• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.189-196
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• 2023

This paper analyzes the level of satisfaction of two groups of teachers who were educated about artificial intelligence using App Inventor. The participants were 13 pre-service and 9 in-service elementary school teachers and the data was collected using a questionnaire. As a result of the study, in-service teachers were all more satisfied than pre-service teachers in terms of interest, difficulty, and participation in the education. In addition, the questions investigating whether education helped motivate learning of artificial intelligence and whether there is a willingness to apply it to elementary classes in the future were also more positive for in-service teachers than for pre-service teachers. In general, pre-service teachers had somewhat more negative views than in-service teachers, but they were more positive than in-service teachers in terms of whether the education helped improve their understanding of artificial intelligence and whether they were willing to participate in additional education. Analysis of the Mann-Whitney test to see if there was a significant difference in satisfaction between the two groups showed no significance. This may be because most of the students in the two groups already had block-type or text-type programming experience, so they were able to participate in the education without any special resistance or difficulty with App Inventor, resulting in high levels of satisfaction from both groups. The results of this study can provide basic data for the future development and operation of programs for artificial intelligence education for both pre-service and in-service elementary school teachers.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.725-739
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• 2022

This study aims to analyze what elementary school pre-service teachers focused on and what challenges they faced in designing and producing a guided science inquiry program based on augmented reality (AR) and to provide some implications for teachers' professionalism and teacher education. To this end, focusing on the cases of pre-service teachers who designed and created AR-based guided inquiry programs, the researchers extracted and categorized the pre-service teachers' focus and challenges from the program design and production stages. As a result, in the program design stage, the pre-service teachers tried to construct scenarios that could promote students' active inquiry process. At the same time, drawing on the unique affordances of AR, the pre-service teachers focused on creating vivid visual data in a 3D environment and making meaningful connections between virtual and real-world activities. The pre-service teachers faced challenges in making use of the advantages of AR technology and designing an inquiry program due to a lack of background knowledge about CoSpaces, a content creation program. In the program production stage, the pre-service teachers tried to make their program easy to handle to improve students' concentration on inquiry activities. In addition, challenges of programming using CoSpaces were reported. Based on these results, educational implications were discussed in terms of the pedagogical uses of AR and teachers' professionalism in adopting AR in science inquiry.

• 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.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.48-71
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• 2024

Typhoons are representative oceanic and atmospheric phenomena that cause interactions within the Earth's system with diverse influences. In recent decades, the typhoons have tended to strengthen due to rapidly changing climate. The 2022 revised science curriculum emphasizes the importance of teaching-learning activities using advanced science and technology to cultivate digital literacy as a citizen of the future society. Therefore, it is necessary to solve the temporal and spatial limitations of textbook illustrations and to develop effective instructional materials using global-scale big data covered in the field of earth science. In this study, according to the procedure of the PDIE (Preparation, Development, Implementation, Evaluation) model, the inquiry activity data was developed to visualize the track of the typhoon using the image data of GK-2A. In the preparatory stage, the 2015 and 2022 revised curriculum and the contents of the inquiry activities of the current textbooks were analyzed. In the development stage, inquiry activities were organized into a series of processes that can collect, process, visualize, and analyze observational data, and a GUI (Graphic User Interface)-based visualization program that can derive results with a simple operation was created. In the implementation and evaluation stage, classes were conducted with students, and classes using code and GUI programs were conducted respectively to compare the characteristics of each activity and confirm its applicability in the school field. The class materials presented in this study enable exploratory activities using actual observation data without professional programming knowledge which is expected to contribute to students' understanding and digital literacy in the field of earth science.

• 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.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.