents each student produced to complete the class project. The process of data analysis was supported by QSR NVivo. The findings of the study implied that students experienced cognitive and affective challenges for their information seeking and use required for the tasks and suggested that technological and individual conferencing would motivate the students to continue their information seeking and use. We then conclude the study with some important implications that can be used as a basis for designing information literacy instructions for students with low academic achievement.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.