• Journal of The Korean Association For Science Education
• /
• v.35 no.5
• /
• pp.805-815
• /
• 2015

The Korean education curriculum is making efforts to improve education to foster competencies that the future society demands through the 2007 and 2009 revised curriculum. The revised curricula focus on enhanced articulation for the quality curriculum. In this study, the curriculum is analyzed for vertical and horizontal articulation. In addition, the study found a problem in Korea's curriculum through international comparison and sought improvement. Furthermore, the study compared internationally articulation of the concept of photosynthesis, of which the results are as follows. First, our science curriculum focuses on vertical articulation and has relatively neglected the problem of horizontal articulation. To compensate for this problem, curriculum design should introduce aspects of 'nature' and 'environment' and should consider the interests and concerns of students, as countries with high horizontal articulation do. Second, the actual education field has a problem with the a lack of continuity and sequence because of concentration of concept in a specific grade or simply repeating the concept across multiple grades. These results have led to alternative proposals that should arrange basis of concept configuration such as 'Big Idea' and should establish the adoption of 'systems' frequently appearing in the other curricula. Finally, there may be mentioned a lack of research on students' learning progression, which can be a common standard of horizontal and vertical articulation. Research on learning progression has been a trend overseas, but there exists no study to fit Korea's situation, so education fields need to conduct the appropriate research on learning progression as part of the commitment to high-quality curriculum.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_4
• /
• pp.1319-1326
• /
• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of KIISE:Software and Applications
• /
• v.36 no.3
• /
• pp.236-243
• /
• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.23-34
• /
• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Journal of The Korean Association For Science Education
• /
• v.21 no.4
• /
• pp.697-709
• /
• 2001

Researchers on conceptual change have been proposed that confronting a cognitive conflict situation would be important for a student to change his/her preexisting conception. There have been reported that there are three different methods of producing a cognitive conflict situation; the first is logical argument(LC), the second is demonstration of an actual phenomenon(DC), and the third is kinesthetic conflict which is a kind of physical experience(EC). In this study, the researcher tried to find out the differences in the conceptual changes by the three different conflict situations. Seventy two high school students were chosen in a high school in Kyungkido, Korea. The students were tested four times; pretest, posttest, one week delayed posttest, and one month delayed posttest. Six different test situations on mechanics were developed for this study. Test item for each situation was developed. Each item consisted of a multiple choice question and explanation of the choice. The result showed a clear differences among the three conflict groups. In general, kinesthetic conflict which is a kind of physical experience(EC) was proved to be the most efficient strategy for the conceptual change; however, logical argument(LC) seemed to be the least efficient. However, the effectiveness was not uniform from situation to situation. Results of some items showed that even the LC was quite good for the conceptual change. Therefore, it seems to be important to develope appropriate method for the target concept.

• Journal of Korea Water Resources Association
• /
• v.54 no.12
• /
• pp.1285-1294
• /
• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of Appropriate Technology
• /
• v.6 no.2
• /
• pp.200-207
• /
• 2020

The purpose of the establishment of Fab Lab is to promote the sustainable development of local communities around the world. To this end, The Fab foundation are preparing a resource-circulating society that maintains a city's self-sufficiency rate of 50% or more by 2054. In developed countries, Fab Lab is not only a manufacturing space for startup support, but an open innovation space for learning and creation. In addition, in emerging countries, Fab Lab is playing a role as a digital production center to create and share appropriate new technologies by reflecting the needs of local communities. India has 70 Fab Labs, the largest emerging country, ahead of Russia's 48. India's Fab Lab is conducting a collaboration project through regular meetings held every six months. The subject of this study, Fab Lab Vigyan Ashram, is defined as a place to transfer the concept of digital lab to alternative schools in rural India. In this study, we looked at a case in which an alternative school for an agricultural community called Vigyan Ashram, the modern version of the Gurukula system, successfully combined with the digital fabrication called Fab Lab to become a new citizen-led making community of the 4th Industrial Revolution. Based on this, we explored the development model of the Indian Fab Lab that fits the local situation.

• Journal of Industrial Convergence
• /
• v.20 no.5
• /
• pp.39-59
• /
• 2022

We analyze the perceptions and requirements of early childhood teachers for artificial intelligence(AI) education to develop an AI education program for 5-year-olds. As for the research methodology, we conducted a survey and an in-depth interview to extract the AI educational elements centering on the analysis stage, the first stage of the ADDIE model. The research result is that first, it is necessary to design a curriculum that combines the contents of early childhood education and AI education to be naturally accepted as AI education for 5-year-olds. Second, an evaluation tool for AI education that can showcase the teacher's reflection should be developed systematically. Third, it is necessary to support a play-centered AI education support and environment for early childhood teachers. Lastly, it is essential to establish a system that can be continuously operated in the field of early childhood education in consideration of AI education in the non-curricular curriculum. It is expected that in the future, a play-oriented AI education program for 5-year-olds will be developed to spread awareness of AI education for infants and present an AI education approach for each age and stage of learners.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.519-531
• /
• 2024

The purpose of this study is to analyze the aspects and characteristics of educational innovation planned and implemented at the university site targeting universities in Busan, Ulsan, and Gyeongnam, and to explore their limitations and tasks. For this purpose, we analyzed the contents of innovation strategy programs among the plans of 17 universities in the national innovation support projects in Busan, Ulsan, and Gyeongnam area. First, the university innovation strategy was divided into input, process, infrastructure, and other factors, and among them, the process factor was divided into education, research, and industry-university cooperation to examine the aspects and characteristics of innovation. As a result of the study, the aspects of university innovation at universities in Busan, Ulsan, and Gyeongnam were analyzed in the areas of education, research, and industry-academia cooperation. Characteristics of innovation were emphasis on convergence education, competency development, smart system foundation, introduction of innovative teaching and learning techniques, consumer-centeredness, and regional linkage. The limitations and tasks of university innovation revealed through the research are as follows. First, a specialized university innovation business structure should be prepared in consideration of the context of local universities. Second, established strategies with high innovativeness must be implemented and sustained, and consensus among members is required for this. Third, the innovation of universities should not mean the centralization of academics, and the role and efforts of universities as a research institutions should be improved. Fourth, it should not be overlooked that more important than the visible innovation strategy of university innovation is the education innovation that occurs directly to students as a result of the education effect.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.201-220
• /
• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.