• 한국정보시스템학회지:정보시스템연구
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• 제23권3호
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• pp.47-71
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• 2014

The purpose of the present study is to verify the structural relationship among system quality, information quality, service quality, perceived ease of use, perceived usefulness, satisfaction, intention on continuous use of mobile learning in cyber university. For this study, W cyber university in Korea was chosen to conduct web survey. The subjects were 283 students who participated in W's cyber university courses. A hypothetical model was composed of system quality, information quality, service quality, perceived ease of use and perceived usefulness as exogenous variables, satisfaction and intention on continuous use of mobile learning as endogenous variables. The result of this study through structural equation modeling analysis is as follows: First, information quality only affect satisfaction, Second, perceived ease of use, perceived usefulness and satisfaction significantly affect intention on continuous use of mobile learning. These results imply that information quality should be considered for the design and development of mobile learning contents. Also, perceived ease of use, perceived usefulness and satisfaction is important to enhance intention on continuous use of mobile learning. This study proposes strategies for successful mobile learning in cyber university.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• 통상정보연구
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• 제9권2호
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• pp.103-120
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• 2007

The knowledge based economy requires more and more people to learn new knowledge and skills in a timely and effective manner. These needs and new technology such as computer and Internet are fueling a transition in e-learning. According to specialist's opinion, imagination experience studying is generalized, and learning environment that language barrier by studying, multi-language studying Machine that experience past things that disappear through simulation, and travel area, and experience future changed state disappears is forecasting to come. This is previewing finally that it may become future education that education and IT, element of entertainment is combined. Already, became story that argument for party satellite of e-Learning existence passes one season already. e-Learning is utilized already in all educations that we touch by effectiveness by corporation's competitive power improvement and implement of lifelong education in educational institutions through present e-Learning. It is obvious that when see from our viewpoint which is defining e-Learning by one industry and rear by application to education as well as one new growth power about these, e-Learning industry becomes very important means that can solve dilemma of growth real form. Only, special quality of digital industry that e-Learning is being same with other digital industry and repeat putting out a fire rapidly, and is repeating sudden change that these evolution is not gradual growth of accumulation and improvement of technology that is appearing consider need to. In the meantime, we need to observe about evolution of Information Technology. Because there is some scholars who e-Learning's concept foresees to evolve by u-Learning.(although, a person who see that these concept is not more in marketing terminology by some scholars' opinion is). This u-Learning's concept means e-Learning that take advantage of ubiquitous technology as Ubiquitous-Learning's curtailment speech. Ubiquitous, user means Information-Communication surrounding that can connect to network freely regardless of place without feeling network or computer. There is controversy about introduction time regarding these direction, but e-Learning is judged to evolve by u-Learning necessarily. Because keep in step and age that study all contents that learner wants under environment of 3A (any time, any whrer, any device) by individual order thoroughly is foreseen to come in ubiquitous learning environment that approach more festinately.

• 한국멀티미디어학회논문지
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• 제12권5호
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• pp.757-766
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• 2009

본 연구에서는 학습자 행동모델을 이용하여 개별적인 학습 환경을 제공할 수 있는 적응적 하이퍼미디어 학습 시스템을 제안하였다. 본 시스템에서는 학습자의 학습행동정보를 실시간으로 추적하여 관리할 수 있는 LBML을 제안하였다. 제안 시스템은 학습행동정보 수집시스댐과 적용적 학습지원시스템으로 구성된다. 학습행동정보 수집시스템은 웹 2.0기술을 이용하여 SCORM CMI 데이타 모델을 기반으로 학습자의 학습행동정보를 실시간으로 수집한다. 수집된 학습행동정보는 LBML 스키마를 기반으로 개별 학습자의 LBML 인스턴스로 저장된다. 적웅적 학습지원시스댐에서는 LBML 인스턴스를 분석하여 학습자의 반웅에 대한 즉각적인 피드백을 제공할 수 있는 규칙기반 학습지원모률과 상호작용적 학습지원모듈을 개발하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• Journal of Information Technology Applications and Management
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• 제21권3호
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• pp.53-64
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• 2014

It is important to keep learners' feeling positive during learning to enhance learning performance. According to flow theory,challenge-skill balance is a precondition for flow experience: Learners feel anxiety when the challenge of learning is higher than their ability, feel boredom when the challenge of learning is lower than learners' ability, and engage in flow status when the challenge of learning matches the learners' ability. However, the current empirical study reveals that emotions related to enjoyment may appear when the learners' skill is equal to or higher than the learning challenge. Nevertheless, boredom emotion may appear when learners perceive the courses are difficult but unimportant. These empirical survey results revealed the necessary of rethinking the appearance of boredom and enjoyment emotions in computer education.

• Asia pacific journal of information systems
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• 제17권1호
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.