• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Journal of Korean language education
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• v.29 no.2
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• pp.101-123
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• 2018

The purpose of this paper is to develop a training program for overseas Korean language teachers focus on preservice teacher. This thesis based on the 2017 Korean Language (prospective) Teacher Overseas Dispatch and Practical Training Assistance Project (Almaty, Kazakhstan region). The present task established prospective teachers, career teachers, and local teachers as the targets of each assignment. We focused on developing a program that could match each of these characteristics. For prospective teachers, the program was designed and conducted with the goal of "improving real expertise through practical training," whereas for career teachers the program was developed with the goal of "improving leadership" and "retraining teachers" by focusing on their abilities as middle managers to build and maintain foreign and domestic networks. For local teachers, the goal was to provide "retraining as Korean language teacher certification". The limitations of those unable to attend domestic meetings were alleviated through training, workshops, and meetings, and a program was developed for real education practical training. For prospective teachers and career teachers in particular, the program was designed to center on a system of collaboration in which classes based on international Project Based Learning(iPBL) were conducted, and groups prepared practical training and practice modules.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.9-15
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• 2023

A common language for modeling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint-based approach that can be implemented along with an ordinary lab task. Some keywords are highlighted to indicate class diagram components and make students understand the textual descriptions. The experimental results indicate significant improvement in students' learning skills. Furthermore, the majority of students also positively responded to the survey conducted in the end experimental study.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.180-186
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• 2024

A common language for modelling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently in order to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint based approach that can be implemented along with an ordinary lab task. Some keywords are heighted to indicate class diagram component and make students to understand the textual descriptions. The experimental results indicate significant improvement in students learning skills. Furthermore, majority of students also positively responded to the survey conducted in the end experimental study.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.148-155
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• 2024

Despite the increasing importance of irony and sarcasm detection in the field of natural language processing, research on the Korean language is relatively scarce compared to other languages. This study aims to experiment with various models for irony detection in Korean text. The study conducted irony detection experiments using KoBERT, a BERT-based model, and ChatGPT. For KoBERT, two methods of additional training on sentiment data were applied (Transfer Learning and MultiTask Learning). Additionally, for ChatGPT, the Few-Shot Learning technique was applied by increasing the number of example sentences entered as prompts. The results of the experiments showed that the Transfer Learning and MultiTask Learning models, which were trained with additional sentiment data, outperformed the baseline model without additional sentiment data. On the other hand, ChatGPT exhibited significantly lower performance compared to KoBERT, and increasing the number of example sentences did not lead to a noticeable improvement in performance. In conclusion, this study suggests that a model based on KoBERT is more suitable for irony detection than ChatGPT, and it highlights the potential contribution of additional training on sentiment data to improve irony detection performance.

• English Language & Literature Teaching
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• v.7 no.1
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• pp.1-19
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• 2001

The current status of in-service teacher development shows that teachers' awareness can be enhanced through critical reflection. This study shows how an English teacher improved her own teaching situation through action research. It reports back the action research the teacher-researcher carried out in the EFL classroom setting. Aiming to improve the pupils' English speaking ability, the teacher introduced 'Task-based Language Teaching (TBLT)' to the English class. The teacher and the pupils took part in the evaluation process of learning and teaching. It was found that the new approach to teaching speaking helped the pupils improve speaking ability and take an active role in learning process. It is further suggested that teacher-initiated action research can be done in collaboration with colleagues, administrators and researchers.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.262-273
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• 2024

We provide a detailed analysis of the data processing and model training process for vulnerability classification using Transformer-based language models, especially sentence text-to-text transformers (ST5)-XXL and XLNet. The main purpose of this study is to compare the performance of the two models, identify the strengths and weaknesses of each, and determine the optimal learning rate to increase the efficiency and stability of model training. We performed data preprocessing, constructed and trained models, and evaluated performance based on data sets with various characteristics. We confirmed that the XLNet model showed excellent performance at learning rates of 1e-05 and 1e-04 and had a significantly lower loss value than the ST5-XXL model. This indicates that XLNet is more efficient for learning. Additionally, we confirmed in our study that learning rate has a significant impact on model performance. The results of the study highlight the usefulness of ST5-XXL and XLNet models in the task of classifying security vulnerabilities and highlight the importance of setting an appropriate learning rate. Future research should include more comprehensive analyzes using diverse data sets and additional models.

• English Language & Literature Teaching
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• v.15 no.3
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• pp.171-198
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• 2009

The current study explored a pedagogical possibility of utilizing magic as a source of communicative tasks for young learners in developing their English speaking proficiency. Fifteen primary school students participated in the study, which consisted of a 17-week period of task-based English instruction and data collection. The participants were instructed to accomplish various types of magic task through collaborative group interaction. The data collected for the study pertained to the students' linguistic outputs, interactions in group and attitudes to English learning. They were analyzed for how magic tasks affect the students' English proficiency developments and group interactions. The study results suggested the significant improvement in the students' English speaking proficiencies. They revealed that magic tasks contributed to a) enhancing the motivation to speak in English, b) stimulating the creative and problem-solving processes, and c) providing the sufficient opportunity to repeat and internalize the target expressions. The study results also indicated that the students' satisfaction with their group members and tasks seemed to have positive influences on their interactions in group and English proficiency development. Further discussion and pedagogical implications are provided as well as the study limitations.