• 대한공업교육학회지
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• v.43 no.2
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• pp.79-96
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• 2018

In this study, we look at the effect that the achievement goal of specialized high school students has on academic self-efficacy and the difference in academic self-efficacy depending on achievement goal orientation. The purpose of this research is to help students to efficiently increase their academic self-efficacy, develop research and study life guidance measures to improve negative factors, and select professors and learning methods. To achieve the purpose, survey was conducted with achievement goal orientation measurement tools(26 questions) and academic self-efficacy measurement tools(28 questions) for 745 students of 18 specialized technical high school students in 5 districts. The results of this study are as follows. First, preference to task difficulty and self-controlling efficacy have highly positive correlations with mastery goal orientation and confidence and mastery avoidance goal orientation have highly negative correlations each other. Second, achievement goal orientation form of specialized high school students were divided into 5 forms; 'execution avoidance(34.8%)', 'mastery orientation(20.8%)', 'approach(17%)', 'avoidance competition(14.9%)', and'mastery avoidance(12.5%)'. In preference to task difficulty, 'approach'group showed the highest average point and 'mastery avoidance'showed the lowest average point. The average point of 'approach' group was higher than other groups in confidence, but 'mastery orientation' group showed the highest average point. Through the results of this study, academic self-efficacy makes an effect by a certain direction in accordance with achievement goal orientation and it's necessary to access academic problems differently according to student's goal directivity. Therefore, it's necessary to provide educational method by student type based on explanation about academic self-efficacy of achievement goal orientation of specialized high school students and analysis on achievement goal orientation form.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.763-773
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• 2017

The purpose of this study is to investigate the impact of Argument-Based Inquiry (ABI) strategy on student's character competency. For this study, 51 grade 11 students (two classes) were selected to the role assignment (ABI-R group) and 46 students (two classes) were assigned to the non-role assignment group (ABI group). In the result, the role assignment group (ABI-R group) showed a statistically higher change in character competency than the group without role assignment (ABI group). Particularly, the ABI-R group has significantly higher grade than ABI group in empathy, responsibility, and respect among the sub-factors of character competency. However, in the case of the cooperation factor of character competency, the ABI group showed statistically significant higher grade than ABI-R group. The results of this study showed that Argument-Based Inquiry (ABI) as teaching and learning strategies in science can contribute to the enhancement of human character competency. In addition, we suggest that students should be actively involved in the class through role assignment, but it is necessary to present the class situation so that they can be actively engaged according to the problem situation rather than being fixed in a given role.

• Korean Journal of Food Science and Technology
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• v.44 no.4
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• pp.428-433
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• 2012

Probiotics and their products, such as yogurt and cheese have been widely consumed in many countries with proven health benefits including anti-microbial activity and anti-diarrheal activity. LHFM (Lactobacillus helveticus - fermented milk) is a processed skim milk powder, fermented by a probiotics, L. helveticus IDCC3801. In the present study, we aimed to investigate the neuroprotective effects and the cognitive improvements of LHFM. LHFM itself did not show any cytotoxicity to the human neuroblastoma cell line, SH-SY5Y; however, it dose-dependently protected against glutamate-induced neuronal cell death. LHFM also attenuated scopolamine-induced memory deficit in Y-maze and Morris-water maze. In the analysis of hippocampus after a behavior test, LHFM significantly increased the acetylcholine level and also inhibited acetylcholine esterase activity. Therefore, the raised acetylcholine release partially contributes to the improvement of learning and memory by a treatment with LHFM. These results suggest that LHFM is an effective material for prevention or improvement of cognitive impairments caused by neuronal cell damage and central cholinergic dysfunction.

• Journal of Korean Home Economics Education Association
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• v.20 no.1
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• pp.137-152
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• 2008

This study examined the organization and operation of home economics curriculum of specialized middle school in the form of regular school among alternative schools and analyzed the perceptions of teachers and students about home economics class. Interviews were conducted with teachers of 6 specialized schools in order to determine the operations and teachers' perceptions of home economics education. Students' perceptions for home economics class were gathered through surveys with students from the 3 (of the original 6) schools that authorized the questionnaire survey. The final analysis utilized 205 student responses. Survey data were analyzed using the SPSS program. The results of the research were as follows: First, home economics education within specialized middle schools was mostly conducted according to the form of the technology-home economics curriculum, which is the national common basic curriculum. Compared to the 7th national curriculum, the class of technology-home economics curriculum in 4 schools occurred 1 hour less each week. Each school incorporated various specialized curricula related to home economics. Second, as for the operation of home economics education in specialized schools, most home economics classes were conducted by teachers who had majored (or minored) in home economics. Moreover, all but 1 school, which used self-made materials, used the national textbook and dealt with the entire content of the textbook. For teaching-learning methods and instructional media, various means were utilized. For evaluation methods, most schools based grades on paper-and-pencil tests(50-60%) and performance tests(40-50%). Third, among teachers' perceptions of home economics education, the meaning of home economics education was focused on practical help and the pursuit of home happiness; the purpose was to realize the happiness of students and their homes by applying these to actual living, and increase students' ability to see the world. In regards to difficulties in educational operations, most pointed out poor conditions of practice rooms. As for differences from general schools, most teachers mentioned the active communication with students. Fourth, through the home economics class, it was found that students perceived the goal of technology-home economics curricula as lower than average. Among students' perceptions about home economics class, most were negative. Perceptions about goal of technology-home economics curricula and home economics class also showed meaningful differences according to each school. Students of the school, which had more home economics class hours and specialized curricula related to home economics, perceived more positively. Also, students who were more satisfied with school and learned from a teacher who majored in home economics tended to perceive home economics class more positively.

• 대한공업교육학회지
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• v.44 no.2
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• pp.104-122
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• 2019

In the era of the fourth industrial revolution, maker education is drawing attention as a method of student-led education. At a time when interest in maker education is also growing in technology education, figuring out what stage of concern(SoC) a middle school technology teacher is critical to effective implementation. This study analyzed SoC in maker education by layer sampling among 400 middle school technology teachers using Concerns-based adoption model. SoC was then obtained by measuring the origin using the SoCQ and then presenting it as a SOCQ profile. Gender, training experience with two lower variables were analyzed using t verification, working cities, teaching experience with more than three lower variables were analyzed using one-way ANOVA. Studies showed that SoC in maker education of middle school technology teachers showed the most similar characteristics to that of non-users. The difference in concern depending on gender was that male teachers were more concerned in maker education than female teachers. The difference in concern depending on the working city was that teachers working in the township were more concerned in the maker education than teachers working in the large city, and the difference in concern depending on the teaching career was higher among teachers with middle experience than those with low and high experience. There was also a higher stage of concern in maker education than in teachers without training experience. Therefore, it is necessary to provide middle school technology teachers with an introduction to the maker education and various information, teaching, learning and evaluation data to enhance overall concern and to support the use and evaluation of the maker education in the classroom by providing various teacher training and consulting on the maker education in the future. Further, through further study, we should conduct study that analyzes both Stage of Concern, Level of Use and Innovation Configuration, to put in the effort for effective settlement of maker education.

• Journal of Science Education
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• v.33 no.1
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• pp.12-30
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• 2009

The purpose of this study was to analyze recognition characteristics of science gifted students on the earth system based on their thinking style. The subjects were 24 science gifted students at the Science Institute for Gifted Students of a university located in metropolitan city in Korea. The students' thinking styles were firstly examined on the basis of the Sternberg's theory of mental self-government. And then, the students were divided into two groups: Type I group(legislative, judicial, global, liberal) and Type II group(executive, local, conservative) based on Sternberg's theory. Data was collected from three different type of questionnaires(A, B, C types), interview, word association method, drawing analyses, concept map, hidden dimension inventory, and in-depth interviews. The findings of analysis indicated that their thinking styles were characterized by 'Legislative', 'Executive', 'Anarchic', 'Global', 'External', 'Liberal' styles. Their preference were conducting new projects and using creative problem solving processes. The results of students' recognition characteristics on earth system were as follows: First, though the two groups' quantitative value on 'System Understanding' was very similar, there were considerable distinctions in details. Second, 'Understanding the Relationship in the System' was closely connected to thinking styles. Type I group was more advantageous with multiple, dynamic, and recursive approach. Third, in the relation to 'System Generalization' both of the groups had similar simple interpretational ability of the system, but Type I group was better on generalization when 'hidden dimension inventory' factor was added. On the system prediction factor, however, students' ability was weak regardless of the type. Consequently, more specific development strategies on various objects are needed for the development and application of the system learning program. Furthermore, it is expected that this study could be practically and effectively used on various fields related to system recognition.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6234-6241
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• 2014

The purpose of this study was to examine the status of medical staff stress and accommodating manners on the death of patients in a hospital setting for serving the basic information to develop a death education program of medical personnel from April 1 to April 30, 2014. A survey was performed on 353 medical personnel at K university hospital, located in Daejeon metropolitan city. Frequency analysis, chi-square test, and independent t-test were used to analyze the data. The results showed that 'to understand the value of the time and preparedness of a meaningful future' were the most important perspectives on the contents of death education (p<0.05), 'in order to change perceptions and attitudes toward death positively' was the most important reason why they required death education'(p<0.05), 'case-based teaching and problem-based learning' was the most effective way of death education (p<0.05), 'negative or hostile response of a patient's guardian to medical personnel' was the largest stress that medical personnel confront upon witnessing a death'(p<0.05). An understanding of the death of patients by medical personnel and an awareness of the need for death education will help improve the understanding of the patient, their guardian, and medical personnel themselves. The main findings will contribute to the development of a specific death education program on the medical personnel in a hospital setting.