• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.3
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• pp.61-70
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• 2024

This study aimed to analyze the impact of perceived usefulness, ease of use, and satisfaction with ChatGPT on the intention to use it. Data were collected through an online survey, and the results showed differences in perceived usefulness, ease of use, and intention to use according to the demographic characteristics of the research subjects. Furthermore, a multiple regression analysis was conducted to examine the impact of ChatGPT's usability, ease of use, and satisfaction on the intention to use. The results indicated statistically significant differences in perceived usefulness, ease of use, and intention to use ChatGPT between students in different academic years. In addition, perceived usefulness, ease of use, and satisfaction with ChatGPT showed a significant positive influence on the intention to use it. This study is significant as it analyzes the intention to use ChatGPT, considering the role of generative AI in digital education and innovative teaching methods in the educational context.

• Ecology and Resilient Infrastructure
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• v.2 no.1
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• pp.12-21
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• 2015

Social demand for ecological engineering and technology has increased in tandem with national economic growth in order to improve the environmental capacity of civil infrastructures. To meet this demand, the Korean Society of Ecology and Infrastructure Engineering (KSEIE) was established in January 2013 and has contributed to the development of ecological engineering technologies. However, the establishment of an educational system for human resources training in ecological engineering is still at an early stage, and it is imperative to develop a curriculum for producing the human resources that can understand and apply ecological principles and functions and that is equipped with the abilities required for ecological conservation, restoration, and creation. As part effort, the KSEIE held a forum, entitled Founding the Education for Ecological Engineering, to discuss the establishment of the education system for ecological engineering in Korea. In this paper, based on the discussions and suggestions made during the forum, we analyzed the current status of ecological engineering education in various disciplines - civil and construction engineering, biology and environment, and landscape planning - in domestic universities, and attempted to seek possible solutions based on the cases of foreign universities. Generally, ecology and other application curricula are taught as fragmented subjects and fields in domestic universities. The development of new education strategies and systematic curricula for multidisciplinary education, ecological response to climate change, and the expansion of research fields is required.

• Communications of Mathematical Education
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• v.36 no.1
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• pp.1-21
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• 2022

The purpose of this study is to analyze the current situation of EduTech, which is proposed as a way to build a flexible learning environment regardless of time and place according to the use of digital technology in mathematics subjects. The process of designing classes to use the EduTech platform, which is still in the development introduction stage, in public education is still difficult, and research to observe its effects and characteristics is also in its early stages. However, in the stage of preparing for future education, it is a meaningful process to grasp the current situation and point out the direction in preparation for the future in which EduTech will be actively applied to education. Accordingly, the current situation and utilization trends of EduTech at home and abroad were confirmed, and the functions and roles of EduTech platforms used in mathematics were analyzed. As a result of the analysis, the EduTech platform was pursuing learners' self-directed learning by constructing its functions so that they could be useful for individual learning of learners in hierarchical mathematics education. In addition, we have confirmed that the platform is evolving to be useful for teachers' work reduction, suitable activities, and evaluations learning management. Therefore, it is necessary to implement instructional design and individual customized learning support measures for students that can efficiently utilize these platforms in the future.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.61-71
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• 2021

In a situation where the online learning is expanding due to COVID-19, the current maker education has limitations in applying it to classes. This study is to design the class of online maker education using artificial intelligence tools in elementary school. Also, it is to identify the responses to it and to confirm whether it helps improve the learner's computational thinking and creative problem solving ability. The class was designed by the literature review and redesign of the curriculum. Using interveiw, the responses of instructor and learners were identified. Pre- and post-test using corresponding sample t-test was conducted. As a result, the class consisted of ten steps including empathizing, defining making problems, identifying the characteristics of material and tool, designing algorithms and coding using remixes, etc. For computing thinking and creative problem solving ability, statistically significant difference was found. This study has the significance that practical maker activities using educational artificial intelligence tools in the context of elementary education can be practically applied even in the online environment.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.449-457
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• 2024

AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

• Journal of Life Science
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• v.18 no.10
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• pp.1384-1389
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• 2008

The purpose of this study was to find out the effects of aerobic training plus diet on blood lipids and apolipoproteins (Apo) in obese children. Sixteen healthy obese boys (ages 10.9; body mass index (BMI) $\geq$95th percentiles for age and sex) participated in this study. The aerobic training program consisted of 40 minutes hiking on a mountain, 60 minute of basketball and football dribbling at an intensity of 60-70% of HRmax, and was performed 5 days a week for 9 weeks. The diet prescription was 2,100 kal/day according to the recommended dietary allowance for 10-12 year old Koreans. All subjects stayed in a training camp for 9 weeks. The results of this study were as follows; Blood lipid profiles including total cholesterol (TC), triglyceride (TG) high density lipoprotein cholesterol (HDL-C) and TC/HDL-C ratio were significantly improved after the 9 week program, but there was no significant change in low density lipoprotein (LDL-C). Apolipoprotein profiles, Apo AI, AII, B, CII and CⅢ were all significantly decreased after the 9 week program, but there were no significant difference in Apo AI/AII ratio and Apo B/AI ratio. These results indicate that aerobic training together with a healthy diet can induce positive changes on blood lipid profiles, Apo AII, B and CII in obese children.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.73-82
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• 2024

In 2017, the United Nations reported that the population aged 60 and above was increasing more rapidly than all younger age groups worldwide, projecting that by 2050, the population aged 60 and above would constitute at least 25% of the global population, excluding Africa. The world is experiencing a decline in the rate of increase in the working-age population due to global aging, and the younger generation tends to avoid difficult and challenging occupations. Although theoretically, AI equipped with artificial intelligence can replace humans in all fields, in the realm of practical information security, human judgment and expertise are absolutely essential, especially in ethical considerations. Therefore, this paper proposes a method to retrain and reintegrate IT professionals aged 50 and above who are retiring or seeking career transitions, aiming to bring them back into the industry. For this research, surveys were conducted with 21 government/public agencies representing demand and 9 security monitoring companies representing supply. Survey results indicated that both demand (90%) and supply (78%) unanimously agreed on the absolute necessity of such measures. If the results of this research are applied in the field, it could lead to the strategic development of senior information security professionals, laying the foundation for a new market in the Korean information security industry amid the era of low birth rates and longevity.

• Nutrition Research and Practice
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• v.3 no.3
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• pp.171-179
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• 2009

Onto the world-fastest ageing of society, the world-lowest fertility rate prompted a development of various policies and programs for a betterment of the population in Korea. Since the vulnerability of young children of low socio-economic class to malnutrition was clearly shown at the in-depth analysis of the 2001 Korea National Health and Nutrition Examination Survey data, an effort to devise supplemental nutrition care program for pregnant/breastfeeding women, infants and preschool children was initiated. The program was designed to offer nutrition education tailored to fit the needs of the participants and special supplementary foods, using USDA WIC program as a benchmark. Based on the dietary intake of those age groups, target nutrients were selected and their major food sources were searched through nutrient content of foods and dietary pattern analysis. As a result, we developed 6 kinds of food packages using combinations of 11 different food items. The amount of each item in a food package was determined to supplement the intake deficit in target nutrients. Nutrition education in $NutriPlus^+$ aims to improve the nutrition knowledge, attitude, and dietary behaviors of the participants, and is provided through group lessons, individual counseling sessions and home visits. Breastfeeding is promoted with top priority in education for the health of both mother and baby. The eligibility guidelines were set for residency, household income, age, pregnancy/breastfeeding and nutritional risk such as anemia, stunting, underweight, and/or inadequate nutrient intake. Income eligibility was defined as household income less than 200 percent of the Korean poverty guidelines. A pilot study to examine the feasibility of program implementation was run in 3 public health centers in 2005 and expanded to 15 and 20 in the following 2 years. The result of 3-year pilot study will be reported separately along with the ultimate nationwide implementation of the $NutriPlus^+$ in 2008.