• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.399-408
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• 2023

This study developed and applied a maker education program for an elementary school's science unit on 'Animal Life'. It examined the program's impact on students' academic achievement and creative problem-solving ability. The theme of the maker education program was 'creating a robot virtual reality (VR) exhibition hall mimicking animal characteristics'. It explored scientific concepts and creatively created a robot VR exhibition hall in accordance with the TMI maker education model. Findings revealed that the program significantly improved students' academic achievement and creative problem-solving ability (p<.05). This study provides evidence for the effectiveness of maker education in elementary school science classes and suggests that using maker education can increase students' interest in and engagement with science learning. To implement maker education more actively in elementary school science classes, stakeholders should develop various topics and programs. Additional research investigating the effectiveness of maker education in different age groups and various other areas of elementary science education is required to generalize the results of this study. Moreover, educational and teacher capacity building is required for educators to utilize maker education effectively.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.355-360
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• 2023

Recently, with the development of digital, children are exposed to a lot of media media. Reading activity decreases, making it difficult to acquire information from books or organize and remember acquired information. Since education with augmented reality increases children's participation and immersion in learning, we proposed a reading activity application with augmented reality technology to increase children's reading interest and creativity. In addition, based on the five nurturing processes, we designed a play-oriented reading activity for the application. In addition to the application suggestions, we designed a play-centered lesson model so that it can be used in actual lessons. In order to analyze the conceptual thinking framework according to the lesson model design, we visited an actual daycare center and conducted a class attended by an expert. Experts who participated were asked to fill out a pre-produced questionnaire to review the suitability of the reading activity class model and operation, and the feasibility was examined. Our lesson model design was based on limited book content, and due to ethical concerns, large-scale experiments with children could not be conducted, so the results of the study are not representative of the whole. However, it is significant that the possibility of running a new reading activity class based on the Nuri course has been examined and proposed.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Education of Primary School Mathematics
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• v.26 no.3
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• pp.199-217
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• 2023

Affective characteristics have been consistently emphasized in mathematics education, but students' confidence or interest in mathematics has not changed significantly. This study analyzes the factors affecting the affective characteristics according to students' academic achievements, which have not been studied so far. The study was surveyed 593 students in the 5th and 6th grades, divided into school mathematics education and social-environmental factors. As a result of the study, students cited 'mathematics class at school' as the factor that had the most influence on their affective characteristics, regardless of academic achievement. Excluding 'mathematics classes at school', upper level students said that 'private education' and 'college entrance exams and jobs', had the most influence on their affective characteristics. Middle level students said that 'assessment at school' and 'private education' had the most influence on their affective characteristics. Lower-level students said that 'school evaluation' and 'mathematics textbook' had the most influence on the affective characteristics. In particular, as the academic achievement level decreased, students' participation in classes decreased rapidly. Most students said that the mathematics content they were learning was too difficult for that reason. Considering these research results, it would be effective to apply methods according to students' academic achievement to some extent in order to improve affective characteristics.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.479-489
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• 2022

In order to understand how to increase the use of virtual training content at K University's online lifelong education institute, this study examined the use experience, content recognition, field practice replacement, and requirements, focusing on the examples of operating institutions. To this end, 12 institutions that operated virtual training contents distributed by the K University Online Lifelong Education Center in 2020 were selected for in-depth interviews and qualitative analysis was conducted on the interviews of 11 institutions. As a result of the analysis, first, the experience of using the contents of the virtual training operating institution was aimed at changing the educational environment, supplementing theoretical learning, and improving the sense of practice. Second, according to a survey on the recognition of virtual training content, if the importance and utilization of the content are high, it can be replaced by on-site practice in non-face-to-face classes, such as experiences of facilities and equipment, attracting interest and attention. Third, in many cases, the perception of replacement for field practice is not unreasonable to use as a pre-training material for field practice, but it is difficult to replace field practice. Fourth, content quality improvements can be summarized as content quality improvement, content access and manipulation improvement, dedicated device development, training for instructors, and curriculum systematization. Fifth, institutional requirements include improving the quality of virtual training content itself, equipment support, curriculum systemization and characterization, systematic curriculum and detailed content sharing, detailed guidance on using virtual training content, introducing how to use content, and recruiting instructors. This study is meaningful in that it sought ways to improve the utilization of virtual training content based on the perception of virtual training content operating institutions.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.539-547
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• 2023

In recent years, university education has emphasized a learner-centered education model with a change in educational paradigm. This study aims to explore students' diverse opinions and improve the quality of education by analyzing the open-ended responses of university lecture evaluations using topic modeling. To this end, a total of 45,001 open-ended responses based on the results of lecture evaluations from 2017 to 2022 in non-metropolitan universities were divided into majors and liberal arts, and a short-form optimized Biterm Topic Modeling (BTM) analysis was conducted. As a result of the analysis, major lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward questions and discussions", "attitude toward attendance and grading", "attitude toward practical activities and presentations", and "attitude toward communication and collaboration", while liberal arts lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward grades and evaluations", "attitude toward attendance and syllabus", "attitude toward academic knowledge and interest", and "attitude toward communication and questions". The results of this study, which analyzed various feedback from students, provide insights that can be used to compare the characteristics of majors and liberal arts courses and improve teaching and learning experiences.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.53-66
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• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.170-184
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• 2024

This study focused on the practical research needed to improve elementary school science lesson plans. Specifically, a video clip-based pedagogical reasoning activity that included elementary student misconceptions was presented and the influences of this activity on preservice teachers' science lesson planning were assessed. First, the eight preservice teacher participants were asked to write a lesson plan for a dissolution and solution unit, after which a first semi-structured interview was conducted. Then, the participants participated in a video clip-based pedagogical reasoning activity. Based on the activity results, the participants revised their previously planned lessons, and second semi-structured interviews were conducted. The data from the preservice teachers' lesson plans and interview transcripts were analyzed using a constant comparative method to investigate the lesson plan changes. It was found that after the video clip-based pedagogical reasoning activity, the preservice teacher tightened the activity or changed the material to understand the students' thinking processes. In addition, they supplemented their goals and assessment criteria to accommodate the diverse students' thinking. Some also specified motivational strategies that considered student interests, motivation, and possible misconceptions. However, some preservice teachers still set goals that did not sufficiently account for student misconceptions and some planned the student assessments based only on the learning goals rather than the students' thinking. The few preservice teachers were able to develop motivational strategies that considered interest, motivation, and misconceptions. The preservice teachers claimed that they had difficulty predicting the misconceptions and connecting these to the lesson content. Discussions were then held to assist the preservice teachers to consider possible student misconceptions when planning their lessons.

• 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.