• English Language & Literature Teaching
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• v.17 no.3
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• pp.177-194
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• 2011

This study examines the use of discourse markers in cross-cultural communication between EFL learners in an e-learning environment. The study analyzes the use of discourse markers in a corpus of an interactive web with a bulletin board system through which college students of English at Japanese and Korean universities interacted with each other discussing the topics of local and global issues. It compares the use of discourse markers in the learners' corpus to that of a native English speakers' corpus. The results indicate that discourse markers are useful interactional devices to structure and organize discourse. EFL learners are found to display more frequent use of referentially and cognitively functional discourse markers and a relatively rare use of other markers. Native speakers are found to use a wider variety of discourse markers for different functions. Suggestions are made for using computer corpora in understanding EFL learners' language difficulties and helping them become more interactionally competent speakers.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.56-62
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• 2023

In the educational environment of the post-COVID-19 era, metaverse learning, which can improve the disadvantages of online learning and improve learning outcomes, is attracting attention. Metaverse is expected to play an important role as a learning experience platform (LXP) that can provide immersion and experience for learners. In order to successfully introduce and utilize metaverse learning that utilizes the metaverse platform, teachers' knowledge of metaverse-related technologies and pedagogical convergence is important. So far, teacher knowledge for educational use of the metaverse has not been explored. In this regard, this study explored the TPACK (Technological, Pedagogical And Content Knowledge) framework as a teacher's knowledge system for metaverse learning. Based on this, this study designed the class contents of metaverse learning. The results of this study are expected to diffuse the importance of TPACK required for metaverse learning and contribute to the development of teachers' competence.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.5-27
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• 2016

This research aims to explore the acceptance of Moodle as a teaching and learning tool by the faculty of the Department of Information Studies (IS) at Sultan Qaboos University (SQU) in the Sultanate of Oman. The researchers employed the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioural intention of SQU faculty members to employ Moodle in their instruction. Data were collected by the interview method. Results showed the emergence of two faculty groups: one uses Moodle and one does not use Moodle. In group that uses Moodle, performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention are positively related, thereby influencing the faculty members' use behavior. In addition to the aforementioned UTAUT constructs, four additional factors affect Moodle's adoption. These moderators are gender, age, experience and the voluntariness of use, amongst which gender exhibits the least influence on Moodle adoption. That is, male and female faculty generally both use the learning platform. Although some members of the group that does not use Moodle exhibit optimistic performance expectancy for technology, the overall perception in this regard for Moodle is negative. The other UTAUT constructs exert no influence on this group's adoption of the learning platform.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.11-19
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• 2013

In this study, we analyze how the difficulties of e-learning firms' management affect to the maturity of the practical use of e-learning research & development (R&D) policies. And we explore the method that can enhance the effectiveness of policy. In the pursuit of this purpose, we use the 2012 South Korea e-learning industry survey data. Using variables of recognition of policy, experience of policy, and intention to use of policy, we find the maturity model of six stages. And we analyze the impact of the difficulties of operation, technology development, marketing to the maturity model. As a result, the more e-learning firms have problems of fund management and technology commercialization, they are located the higher maturity of the use of policy. Based on the results of these studies, we discuss the implication for how can enhance the effectiveness of policies.

• The Journal of Sustainable Design and Educational Environment Research
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• v.2 no.1
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• pp.105-112
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• 2002

New buildings are to be needed for an aging society. But, there may be a solution on the multi-purpose & mixed-use existing facilities. Therefore this study is focused on verification of mixed-use school facilities's potentialities. If possible, I want to suggest some directions of renewal primary school planning. The results of this study are as follows : 1) It is important to make a policy for improving the educational environment, 2) Future buildings must be planned by the principles based on the lifelong learning, 3) Mixed-use building blocks need to be laid out by the concept of accessibility, 4) We can use existing primary school's areas as a elderly welfare facilities without any renovation and increasing areas.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.

• Asia Pacific Journal of Corpus Research
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• v.4 no.1
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• pp.61-71
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• 2023

This study examines the effects of data-driven learning (DDL)-an approach employing corpora for inductive language pattern learning-on error identification in second language (L2) writing. The data consists of error identification instances from fifty-five participants, compared across different reference materials: the Corpus of Contemporary American English (COCA), dictionaries, and no use of reference materials. There are three significant findings. First, the use of COCA effectively identified collocational and form-related errors due to inductive inference drawn from multiple example sentences. Secondly, dictionaries were beneficial for identifying lexical errors, where providing meaning information was helpful. Finally, the participants often employed a strategic approach, identifying many simple errors without reference materials. However, while maximizing error identification, this strategy also led to mislabeling correct expressions as errors. The author has concluded that the strategic selection of reference materials can significantly enhance the effectiveness of error identification in L2 writing. The use of a corpus offers advantages such as easy access to target phrases and frequency information-features especially useful given that most errors were collocational and form-related. The findings suggest that teachers should guide learners to effectively use appropriate reference materials to identify errors based on error types.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.

• Journal of Information Technology Services
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• v.22 no.6
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• pp.17-35
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• 2023

This study investigated how helpful the use of generative AI such as ChatGPT is in conducting engaged learning at each university. In this study, based on the experiences of users using generative AI technology, we analyzed the relationship between usability and ease in consideration of the characteristics of learners, and examined whether there is an intention to continue using generative AI technology in the future. In this study, in order to verify the factors affecting the intention to use ChatGPT technology in order to solve the problems given in the participating classes, we examined previous papers based on the Technology Acceptance Model (TAM) and the Information System Success Model (IS), extracted the factors affecting the intention of ChatGPT technology, and presented the research model and hypothesis. Empirical research on the continuous use of generative AI in participatory learning using ChatGPT was conducted to determine whether it is suitable for long-term and continuous use in the educational environment, and whether it is sustainable by examining the intention of learners to continue using it. First, user satisfaction was positively related to the intention to continue using generative AI technology. Second, if the user experience has a great influence on the intention to continue using ChatGPT technology, and users gain experiences such as usefulness, interest, and effective response in the process of using the technology, the evaluation of the technology is positively formed and the intention to continue using it is high. Third, the ease of use of the technology also showed that it was intended to be used continuously when an environment was provided in which users could easily and conveniently utilize generative AI technology.