• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.3
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• pp.174-182
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• 2017

Objectives: This study was conducted in order to develop a computerized test to measure the level of mathematic achievement and related cognitive functions in children and adolescents in South Korea. Methods: The computerized Comprehensive Learning Test-Mathematic (CLT-M) consists of the whole number computation test, enumeration of dot group test, number line estimation test, numeral comparing test (magnitude/distance), rapid automatized naming test, digit span test, and working memory test. To obtain the necessary data and to investigate the reliability and validity of this test, 399 children and adolescents from kindergarten to middle school were recruited. Results: The internal consistency reliability of the CLT-M was high (Cronbach's alpha=0.76). Four factors explained 66.4% of the cumulative variances. In addition, the data for all of the CLT-M subtests were obtained. Conclusion: The computerized CLT-M can be used as a reliable and valid tool to evaluate the level of mathematical achievement and associated cognitive functions in Korean children and adolescents. This test can also be helpful to detect mathematical learning disabilities, including specific learning disorder with impairment in mathematics, in Korea.

• Journal of Korean Public Health Nursing
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• v.7 no.2
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• pp.67-76
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• 1993

This study was conducted to investigate the nurses' recognition extent of nursing power and related factors. It was performed in a private university hospital using questionaire papers from Jul. 11 to Jul. 20, 1993. The collected data were in number of 209, and they were handled and analyzed by computer program (SPSS). Through above process, we got meaningful results as follow. 1. The factor concerned with whether the nursing department show its independent power or not was 4.1 on an average. Consequently above results showed that the independent power roles as a important factor. The factor concerned with study and research was 3.9 on an average. So that the extension of learning was also a comparatively important nursing power. But ensuring good material environment was merely 1.6 on an average. So it was understood as less important nursing power. 2. Between the factor concerned with enforcement of independence of nursing department and the factor concerned with extension of learning, there was positive correlation of 0.32 Pearson's Correlation Coefficent. It can be interpreted as the effort of extension of learning go side by side with the showing of independent power of nursing department. As a result the power of nursing become strong. And between the factor concerned with enforcenment of independence of nursing department and the ensuring o(good material environment, there was negative correlation of -0.28 Pearson's Correaltion Coefficint. It can be interpreted as the stronger the independent power of nursing, the more overlooked the recognized of material environment of nurses.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.169-176
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• 2022

This study was conducted to find out the relationship between the perception of the necessity and importance of creative teaching methods and core competencies in universities. A total of 142 people who voluntarily agreed to participate in the study were selected and surveyed using the Naver online questionnaire. It was found that the necessity and importance of creative teaching methods was related to the sub-factors of core competencies such as practical field competency, problem-solving competency, convergence competency, communication competency, job ethics, community consciousness. University education should actively introduce and apply creative teaching methods such as action learning and PBL, focusing on improving core competencies, which are actual abilities to be performed in the field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4476-4491
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• 2021

Video service providers tend to face user network problems in the process of transmitting video streams. They strive to provide user with superior video quality in a limited bitrate environment. It is necessary to accurately determine the target bitrate range of the video under different quality requirements. Recently, several schemes have been proposed to meet this requirement. However, they do not take the impact of visual influence into account. In this paper, we propose a new multi-category model to accurately predict the target bitrate range with target visual quality by machine learning. Firstly, a dataset is constructed to generate multi-category models by machine learning. The quality score ladders and the corresponding bitrate-interval categories are defined in the dataset. Secondly, several types of spatial-temporal features related to VMAF evaluation metrics and visual factors are extracted and processed statistically for classification. Finally, bitrate prediction models trained on the dataset by RandomForest classifier can be used to accurately predict the target bitrate of the input videos with target video quality. The classification prediction accuracy of the model reaches 0.705 and the encoded video which is compressed by the bitrate predicted by the model can achieve the target perceptual quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.8-9
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• 2021

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.14-25
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• 2018

In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.

• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.172-180
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• 2014

The first purpose of this study is to distinguish difficult chapters in 'Speed of objects' chapter and find the factors which give difficulty to the teachers and students. Also, it attempts to compare the students' assessment scores with the degree of difficulty in teaching and also with the degree of difficulty in learning. This report is expected to help science teachers develop their PCK(Pedagogical Content Knowledge) for teaching the chapter professionally. 15 teachers who had taught the 'Speed of Objects' chapter and their 386 students took part in the survey to acquire information about the difficulties in teaching and learning. 386 students also received a test to examine their understandings of the chapter. The results of this study are as follow; First, the degree of teachers' and students' difficulty is only affected by the contents, and the degree of onerousness felt by teachers is higher than that of students. Second, The topics caused higher difficulty to teachers were 'Understanding the meaning of motion(2nd lesson)', 'Understanding the meaning and unit of speed(5th lesson)', 'Changing unit of speed(6th lesson)', 'Drawing a distance-time graph(7th lesson)', and 'Understanding the relative motion(10th). The topics that led higher difficulty to students were the contents of 5th, 6th, and 7th lessons. Third, the 'Speed of Objects' chapter can be divided into 4 types of difficulty according to the degree of teaching and learning; 'Strong difficulty', 'Learning difficulty', 'Weak difficulty', and 'Teaching difficulty'. Last, students showed low achievement to the tasks that were related with 'Strong difficulty' and 'Teaching difficulty'.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.523-531
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• 2018

The purpose of this study based on descriptive research is to examine the influences on the self-regulated learning ability due to nursing students' achievement-goal and to make sure mediating effect of autonomous support of professor. A convenience sample of 294 nursing students were recruited from a college in C province from April to May, 2018. Data are analyzed using SPSS/WIN 21.0 program. As a result, it is turned out that there are positive correlation between mastery goal, performance approach goal, self-regulated learning ability and autonomous support of professor. In the Sobel test results, autonomous support of professor had a partial mediating effect between mastery goal and self-regulated learning ability(Z=3.922, p<.001). So, a new teaching method should be developed through the convergence of related factors of self - regulated learning ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.126-137
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• 2020

This study provided implications for applying learner-centered teaching methods in the field of management education by adopting flipped learning and team-based learning to Business Communication classes and operating the classes according to its characteristics. Business communication capability, especially related to drawing up a document or a written report, is considered one of the crucial factors for making individuals valuable in an organization. For the identification of the subject outputs, a total of 64 students from the first and second semesters in 2018 were sampled, and the Wilcoxon signed rank test and paired t-tests were carried out. The results show that all types of communication capabilities have significantly increased at the end of each of the semesters. Also, the overall satisfaction level proved to be higher at the end rather than at the beginning of each semester. This study is especially meaningful because the results suggest concrete ways to apply learner-centered teaching methods for business education.