• Journal of Korean language education
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• v.29 no.1
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• pp.85-108
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• 2018

This study pointed out the reality that many novice Korean language teachers who lack rater training are scoring the learners' writing skill. The study performed and analyzed a case where pre-service teachers were educated in order to explore the possibility of promoting rater training in a Korean language teacher training course. The pre-service teachers majoring in Korean language education at the graduate school scored TOPIK compositions and were provided feedback by the FACETS program, which were further discussed at the rater meeting. In three scoring processes, the raters scored with conscious of own rating patterns and showed positive change or over correction due to excessive consciousness. Consequentially, ongoing training can improve rating ability, and considering the fact that professional rater training is hard to progress, the method composed of FACETS analysis and rater training revealed positive effects. On the other hand, the rater training including native Korean and non-native(Chinese) speakers together showed no significant difference by mother tongue but by individual difference. This can be interpreted as a positive implication to the rating reliability of non-native speakers possessing advanced Korean language abilities. However, this must be supplemented through extended research.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.125-136
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• 2012

In most child-care facilities, homeroom teachers take the responsibilities for safety education, which has been conducted in class hours on a regular basis. It was found that most homeroom teachers are lacking in teacher training opportunities, and the younger they are, the less training experience they get. Teachers with fewer teaching careers had a greater interest in safety education and training courses. In addition, homeroom teachers have been utilizing monthly toddler magazines as a method to acquire knowledges for safety education and prevention of safety accidents. The safety accidents which have most frequently occurred in care-care facilities turned out to be stumble and tear, and infants aged 3 years or younger were found to be easily exposed to the safety accidents mainly due to the frolic between peers during the free-choice activity time. The homeroom teachers recognized only traffic safety education among the range of safety training courses, which varied depending on teaching career such as traffic safety education and indoor/outdoor safety environment training, etc. In addition, it turned out that the safety training methods were limited to the utilization of discussion techniques, role-playing, description and demonstration.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.135-146
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• 2018

The Modified Voiced Lip Trill (MVoLT) training is a variant of voiced lip-till training characterized by increased loudness, lowered laryngeal position, and lip contact facilitated with fingers. The purpose of the current study was to assess the effect of the MVoLT training program on vocal changes of musical singing theater students. A total of 32 musical theater students (17 males and 15 females, age ranging from 18 to 29) participated in the study. For about three months, each participant was tutored using a systematic program focussing on the MVoLT training, accompanied by certain facilitating strategies. Pre- & post-training multi-dimensional vocal characteristics were assesed and compared. Results showed that cepstral peak prominence during vowel phonation increased after training, while its standard deviation and Cepstral Spectral Index of Dysphonia decreased. When an aerodynamic assessment was performed, maximum phonation time, subglottal pressure, mean airflow rate increased, while electroglottographic measures did not change. In addition, decreased psychometric measures, higher maximum pitch, and increased vocal range were noted after training. In conclusion, the MVoLT was proven to have a potential as an effective and safe training method for musical theater singing.

• The Korean Journal of Emergency Medical Services
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• v.26 no.1
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• pp.7-19
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• 2022

Purpose: The purpose of this study is to evaluate the interrupted chest compression time during the use of an automated external defibrillator (AED) depending on different AED practice training methods, and to report differences in self-efficacy before and after training. Methods: We enrolled university freshmen who have had cardiopulmonary resuscitation (CPR) training but have not or have had AED training but over 6 months. We examined differences between the group that practiced only shockable rhythms during training and the group that practiced both shockable and non-shockable rhythms. Results: A total of 72 individuals participated in this study, with 36 individuals each in the control and experimental groups. There was no statistically significant difference in the proficiency of AED usage between the two groups. In non-shockable cases, the experimental group showed shorter chest compression interruption time than the control group (2.30±1.21sec vs. 3.16±1.73 sec; p<0.01). In terms of self-efficacy before and after training, both groups showed higher self-efficacy after than before training. Conclusion: Individuals who underwent training that provided practice on both shockable and non-shockable rhythms had a shorter interrupted chest compression time when using the AED.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.294-303
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• 2022

The purpose of this study is to examine the effects of sensory integration training on children with developmental delays. To achieve this goal, an educational experiment is conducted in five main areas: gross motor ability, fine motor ability, adaptive ability, language and social ability in children with developmental delay. The study subjects were children with developmental delays aged 3-6 years diagnosed at Beijing Institute of Pediatrics and Beijing Medical University and received sensory integration intervention and homebased training at the Golden Rain Forest Beijing Tongzhou Center from 2018 to 2021. According to the purpose of the analysis, the data collected are subjected to descriptive statistics using SPSS 21.0 statistical program, Two-way MANOVA analysis, and data analysis method of multivariate analysis is used to process the collected data. In addition, a total of 39 subjects were selected, including 19 children who received sensory integration training and 20 children who only received family training. The results show that the sensory integration training group outperformed the home training group in all aspects and developmental quotient, but the home training group also showed higher levels of significance for improvements in gross motor, fine motor and developmental quotient.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.410-417
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• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.163-173
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• 2020

In this study, on-site corporate instructors participated as student-cum-workers in a degree-linked high skills Meister course to improve job competency and practical ability as proposed in the Work-Study Career Vision. Evaluation questions were then developed and their validity was verified by assessing satisfaction related to expected goals in enhancing advanced training guidance and competency as an evaluator. Satisfaction assessment was conducted based on training preparation, training implementation, training effectiveness and training administration. The Delphi Method was adopted and a total of 48 items were developed in 6 categories under 4 main areas. There were 7 evaluation items on the satisfaction of training course development under training preparation, 21 evaluation items related to the satisfaction of Off-JT and OJT courses under training implementation, 16 evaluation items related to the satisfaction of increased competency as an on-site corporate instructor and the satisfaction of enhanced practical skills and skills application at work under training effectiveness, as well as 6 evaluation items to assess satisfaction with administrative support under training administration. The final conformity assessment was conducted based on the stability, content validity ratio, consensus and convergence indicators of the developed items. Results of this study do not only apply to quality management of the high skills Meister course which is being promoted as a pilot project for work-study programs, but also serves as a rationale that may be considered as a basic research tool in the collection of various opinions to derive overall system improvement factors for the work-study high skills Meister course.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.