This study aims to investigate the online class preference depending on students' gender and school level. To achieve this aim, the study conducted a survey on 4,803 elementary, middle, and high school students in 17 regions nationwide. The valid data of 4,524 were then analyzed using the Apriori algorithm to discern the associated patterns of the online class preference corresponding to their gender and school level. As a result, a total of 16 rules, including 7 from elementary school students, 4 from middle school students, and 5 from high school students were derived. To be specific, elementary school male students preferred software-based classes whereas elementary female students preferred maker-based classes. In the case of middle school, both male and female students preferred virtual experience-based classes. On the other hand, high school students had a higher preference for subject-specific lecture-based classes. The study findings can serve as empirical evidence for explaining the needs of online classes perceived by K-12 students. In addition, this study can be used as basic research to present and suggest areas of improvement for diversifying online classes. Future studies can further conduct in-depth analysis on the development of various online class activities and models, the design of online class platforms, and the female students' career motivation in the field of science and technology.
Ji, Hyo-Sang;Kim, Jae-Sung;Kim, Ri-Won;Kim, Jeong-Joon;Han, Ik-Joo;Park, Jeong-Min
Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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v.7
no.8
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pp.327-342
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2017
In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.
Here are background, method, scope, main contents of this research. As the interests increased in recent about the construction in complex and diverse areas, construction is locally connected to human life like to coexistence of the technology and culture. The local development should not be fragmentary construction to improve local recycling ability. Local society should be inherited by modern cultural perspective through a variety of local culture and coexistence. Effective decision making analysis is necessary to build a livable area with a combination of high-tech industry. For this reason, this paper will study the political analysis for decision making at the planning stage of construction in point of fusion of technology and culture by using unstructured data analysis. Conclusion is as in the following. Local planning stage of construction describes diverse meanings of intangible and intangible factors as political factor. Technology factors have various qualitative and quantitative factors in construction field. Understanding decision making at the planning stage of construction means not only visible 'technology factor' such as structure, method, shape, and so on, but also invisible 'culture factor' such as spirit of age, religion, learning, and life-style reflected in formation process of space, and insight of brain power about art.
The purpose of this study is to use K-MOOC, which has limitations in utilization because it is centered on theory delivery, to derive tasks to activate the teaching methods of instructors, and to implement the derived tasks using the metaverse platform. to develop a prototype. According to the purpose of the study, the study was conducted as follows. First, from October 4 to November 15, 2022, a Delphi survey was conducted on 21 experts with experience of consulting, research, class development, and operation related to the K-MOOC project. Second, in order to realize the tasks in the teaching method field derived from the Delphi survey, matching with the teaching method class model elements to result of Delphi survey was applied was carried out. Finally, based on the results of expert Delphi and the elements of the class model applicable to the metaverse platform, a teaching method was developed. Through the process of the study, a total of 16 detailed items were derived for the teaching method-related tasks for the activation of K-MOOC: support strategic tasks, teaching method competency, aspect of class design, evaluation and sharing of learning outcomes. By applying the metaverse, the teaching model elements for K-MOOC revitalization were derived from four categories: self-directed repetition, individualized problem solving, practice opportunity expansion, and immediate feedback, and matched with the first 16 detailed items. A four-step teaching model was completed: course attendance (step 1), mission analysis by individual level (step 2), sharing of mission solutions (step 3), and mission evaluation and feedback (step 4). Through the results of this study, the possibility of using the metaverse as a teaching practice platform was confirmed even in terms of the introduction and development of specialized techniques.
If we may practice the nutrition education planned on the basis which carefully grasped the inappropriate behavioral determinants of middle-school students, it might be an effective method achieving the change in perception and behavior improving the distorted perception about the ideal body shape, so we are to suggest the 8 week program of body shape perception improvement for successful nutrition education as follows. The body shape perception improvement program is a step-by-step group consulting program. At the introduction stage, we let them understand the meaning of true beauty and body change of teenage period and forming of sexual identity. At the stage of perception conversion, we let them have the opportunity to observe the status of body perception of the teenager and self-observation. At the stage of correction, we let them criticize the distorted body image in the society with mass media at the same time with the self-reflection. At the stage of maintenance and evaluation, we suggested the behavior guidance while preparing it. Setting this as the basis, we applied the contents such as the evaluations through cultural sharing events making somethings while directly participating. As the target groups to practice education were middle school students, we considered the learning level and behavioral features of the middle school students, and composed the programs including the methods such as role play, watching real things, media production, discussions and experiences. If the program of body shape perception improvement developed at this study could be utilized at the field of schools, the teenagers can change their ways of thought naturally avoiding the view about unified appearance rightly perceiving negative self-image that the teenagers can have and if the group consulting can be practiced regularly at each school, many students may experience the change in perception, so it might solicit the improvement of health of the families and local societies as well as that of the individual student.
Parkinson's disease is a degenerative neurological disease that affects even basic daily life movements due to impairment of body function caused by a lack of dopamine, which is charge of the body movement. Presently, it is hard to cure Parkinson's disease entirely with medical technology, so movement therapy as a solution to delay and prevent disease is getting more attention. Therefore, this study aims at desiging and disseminating a body movement program that concentrates on individual self-care and balacing the state of body and mind by applying the Feldenkrais Method® to patients with Parkinson's disease. The Feldenkrais Method® is a mind-body perceptual learning method using body movements. It is a methodology that re-educates the nervous system by connecting the brain and behavior as a function of neuroplasticity. In this study, the body movement program developed and verified by the researcher was modified and supplemented with a focus on the self-awareness of the Feldenkrais Method®. A 24-session physical exercise program was composed of 5 stages to improve the self-management ability of patients with Parkinson's disease. The stages include self-awareness, self-observation, self-organization, self-control, and self-care. The overall changes recognize one's condition and improve one's ability to detect modifications in the internal sense and external environment. In conclusion, the body movement program improves the body movement program improves mental and physical functions and self-care for Parkinson's disease patients through the Feldenkrais method. The availability of the program's on-site applicability remains a follow-up task. Furthermore, it is necessary to establish a systematic structure to spread it more widely through convergent cooperation with the scientific field applied with metaverse as a reference for the wellness of the elderly.
Interaction methods and technologies for mutual exploration based on user behavior are evolving variously. Especially, in recent years, with the development of a wide range of sensors, they have developed from contact to non-contact methods. However, developers' senseless definitions of the interaction methods have made the exploration process quite complicated, which rather creates the hassle of users needing to learn the interaction guide defined by the developers before experiencing the exhibition contents. In this context, in order to make visitors smoothly communicate with exhibition contents, a preliminary study on easy interaction for users of various ages is needed, and in particular, research on improving the usability of user interaction is also essential when developing non-contact exhibition contents. So, in this study, a method to reduce the confusion between developers and users was sought by researching non-contact interaction that could be universally interacted with in the field of exhibition contents and proposing behavior designs. First, based on the narrative structure of cultural resources, existing studies were reviewed and the points of interactions as cultural contents were derived. Then the most efficient search process was selected among non-contact behaviors based on hand gestures that allow users to naturally guess and learn interaction methods. Furthermore, on the basis of the meaning of non-linear narrative-based interaction and the analysis results of spatial behavior elements, affordance behavior with high learning effect and efficiency was derived. Through this research process, an action that helps users to understand non-contact interaction naturally in the process of exploring exhibition-type cultural contents and to utilize non-contact interaction in the process of immersion in exhibition contents is proposed as a final model.
The Journal of the Convergence on Culture Technology
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v.9
no.2
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pp.381-388
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2023
This thesis is purposed to develop a regional-customized education program selection model using big data analysis. Based on the literature review, the concepts and characteristics of big data and lifelong education are analyzed. In addition, this thesis presents how to collect the data for lifelong education and to use big data suitable for the characteristics of lifelong education. Based on these results, a regional- customized lifelong education program selection model is developed. The regional customized lifelong education program model is developed by the following six steps. The customized education program model proposed in this study has a high degree of flexibility in terms of practical use, as it can be utilized in real-time data provision methods such as the nationally approved Lifelong Learning Personal Status Survey without the need for analysis one year later, allowing for selective analysis and future predictions. It is clear that there is a significant need and value for big data in the education field. Furthermore, all programs used in the sample model are provided free of charge, and due to the programming nature, the community is actively engaged in exchanges, making it very easy to modify and improve for the development of a more complete education program model in the future.
The purpose of this study is to examine the anxiety arising from the budgetary and mental problems of graduate school students. Three graduate students majoring in Earth science examined conflict situations by using a narrative inquiry technique. First, participants become psychologically unstable due to a lack of knowledge in the field of Earth science, lack of mentors, lack of information related to academic schedules, late start compared to others, financial difficulties, and discrimination in the scholarship system. Second, participants felt hope from the perception that their lives are valuable, that they can change students for the better, and that they are developing themselves. Third, with their hope, the study participants bore the previously mentioned inferior situation mentioned above. They are, however, torn between becoming secondary school teachers and attempting to reroute their career path due to certain circumstances. Based on the results of the examination, the following conclusions were drawn. First, there should be improvements from graduate school management based on collecting and scrutinizing the demands of students in the to fulfill their needs. Second, providing psychological counseling for students who have problems overcoming their anxieties. This study expects graduate schools to not only emphasize training of incumbent teachers, but also suggest ways that can satisfy students to make better learning environment for all its members.
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%.
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