• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.57-69
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• 2017

Due to the development of technology even in learning and education area, many studies have begun to make a new attempts to research by using SNS, breaking away from traditional learning methods. However, the limitations of these studies are restricted only to the use of wireless Internet and writing on Web sites. This study aims to conduct a research on English learning activities that utilize various technologies such as Bigdata, Facebook, Social Network Services (SNS) and English applications. In addition, this study looks into how these modern technologies can be integrated in the classrooms and which activities can be applied in the English classroom. This research is to suggest effective English learning methods through a thorough investigation on the effectivity of various technologies based on the Web 2.0 such as Flickr, blogs, MySpace, and online discussion board within the context of the English learning. To verify the effect of the study, the subjects are divided into experimental and control group. The experiment is proceeded with pre- and post-test. The experimental group is designed to verify the effects using SNS tools such as Facebook, Bigdata, and Online Massive Learning. A survey is conducted to determine the preference of utilizing social networking sites and to analyze the effects in class. The result is that the average scores for experimental group have improved more than the average of control group. The comparison of pre and post-test of the experimental group shows that the significance of the higher and median group was statistically significant at the p<0.01.

• Journal of Gifted/Talented Education
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• v.19 no.3
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• pp.457-476
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• 2009

The research aimed to investigate characteristics of middle school students in a biology class as science gifted education in terms of self-regulated learning abilities, personality traits and learning preferences. The twenty subject in the study responded to questionnaires of a self-regulated learning ability instrument, a personality trait tool, and a learning preference survey in March, 2009. It was found that the research subjects showed higher levels of cognitive strategies, meta-cognition, and motivation than those students in a previous study(Jung et. al., 2004), while environment was opposite. The level of cognitive strategies was significantly correlated with meta-cognition(r=.610, p=.004) and motivation (r=.538, p=.014) and meta-cognition with environment(r=.717, p=.000). Those students who showed highest levels of self-regulated learning ability displayed various personality traits. One male student with the highest level of self-regulated learning ability showed a personality of hardworking, tender-minded, and conscientious traits and wanted to be a medical doctor. The female student with the second highest level of self-regulated learning ability presented a personality as creative, abstract and divergent thinker and she showed a strong aspiration to be a world-famous biologist with breakthrough contribution. The five students with highest levels of self-regulated learning ability showed a common preference in science learning: they dislike memory-oriented and theory-centered lecture with note-taking from teacher's writings on chalkboard; they prefer science learning with inquiry-oriented laboratory work, discussion among students as well as teachers. However, reasons to prefer discussion were diverse as one student wants to listen other students' opinions while the other student want to present his opinion to other students. The most favorable science teachers appeared to be who ask questions frequently, increase student interests, behave friendly with students, and is a active person. In conclusion, science teaching for the gifted should employ individualized teaching strategies appropriate for individual personality and preferred learning styles as well as meeting with individual interests in science themes.

• Journal of Korean Society of Rural Planning
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• v.15 no.2
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• pp.111-120
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• 2009

The purpose of this study was to analyze the lifestyles of tourists visiting rural experience tourist destinations and the influential relations of the attributes to affect rural experience tourism. The research findings show that the lifestyles of tourists had significant impacts on their preference for rural experience programs(0.2502/3.0l2). Second, their lifestyles had also significant impacts on the need for rural experience tourist destinations(5.039/3.363). Third, their preference for rural experience programs had significant influences on their intentions for revisits(0.386/3.l60). Fourth, their preference for rural experience programs had significant influences on their intentions for word of mouth(1.448/8.073). Fifth, their need for rural experience tourist destinations had significant impacts on their intentions for revisits(1.940/5.594). And finally, their need for rural experience tourist destinations had no significant influences on their intentions for word of mouth(-1.0611-1.421). According to the analysis results of the regression coefficient of the measuring model, enjoying leisure(1.130/6.775) and pursuing health(1.110/9.001) were large influential factors in lifestyle; pursuing learning(1.47317.946) was the biggest influential factor in preference for rural experience programs; and a natural environment(1.220/8.990) was the biggest influential factor in the need for rural experience tourist destinations.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.49-54
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• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.65-72
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• 2007

This paper aims to provide information to generate a statistical load model of an educational server by analyzing workload of an e-Learning sewer at Dankook University. The result of the analysis shows file size distribution, access frequency and transmission volume for each file type, access interval, changes in preference and clients access rate by networks. In particular, it had different results from previous studies about video file's size distribution and file distribution based on access frequency. This is because the characteristics of e-learning are influenced by using authoring tools for making into video file and by freeing the number of students who register for a course. The result in this paper can be used as a basic data for studies designed to improve e-learning system architecture and server performance.

• Journal of The Korean Association For Science Education
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• v.32 no.2
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• pp.402-414
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• 2012

This study investigated the effects of the instructions using analogies in learning the concept of saturated solution by the analogy presentation types and the verbal learning styles upon the mapping understanding, the mapping errors, and the perceptions of the instruction. Fifth graders (N=123) at an elementary school were selected and assigned to VA (n=63) and VPA (n=60) groups. As a pretest, a test on the verbal learning style was administered. The students in the VA group learned the target concept with a verbal analogy, while those in the VPA group learned it with a verbal/pictorial analogy. After the students learned it, a mapping understanding test was administered. The students in the VPA group also administered the test on the perceptions of the instruction and some of them were interviewed in depth. The results revealed that the scores of the students with strong verbal learning preference in the VPA group were significantly lower than those in the VA group in the mapping understanding test. However, the scores of the students with weak verbal learning preference were not significantly different between the two groups. Five types of mapping errors were identified: failure to map, mismapping, rash mapping, impossible mapping, and mapping of a surficial feature. According to students' verbal learning styles, there were some differences in the frequencies of mapping errors in the two groups. Many students in the VPA group, regardless of their verbal learning styles, had positive perceptions of the instruction in various cognitive and motivational aspects. However, some of them also pointed out a few difficulties of the instruction. Educational implications of these findings are discussed.

• Korean Journal of Environment and Ecology
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• v.26 no.3
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• pp.454-462
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• 2012

This study was to research the effective use of green rooftop space, facilities and gardening, targeting members from community rehabilitation centers with disabilities. The three community rehabilitation centers studied were, Namyangju Center located in a rural area, Seoul Center located in a urban area and Siheung Center located in both a rural and urban area. We analyzed the difference in preference on the basis of each local community area. In fact, it indicated that 50% of each center knew about the green rooftop at their facilities and its use as a place for taking walks and conversation. It also showed that there was the high preference for priority objects such as a bench, pergola and trash can. Also the preference for natural visualizations like herbal or ornamental plants. The study showed a high preference to a small vegetable plot, hands on gardening and ecological wetland. It also indicated that there was a high preference for experience in nature programs on the rooftops (28.9 %) versus the rate of horticultural programs (27%). Therefore, it proves that the composition of a green rooftop at a community rehabilitation center should be differentiated so that the green rooftop can be a place not only for resting, but also great for a natural learning experience and gardening therapy for people with disabilities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.