• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.5
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• pp.239-252
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• 2008

To facilitate the processes of e-learning resource description, discovery and reuse, e-learning objects should be appropriately described and classified using standard metadata that need to be published in a registry to reduce duplication of effort and enhance semantic interoperability. This paper describes how standard ebXML registries can be used for semantic grid computing for annotating, storing, discovering and retrieving e-learning object metadata. For semantic annotation of e-learning objects, IEEE Learning Object Metadata (LOM) is adopted as the metadata ontology. In order to support the e-learning metadata ontology in interoperable ebXML registries, a mapping scheme between LOM and ebXML Registry Information Model (RIM) is proposed. The usefulness of sharing e-learning object metadata is demonstrated by prototyping a semantic registry based on the scheme.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.77-84
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• 2009

Along with developments of information and communication technologies, internet has spread not only all over the society, but also our everyday life deeply. Also requirements for e-learning using internet in the educational aspect have a great influence on the changes of school educations. The benefits of e-learning are many, including cost-effectiveness, enhanced responsiveness to change, consistency, and timely contents. Therefore, the e-learning has been introduced to the universities. However, the e-learning is operated inefficiently because of introduction to the university with no definite idea about effects of education and economy in the university. Therefore, in this paper we analysed the category of e-learning based the curriculum operation forms in the university, surveyed tests about students preference and the studied what is desirable e-learning operation forms.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2540-2542
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• 2003

This paper presents an internet-based self-learning educational system which can be enhancing efficiency in the learning process of Java language. The proposed self-learning educational system is called Java Web Player(JWP), which is a Java application program and is executable through Java Web Start technologies. In this paper, three important sequential learning processes : concept learning process, programming practice process and assessment process are integrated in the proposed JWP using Java Web Start technologies. This JWP enables the learners to achieve efficient and interesting self-learning since the learning process is designed to enhance the multimedia capabilities on the basis of educational technologies. Also, online voice presentation and its related texts together with moving images are synchronized for efficient language learning process. Furthermore, a simple/useful compiler is included in the JWP for providing language practice environment such as coding, editing, executing and debugging Java source files. Finally repeated practice can make the learners to understand easily the key concepts of Java language. Simple multiple choices are given suddenly to the learners while they are studying through the JWP and the test results are displayed on the message box.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2799-2823
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• 2019

With the help of learning analytics, MOOCs have wider potential to succeed in learning through promoting self-regulated learning (SRL). The current study aims to apply and validate visualization design guidelines for a MOOC dashboard to enhance such SRL capabilities based on learning analytics. To achieve the research objective, a MOOC dashboard prototype, LM-Dashboard, was designed and developed, reflecting the visualization design guidelines to promote SRL. Then, both expert and learner participants evaluated LM-Dashboard through iterations to validate the visualization design guidelines and perceived SRL effectiveness. The results of expert and learner evaluations indicated that most of the visualization design guidelines on LM-Dashboard were valid and some perceived SRL aspects such as monitoring a student's learning progress and assessing their achievements with time management were beneficial. However, some features on LM-Dashboard should be improved to enhance SRL aspects related to achieving their learning goals with persistence. The findings suggest that it is necessary to offer appropriate feedback or tips as well as to visualize learner behaviors and activities in an intuitive and efficient way for the successful cycle of SRL. Consequently, this study contributes to establishing a basis for the visual design of a MOOC dashboard for optimizing each learner's SRL.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1252-1256
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• 2007

U-learning (ubiquitous learning) systems, which deliver learning materials anytime and anywhere, allow learners to watch live lectures on PDAs, tablet PCs and notebook computers via broadband and wireless Internet. These systems have various problems; first, terminal devices are expensive, and it is difficult to maintain their efficiencies. Secondly, Internet does not guarantee quality of service (QoS), and in general it does not provide real-time services. Finally, the security of these systems is weaker in a local network than in an external network. The USB-based terminal system based on the OSGi service platform was designed as a ubiquitous system, in order to solve those problems. The USB terminals, used in this system, are inexpensive, and it is easy to maintain their performances. Also, this system solves the problems of security in a local network and provides guaranteed QoS. To accomplish this, the number of USB terminals connected to the system has to be limited according to the formula proposed in our paper. This system uses the OSGi specification as a middleware. It supports the discovery mechanism of the USB terminals, maintenance and administration of the system. Finally, this paper shows a driver's license testing system as an example u-learning application1.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.71-80
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• 2010

The purpose of this research is to help IPTV (Internet Protocol Television) commercialization using newly produced educational contents in the area of entertainment and education which currently popular in the market. It is called, Stimulus $Tester^{TM}$, endow reaction time from the feedback of learning system, using a non-direct method, for example, a remote controller. Reaction time is the learning efficiency promotion mechanism that learner ascertain the learning condition of oneself by the time with solved questions from the solving the question in given time. Reaction time also play a key role that the learner may go through course which distribute the point to PC from Server. If this system is ready, we expect that the educational industry will gradually spread out. To verify the learning efficiency of this system, we concluded that the learning improvements, by an Internet-based and a paper-based test, of the increase by 51%, from 2.47min to 1.27min, during reaction of 7 days.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.75-81
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• 2012

With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user's preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.201-207
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• 2012

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.608-627
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• 2016

Wireless mesh networks (WMNs) based on IEEE 802.11s have emerged as one of the prominent technologies in multi-hop communications. However, the deployment of WMNs suffers from serious interference problem which severely limits the system capacity. Using multiple radios for each mesh router over multiple channels, the interference can be reduced and improve system capacity. Nevertheless, interference cannot be completely eliminated due to the limited number of available channels. An effective approach to mitigate interference is to apply dynamic channel switching (DCS) in WMNs. Conventional DCS schemes trigger channel switching if interference is detected or exceeds a predefined threshold which might cause unnecessary channel switching and long protocol overheads. In this paper, a P-learning based dynamic switching algorithm known as learning automaton (LA)-based DCS algorithm is proposed. Initially, an optimal channel for communicating node pairs is determined through the learning process. Then, a novel switching metric is introduced in our LA-based DCS algorithm to avoid unnecessary initialization of channel switching. Hence, the proposed LA-based DCS algorithm enables each pair of communicating mesh nodes to communicate over the least loaded channels and consequently improve network performance.