• Journal of Korean Home Economics Education Association
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• v.20 no.3
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• pp.1-16
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• 2008

The purpose of this study is to investigate the perception of middle schooler's lesson of 9th grade "Housing Education" Questionnaire survey was conducted with 394 middle school students in Gyeongnam in 2006 and the dada analyzed by the descriptive statistics, t-test, One-way ANOVA, Duncan's multiple range test, Pearson correlation and multiple regression analysis by using SPSS 14. program. The result of this study were as follows. The unit 'Use of dwelling space' was perceived useful in real life, interesting in explanation & visual materials of the text, taught easily to understand the students and ranked highest in class satisfaction compared with the rest two units. The unit 'Indoor environment & equipment' was least interesting, most difficult and ranked lowest in class satisfaction. The unit 'House management & repair' was relatively interesting and easy, but not useful in real life, not much interesting in explanation & visual materials in text, taught difficult by teachers. The class satisfaction ranked second of the units. Female students perceived that the unit 'Use of dwelling space' was easy, but male students perceived that all the units were interesting in the explanation & visual materials in textbook, various teaching-learning methods, interesting experiment & practice, and was satisfied with the unit 'House management & repair'. As their academic achievement of "Technology & Home Economics" was lower, they perceived that the education contents and various experiment & practice were more difficult. The class satisfaction of middle schoolers about the unit "Housing Education" were affected by how easily the teacher taught the subject, making it more understandable, the level of difficulty of the contents and the level of interest of explanation & visual materials of text.

• Korean Journal of Optics and Photonics
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• v.28 no.5
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• pp.203-212
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• 2017

A scientific model refers to a conceptual system that can describe, explain, and predict a particular physical phenomenon. The co-construction of the scientific model is attracting attention as a new teaching and learning strategy in the field of science education and various studies. The evaluation and modification of models compared with the predicted models of data from the real world is the core of modeling strategy. However, there were only a limited data provided by the teacher in many studies of modeling comparing the students' predictions of their own models. Most of the students were not given the opportunity to evaluate the suitability of the model with the data in the real world. The purpose of this study was to develop a scientific model co-construction program that can evaluate the model by directly comparing the predicted models with the observed data from the real world. Through a collaborative discussion between teachers and researchers for 6 months, a 5-session scientific model co-construction program on the subject 'image formation by convex lenses' for second grade middle school students was developed. Eighty (80) students in 3 classes and a science teacher with 20 years of service from general public co-educational middle school in Gyeonggi-do participated in this 2-week program. After the class, students were asked about the helpfulness and difficulty of the class, and whether they would like to recommend this class to a friend. After the class, 95.8% of the students constructed the scientific model more than the model using the construction rule. Students had difficulties to identify principles or understand their friends, but the result showed that they could understand through model evaluation experiment. 92.5% of the students said that they would be more than willing to recommend this program to their friends. It is expected that the developed program will be applied to the school and contribute to the improvement of students' modeling ability and co-construction ability.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.325-340
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• 2022

This study attempts to analyze the causes of low affective achievement of elementary, middle, and high school students in Korea in science. To this end, a total of 27 students, three to four students per grade, were interviewed by grade from the fourth grade of elementary school to the first grade of high school, and a total of nine teachers were interviewed by school level. In the interview, related questions were asked in five sub-areas of the 'Indicators of Positive Experiences about Science': 'Science Academic Emotion', 'Science-Related Self-Concept', 'Science Learning Motivation', 'Science-Related Career Aspiration', and 'Science-Related Attitude'. Interview contents were recorded, transcribed, and categorized. As a result of examining the causes of low science academic emotion, it was found that students experienced negative emotions when experiments are not carried out properly, scientific theories and terms are difficult, and recording the inquiry results is burdensome. In addition, students responded that science-related self-concept changed negatively due to poor science grades, difficult scientific terms, and a large amount of learning. The reasons for the decline in science learning motivation were the lack of awareness of relationship between science class content and daily life, difficulty in science class content, poor science grades, and lack of relevance to one's interest or career path. The main reason for the decline in science-related career aspirations was that they feel their career path was not related to science, and due to poor science performance. Science-related attitudes changed negatively due to difficulties in science classes or negative feelings about science classes, and high school students recognized the ambivalence of science on society. Based on the results of the interview, support for experiments and basic science education, improvement of elementary school supplementary textbook 'experiment & observation', development of teaching and learning materials, and provision of science-related career information were proposed.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.