• Journal of Korea Game Society
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• v.9 no.4
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• pp.67-80
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• 2009

In this paper, we described the development of a Korean Manual Alphabet (KMA) learning game with avatar. KMA letters correspond to the vocabulary of Korean Sign Language (KSL) when spelling a word. Each KMA letter corresponds to a letter of the Korean Alphabet (KA) and KA is represented as hand shapes by sign language user. We developed a KMA learning game for a beginner to learn KMA letters from sign language avatar and practice KMA presentation easily. The system composed of sign language teacher avatar GUI popup window based on OpenGL, KMA letter recognition module, KA letter raining game module and USB camera. A user learns a KMA letter with expressing KA syllabic from avatar and inputs a KMA letter to the system using USB camera. We evaluated the efficiency of the developed system through the verification of users.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• Smart Media Journal
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• v.12 no.9
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• pp.19-27
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• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.65-86
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• 2021

In the foreign language education, learners are an important part of education, however in the Korean language education, the study of learners was insufficient compared to the contents of education, teaching methods and textbooks. Therefore, it is meaningful to analyze how learner research, especially learning strategy research, has been conducted and derive areas that need research for better education. In this study, co-word analysis was conducted on the titles of academic journals and dissertations in order to analyze the learning strategy research in Korean language education. I found it is about "reading" that the most studies related to Korean language learners' learning strategies were conducted and those studies' subjects mostly were 'Chinese international students' and 'marriage-immigrants'. In addition, the results of the subgroup analysis on the research topic show four major subgroups: a group related to 'reading for academic purposes', a group related to 'request, rejection, conversation, etc.', a group related to 'writing', and a group related to 'vocabulary, listening'. This shows that the researchers' major interests in studying Korean learner's strategies are "reading" and "speaking" and their studies have been concentrated in the specific areas. Therefore, it is necessary for researchers to study various functions and subjects in Korean language learner's learning strategies.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.21-32
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• 2014

Recently, the number of Koreans who use smartphones has increased drastically; many use smartphones to learn English. In this study, one hundred Korean adult ELT (English language teaching) learners were surveyed to investigate their use of smartphones and factors influencing such use. For comparison, sixty-two students of a Korean cyber university were surveyed; these students were able to study using their smartphones in a smart campus environment. The research results showed that both groups positively used smartphones frequently, and that many intended to continue using them. With regard to ELT, both groups intended to learn English using their smartphones. Furthermore, they preferred certain types of ELT content: thirty-minute or less learning sessions, receptive English skills that focused on listening and reading, and short units of framed language items such as pronunciation and vocabulary. However, few of the respondents in both groups installed ELT apps on their smartphones, and few of the ELT apps satisfied them. The cyber university students responded similarly about smartphone use, although their responses regarding smartphone use for ELT purposes were less positive. These results indicate that the goal of cyber universities in achieving optimum learning outcomes through smart learning and the smart campus has not yet been realized.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.509-519
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• 2017

This study addresses the effective utilization of film music as learning material for language, target culture, and ethics education. Music is intertwined with language and culture, and even with ethics. This study focuses on the potential power of film music in the processes of teaching and learning in a classroom. For this purpose, five songs are selected from the soundtrack of Disney's famous animation The Lion King: "Circle of life", "I just can't wait to be king", "Be prepared", "Hakuna Matata", and "Can you feel the love tonight?", and concrete learning activities are suggested based on these. Using these five songs, gap-filling and singing-recoding tasks are proposed as listening and speaking activities respectively. Film music is also very useful in learning vocabulary, sentence structure, and grammar. Learners participate in a writing activity involving creating their own lyrics for the tunes reflecting their experiences. Next, for culture education, a teacher asks their students to discuss about, and be aware of, food culture using a specific character's song. Finally, for ethics education, a philosophy of life, natural logic, leadership qualities, and the motto Hakuna Matata("no worries") are explored and discussed through an analysis of the lyrics. The open-ended questionnaire survey is conducted. The result shows that music has a positive effect on culture and ethics education. Film music can be effective in learning a second language, target culture, and ethics.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.23-30
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• 2019

The development of content for language education using AR/VR technology is a necessary task to be pursued in line with commercialization of 5G. Research on service platform for systematic management and service is currently being carried out by global companies competitively, The unique language education service model for unique areas of culture has the right to pursue R & D jointly with Korea and China. In this study, we applied the developed "Korean language education service platform for Chinese people based on e-learning" to improve the acceptance of AR/VR contents and applied AR/VR technology to video-based language education contents. And to present a new paradigm of language education. Contents development is to develop AR-based vocabulary learning services, develop experiential learning contents for VR-based step-by-step situations, and gradually develop contents to enable beginner / intermediate / advanced language education services. The service platform enables management of learning management and learning contents, and complies with metadata attributes to complete a platform capable of accommodating large capacity AR/VR contents. In the future, systematic research will be carried out in order to develop as a portal for educational services through development of various contents using mixed reality technology.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Korean Journal of Human Ecology
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• v.16 no.4
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• pp.699-710
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• 2007

The study purposes to explore the effects of individual characteristics and home environments of children on their bilingual language aquisition, that is, to examine whether their English language competency is different from their Korean language competency depending on those variables. Thus English or Korean language competency of children who had had early exposure in English learning were studied in terms of child's individual characteristics such as age, gender, exposure period to English, intelligence, and experiences of visiting English-speaking countries, and home environments such as parental age, educational level, income level, their perceived English competency, their perceived significance of English and Korean language, and the frequency of using English at home. 72 children who went to English kindergarten were tested with Peabody Pictures Vocabulary Test-Revised (PPVT-R) in Korean version and in English version respectively. The results show that child's intelligence and experiences of visiting English-speaking countries influence their Korean language competency. Also child's age, exposure period to English and experiences of visiting English-speaking countries influence their English language competency. Moreover their mother's educational background, father's English fluency, mothers' English fluency, and the frequency of using English at home influence child's English language competency, whereas any variables did not influence child's Korean language competency. Accordingly, child's English and Korean language competencies are related to each other.