• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.872-877
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• 2018

STEAM is an acronym for Science, Technology, Engineering, Arts, and Mathematics. It is considered important to equip students with a creative thinking ability and the core competences required in future society, helping them devise new ideas emerging from branches of study. This study is about the convergence of instructional design in private finance for the life sciences, which aims to foster talent through problem-based learning (PBL). Skills like collaboration, creativity, critical thinking, and problem solving are part of any STEAM PBL, and are needed for students to be effective. STEAM projects give students a chance to problem-solve in unique ways, because they are forced to use a variety of methods to solve problems that pop up during these types of activities. The results of this study are as follows. First is the structured process of convergence lessons. Second is the convergence lesson process. Third is the development of problems in the introduction of private finance and the life sciences for a convergence lesson at Dornod University. Learning motivation shows the following results: understanding of learning content (66.6%), effectiveness (63.3%), self-directed learning (59.9%), motivation (63.2%), and confidence (63.3%). To make an effective model, studies applying this instructional design are to be implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.7-12
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• 2018

In the society where the paradigm of knowledge is rapidly changing and developing, convergence emphasizing the connection between knowledge and technology in various fields is significant. In order to cultivate these creative-convergent talents, STEAM(Science, Technology, Engineering, Arts, and Mathematics) is being considered important to make them equipped with creative thinking ability and core competence required in the future society and help them devise new ideas escaping from the branches of study. This study is about convergence instructional design of computer with marketing subject, which aims to foster talent. The results of this study are as follows. First, the structured process of convergence lessons. Second, the convergence lesson was based on a cyclic process with steps : selection of the subject concerned, selection of a topic, designing the lesson, mapping out the lesson plan and developing problems, having a final discussion on the whole lesson, performing the lesson and evaluating the lesson. Third, the development of the problems about the introduction of computer engineering and Internet marketing subject for convergence lessons. To make an effect of this model, studies applying this instructional design to many lectures should be implemented.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.79-88
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• 2017

As the age in which the importance of sensitivity has increased, education for the future generation regarding emotion engineering, affective recognition and cognitive science have taken center stage. We measure human's emotion quantitatively, analyze evaluation and apply them to various services in life, which are based on human technology. Therefore, we need the education which is related to emotion science to cultivate talented people. The goal of this paper is to suggest the possibility of emotion science education and effective methods through development of the STEAM (Science, Technology, Engineering, Arts, Mathematics) program which can teach emotion science to early elementary school students by applying it to pilot classes. For this study, first, we build a program, 'The mind made by figure' for student of early elementary school. The method of STEAM was used in this program, because it is an effective system to educate the emotion science. We recognize the needs and value of this program development through theory and benchmarking of STEAM related to emotion science. And then the contents of class, activities, course book and kit are designed with elementary school textbook of pertinent grade. Secondly, we analyze the result which is applied in two pilot classes of second grade by satisfaction survey and teacher interview. As a result, the average of satisfaction level was very high (4.40/5), Class participation was especially high. Third, we discuss the ability, value and limits of this program based on the result of analysis. The outcome of this research shows that students of early elementary school who have difficulty in understanding science can approach the education program related to emotion science with ease and interest. We hope this education will help students understand emotion science effectively, and to train people to lead the emotion centered era.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.