• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.983-991
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• 2003

The Web-based instruction on the internet provides lots of learners with the related information and knowledge beyond time and space. But in the Web-based instruction, there is a problem that the teaming process statuses for learners can be known only through an exam. This paper introduces a web monitoring method to check whether the learner has some problems in learning process and to be able to find out the students with the problems. In the method this paper proposes a learning time prediction model for predicting the proper next study time intervals based on the learner`s learning times and grades on Previous learning units. This method provides the educator with the learning Process statuses for learners. The Loaming prediction model for web-based monitoring can be used to stimulate learners to take the good teaming processes by sending automatically alerting messages if their real teaming times exceeds on his predicted learning time interval. The results of the estimation through case study on the web-based monitoring to use the teaming time prediction model show that most of on-line learners with Poor teaming process statuses get poor grades. In addition, the results show that learner`s poor habits keep going on without change.

• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.359-379
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• 2019

This study aims to suggest the ways to reinforce academic library's supports for users' class·learning activities. For this purpose, this study collected the development plans of academic libraries in Korea, and analysed the plans for supporting class·learning activities. As a result, it is shown that the most libraries emphasized 'expansion of learning material' and marked it on development plan. As subsequent plans, libraries provided the action plans of 'expansion of reading education and reading programs', 'expansion of electronic materials', 'expansion of characterized materials' one after another. This study suggests 'user-centered collection development and expansion of learning materials', 'activation of library services making use of big data', 'enlargement of engagement services for handicapped and foreign students' as ways to strengthen the services of academic libraries to support class·learning activities of users.

• Journal of Gifted/Talented Education
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• v.21 no.2
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• pp.255-268
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• 2011

The 21st century is called as the "Age of knowledge flood", and thus the importance of the ability which can use knowledge creatively is more emphasized. Also, not only individual problem solving but also solving problems through effective communication skills with group members are needed, and therefore, it is requested to train potential gifted learner working together with others to practice cooperation and eventually grown up as a competitive human resource to adapt successfully in future environment. In this paper, to show the effectiveness of cooperative learning in gifted learners, members for cooperative learning group has been selected using whole brain theory from the 42 gifted middle school students who participated in summer gifted learner vacation program. From the analysis of the learners' learning motivation and frequency of interactions whole brain based cooperative learning is effective for enhancing both learning motivation and interactions. Therefore, the whole brain based cooperative learning is an effective pedagogy for enhancing the motivation as well as facilitating interactions within gifted learners.

• Journal of Korea Game Society
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• v.16 no.3
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• pp.7-16
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• 2016

This research aims to suggest a gamified smart learning design for Korean EFL young learners' speaking proficiency. Gamification is the use of game-thinking and game mechanics in non-game contexts to engage users in solving problems. Thus, the gamified smart learning as gamification in education is designed not only to elicit students' participation but also to enhance speaking skills. Especially, this research based on the results of a pilot study is focused on easing the burden of homework as well as engaging the speaking English game for the primary students with a relatively short attention span. The game elements utilized in this study are competition, rewards, customized characterization and so on. Kakao Talk is selected for this gamified smart learning research because of its ease of accessibility, and multiple applicable functions for language learning such as voice recording, text messaging and sharing videos or photos. Gamification in smart learning can be a means of productive approach to contemporary EFL teaching and learning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.337-347
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• 2020

In CRYPTO 2019, Gohr presents that Deep-learning can be used for cryptanalysis. In this paper, we verify whether Deep-learning can identify the structures of S-box. To this end, we conducted two experiments. First, we use DDT and LAT of S-boxes as the learning data, whose structure is one of mainly used S-box structures including Feistel, MISTY, SPN, and multiplicative inverse. Surprisingly, our Deep-learning algorithms can identify not only the structures but also the number of used rounds. The second application verifies the pseudo-randomness of and structures by increasing the nuber of rounds in each structure. Our Deep-learning algorithms outperform the theoretical distinguisher in terms of the number of rounds. In general, the design rationale of ciphers used for high level of confidentiality, such as for military purposes, tends to be concealed in order to interfere cryptanalysis. The methods presented in this paper show that Deep-learning can be utilized as a tool for analyzing such undisclosed design rationale.

• Korean journal of medical education
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• v.30 no.4
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• pp.295-308
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• 2018

Purpose: Although medical curricula are now better structured for integration of biomedical sciences and clinical training, most teaching and learning activities still follow the older teacher-centric discipline-specific formats. A newer pedagogical approach, known as Collaborative Learning Cases (CLCs), was adopted in the medical school to facilitate integration and collaborative learning. Before incorporating CLCs into the curriculum of year 1 students, two pilot runs using the action research method was carried out to improve the design of CLCs. Methods: We employed the four-phase Kemmis and McTaggart's action research spiral in two cycles to improve the design of CLCs. A class of 300 first-year medical students (for both cycles), 11 tutors (first cycle), and 16 tutors (second cycle) were involved in this research. Data was collected using the 5-points Likert scale survey, open-ended questionnaire, and observation. Results: From the data collected, we learned that more effort was required to train the tutors to understand the principles of CLCs and their role in the CLCs sessions. Although action research enables the faculty to improve the design of CLCs, finding the right technology tools to support collaboration and enhance learning during the CLCs remains a challenge. Conclusion: The two cycles of action research was effective in helping us design a better learning environment during the CLCs by clarifying tutors' roles, improving group and time management, and meaningful use of technology.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.119-129
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• 2009

In The increasing use of computers and high-speed Internet network has greatly influenced education, causing a veering away from the typical and traditional way of delivering instruction. Specifically, the various kinds of Web-based multimedia technology, the interactive activities on the Internet, and satellite broadcasting technology are accelerating the emergence of a virtual-lectures-based educational model, which transcends time and space. Such virtual lectures make it possible for the entire teaching-learning process to be done in a virtual learning environment, thus giving rise to problem regarding learning guidance, feedback, and appraisal. In this paper, we propose a system for attendance appraisal for learner participation degree based virtual lecture, an appraisal element in virtual learning environments. This appraisal model can set the elements of virtual learning environments in such a way as to reflect in the attendance appraisal of the opened virtual learning environment information regarding the learner's participation in class. In addition, this model motivates the learners to actively participate in the virtual learning environment and to support instructors by accomplishing the activities that are needed for attendance appraisal.