• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.425-433
• /
• 2023

In this study, we developed a virtual artist play lesson model using gamification concepts and AI-generated art programs to foster creative thinking in freshman art majors. Targeting first-year students in the Digital Media Art Department at Sichuan Film & Television University in China, this course aims to alleviate fear of artistic creation and enhance problem-solving abilities. The educational model consists of four stages: persona creation, creative writing, text visualization, and virtual exhibitions. Through persona creation, students established their artist identities, and by introducing game-like elements into writing experiences, they discovered their latent creativity. Using AI-generated art programs for text visualization, students gained confidence in their creations, and in the virtual exhibitions, they were able to enhance their self-esteem as artists by appreciating and evaluating each other's works. This educational model offers a new approach to promoting creative thinking and problem-solving skills while increasing learner engagement and interest. Based on these research findings, we expect that by developing and implementing educational strategies that cultivate creative thinking, more students will grow their artistic capacities and creativity, benefiting not only art majors but also students from various fields.

• Journal of the Korean Society for Library and Information Science
• /
• v.57 no.2
• /
• pp.379-408
• /
• 2023

This case study reports on the redevelopment of a course, Local Culture Information Theory offered by the Department of Library and Information Science at C University, into a capstone design course using a project-based learning approach. In collaboration with a local community youth organization, the redesigned course provided an opportunity for LIS students to develop and implement a digital literacy program that enabled high school students to use a variety of digital multimedia technologies to complete a project of digital Human Library featuring video, audio, and digital are such as webtoons. Through semi-structured interviews with 5 students and 3 staff from partner organizations, this study reports on course development process, the establishment of local partnerships, project outcome, as well as suggestions for improvements. In addition, a qualitative analysis of the participating students' interview responses using the Framework for 21st Century Learning (P21) found they developed and improved 11 skills across three core areas: life and career skills including self-direction, project management, collaboration with diverse teams, flexibility, responsibility, leadership; learning and innovation skills including communication and collaboration, problem-solving, creativity, and critical thinking; and information, media, and technology skills through media creation. Lessons learned and recommendations from this case study may be useful for other LIS programs and faculty interested in implementing project-based learning or developing capstone design courses.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.205-218
• /
• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Therapeutic Science for Rehabilitation
• /
• v.12 no.4
• /
• pp.23-37
• /
• 2023

Objective : This study systematically reviewed the collaborative team interventions of the Individualized Education Plan (IEP) using the International Classification of Functioning, Disability, and Health-Children and Youth (ICF-CY) framework to establish the professional domain of occupational therapists in Korea and their role as experts in IEP cooperative team interventions in special education. Methods : Articles were collected from the EBSCOhost, ProQuest, and PubMed databases. International search terms included "Special education," "Individualized education plan (IEP)," "IEP process," "IEP implementation," and "Occupational therapy." The study period was limited from January 2013 to February 2023, and the final 10 studies were analyzed using secondary classification. Results : Most studies were randomized experiments targeting individuals with autism, and often employed environmental improvements. The IEP collaborative team interventions using the ICF-CY framework emphasized goals related to activity (five studies), participation (four studies), and body structure/function (one study). Conclusion : Occupational therapists play a crucial role in collaborative IEP team interventions. This study established expertise in the context of special education in South Korea.

• Journal of Practical Engineering Education
• /
• v.15 no.3
• /
• pp.605-614
• /
• 2023

In In the era of digital transition, AI-based personalized services are emerging in the field of education. This research aims to examine the development strategies for implementing AI-based learning services in school. Focusing on AI-based math learning service "Math Cell" developed by i-Scream Edu, this study surveyed the functional requirements from the perspective of an educator. The results were analyzed for importance and suitability using IPA, and expert opinions were surveyed to explore specific development directions for the service. Consequently, importance in all areas such as diagnosis, learning, evaluation, and management averaged 4.82 and performance averaged 4.56, showing excellent results in most questions, and in particular, importance was higher than performance. Among certain detailed functions, concept learning, customized task presentation, evaluation result analysis function, dashboard-related functions, and learning materials in the dashboard were not intuitive for students to understand and had to be supplemented. This study provides meaningful insights by summarizing expert opinions on AI-based personalized mathematics learning services, thereby contributing to the exploration of the development strategies for "Math Cell".

• Korean Journal of Ecology and Environment
• /
• v.56 no.4
• /
• pp.339-347
• /
• 2023

Dissolved organic matter (DOM) is a key component in the biogeochemical cycling in freshwater ecosystem. However, it has been rarely explored, particularly complex river watershed dominated by natural and anthropogenic sources, such as various effluent facility and livestock. The current research developed a new analytical method for TOC/TN (Total Organic Carbon/Total Nitrogen) stable isotope ratio, and distinguish DOM source using stable isotope value (δ¹³C-DOC) and spectroscopic indices (fluorescence index [FI] and biological index [BIX]). The TOC/TN-IR/MS analytical system was optimized and precision and accuracy were secured using two international standards (IAEA-600 Caffein, IAEA-CH-6 Sucrose). As a result of controlling the instrumental conditions to enable TOC stable isotope analysis even in low-concentration environmental samples (<1 mgC L^-1), the minimum detection limit was improved. The 12 potential DOM source were collected from watershed, which includes top-soils, groundwater, plant group (fallen leaves, riparian plants, suspended algae) and effluent group (pig and cow livestock, agricultural land, urban, industry facility, swine facility and wastewater treatment facilities). As a result of comparing characteristics between 12 sources using spectroscopic indices and δ¹³C-DOC values, it were divided into four groups according to their characteristics as a respective DOM sources. The current study established the TOC/TN stable isotope analyses system for the first time in Korea, and found that spectroscopic indices and δ¹³C-DOC are very useful tool to trace the origin of organic matter in the aquatic environments through library database.

• Journal of Service Research and Studies
• /
• v.13 no.4
• /
• pp.173-190
• /
• 2023

In recent times, with the development of virtual convergence technologies, the market for the Metaverse, a digitally virtual space that combines virtuality and reality, is experiencing significant growth. These Metaverses are realizing new value in both reality and virtual spaces through the development of diverse services and content. However, existing research on the Metaverse mostly revolves around its conceptualization and categorization, with limited exploration of intentions to use the Metaverse. Consequently, this study examined the impact of Metaverse service characteristic factors on trust and intention to use within the Metaverse. The results of this study are as follows. First, among the service characteristic factors of the Metaverse, presence, interactivity, and playfulness were found to have a positive impact on Metaverse trust. On the other hand, informativeness was found not to have a significant influence on trust in the Metaverse. Second, Metaverse trust was found to have a positive impact on intention to use the Metaverse. Based on the research results above, this study aims to propose effective communication strategies for activating the Metaverse and developing services within the Metaverse platform.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.5
• /
• pp.127-136
• /
• 2023

Gyeonggi Province has established a bus information system to provide real-time bus arrival information, aiming to make bus usage convenient for its residents. While the Gyeonggi bus information system is becoming more advanced through the application of IT technology, there are still information-vulnerable groups finding it difficult to use. In particular, the elderly have a low level of digital information literacy and habe difficulty using it. In this regard, this study aims to address the information usage disparity among the elderly in public transportation by utilizing expert in-depth survey methodology known as the Delphi technique. The study classified the policy initiatives that Gyeonggi Province should undertake into three categories: user education and expanded promotion, technological development and dissemination, and providing convenient usage environment. Through two rounds of surveys, the study assessed the priority of ten specific sub-tasks within these categories. Additionally, it gathered opinions on the effectiveness and feasibility of each item. The results yielded prioritization and evaluation of effectiveness and feasibility for nine sub-tasks. Based on these outcomes, the study proposed future projects that Gyeonggi Province should implement to address the information disparity among the elderly, offering a comprehensive approach to bridge the gap.

• Journal of Venture Innovation
• /
• v.5 no.4
• /
• pp.23-39
• /
• 2022

This study was conducted to empirically analyze the factors influencing the intention to continue using live commerce, which was active as digital contacts become more prevailed due to the progress of the 4th industrial revolution and the COVID-19 pandemic. A research model was established by paying attention to the characteristics of influencers among various influencing factors. Influencer characteristics were subdivided into attractiveness, professionality, awareness, and entertainment. In addition, the dual mediating effect of interaction and trust was also tested between influencer's characteristics and the intention to continue using of live commerce. To this end, a survey was conducted targeting people who have experience using live commerce, and 300 valid samples were analyzed. The empirical analysis utilized SPSS 25.0 and Process Macro 4.0. As a result of the empirical analysis, it was found that attractiveness, professionality, awareness, and entertainment derived from the characteristics of influencers all had a significant positive (+) effect on the intention to continue using live commerce. The impact of the influence of variables that directly affect the intention to continue using was in the order of entertainment, awareness, professionality, and attractiveness. On the other hand, as a result of examining the dual mediating effect of interaction and trust, it was found that they all tested a mediating role between attractiveness, professionality, awareness, entertainment, and intention to continue using live commerce. Based on these research results, the academic and practical implications of this study were presented.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.10
• /
• pp.455-460
• /
• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.