• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.295-304
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• 2015

This study intended to examine the correlation of career self-regulation (plan and check-up, positive thinking, career feedback, environment formation for career) and career identity (career decision, indecisiveness, career indecision) caused by parental career support (informative, emotional, financial, and empirical) among freshmen, sophomores, and juniors in the radiotechnology department. For assessment, a survey was conducted and according to the results, there existed correlation as follows. Regarding parental career support, emotional support is plan and check-up (r=.25, p<.001), Career feedback (r=.54, p<.001), and positive thinking (r=.46, p<.001) showed high positive correlation while informative support showed correlation in all factors showing high correlation with environment formation for career (r=.22, p<.001), plan and check-up (r=.20, p<.001), career feedback (r=.24, p<.001), and positive thinking (r=.26, p<.001). Financial support career feedback (r=.33, p<.001) and positive thinking (r=.34, p<.001) showed somewhat higher correlation. All factors of environment formation for career (r=.18, p<.001), plan and check-up (r=.25, p<.001), career feedback (r=.37, p<.001), and positive thinking (r=.30, p<.001) showed high correlation. Informative support showed high correlation only with career decision (r=.27, p<.001) and financial support also showed high correlation only with career decision (r=.18, p<.001). Also, empirical support was somewhat highly correlated only with career decision (r=.23, p<.001). Regarding school-year difference depending on parental career support, there was significant difference between emotional support (F=8.52, p<.001), financial support (F=8.97, p<.001), and empirical support (F=5.36, p<.05) while informative support was dismissed. Regarding school-year difference depending on career self-regulation, there was significant difference between career feedback (F=8.48, p<.001) and positive thinking (F=16.29, p<.001) while environment formation for career and plan and check-up were dismissed. Regarding school-year difference depending on career identity, there was significant difference between career indecision (F=4.01, p<.05) and career decision (F=11.72, p<.001) while indecisiveness was dismissed. According to the analysis results, parents' active support to their child like respecting and listening to their opinion on career, provision of career related experience or information, and provision of necessary financial aid for their study or academic preparation made the students plan and exploring their career, examine accomplishment progress, have positive idea to realize their objectives. In addition, the students were able to establish the objective of their career by forming the environment that helped them realize their objectives by seeking advices and encouragement from surroundings. Meanwhile, the parents' attitude to respect and listen to their child's career related opinion affected their career decision and indecision. Although informative support helped the students' career decision, financial and empirical support caused effect only to career decision.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.15-28
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• 2016

The purpose of this study is to investigate the changes of students' modeling ability in terms of 'meta-modeling knowledge' and 'modeling practice' through co-construction of scientific model. Co-construction of scientific model instructions about astronomy were given to 41 middle-school students. The students were given a before and after instruction modeling ability tests. The results show that students' 'meta-modeling knowledge' has changed into a more scientifically advanced thinking about models and modeling after the instruction. Students were able to be aware that 'they could express their thoughts using models', 'many models could be used to explain a single phenomena' and 'scientific models may change' through co-construction modeling process. The change in the 'modeling practice' of the students was divided into four cases (the level improving, the level lowering, the high-level maintaining, the low-level maintaining) depending on the change of pre-posttest levels. The modeling practice level of most students has improved through the instruction. These changes were influenced by co-construction process that provides opportunities to compete and compare their models to other models. Meanwhile, the modeling practice level of few students has lowered or maintained low level. Science score of these students at school was relatively high and they thought that the goal of learning is to get a higher score in exams by finding the correct answer. This means that students who were kept well under traditional instruction may feel harder to adapt to co-construction of scientific model instruction, which focuses more on the process of constructing knowledge based on evidences.

• Cartoon and Animation Studies
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• s.37
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• pp.393-411
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• 2014

The volume of world market for serious game in year 2015 is expected to be about 9.6 trillion, and the volume of educational serious game market is expected to surpass half of the whole serious game market. In Korea, the development of game for educational purpose has dominated around the education enterprises since late 90s. In 2008, 'Serious Game Forum' was founded led by the Ministry of Culture, Sports, and Tourism with experts from many fields in the society and there were progressing of making policies and plans for potential development of the serious game industry, but the effects were not successful than expected. In 2012, the Ministry of Education, Science, and Technology announced commercialization policy of digital textbook by 2015 and the serious game for educational purpose got attention again. Then, the serious game market became more vigorous with the dispersion of smart devices.13) As a result, the serious games on the smart devices or interlocking between the online and smart devices became an important issue in development rather than the online only serious games. Math field has international competitive power through export in the educational serious game market which takes more than half of the serious game market. Therefore, developing serious game for math education is a good area to raise competitiveness in domestic and international game industries. Moreover, it has no received preferences from students and parents although it has high potential for positive change of individuals and society. The reason is that students recognize it as educational content rather than a game and they avoid it, while parents recognize it as game but not an education. This phenomenon happens because the game elements and educational elements are not properly mixed but focused only on education or emphasized only the fun factors of game when it was developed. Therefore, the purpose of this research is to suggest a direction of developing serious games effectively combining with elementary math for elementary students to get interested in math while playing games. The research will analyze the current elementary math textbooks and find contents which may be combined with the game genre that elementary students enjoy playing these days. This research received advice from serious game developers and math education expert group to reflect the inclination of elementary school students, and respond to the demands from parents and educational institutions, and suggested a direction of developing serious games for effective math education.

• Journal of The Korean Association For Science Education
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• v.31 no.8
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• pp.1092-1109
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• 2011

The purpose of this study was to analyze of the effects of instructional supervision emphasizing reflective thinking on science teaching of elementary teachers. The participants in this study were two teachers. This study was divided in former, middle, and later periods, and consisted of monitoring their own teaching, interviewing, journal writing, discussion with peer teachers and teacher training. Data included descriptions of nine science classes, nine interviews, seven journals and the journals of the researcher. Data analysis tools were the frameworks of the questions, feedback, teaching methods, elements of teaching behavior, and reflection levels. This study employed qualitative research, analysis of the frequency of data, and quoting of descriptions related to the result. The results of this study were as follows: First, teachers showed mainly technical reflection, but changed to show more practical reflection, and critical reflection in the later period of instructional supervision. Second, instructional supervision emphasizing reflective thinking on science teaching for elementary teachers meaningfully changed the question, feedback, teaching methods and teaching elements of teachers. From the results of this study, instructional supervision emphasizing reflective thinking on science teaching for elementary teachers can be considered an effective method in improving teaching elementary science, and instructional supervision used in this study made possible the higher level of reflection and appropriate teaching behavior.

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.177-189
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• 2020

In this study, we implemented an education program to improve the responsive teaching ability of pre-service science teachers, and analyzed the responsive teaching practices revealed during the program process. Through this, we derived the types and characteristics of responsive teaching practice, identified factors that made it difficult for pre-service teachers to practice, and obtained empirical data on under what conditions the responsive teaching capacity of pre-service teachers was developed. For this purpose, a practice-based teacher education program was designed and carried out for 14 pre-service teachers who had no experience in responsive teaching. The program consists of four steps; observation of class, practice through rehearsal, application in practicum, and post-reflection on educational practice. In particular, qualitative analysis was conducted on the types of responsive teaching and their detrimental factors revealed during application in practicum. As a result of the analysis, four types were derived; discriminator type, communicator type, guide type, and facilitator type. Each type was identified as having a common responsive teaching step element. The education program implemented in this study was effective for pre-service teachers to recognize the importance of student-participation class and the educational effect of responsive teaching. However, three barriers that prevented pre-service teachers from responsive teaching practice were also analyzed. First was the pressure to achieve specific learning goals within a given class time. Second was the rigid belief of the fixed curriculum. Third was the obsession that the teacher should lead the class. Based on these results, it was suggested that in order to improve the responsive teaching ability of pre-service teachers, it is necessary to support the recognition of breaking out of the thinking the time constraint, the flexibility of the curriculum, and the role of teacher as a class supporter.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.31-53
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• 2014

The purpose of this study is to analyze how the elements of activating happiness education suggested by Noddings is reflected in the Home Economics part of Middle School Technology-Home Economics Textbook Volume 1 of 2009 Curriculum Revision. The introduction style of unit, sub-unit, and small chapter, the objectives, the body contents, the activity resources, the tables/diagrams/pictures, the supplementry and advancedl materials, and the wrap-up and evaluation of sub-unit and units of Home Economics part of Technology Home Economics textbook volume 1 were analyzed. Noddings suggested the elements for activating happiness education in five areas of personal life sector including 'home making', 'place and nature', 'parenting', 'chracter and spiriual experiences', and 'growth of interpersonal relationships' and two areas of public one including 'preperation for work' and 'community, democracy and voluntary activities'. The specific elements in seven sectors of activating happiness education were extracted using the content analysis. How the elements of those suggested by Noddings were reflected in the various parts of the textbook were analyzed in terms of the closeness of approaches, contents, and procedures between Noddings's and textbook. The major findings of this study were as follows: 1. The degree to which the elements of activating happiness education were reflected in the textbook differed by each unit. The elements of activating happiness education were reflected the most frequently in the unit of 'Understanding Adolescence' and the least frequently in the unit of 'Self-management of Adolescence'. 2. Although the elements of activating happiness education were generally reflected in all the elements of a textbook, these elements were relatively more reflected in the body contents and tables/diagrams/pictures.