• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.24-33
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• 2021

This study examined a semantic relationship expressed in college presidents' welcome greetings in order to explore the promotion strategies and future direction of universities in Daegu & Gyeongbuk provinces in South Korea. Greetings were collected from university websites as of September, 2020. According to word frequency analysis, "everyone," "welcome," and "visiting" were mostly used in the headlines. In the body texts, "college" and "education" were frequently paired. While the two- & three-year colleges focus on industrial and technical capabilities, four-year universities tend to emphasize educational excellence and academic research performance. This study is valuable in that it understands the direction that universities in Daegu and North Gyeongsang Province put forward amid the decreasing school-age population and the changing social environment.

• Knowledge Management Research
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• v.22 no.3
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• pp.91-106
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• 2021

With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.

• The Journal of Korean Association of Computer Education
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• v.22 no.2
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• pp.29-38
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• 2019

The evaluation of programs related to SW development often only provides the right answer of the student's program. The purpose of this study was to provide the baseline data about the contents of the program evaluation support the teacher's class and which part should be considered important in partial scoring. To accomplish the goal, we had two months of Python lessons for 90 middle school students in free-semester and analyzed 1185 source codes collected during the lessons. Result of analysis, many students made mistakes about syntax errors and teachers considered logic errors as important. Based on the result, it is necessary to reduce the student's syntax errors and teachers need to evaluate student's program with considering the importance of logical aspects and necessary to devise a partial scoring. This study has significance about consideration of program evaluation from the perspective of learning support and evaluation.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.236-249
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• 2018

The purpose of this study is to examine the effects of educational activities based on oriental mythology on young children's creativity and personality. A total of 14 sessions were applied to 36 children (18 experimental group and 18 control group). For this study, myths in Shan-hai jing, The Classic of Mountains and Seas), which is the origin of oriental mythology, were used as basic data of class activities and developed to experimental groups. The collected data were analyzed using SPSS 21.0 statistical program and covariance analysis (ANCOVA) was conducted with covariance score of the group after the activity. The results of this study were as follows: First, integrated educational activities using oriental mythology had a positive effect on all children's creativity and all sub-factors. Second, integrated educational activities using Oriental mythology have a positive effect on children's personality. The expansion and transformation of educational texts to oriental mythology is not only helpful for young children's creativity and personality development, but also enables them to experience cultural balance through their new understanding of the Orient.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.93-100
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• 2019

Recently, in various business fields, companies are concentrating on providing chatbot services to various environments by adding artificial intelligence to existing messenger platforms. Organizations in the field of job placement also require chatbot services to improve the quality of employment counseling services and to solve the problem of agent management. A text-based general chatbot classifies input user sentences into learned sentences and provides appropriate answers to users. Recently, user sentences inputted to chatbots are inputted as short texts due to the activation of social network services. Therefore, performance improvement of short text classification can contribute to improvement of chatbot service performance. In this paper, we propose T-EBOW (Translation-Extended Bag Of Words), which is a method to add translation information as well as concept information of existing researches in order to strengthen the short text classification for employment chatbot. The performance evaluation results of the T-EBOW applied to the machine learning classification model are superior to those of the conventional method.

• 한국체육학회지인문사회과학편
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• v.56 no.1
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• pp.99-110
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• 2017

The purpose of this study was to focus on the social influence of the movie , to review the meaning and symbolism of Koreans living in Japan (ざいにち) and rugby in the movie, and to eventually reveal the messages from the movie. The research method was text analysis. As a result, Koreans living in Japan were represented in 2 ways. Koreans living in Japan were represented as Homo Sacer, the contradictory being, located inside society by Japan's sovereignty but considered as outsiders. Meanwhile, the identity of Koreans living in Japan were represented clearly as Korean and they were acknowledged in Japan as proud and capable. The rugby in the movie has 2 symbolic meanings. First, it was the most important and effective way to prove Korean existence in Japan by representing the struggle for recognition, additionally it also carried an important message about their ideal society.

• Journal of Industrial Convergence
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• v.19 no.3
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• pp.61-65
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• 2021

This study aimed to analyze effects of Arduino-based education for secondary school students through meta-analysis. Prior studies including journal articles and theses were selected through rigorous review. The researcher calculated the overall effect size and effect sizes by categorical variables. Research findings are as follows. First, the total effect size of Arduino-based education was 0.537. Second, the effect sizes by type of dependent variables were the affective domain 0.849 and the cognitive domain 0.479. Third, the effect sizes by school level were the middle school 0.796 and the high school 0.474. Fourth, the effect sizes by subject areas were music 1.255, science 0.562, technology 0.443, and information 0.429. Fifth, the effect sizes by types of programming were the graphic-based programming 0.543 and the text-based programming 0.376.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.107-126
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• 2021

In e-commerce, rapid and accurate automatic product classification according to product information is important. Recent developments in deep learning technology have been actively applied to automatic product classification. In order to develop a deep learning model with good performance, the quality of training data and data preprocessing suitable for the model are crucial. In this study, when categories are inferred based on text product data using a deep learning model, both effects of the data preprocessing and of the selection of training data are extensively compared and analyzed. We employ our CNN model as an example of deep learning model. In the experimental analysis, we use a real e-commerce data to ensure the verification of the study results. The empirical analysis and results shown in this study may be meaningful as a reference study for improving performance when developing a deep learning product classification model.