• Journal of The Korean Association of Information Education
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• v.26 no.3
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• pp.187-195
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• 2022

Recently, with interest in metaverse, attempts are being made to utilize the metaverse platform in various forms. In this paper, we focused on the educational application potential of metaverse, and developed and applied a metaverse STEAM program to provide an effective learning experience to learners in non-face-to-face educational situations. The developed program utilizes Minecraft and ZEPETO, familiar to students, as metaverse learning platforms, and consists of a total of 16 lessons of 5 modules in the form of modules so that alternative classes can take place in the educational field. Through this, the learner's change in STEAM attitude and learning satisfaction were measured, and through the developed STEAM program, the learner's interest, consideration, communication, usefulness, self-concept, self-efficacy, and career choice areas significantly increased. In addition, positive results were confirmed in all areas of the learner satisfaction test related to satisfaction, interest, and overall class. In the future, considering the characteristics of the metaverse, it is necessary to break free from the constraints of time and space to communicate anew, and various learner-centered educational approaches based on a high degree of freedom and immersion should be implemented.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.367-376
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• 2022

In this study, the user experience was analyzed from the learner's point of view, focusing on the metaverse platform 'Spatial'. SUS(System Usability Scale) was used to evaluate the usability of the metaverse platform 'Spatial' in a college class, and the Magnitude estimation technique was used to evaluate the immersion and satisfaction with the class. In addition, a questionnaire survey was used to collect user experience opinions on the use of 'Spatial' as a teaching tool. Looking at the usability evaluation results of the 'Spatial' system, the students evaluated the usability, immersion, and satisfaction quite positively. Looking at the user experience of metaverse platform 'Spatial', it was found that students highly valued Metaverse as an educational tool that can provide a place for many people to gather and communicate even in a non-face-to-face space. Compared to other online platforms, metaverse has advantages in ease of use, interaction, immersion, and interest. In particular, in addition to keyboard, touch, and display, interaction using the five senses such as voice, motion, and gaze was recognized as a great advantage. On the other hand, it was found that high openness, freedom, and interest factors can both promote learning and inhibit learning. Nevertheless, it is judged that the metaverse platform 'Spatial' can be effectively applied in college classes because it enables various interactions between instructor and learner or between learner and learner.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.439 -447
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• 2023

This paper aims to develop a map for optimized class space using ZEP among the metaverse platforms. As a research method, the classroom space was organized so that the subject of learning became a learner, and the classroom space was modified and supplemented to optimize while being applied to elementary school computer classes. The contents of the study investigated learners' prior perception of metaverse, and compared and analyzed the advantages and disadvantages of the metaverse platform. In addition, the map was designed by reflecting the results of the survey, and after applying the map to the class, necessary APIs and apps were installed to supplement it. As a result, the learner became the subject of learning in the metaverse space, freely identified the space, and actively participated in the class. In particular, we found that students who were passive offline and those who had a low participation rate due to lack of skills participated more actively. In particular, students who were passive offline or whose participation was low due to lack of skills participated more actively. If API and JavaScript programs are added to collect log data of learners for learning analysis, real-time feedback is possible for learners, and learner feedback is possible for instructors with statistical data. If this is possible, the metaverse space can fully expect the role of a learning assistant for learners and a teaching assistant for instructors.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.837-842
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• 2023

This study analyzed the awareness and actual condition on metaverse utilization in education for design major students. An online survey was conducted for 14 days from May 10 to 23, 2023, targeting 120 students majoring in design at D University. The evaluation method of the questionnaire was a nominal scale and a 5-point scale, and the questionnaire results were analyzed through SPSS 29.0. First, it is necessary to sufficiently share the advantages of metaverse utilization in education with students, and to provide basic literacy programs utilizing the characteristics of metaverse and supporting class activities. Second, students' response will be higher in studio classes where practical training is conducted rather than information delivery or understanding-oriented lectures. Third, in order for the metaverse to become a means of education in the digital transformation era rather than just a temporarily medium in COVID-19 era, specific and systematic design education programs reflecting the characteristics of the metaverse need to be continuously developed. In addition, it is important for instructors to actively review the use of the metaverse and search for various ways to utilize it.

• Human Ecology Research
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• v.61 no.4
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• pp.673-686
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• 2023

The purpose of this study was to identify home economics (HE) teachers' concerns about and perceptions of HE education using the latest technologies in the era of the 4th Industrial Revolution and to reveal whether they differ according to teachers' general background variables. The questionnaire survey method to measure HE teachers' concerns and perceptions of HE education using the latest technologies in the era of the 4th Industrial Revolution was conducted online using the Google Questionnaire from which 150 responses were received. The main results were as follows. Firstly, HE teachers scored an average of 3.46 out of 5 for the latest technology. Among these interests in the latest technology, interest in "augmented reality and virtual reality technologies" scored the highest at an average of 3.80, while interest in "neural network machine learning" (2.78) was low. HE teacher's concerns about HE education using the latest technologies in the era of the 4th Industrial Revolution were high, with an average score of 4.40. Among these concerns for the latest technology, "concern about the results of HE education using the latest technology" scored the highest at 4.53. HE teachers' anxiety about the latest teaching technology in the era of the 4th Industrial Revolution was moderate, averaging 3.05. The highest form of anxiety was "anxiety about the impact on the job" (4.03) and the lowest was fear of "the disappearance of the teacher's job" (2.50). HE teachers' innovation resistance to the latest teaching technology was low at 2.18. Expectations of the latest technology in HE classes in the era of the 4th Industrial Revolution averaged 3.85, slightly higher than the middle of 3.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.1-16
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• 2021

The purpose of this study was to investigate the needs of middle school students for the practical reasoning in a distance learning environment, to verify the needs differences based on the learner's personal characteristics, student-teacher interaction, and student-student interaction, and to investigate the relationships among student-teacher interaction, voluntary participation of students, and the students' perception of the extent to which practical reasoning is implemented in distance learning. For this purpose, 1,842 middle school students from seven schools in Gyeonggi, Daejeon, Chungbuk, and Sejong areas were surveyed online to investigate the importance of the practical reasoning questions and the how much practical reasoning is implemented in current distance learning. Among them, 1,095 responses were used for final analysis and descriptive statistics, independent sample t-test, one-way ANOVA, and path analysis were conducted. As a result of the study, first, middle school students acknowledged that the practical reasoning was important with the importance average 3.76. Based on the locus for focus model, the priorities of the needs in home economics class were examined, and the values and importance of the problem, and the ramification of the solution were considered to be of high priority. Second, characteristics of middle school students, student-teacher interaction and student-student interaction were found to have positive influence on needs for practical reasoning, while no difference were found by gender or voluntary participation in distance learning. Third, the voluntary participation of students and the student-teacher interaction in distance learning had a positive (+) significant effect on perceived implementation of practical reasoning, yet negative (-) significant effect on needs for practical reasoning.