• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.471-481
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• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.

• Journal of Christian Education in Korea
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• v.66
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• pp.343-382
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• 2021

This study is a case study in which the interactive non-face-to-face classes using Edutech were applied to the Department of Christian Education. The subjects were 20 students from the Christian education department of A University located in the metropolitan area. The course was 'Instructional Methods and Educational Technology' in the first semester of 2020. In theory, I studied non-face-to-face classes and interaction, and edutech and interaction. Afterward, it designed and developed interactive non-face-to-face classes using edutech. The interactive non-face-to-face classes using edutech were developed as a process of applying Flipped-PBL based interactive edutech. In addition, Edutech was selected for active interaction according to the Flipped-PBL process to be carried out in a non-face-to-face situation. In particular, in the process of developing the problem of PBL, it was built around the situation of the church. As a result of applying the class, first, learners showed high satisfaction and interest in the class. Second, positive transference appeared in the space of learning and the space of living. Third, interactive non-face-to-face classes using Edutech have generated active interaction. In particular, interactive edutech and learning methods have become the main factors enabling active interaction. Through this, learners have improved learning efficiency, immersion, and satisfaction. Also, as an alternative to face-to-face classes, I was able to experience online classes. In other words, the satisfaction and interest of learning, and the transference of learning space, were also possible through active interactions generated through learning methods using interactive Edutech used in class. Furthermore, disabilities in the online communication(Internet) environment and learners' unfamiliarity with the online environment have been found as factors that hinder learning satisfaction and interaction. During learning, obstacles to the online communication environment hinder the utilization of interactive Edutech, preventing active interactions from occurring. This results in diminishing satisfaction and interest in learning. Therefore, we find that designing interactive non-face-to-face classes using Edutech requires sufficient learner learning and checking of the online communication(Internet) environment in advance for Edutech and learning methods. In response, this study confirmed the possibility by applying interactive non-face-to-face classes using Edutech to Christian education classes as an alternative method of education that allows active interaction and consistent transference of learning and life. Although it is a case study with limited duration and limitations of the number of people, I would like to present the possibility as an alternative Christian education method of an era where the direction of online classes should be presented as an alternative to a face-to-face class.

• Journal of the Korean Society of Earth Science Education
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• v.12 no.1
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• pp.82-93
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• 2019

The purpose of this study is to analyze the results of satisfaction for learning eco-system on the teacher training program conducted at the IS-Geo (International School for Geoscience Resources) which is KIGAM (Korea Institute of Geoscience and Mineral Resources), and to determine the satisfaction and educational effects of the teacher training programs on elementary and secondary teachers. And then, to suggest improvement points in the future operation of the teacher training program at the IS-Geo. Therefore, we conducted questionnaire of satisfaction for learning eco-system based on the data collected by a survey of 98 elementary and secondary teachers who participated in the teacher training program at the IS-Geo, from July 2017 to August 2018. The research results are as follows. First, the results of satisfaction for learning eco-system showed high values of 4.58 or higher in both the elementary and secondary programs, and the teacher training program conducted by the IS-Geo had a positive effect on the training participants. Second, internal factors indicating learning motivation and learning development were elementary teacher training 4.70 and secondary teacher training 4.64, and it is necessary to develop training contents and programs by classifying them into majors other than the earth science department. Third, intermediate factors indicating contents of education and learning curriculum were 4.67 for an elementary teacher training program and 4.72 for secondary teacher training program. In addition, in order to operate the teacher training program according to the purpose of science and technology culture, it is necessary to develop a teaching-learning model and to improve the quality of teaching. Fourth, external factors indicating learner support and quality of instructors were 4.83 for an elementary teacher training program and 4.72 for a secondary teacher training program. In particular, it is necessary to develop teaching materials that can be used immediately in school classes and can generate interest.

• Journal of The Korean Association For Science Education
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• v.39 no.3
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• pp.363-377
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• 2019

This study reveals the aspects of subjectivity in the test results in a science-specific aspect when assessing science-related affective characteristic through self-report items. The science-specific response was defined as the response that appear due to student's recognition of nature or characteristics of science when his or her concepts or perceptions about science were attempted to measure. We have searched for cases where science-specific responses especially interfere with the measurement objective or accurate self-reports. The results of the error due to the science-specific factors were derived from the quantitative data of 649 students in the 1st and 2nd grade of high school and the qualitative data of 44 students interviewed. The perspective of science and the characteristics of science that students internalize from everyday life and science learning experiences interact with the items that form the test tool. As a result, it was found that there were obstacles to accurate self-report in three aspects: characteristics of science, personal science experience, and science in tool. In terms of the characteristic of science in relation to the essential aspect of science, students respond to items regardless of the measuring constructs, because of their views and perceived characteristics of science based on subjective recognition. The personal science experience factor representing the learner side consists of student's science motivation, interaction with science experience, and perception of science and life. Finally, from the instrumental point of view, science in tool leads to terminological confusion due to the uncertainty of science concepts and results in a distance from accurate self-report eventually. Implications from the results of the study are as follows: review of inclusion of science-specific factors, precaution to clarify the concept of measurement, check of science specificity factors at the development stage, and efforts to cross the boundaries between everyday science and school science.

• Journal of Christian Education in Korea
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• v.70
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• pp.207-238
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• 2022

Faith education focuses on 'changing the direction of life' that pursues the life of Christ. This is possible only when the message of the Bible is embodied in life, not by accumulating biblical knowledge. Today, however, faith education does not allow biblical messages to be embodied in life. This is the result of focusing on knowing the Bible itself, instead of guiding the faith education to meet the Bible and the experience of human life. Church education emphasized the inner faith of individuals rather than changes in life and practice, preparing for the afterlife, and mostly for the training and quantitative growth of the church. As a result, in the COVID-19 era, Protestants showed an immature appearance that only cared about the safety of the church, and social trust in Protestants was lost. Therefore, faith education should educate what life of the Bible and the experiences of the learner will meet and respond to God in order for the Bible's message to be realized in life. I tried to find out how to prepare for this faith education in detail. So I would like to look at "The preparation process for religious classes related to experience" compiled by the German Protestant Lutheran Bavarian Presbyterian Church and present tasks for application to the Korean Church. Preparation for experience-related religious classes consists of five courses. It is a personal meeting, a theological orientation, a pedagogical orientation, a pedagogical decision, and a summary of the progress plan. The main purpose of this process is to learn how biblical believers interpreted their experiences in life from the perspective of faith and tried to overcome the problem. Faith education related to experience deals with the essence of faith education, not one of the Bible teaching methods. Although the field of education is in the social change of expanding from the real world to the virtual world, the essential nature of faith education cannot change. Therefore, research and application of faith education related to experience in Korean churches will help the biblical message to be embodied in Christian life.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.278-294
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• 2022

To date, cultural property education has seen rapid quantitative growth due to national and personal needs. However, qualitative growth is lacking. The objectives of cultural property education have not been established, and therefore, even its identity is not clear. The most pressing issue at present in cultural property education is to first set objectives. This study aimed to analyze the objectives of current cultural property education, identify the problems, and set new objectives to meet significant national and personal needs in terms of education. The problems with the objectives of current cultural property education are that the persons interested in the education do not understand the concept of the education objectives clearly and that the objectives do not contain much actual content of the education. Also, the objectives of the education do not take into account the dynamic competencies and interests of the learners and do not satisfy the changes of the times. To solve these problems, new cultural property education, called 'U.V.E.C.,' was offerred. U.V.E.C. education is aimed at understanding cultural properties, recognizing their value, and enjoying them, and at creating culture. The objectives of U.V.E.C. cultural property education were set such that they can be modified flexibly in a learner-centric way with clear and practical format and contents. Based on this direction, stepwise objectives were set including overall objectives, detailed objectives, and practice objectives, and objective cases of each step were proposed. Considering the generality of the education and the distinct characteristics of the cultural properties, the U.V.E.C. education objectives took into account the diversity of behavioral objectives, clearness in statements, the objectives of problem solving, the initiative of learners and openness for expression outcomes. The U.V.E.C. objectives are clear and specific so that teachers can enhance their pedagogical efficiency and learners are able to develop interesting and diversified competencies. In addition, it is expected that the U.V.E.C. objectives will significantly affect objective setting for education on cultural properties which have not been studied widely. Further systemic and specific studies on the contents and methods of the U.V.E.C. education would help to change the overall education on cultural properties and position the field as a new academic area.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.