• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.221-228
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• 2006

With the increasing use of the Internet improved Internet technologies as well as web-based applications, the effectiveness assessment of e-Learning has become one of the most practically and theoretically important issues in both Educational Engineering and Information Systems. This study suggests a research model, based on an e-Learning success model, the relationship of the e-learner's self-regulated learning strategy and the quality perception of the e-Learning environment. This research model focuses on the learning environment and on e-learning strategy. The former consists of learning management system, learning content quality and service quality that are provided by e-Loaming. The latter refers to the learners' self-regulated learning strategy. We will show the validity of the model empirically.

• East Asian mathematical journal
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• v.33 no.4
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• pp.413-429
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• 2017

This study is a basic study for developing a mathematical learning application program that can be used in smart devices for adaptive learning. We selected 20 mathematical learning applications including middle school contents and analyzed learning types. And we analyzed the contents and the learning process. As a result, most learning types of mathematics learning applications were problem-centered. Contents analysis results showed that the most applications have achievement goals. The factors that induce interest in learning were lacking and feedback was not provided sufficiently. Analysis of the learning process showed that most of the math learning applications were classified according to their purpose and characteristics.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.633-640
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• 1999

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like Q-learning and TD(Temporal Difference)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present COMREL(COMpressed REinforcement Learning) algorithm for finding the shortest path fast in a maze environment, select the candidate states that can guide the shortest path in compressed maze environment, and learn only the candidate states to find the shortest path. After comparing COMREL algorithm with the already existing Q-learning and Priortized Sweeping algorithm, we could see that the learning time shortened very much.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1526-1533
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• 2018

This study aims to provide implications for mobile foreign language vocabulary learning app by analyzing the effects of mobile vocabulary learning app based on character animation. For this purpose, we applied the learning application designed with character animation and text, and the application designed with text only to two groups of learners, and analyzed the effect. As a result, we found that application designed with character animation and text was useful in recognition frequency and duration concerning learning. Regarding learning outcomes, we found that it is useful not only in memory but also in learning interest and motivation. This study provides implications for learning method and design development of mobile-based foreign language vocabulary learning application which actively using recently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Educational Technology International
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• v.22 no.2
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• pp.255-283
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• 2021

In the early 2020, COVID-19 changed the traditional way of teaching and learning. This paper aimed to explore the impact of college students' perception of course quality on their online learning satisfaction. A total of 4,812 valid samples were extracted, and the difference analysis and hierarchical regression analysis were used to make an empirical analysis of college students' online learning satisfaction. The research results were as follows. Firstly, there was no difference in online learning satisfaction among students by gender and grade. Secondly, learning assessment, course materials, course activities and learner interaction, and course production had a significant positive impact on online learning satisfaction. Course overview and course objectives had an insignificant correlation with online learning satisfaction. Thirdly, the total effect of online learning satisfaction was as follows. Course production had the greatest effect, followed by course activities and student-student interactions, followed by course materials. It was the learning evaluation that showed the least effect. This study can provide empirical reference for college teachers on how to continuously improve online teaching and increase students' satisfaction with online learning.

• Educational Technology International
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• v.23 no.1
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• pp.41-66
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• 2022

Vocabulary learning plays an important role in language learning. This study explored a new paradigm based on social networking site (SNS) supported collaborative learning for vocabulary learning. SNS supported collaborative learning (SSCL) can effectively promote learners' engagement, interest and motivation by providing a more communicative and interactive environment. However, vocabulary learning studies on SSCL mainly focused on the effectiveness and influencing factors, lacking specific instructional strategies. Therefore, this study aims to develop instructional strategies that guide instructors to create an SSCL environment for facilitating vocabulary learning. The final instructional strategies are composed of three stages according to the course process, consisting of 8 general strategies and 21 specific guidelines. The content validity was ensured by four experts in the field of educational technology. The instructional strategies were then applied in an actual classroom with 16 students. The positive responses from the instructor and learners indicated that SSCL can be reasonably incorporated into the current curriculum to provide effective learning opportunities and to promote learners' vocabulary learning.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.166-175
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• 2023

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Q-learning.