• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.451-459
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• 2019

Various policies have been implemented for human resources development in preparation for future society. Among the policies, STEAM education, SMART education and SW education are representative examples. In order for these policies to be implemented effectively in the school setting, teachers' positive perception and teaching competency are required. In consideration of the importance of pre-service teacher education, this study analyzed the current status of elementary pre-service teachers' perception and teaching readiness on STEAM education, SMART education and SW education, and sought implications that can be reflected in pre-service teacher education. The results of the study showed that the pre-service teachers' perception on the necessity of each policy was very high, and the understanding level of each policy was relatively high. Compared with this, it was found that pre-service teachers lacked training experience related to each policy, and the level of readiness for teaching was very low. As the most important task to be solved, many pre-service teachers selected the implementation of teacher education and seminars, and the distribution of instructional programs and materials. As the result of analyzing the difference according to pre-service teachers' individual characteristics, differences were found according to the level of their ICT utilization ability. Based on the results of this study, implications to be reflected in pre-service teacher training processes were suggested.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.39-47
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• 2021

The construction of high-quality input features through effective segmentation is essential for increasing the sentence comprehension of a language model. Improving the quality of them directly affects the performance of the downstream task. This paper comparatively studies the segmentation that effectively reflects the linguistic characteristics of Korean regarding word and sentence classification. The segmentation types are defined in four categories: eojeol, morpheme, syllable and subchar, and pre-training is carried out using the RoBERTa model structure. By dividing tasks into a sentence group and a word group, we analyze the tendency within a group and the difference between the groups. By the model with subchar-level segmentation showing higher performance than other strategies by maximal NSMC: +0.62%, KorNLI: +2.38%, KorSTS: +2.41% in sentence classification, and the model with syllable-level showing higher performance at maximum NER: +0.7%, SRL: +0.61% in word classification, the experimental results confirm the effectiveness of those schemes.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Journal of vocational education research
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• v.30 no.4
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• pp.1-22
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• 2011

Paradoxically, leadership has not only positive effects but also negative effects. The purpose of this study is to examine supervisors' dysfunctional leadership behaviors, antecedents and results in order to draw HRD implications for protecting organizations and employees from dysfunctional leaders and provide implications for leadership development. A qualitative research method based on semi-organized interviews with 28 employees from S-group was used. The results of this study show that the dysfunctional leadership behaviors were associated with ten behavioral categories: belittling and insulting the subordinates, authoritative and arbitrary behaviors, self-aggrandizement, biased preference for certain personnel, arrogance, micro-managing, inability to change and adapt, discordance between words and actions, over-dependance on supervisor, lack of ethics and values. Dysfunctional leadership behaviors were casually attributed to 'personal traits & experience', 'task characteristics', and 'internal & external environments of the organization'. Finally, the results of supervisor's dysfunctional leadership behaviors on employees and the organizational effects were 'increased turnover rate', 'declining work efficiency', 'collapsing morale', 'retraining innovative thinking', 'passive working culture', 'discouraging organizational vitality', 'discouraging organizational synergy', 'losing loyalty' and 'declining trust on supervisor'.

• 한국노년학
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• v.32 no.3
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• pp.747-758
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• 2012

Detecting probable Alzheimer's disease (AD) at an early stage is crucial in slowing the progression of the disease and initiating drug therapy for more effective symptom management. Therefore, this study aimed to identify linguistic features that allow us to distinguish between patients with AD and normal controls. This paper reports on characteristics of spontaneous speech in subjects in three stages of AD (questionable, mild, moderate) compared with education- and age-matched normal controls. Four components of speech were measured in Korean native speakers with AD and normal aging: speech tempo, hesitation (measured in seconds), rate of articulation errors, and rate of grammatical errors. The results revealed significant differences in most of these speech components among the four groups, including significant differences between normal controls and the questionable AD group in the areas of speech tempo and rate of grammatical errors. Phonological? articulatory ability was preserved in questionable AD, and grammatical ability was preserved in questionable and mild AD. Subjects with moderate AD were severely impaired in grammatical ability. Prospective assessments of spontaneous speech skills using a dialogue and picture-description task are useful in detecting the subtle, spontaneous speech impairments that AD causes even in its early stage.

• Journal of Korean Philosophical Society
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• v.137
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• pp.277-321
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• 2016

Friedrich Nietzsche's philosophy embraces characteristics of existential philosophy and philosophical anthropology. In his book "Thus Spoke Zarathustra", Nietzsche defined human beings as an existence with innate possibility for change, beings that stand at the borderline between "the last man" and "the ${\ddot{u}}bermensch$", raising a question over the meaning of human being's existential healthiness. The anthropological symptoms that Nietzsche's philosophy deals with trigger existential problems, and healing these anthropological symptoms is a precedent to healing an existence. In Nietzsche's philosophy, the ${\ddot{u}}bermensch$ is presented as a prototype of practical man with a healthy existence, born from endeavors to heal the last man prototype of a decadence that was prevalent throughout Europe at the time. Nietzsche found the root cause of nihilism found in Europe in philosophy, religion, metaphysics, and Christianity, and attempted a genealogical investigation on this aspect. In so doing, a philosophical problem surfaced whereby only one truth was used to force diverse existential styles into a uniform style. Nietzsche intensively criticized philosophy and philosophers that only studied truths from metaphysical-Christian-moral perspectives, as they overlooked the foundation of true existence and presented human beings of a feeble mind and will as a result. Nietzsche emphasized the practical role of philosophy that can contribute to the human being's ascent and growth based on realistic conditions of human existence described as the earth, that philosophy that can serve as a basis for existential transformation of human beings and their lives. The task of philosophers is to lay the groundwork for the possibility of changes for all human beings and their realization. This existential practical foundation of philosophy can be called the ${\ddot{u}}bermensch$, as it is healthy man, the "greatest reality" as Nietzsche desired.

• Journal of Science Education
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• v.42 no.3
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• pp.273-292
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• 2018

The lecture is the main method of teaching, and the most common activity of students is 'listening.' Therefore, efficient and positive changes are expected if the researcher analyzes and uses students' listening styles to educate them. In addition, as the learner-centered education is emphasized, the learner's characteristics are becoming more important, and this flow increases the value of the listening styles of the student and that of the learning strategy, which is the student's self-directed learning. Therefore, this study examined whether there are differences in science achievement according to the listening styles and learning strategies by statistical analysis of the data obtained by conducting surveys for students in 5th, 7th, and 10th grades. The results of this study are as follows: First, students' listening styles and learning strategies show significant differences between men and women. Second, students' listening styles and learning strategies show significant differences between grade levels. Third, the level of task-oriented and critical listening types among listening styles produce meaningful differences in science achievement. Fourth, listening style, learning strategy, and science achievement have a significant correlation with each other. Finally, in terms of learning strategy-science achievement, it was shown that basic and complex cognitive strategy had a positive correlation with science achievement.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• Management & Information Systems Review
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• v.38 no.1
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• pp.103-120
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• 2019

Recently, as mobile banking enables to instantly provide the customized service in accordance with customer demand via information technology. With this individual customized service, mobile banking plays a role of transforming the existing offline banking strategies. However, contrary to expectation, the mobile banking service has not been widely used to the extent that it can replace offline banking service. Therefore, the current study aims to explore the antecedents to affect customer's usage of mobile banking. Specifically, the antecedents influencing the intention to use and actual usage of mobile banking include personal-innovation fit, positive psychological capital, and service quality factors, which reflect the innovative technology characteristics of mobile banking. Furthermore, the paper also analyzes the effect of mobile banking service on intention to use and actual usage of mobile banking service. With empirical analysis using Structural Equation Modeling for 195 mobile banking users, the results showed that user's ability fit, value fit, and positive psychological capital positively affected user's future intention to use and actual usage of mobile banking. Furthermore, the current paper also found the significant moderation effect of usage purpose of mobile banking (banking task and online stock exchange) in the relationship between positive psychological capital and intention to use. This study suggests that banks need to develop mobile banking services that reflect customer's IT usability as well as their pursuing purpose and value.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.