• Journal of Korean Home Economics Education Association
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• v.32 no.2
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• pp.59-76
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• 2020

The purpose of this study was to develop and evaluate the teaching/learning process plan of 'housing culture and practical space use' for home economics in middle school according to the problem based learning(PBL) model. The plan consisting of 4-lessons has been developed and implemented following the steps of ADDIE model. Various activity materials (4 scenarios, 6 individual activity sheets, 10 reading texts, and 5 working resources) and visual materials (4 sets of ppt and 4 moving pictures) as well as questionnaire were developed for the 4-session lessons. The plans were implemented to a single class of 21 junior students at H middle school in rural area, Kyeongnam, from 1^st to 12^th of April, 2019. Students highly enjoyed and were satisfied with the whole 4-lessons in aspects such as understanding of the contents, adequacy of materials and activities, and usefulness in one's own daily life. Additionally, they have more actively participated in the lessons than usual and even interested in learning more of such lessons. Students also reported that they highly accomplished the goal of each lesson as well as overall objectives. They showed interest in the major part of PBL lesson such as scenario and group activities. And they engaged themselves in drawing the share housing space plan with '5D planner' web program which they described as the best part of the lessons. The teaching/learning process plan developed in this study may be used as a theme of maker education, which is emerging these days. It can be concluded that the PBL teaching/learning process plans for 'housing values and practical space use' would contribute to improving students' attitude on living with others and ability to manage one's individual life.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Restorative Dentistry and Endodontics
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• v.30 no.3
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• pp.158-169
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• 2005

This study was done to evaluate the shear bond strength between light-cured glass ionomer cement (GIC) base and resin cement for luting indirect resin inlay and to observe bonding aspects which is produced at the interface between them by SEM. Two types of light cured GIC (Fuji II LC Improved, GC Co. Tokyo, Japan and Vitrebond$^{TM}$, 3M, Paul Minnesota U.S.A) were used in this study. For shear bond test, GIC specimens were made and immersed in 37$^{\circ}C$ distilled water for 1 hour, 24 hours, 1 week and 2 weeks. Eighty resin inlays were prepared with Artglass$^{(R)}$ (Heraeus Kultzer Germany) and luted with Variolink$^{(R)}$ II (Ivoclar Vivadent, Liechtenstein). Shear bond strength of each specimen was measured and fractured surface were examined. Statistical analysis was done with one-way ANOVA. Twenty four extracted human third molars were selected and Class II cavities were prepared and GIC based at axiopulpal lineangle. The specimens were immersed in 37$^{\circ}C$ distilled water for 1 hour, 24 hours, 1 week and 2 weeks. And then the resin inlays were luted to prepared teeth. The specimens were sectioned vertically with low speed saw. The bonding aspect of the specimens were observed by SEM (JSM-5400$^{(R)}$, Jeol, Tokyo, Japan) .There was no significant difference between the shear bond strength according to storage periods of light cured GIC base. And cohesive failure was mostly appeared in GIC On scanning electron micrograph, about 30 - 120 $\mu$m of the gaps were observed on the interface between GIC base and dentin. No gaps were observed on the interface between GTC and resin inlay.

• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.565-578
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• 2000

The aim of this study was development of the Straight-Wire Appliance(SWA) suitable lot the treatment or Korean. To accomplish the object of this study, Korean adult with normal occlusion were selected with following criteria : 1) no functional abnormality in the craniofacial area, 2) good dental arch form and posterior occlusal relationship, 3) Angle Class I occlusal relationship, 4) no experience of orthodontic, nor prosthodontic treatment, especially, no dental treatment on labial and buccal surfaces of teeth, 5) good racial profile. Impression were taken for upper and lower dental arches or the selected normal occlusion samples and the orthodontic dental stone models were fabricated. 5 well-trained orthodontists had examined the acquired dental stone models to select study samples which satisfy the Six keys to optimal occlusion of Andrews. 155 pairs of dental stone models (92 pairs of Male, 63 of Female) were finally selected. 3 dimensional digitization were performed with the Coordinate Measuring Machine(CMM, MPC802, WEGU-Messtechnik, Germany) and measuring of Angulation, Inclination, In-and-Out, Molar offset angle and Arch form were accomplished with a measuring software to achieve data for the development of SWA. Before the measurement, error study was performed on the 3 dimensional digitization with CMM, and the analysis of reliability of computerized measuring method adapted in this study and conventional manual method Presented by Andrews was performed. Results of this study were as to)lows : 1. Equi-distance digitization with mesh size 0.25 mm, 0.5 mm and 1.0 mm were acceptable in 3 dimensional digitization of dental stone model with the CMM, and the digitization with 1.0 mm mesh size was recommendable in terms of efficiency. 2. Computerized measuring method with 3 dimensional digitization was more reliable than manual measuring method of Andrews. 3. Data were collected for the development of SWA suitable for the morphological characteristics of Korean with the computerized measuring method with 3 dimensional digitization.

• Communications of Mathematical Education
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• v.24 no.1
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• pp.235-257
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• 2010

Contemporary belief is that the creative talented can create new knowledge and lead national development, so lots of countries in the world have interest in Gifted Education. As we well know, U.S.A., England, Russia, Germany, Australia, Israel, and Singapore enforce related laws in Gifted Education to offer Gifted Classes, and our government has also created an Improvement Act in January, 2000 and Enforcement Ordinance for Gifted Improvement Act was also announced in April, 2002. Through this initiation Gifted Education can be possible. Enforcement Ordinance was revised in October, 2008. The main purpose of this revision was to expand the opportunity of Gifted Education to students with special education needs. One of these programs is, the opportunity of Gifted Education to be offered to lots of the Gifted by establishing Special Classes at each school. Also, it is important that the quality of Gifted Education should be combined with the expansion of opportunity for the Gifted. Social opinion is that it will be reckless only to expand the opportunity for the Gifted Education, therefore, assessment on the Teaching and Learning Program for the Gifted is indispensible. In this study, 3 middle schools were selected for the Teaching and Learning Programs in mathematics. Each 1st Grade was reviewed and analyzed through comparative tables between Regular and Gifted Education Programs. Also reviewed was the content of what should be taught, and programs were evaluated on assessment standards which were revised and modified from the present teaching and learning programs in mathematics. Below, research issues were set up to assess the formation of content areas and appropriateness for Teaching and Learning Programs for the Gifted in mathematics. A. Is the formation of special class content areas complying with the 7th national curriculum? 1. Which content areas of regular curriculum is applied in this program? 2. Among Enrichment and Selection in Curriculum for the Gifted, which one is applied in this programs? 3. Are the content areas organized and performed properly? B. Are the Programs for the Gifted appropriate? 1. Are the Educational goals of the Programs aligned with that of Gifted Education in mathematics? 2. Does the content of each program reflect characteristics of mathematical Gifted students and express their mathematical talents? 3. Are Teaching and Learning models and methods diverse enough to express their talents? 4. Can the assessment on each program reflect the Learning goals and content, and enhance Gifted students' thinking ability? The conclusions are as follows: First, the best contents to be taught to the mathematical Gifted were found to be the Numeration, Arithmetic, Geometry, Measurement, Probability, Statistics, Letter and Expression. Also, Enrichment area and Selection area within the curriculum for the Gifted were offered in many ways so that their Giftedness could be fully enhanced. Second, the educational goals of Teaching and Learning Programs for the mathematical Gifted students were in accordance with the directions of mathematical education and philosophy. Also, it reflected that their research ability was successful in reaching the educational goals of improving creativity, thinking ability, problem-solving ability, all of which are required in the set curriculum. In order to accomplish the goals, visualization, symbolization, phasing and exploring strategies were used effectively. Many different of lecturing types, cooperative learning, discovery learning were applied to accomplish the Teaching and Learning model goals. For Teaching and Learning activities, various strategies and models were used to express the students' talents. These activities included experiments, exploration, application, estimation, guess, discussion (conjecture and refutation) reconsideration and so on. There were no mention to the students about evaluation and paper exams. While the program activities were being performed, educational goals and assessment methods were reflected, that is, products, performance assessment, and portfolio were mainly used rather than just paper assessment.

• Journal of The Korean Association For Science Education
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• v.32 no.1
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• pp.95-112
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• 2012

The research evaluated the effects of the improvements in scientific inquiry for elementary school students and focused on the development and application of the explicit and reflective learning strategy through observation and classification. The explicit and reflective learning strategy was modified and completed with the review of the experts after the development of the draft based on the theoretical approach. The students were evaluated for their academic achievements in scientific inquiry skills before and after taking the course. The results were as follows: First, the steps of the developed learning strategy (1) to motivate, (2) to explore reflectively, (3) to guide explicitly, (4) to inquire explicitly, and (5) to verify reflectively were set to reflect the verification. Second, the results of applying the developed model to the lessons based on the quantitative analysis was effective for observation and classification skills in the quest for improved performance of the whole (the sum of observation and classification, inquiry skills) and the observed features, but there was no effect on classification. Also, the lessons applied the developed teaching strategy and showed effectiveness in improving academic achievement. Particularly in analyzing the relationship between the academic achievement and exploration capabilities, in order to improve academic achievement, the importance of improving inquiry skills was found. Third, the qualitative analysis of teaching and learning strategy developed by applying the lessons of this teacher guide and small group activities through the explicit and reflective observation and classification of the student learning activities showed the significant improvement of ability of the scientific inquiry skills. In addition to the improvement in the abilities of the classification showed after the formation of the most basic observation skills of the scientific inquiry.

• Horticultural Science & Technology
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• v.30 no.5
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• pp.586-595
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• 2012

This study was conducted to determine the effects of horticultural therapy (HT) program using hydroponics on work adjustment skills of students with mental retardation. Based on the critical role transitional model and special education curriculum for agriculture, especially hydroponics, HT program (total 22 sessions) using hydroponics procedure for Lettuce (Lactuca sativa L. 'Asia Heuk Romaine') was developed. Fourteen (10 males, 4 females) graded $1^{st}$ to $2^{nd}$ with intellectual disabilities were recruited from a special education class in a high school located in Inchon, Korea and then a special farm for hydroponics in Inchon, Korea was offered for the HT program. The students with intellectual disabilities participated in the HT program for 4-month (from September to December of 2011, twice a week, approximately 60 minutes per session). Before and after the HT program, the McCarron assessment neuromuscular development, emotional behavioral checklist, interpersonal negotiation strategies, and KEPAD picture vocational interest test were performed by the teachers and horticultural therapists. As the results, the students significantly improved motor performance (p = 0.002), emotional behavioral strategies (p = 0.00), and interpersonal negotiation strategies (p = 0.05). However, no significant difference between before and after the HT program for vocational interest was observed. In conclusion, the HT program using hydroponics, consists of simple and easy tasks so that it would be applicable for the students with intellectual disabilities positively affected to work adjustment skills by improving the motor performance, emotional behavioral strategies, and interpersonal negotiation strategies. Additionally, HT programs using hydroponics with various kinds of vegetables are required to develop and to apply in practical settings for improving work adjustment skills.

• Korean Journal of Health Education and Promotion
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• v.20 no.2
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• pp.35-61
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• 2003

Background： The purpose of this study was to develop a structured and individualized smoking prevention program for adolescents by utilizing a multimedia computer-assisted instruction model and to empirically assess its effect. Method： For the purpose of this study, a guide book of smoking prevention program for middle and high school students was developed as the first step. The contents of this book were summarized and developed into an actual multimedia CAI smoking prevention program according to the Gane ＆ Briggs instructional design and Keller's ARCS motivation design models as the second step. At the final step, the short-tenn effects of this program were examined by an experiment. This experiment were made for middle school and high school students and the quasi experimental design was the pretest - intervention - posttest. The measured data was attitude, belief, and knowledge about smoking, interest in the program, and learning motivation. Result： The results of this study were as follows： First, the guide book of a smoking prevention program was developed and the existing literature on adolescent smoking was analyzed to develop the content of the guide book. Then the curriculum was divided into three main domains on tobacco and smoking history, smoking and health, adolescent smoking and each main domain was divided into sub-domains. Second, the contents of the guide book were translated into a multimedia CAI program of smoking prevention througn Powerpoint software according to the instructional design theory. The characteristics of this program were interactive, learner controllable, and structured The program contents consisted of entrance(5.6％), history of tobacco(30％), smoking and health(38.9％), adolescent smoking(22.2％), video(4.7％), and exit(1.6％). Multimedia materials consisted of text(121), sound and music, image(still 84, dynamic 32), and videogram(6). The program took about 40 minutes to complete. Third, the results on analysis of the program effects were as follows： 1) There was significant knowledge increase between the pre-test and post-test with total mean difference 3.44, and the highest increase was in the 1st grade students of high school(p＜0.001). 2) There was significant decrease in general belief on smoking between the pre-test and post-test with total mean difference 0.28. In subgroup analysis, the difference was significantly higher in the 1st grade of high school (p<0.001), low income class (p<0.001), and daily smokers (p<0.01). 3) There was no significant difference in attitudes on his personal smoking between the pre-test and post-test. 4) The interest in the program seemed to lower as students got older. The score of motivation toward this prevention program was the highest in the middle school 3rd grade. Among sub-domains of motivation, the confidence score was the highest. Conclusion： To be most effective, the smoking prevention program for adolescents should utilize the most up-to-date and accurate information on smoking, and then instructional material should be developed so that the learners can approach the program with enjoyment. Through this study, a guide book with the most up-to-date information was developed and the multimedia CAI smoking prevention program was also developed based on the guide book. The program showed positive effect on the students' knowledge and belief in smoking.