• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.77-92
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• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Korea Science and Art Forum
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• v.32
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• pp.319-334
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• 2018

This research is a case study to make sure the enhancement of students' imagination and creativity through developing and applying the Literature-Art-Science Integrated Education Program. Its research object was totally 25 persons of 29 students of the 1st to the 4 th Grades from Gunsan Sulsan Elementary School. Its research period lasted for 4 months from September to December, 2017, and I, as the research place, used the art room at Gunsan Sulsan Elementary School. The programs were totally 10 sessions with a unit of 1 session per each grade for 2 hours from 1:00 to 3:00 in the afternoon from Monday through Friday. I fixed ten themes of this program-eight plane modeling, and two solid modeling, and finished the work of storytelling during summer vacation. And I arranged their levels as low:middle:high(3:5:2) ones. The former was 'A Film of Monster Gorilla'(L), 'Learning the Spirit of Gyeongju Choi's Family'(M), 'A Tale of My Friend Made of Natural Materials'(L), 'The Reading of My Dream'(M), 'Gathering the Objects in My Mobile'(M), 'A Mock Trial of Marrying Off'(M), 'Painting My Favorite Children's Poem'(H), and 'Painting My Favorite Children's Song'(H), and the latter was 'Seeking for a Bluebird in My Mind'(L), and 'Making My Cherished Object' (M). Then I used the unique art expression technique per each theme, which were in sequence marbling, Korean paper art, combine painting, collage, imaginary painting, imaginary painting, play dough art, imaginary painting techniques. And I delivered to the students the scientific knowledge in terms of growing or manufacturing processes of materials used for making artworks. Prior to and after the processing this program, I surveyed about the students' ability of integrated thinking and emotional experience by 'Figure B Type' and 'Figure A Type' of The Torrance Tests of Creative Thinking, and took statistics with the resultant data. And I executed a paired t-test in order to verify the significance of mean difference in the result of investigation with those data. From the analyzed result according to the elements of creativity and the mean quotients of creativity, there showed a significant difference (t=3.47, p<.01) in 'fluency', and also a significant difference(t=3.59, p<.01) in 'creativity.' Judging from the statistic values of two fields such as the student's ability of integrated thinking and emotional experience, I estimate that over the majority of the students showed the enhancement in self-confident creative expression as well as higher interest and concern through this program. The result that I arranged and analyzed the making process of artworks, the photos of the resultant, etc. as such is as follows : Firstly, from this program being proceeded as art-centered STEAM class, the student's systematic problem-solving ability was improved in his ability of integrated thinking to transform the literary contents into artistic one. Secondly, the student obtained the emotional experience such as interest in the class, self-confidence, intellectual satisfaction, self-fulfillment, etc. through art-centered STEAM class using ten art expression techniques. Thirdly, the student's mind willing to cooperate, communicate with his friends, and care for them was ripened in the process of problem-solving. Fourth, the student's self-confidence was further instilled when presenting famous artists and their artworks in the introduction and finale of ten art expression techniques. Likewise, the statistic values on the fields of student's ability of integrated thinking and emotional experience illustrate that over the majority of the students showed improvement in the ability of creative expression with confidence as well as higher interest and concern upon this program.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Korean Academy of Nursing
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• v.22 no.1
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• pp.81-115
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• 1992

The problem addressed by this study was to determine the effect of nurse - patient agreement on the means to achieve nursing goals in the early postpartum period of primiparous mothers. It was hypothesized that the experimental treatment would result in hegher self-confidence and satisfaction in maternal role performance. This purpose was to contribute to the planning of nursing care to enhance self- confidence and satisfaction in maternal role performance and to the development of relevant nursing theory. Especially, the early postpartum period is crucial toward in recovery from childbirth and attainment of the maternal role. Maternal role attaintment is a complex social and cognitive process of stimulus -response accomplished by learning. Most women attain the maternal role sucessfully. But, some primiparous mothers experience difficultites in attainment of the maternal role due to lack of experience and knowledge. Self-confidence and satisfaction in maternal role performance are important factors in attainment and adjustment to the maternal role (Mercer, 1981a, 1981b ; Lederman, Weigarten, and Lederman, 1981 :Bobak and Jensen, 1985). Nursing is defined as behaviors of nurses add patients that attain nursing goals through action, reaction, interaction, and transaction. For attainment of nursing goals, active participating transactions must occur by agreement on the means to achieve those goals through nurse -patient mutual goal setting and establishment of their active relationships(King, 1981, Ha, 1977). Based on King's theory of goal attainment (1981), this stuy was planned as a non-equivalent control group, non -synchronized quasi -experimental design using agreement on the means to achieve nursing goals in early postpartum as the experimental treatment. The data were collected from July 20 to Sep. 1, 1991 by questionnaires with 60 primiparous mothers planing to breast feed after normal deliveries at W hospital in Pusan, Korea. The subjects were divided into a control group(conventional group) -those admitted from July 20 to Aug. 12, and an experimental group(agreement group) - those admitted from Aug. 13 to Sep. 1. The instument for agreement on the means to nursing goals in the early postpartum period included five steps - identification of disturbances of problems through action, reaction, and interaction with primiparous mothers : mutual early postpartal nursing goal setting : exploration of the means to achieve goals ; agreement on the means (self- care, ealry maternal -infant contact, performance of mothering behavior, and communicating about the infant's behavior and health condition) : implementation of the means. This instrument was developed on the basis of King's elements that lead to transactions in nurse-patient interactions. Lederman et al's (1981) scale for Confidence in ability to cope with tasks of motherhood and Lederman et al's(1981) scale for Mother's satisfaction with motherhood and infant care were used to measure self-confidence and satisfaction in maternal role performance ·with the subjects immediately after admission and on the day of discharge. Self-care performance in the experimental group was measured by self -evaluation tool developed by the investigator from the literature concerned. The tools to measure Pelf-confidence and satisfaction in maternal role performance, and the tool to measure self-evaluation of self-care performance were tested for internal reliability. Cronbach's Alphas were 0.94, 0.94, and 0.63. The data were analysed by using in S.P.S.S. computerized program and included percentage, x²-test, t-test, ANOVA, and Pearson Correlation Coefficient. The conclusions obtained from this study are summerized as follows : 1. The degree of self-confidence in maternal role performance of the total subjects group measured before the experimental treatment was above average with a mean score of 2.77(range 2.14-3.64). Out of 14 items, those with relatively high mean scores were ‘I would like to be a better mother than I am’(3.95), and ‘I have my doubts about whether I am a good mother’(2.87). Those with low mean scores were ‘I know that my baby wants most of the times’(2.28), ‘When the baby cries, I can tell what she /he wants’(2.37), and ‘I have confidence in my ability to care for the baby’(2;50). That is, the self - confidence of Primiparous mothers was considerably high in mothering, but rather low in activities concerning the infant care and understanding of the infant behavior. The degree of satisfaction in maternal role performance of the total subjects group measured before the experimental treatment was high with a mean score of 3.18(range 1.92-3.92). Out of 13 items, those with relatively high mean scores were ‘I am glad 1 had this baby now’(3.75), ‘I play with the baby between feedings when s/he is awake and quiet’(3.67), and ‘I enjoy being a mother’(3.27). Those with low mean scores were ‘I am upset about having too many responsibilities as a mother’(2.78), ‘It bothers me to get up for the baby at night’(2.82), and ‘I get annoyed if the baby frequently interrupts my activities’.(2.82), That is, the satisfaction of primiparous mothers was considerably high in mothering and infant care, but rather low in restraints in time or on the mother's self accomplishment and development. 2. Agreement on the means to achieve nursing goals in the early postpartum period included process of mutual goal setting, exploration of the means to achieve goals, and ahreement in concert means to achieve goals based on the mothers' condition, concerns, self-perception of the nurse - patient interactions. In the process of agreement, there was agreement that the means to achieve goals should be through trust and establishment of active relationships with the nurse through identification of problems according to planned nursing goals and active interaction, such as explanations, teaching, changing of opinions, acceptance or rejection of explanations, and proposing of questions. Therefore agreement on the means to achieve nursing goals in the early postpartum period appears to be an effective nursing intervention for primiparous mothers. 3. The degree of self- confidence in maternal role performance of the exprimental group was higher than that of the control group(t＝3.95, p＜0.01). Out of 14 items, those with higher score in the experimental group were ‘I would like to be a better mother than I am’(t＝1.93, p＜0.05), ‘I know that my baby wants most of the times’(t＝2.75, p＜0.01), ‘When the baby cries, 1 can tell what she/he wants’(t＝2.10, p＜0.05), ‘I have confidence in my ability to care for the baby’(t＝3.72, p＜0.01), ‘I trust my own judement in deciding how to care for the baby’(t＝1.96, p＜0.05), ‘I feel that I know my baby and what to do for him /her’(t＝2.44, p＜0.01), ‘I am concerned about being able to meet the baby's needs’(t＝2.87, p＜0.01), ‘I know what my baby likes and dislikes’(t＝3.26, p＜0.01), ‘I don't know to care for the baby as well as I should’(t＝2.07, p＜0.05), and ‘I am unsure about whether I give enough attention to the baby’(t＝3.04, p＜0.01), That is, the degree of self-confidence in mothering, activities concerning infant care, and understanding of infant behavior of the experimental group was higher than that of the control group. Therefore, the first hypothesis, that the degree of self-confidence in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝3.95, p＜0.01). 4. The degree of satisfaction in the maternal role performance of the exprimental group was higer than that or the control group(t＝2.31, p＜0.05). Out of 13 items, those with higher score in the experimental group were ‘I am glad I had this baby now’(t＝2.29, p＜0.05), ‘I enjoy taking care of the baby’(t＝2.4g, p＜0.01), ‘It is boring for me to care for the baby and do the same thing over and over’(t＝2.87, P＜0.01), ‘I am unhappy with the amount of time I have for activities other than childcare’(t＝2.51, p＜0.01), and ‘When bathing and diapering the baby, I would like to be doing something else’(t＝2.43, p＜0.01). That is, the degree of satisfaction in mothering, infant care, and restraints in time of on the mother's self accomplishment and development in the experimental group was higher than that of the control group. Therefore, the second hypothesis, that the degree of satisfaction in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝2.31, p＜0.05). 5. The third hypothesis, that the higher the degree of satisfaction in materenal role performance, the higher the degree of self-confidence in materenal role performance in the experimental group, was supported (r＝0.57, p＜0.01)

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.177-189
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• 2020

In this study, we implemented an education program to improve the responsive teaching ability of pre-service science teachers, and analyzed the responsive teaching practices revealed during the program process. Through this, we derived the types and characteristics of responsive teaching practice, identified factors that made it difficult for pre-service teachers to practice, and obtained empirical data on under what conditions the responsive teaching capacity of pre-service teachers was developed. For this purpose, a practice-based teacher education program was designed and carried out for 14 pre-service teachers who had no experience in responsive teaching. The program consists of four steps; observation of class, practice through rehearsal, application in practicum, and post-reflection on educational practice. In particular, qualitative analysis was conducted on the types of responsive teaching and their detrimental factors revealed during application in practicum. As a result of the analysis, four types were derived; discriminator type, communicator type, guide type, and facilitator type. Each type was identified as having a common responsive teaching step element. The education program implemented in this study was effective for pre-service teachers to recognize the importance of student-participation class and the educational effect of responsive teaching. However, three barriers that prevented pre-service teachers from responsive teaching practice were also analyzed. First was the pressure to achieve specific learning goals within a given class time. Second was the rigid belief of the fixed curriculum. Third was the obsession that the teacher should lead the class. Based on these results, it was suggested that in order to improve the responsive teaching ability of pre-service teachers, it is necessary to support the recognition of breaking out of the thinking the time constraint, the flexibility of the curriculum, and the role of teacher as a class supporter.

• 대한공업교육학회지
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• v.36 no.1
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• pp.75-94
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• 2011

The purpose of this study is to suggest the basic data in order to examine and perform the plan for activating the team teaching between industry-educational adjunct teachers and vocational teachers in technical high school. The research results are as follows. First, It is found that both teacher groups acknowledge the necessity of the team teaching, but vocational teachers are less likely to recognize the necessity than industry-educational adjunct teachers. Second, In the preparation of team teaching, both two groups of the teachers believe that the preliminary interchange and training between them are to be highly supportive for the activities expected to help teachers. Therefore, it is necessary to have opportunity of communication and narrow the difference of opinions between them by promoting the deep interest about applicable field and sharing the mutual idea between the teachers in the preparation of team teaching. Third, And the two groups recognize that the cooperation and joint establishment of design of team teaching and the individual process or joint progress of class activity are desirable for the proper design of team teaching. Therefore, it is necessary to establish the class environment for the interaction between teachers and students through not only the reciprocal activities between teachers but the interest class by systematically preparing the class design and role division clearly. Fourth, In the practice of team teaching, the two groups believe that the teaching activities can be usually divided and progressed, but it is desirable to work together in the related contents. The vocational teachers recognize that it is necessary to actively interact with students by connecting with the case of industry field. On the other side, industry-educational adjunct teachers think that the learning contents should be selected and organized according to the interests of students by associating with the case of industry field. Fifth, And two groups of teachers recognize that it is desirable to evaluate the grade by reflecting on the assessment by vocational teachers(50%), industry-educational adjunct teachers(50%).

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.53-64
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• 2015

This research was performed of high school science classes. These science classes progressed by using Scratch, and surveyed students' perception after finishing each class. This research was conducted of male students who want to choose department of natural science in the next grade. Those classes are consisted of four classes. This study produced a journal, and contained expressions of their thinking and feeling based on experiences during attending classes and projects. Consequently, that journal was analyzed in view of understanding and perception of Scratch using science classes, and it was also included of utilizing Scratch program. Research shows following three conclusions. First, students preferred Scratch using class to general one. They attend more active with high interest, and they felt senses of accomplishment while they make output by themselves. Second, their studies passed through three stages. These are problem perception, problem solving, and producing. Problem solving stage is especially complicated and difficult stage to students. This stage is consisted of Scratch side and Science side. Scratch side has Design and applying process, and Science side has data gathering and analyzing. Students' comprehension of scientific knowledge is increased and is preserved long time through this stage. Last, students had a hard time using Scratch. Because, it is the first time to them to use that program. Therefore, we deemed that they needed to start this kind of experience at lower grade than they are now, such as middle school stage. It is expected that this type of classes are getting more expanded and more populated as a part of students' core ability.