• Journal of Industrial Convergence
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• 제22권1호
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• pp.137-148
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• 2024

This study aims to analyse the research trends and identify issues in elementary physical education in the new normal era. For this purpose, the taxonomic analysis method proposed by Spradley (2016) was applied, and 43 Korean academic articles were finally categorised and analysed. The findings are as follows. First, due to the changes in the educational environment caused by COVID-19, most of the remote and online physical education classes were conducted as content-oriented classes. It was found that there was a lack of communication between teachers and students in online physical education classes. Second, the difficulties of remote and online physical education classes and online and offline combined physical education classes, as well as research on how to overcome and improve them, were concentrated. Third, the need for evolution of physical education teachers and training of future professionals in line with the methodological transformation of primary physical education and the current situation was raised. In addition, the number of studies utilising blended learning, flipped learning, and new technologies, which have gained attention in primary physical education due to COVID-19, has increased. Based on the findings, we proposed the direction and future tasks of elementary physical education in the new normal era.

• Journal of the Korean Society of Earth Science Education
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• 제13권2호
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• pp.162-174
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• 2020

This study aimed to develop a creative education class program using metaphor, one of the creative thinking techniques, and to examine the effectiveness of the program targeting for randomly sampled 338 students in six middle schools. The creative education class program with the metaphor was developed based on content elements concerning 'astronomy' in 2015 science curriculum revision in South Korea. The program was tested for validity after being modified and supplemented three times by forming a group of experts, and the final version of the program was applied to school education fields during four periods, including block time. To find out the effectiveness of the program and the implementation, creative education class satisfaction test and creative thinking process test were conducted. That is to say, the creative education class satisfaction test was conducted before treatment and the creative thinking process test was implemented both before and after treatment. The results of the study are as follows. First, in this study, the program was developed with the emphasis on students voluntarily and actively participating in creative education programs while utilizing creative thinking methods. Second, the statistical results of the pre- and post-class about the creative education program using the metaphor of creative thinking techniques represented significant results(p<.05). In other words, the two-dependent samples by students' pre-and post-score about the creative education class showed significant statistical test results (p<.05). It turned out that the creative education program using metaphor has had a positive impact on research participants. Third, in regards to the results of the creative education class satisfaction test, 101 out of 338 students(30%) answered 'Strongly Agree' and 137(41%) answered 'Agree', indicating the subjects' satisfaction with the class was high in general. On the other hand, concerning difficulties of the creative class, 137(41%) answered "Lack of time" as the main factor, followed by 98(30%) "Difficulties of problems they were required to solve", 73(22%) answered "Conflicts with friends", and 24(7%) said "Difficulties of contents." These responses were taken into account as considerations for further development of creative education programs.

• Journal of The Korean Association For Science Education
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• 제32권5호
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• pp.903-927
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• 2012

The study aimed to analyze middle school students construction of physics inquiry problems for open inquiry from the viewpoint of variable isolation and clarification, and investigate students' difficulties during the processes of variable isolation and clarification to get implications for teaching and learning strategies for small group open inquiry activities which have been included in the 2007 national curriculum. The participants were 4 students who had attended an outreach program for the science gifted run by a university institution located in Seoul area. They performed an open inquiry on egg drop for 13 lessons for 30 hours. Level descriptions for variable isolation and clarification have been developed and applied to analyze students' inquiry problems and variables included by the problems. Students iterated inquiry processed 5 times and the inquiry problem showed progress gradually. Dependent variables have been isolated ahead and the levels of variable isolation and clarification showed higher than the independent variables. Many kinds of independent variables isolated extensively and the independent variables and control variables have been mingled. One of the reasons why students had some difficulties in isolation of independent variables could be the absence of theoretical models. The realities of school lab could restrict the variable isolation and clarification as well as topic selections. Some sensory or extensive variables such as broken eggs and drop height seem to be salient to be focused on as core variables. Lack of background knowledges could be one of the reasons for students' difficulties in variable clarification, such as theoretical definitions and operational definitions. As a result of lacking background knowledges, students could not construct theoretical models even though they could isolate and clarify variables as scientific lexical definitions. Some perceptions of inquiry as trial and error or reckless establishment of causal relations between variables could be accounted as one reason.

• Journal of Intelligence and Information Systems
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• 제23권3호
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of The Korean Association For Science Education
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• 제43권3호
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• pp.265-276
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• 2023

The purpose of this study was to develop a plan to ensure that students lacking basic science skills acquire the minimum needed science learning ability while completing the common curriculum. We surveyed 27 elementary and secondary science teachers with experience in research and teaching related to basic skills support to investigate their perceptions of the criteria for minimum achievement standards using Consensual Qualitative Research (CQR) and Analytic Hierarchy Process (AHP). The results indicated that the science teachers tended to describe low achievers as lacking science learning competency, accumulating a science learning deficit, and lacking prerequisite knowledge. However, there were some differences in the characteristics that the elementary and secondary teachers paid attention to in students with insufficient science and basic academic skills. Specifically, the secondary teachers demonstrated greater sensitivity towards low learning motivation and difficulties in using scientific symbols, whereas the elementary teachers were more sensitive towards students' attitudes towards science or lack of experience. Furthermore, it has been observed that the prioritization of items, categorized by school level, differs in terms of setting minimum achievement standards to ensure basic skill support. This implies the need to develop minimum achievement standards considering various variables based on the school level. As there are diverse opinions among science teachers, depending on their expertise, regarding the factors to be considered when developing these standards to guarantee science and basic skill support. Based on the findings of the study, policy support is required to enhance teachers' professionalism in developing students' basic skills while considering the individual context and diversity of low achievers. Additionally, it is crucial to establish a shared vision for students lacking basic skills to reduce the gap between national policy and the practices of science teachers in ensuring support for basic skills.

• Journal of Intelligence and Information Systems
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• 제25권2호
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• 제19권2호
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of The Korean Association For Science Education
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• 제31권5호
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• pp.788-800
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• 2011

The purpose of this study was to investigate the elementary school teachers' perception of science writing. In this study, 10 elementary school teachers who have taught in the 3rd or 4th grade science lesson in 2010 were selected. Researchers constructed interview guide in three parts including the teachers' understanding of science writing, the status of science writing teaching and the difficulties of science writing in their classes. For the investigation, semi-structured in-depth interviews with 10 elementary school teachers were conducted individually. The results showed that the elementary school teachers were unfamiliar with the word ‘science writing’ and considered science writing as a writing using science learning contents. Also, they think that teaching science writing in their science lessons was not needed and didn't assess and provide detailed feedback with the students' written works. Most teachers needed teaching materials and assessment tools for science writing. To develop elementary teachers' understanding of the value and use of writing for learning in science, they will need to participate in science writing programs for in-service teachers and various teaching materials and assessment tools should also be developed.

• 대한공업교육학회지
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• 제34권2호
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• pp.128-160
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• 2009

Narratively I have described interactions between two teachers performing an introduction to the engineering class with various situations such as place, teacher, student and subject. I have specifically illuminated a three-dimensional narrative inquiry space embracing the culture of the university, the college of engineering and the ABEEK(Accreditation Board of Engineering Education of Korea)program. The result of the study is as follows: First, in order to stimulate the students' motivation, the teachers have to make not only their class PowerPoint slides match the size of the classroom, but the content of the slides must be condensed with core concepts. They also should utilized some video clips to empower students' interest in the subject within their classrooms. Second, the teachers should do various class activities in the classroom. Instead of spending most of the class time with his/her explanation, it would be advantageous for the teachers to allow the students to perform a task in class. Third, the teachers should ask their students about assignments which are helping students' understanding of the subject and planning of their future. Lastly, the teachers need to design the mid-term and the final tests inducing the students' motivation. Those tests also must test students' creativity and insight of the subject. Thus, the test should consist of an interpretive exercise and an essay type of item thus reducing the multiple choice types of items. There are several limitations to the study. First it is difficult to generalize what we found here because it is a case study. Second, we could not study in depth the effect of the interaction between the two teachers who were performing the introduction to the engineering course during the academic semester. Third, this study just probed into the difficulties of teaching the course. Hence, we have to understand more by focusing on each issue such as adapting to a new learning environment as a student from abroad, a practical experience boosting the students' interest in the introduction to the engineering course, also a practical experience on process based learning-versus result based learning, and an effective management of the student team presentation etc.

• Tunnel and Underground Space
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• 제31권5호
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.