• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.177-189
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• 2016

This study examined the effectiveness of maternity nursing simulations using a high-fidelity simulator for undergraduate nursing students. One-group pretest-posttest design was used. The simulation-based education program consisted of three sessions, including the clinical scenarios about prenatal, childbearing and postpartum care. The program provided for 3 weeks in November 2014. Data was collected before and after the simulation education using self-reported questionnaires, which included simulation effectiveness, problem solving ability, communication skills and self-confidence in maternity nursing. The data of 83 participants were analyzed using the IBM SPSS 20.0 program. After simulation education, the overall score of the simulation effectiveness was 17.4 out of 26.0. Communication skill (t=4.58, p=<.001) and self-confidence in maternity nursing (t=9.70, p=<.001) increased significantly in the posttest. On the other hand, there was no significant change in the problem solving ability. The simulation effectiveness correlated significantly with the problem solving ability (r=.494, p<.001), communication skill (r=.361, p<.001), and self-confidence in maternity nursing (r=.497, p<.001) after simulation-based education. These findings suggest that the high-fidelity simulation in maternity nursing education could be used not only to enhance the nursing competency, but also to deal with the limitations of the clinical practicum in the current situation.

• Journal of The Korean Association For Science Education
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• v.31 no.4
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• pp.609-620
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• 2011

This study examined how pre-service science teachers, who observed classes for the gifted in science, perceive the gifted in science and the education they are getting, and explored what needs to be improved in the classes for the gifted in science. Based on the results of this study, first, pre-service science teachers were negative about the giftedness of the gifted in science. Second, they recognized that various types of classes were not provided. Especially, while theoretical lectures were mostly offered, they recognized that it had a negative influence in developing the potential giftedness of the gifted in science. Third, they were negative about the absence of programs for improving creativity and thinking skills and teaching materials for the gifted in science; however, they were positive about self-directed learning. Fourth, they had a negative opinion on educational facilities and the number of students in classes. Fifth, they recognized that potential giftedness would be developed the most when the lecturer is a professor majoring in the subject. For improvements in the classes for the gifted in science, they referred to revising the distinction focusing on preceding learning, reinforcing teaching methods to improve creative thinking, constructing creative contents regardless of specific grades and curriculum, securing learning materials for the gifted, and the necessity of lecturers specialized in the education for the gifted. Eventually, pre-service science teachers have negative cognitions for the classes for the gifted in science offered by universities, and it was known that they mentioned the necessity of creative educational courses and professional lecturers, not pre-learning for improvements.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.441-446
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• 2020

In this paper, we propose the α-Feature Map Scaling (α-FMS) method which extends the FMS method that was designed to enhance the discriminative power of feature maps of deep neural networks in Speaker Verification (SV) systems. The FMS derives a scale vector from a feature map and then adds or multiplies them to the features, or sequentially apply both operations. However, the FMS method not only uses an identical scale vector for both addition and multiplication, but also has a limitation that it can only add a value between zero and one in case of addition. In this study, to overcome these limitations, we propose α-FMS to add a trainable parameter α to the feature map element-wise, and then multiply a scale vector. We compare the performance of the two methods: the one where α is a scalar, and the other where it is a vector. Both α-FMS methods are applied after each residual block of the deep neural network. The proposed system using the α-FMS methods are trained using the RawNet2 and tested using the VoxCeleb1 evaluation set. The result demonstrates an equal error rate of 2.47 % and 2.31 % for the two α-FMS methods respectively.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.403-409
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• 2020

Gamification can be more fun and interesting than boring and it can be used for variety of educational subjects. Through the fun and flow of the learner, the learner understands the meaning and context of the problem to be solved. It will be able to collect, analyze and creatively solve data in a way that computers understand. Training through gamification will be an effective and memorable coding education for the efficient learning of coding education among the newly designated compulsory education. It is considered to be highly useful as a convergence study to increase tourism experience. Currently, in the field of school, coding education is mainly conducted using entry and scratch, which are educational programming languages, and coding education using gamification is not extensively used in the current education field. It is also expected to be used to increase the tourism experience, and it can be used to enhance the learner's computational thinking ability and creativity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.568-576
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• 2020

This study examines the effects of ethical education using CEDA discussion learning, on nursing professional self-concept, ethical dilemma and ethical decision-making confidence of nursing students. The participants were 57 third year nursing students from a nursing college in C city, and the data were collected from March 4 to May 13, 2019. Ethics education using CEDA discussion learning was conducted for 9 weeks, including 3 weeks of theoretical education and 6 weeks of CEDA discussion. Collected data were analyzed by t-test, one-way ANOVA and paired t-test using the SPSS WIN / 21.0. The results indicate that ethical education using CEDA discussion learning positively affects nursing professional self-concept (t=13.816, p<0.001), ethical dilemma (t=6.205, p<0.001) and ethical decision-making confidence (t=11.950, p<0.001). In addition, the self-concept of nursing professional, ethical dilemma, and ethical decision-making self-confidence are correlated, and nursing professional self-concept differs according to major satisfaction. Our results indicate a necessity to strengthen the ethics education of nursing college students, to help develop positive nursing professional self-concept and confident ethical decision-making in an ethical dilemma.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.5
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• pp.369-379
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• 2017

The purpose of this study was to improve an university tutoring program qualitatively, evaluating and analyzing the case of tutoring program that students attended in order to strengthen their learning competences through CIPP(Context-Input-Process-Product). To do this, a tutoring survey with satisfaction survey was carried out for 90 students who participated in the final reporting meeting among those who joined the tutoring program at the second semester of 2016 year at the B university. In addition, a tutor interim checking workshop was held for the 49 tutors and qualitative analyses were carried out on the reflection papers submitted by students who were involved in the tutoring program. Tutoring program assessments were carried out in terms of several steps and their view points. They were the necessity of tutoring program for the context evaluation, the appropriacy of human-material resources and program planning for the input evaluation, the teaching and learning activity for the process evaluation and the influence, effectiveness, sustainability, transferability for the product evaluation. Survey results showed that the average scores were presented 4.05 for the context evaluation, 3.88 for the input evaluation, 4.08 for the process evaluation and 3.92 for the product evaluation respectively. Similar analysis results were represented from the tutor workshops and the reflection paper analysis results. Learners were satisfied with all the evaluation items, scoring more than the average level. However, it was necessary for the evaluation items that were rated low range to improve the program through detail modification plans.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.292-300
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• 2016

The purpose of this study was to investigate the effects of case-based learning about infants and toddlers on healthcare department students, using a video in an emergency care environment. A total of 57 students from a healthcare department of D university in J city were enrolled. They were divided into two groups: The experimental group (n=29) and the control group (n=28). This study is pre-post designed with a non-equivalent control group. The experimental group received a 1-week education for a duration of 3 weeks (3 sessions in total) with 180 minutes per session. The control group received a traditional curriculum of lecture. Before and after the education, we measured the knowledge and skill confidence of emergency care toward infants and toddlers, the academic self-efficacy, and problem solving ability. Data collection and intervention were carried out from November to December of 2014. Data were analyzed with x2-test, paired t-test, unpaired t-test with SPSS version 20.0 Program. The experimental group showed a significantly higher improvement of skill confidence of emergency care toward infants and toddlers (P<001), as well as preferred task difficulty among sub-items of academic self-efficacy (p=.029), approach avoidance style (P=.001), and problem solving confidence (p=.040) among sub-items of problem solving ability on preference compared with the control group. In this study, a case-based learning was verified to be an effective teaching method to enhance professional competency of healthcare department students. The findings from this study suggest that a case-based learning using various educational contents should be developed, expanded, and carried out to promote better learning.

• Journal of Gifted/Talented Education
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• v.19 no.3
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• pp.457-476
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• 2009

The research aimed to investigate characteristics of middle school students in a biology class as science gifted education in terms of self-regulated learning abilities, personality traits and learning preferences. The twenty subject in the study responded to questionnaires of a self-regulated learning ability instrument, a personality trait tool, and a learning preference survey in March, 2009. It was found that the research subjects showed higher levels of cognitive strategies, meta-cognition, and motivation than those students in a previous study(Jung et. al., 2004), while environment was opposite. The level of cognitive strategies was significantly correlated with meta-cognition(r=.610, p=.004) and motivation (r=.538, p=.014) and meta-cognition with environment(r=.717, p=.000). Those students who showed highest levels of self-regulated learning ability displayed various personality traits. One male student with the highest level of self-regulated learning ability showed a personality of hardworking, tender-minded, and conscientious traits and wanted to be a medical doctor. The female student with the second highest level of self-regulated learning ability presented a personality as creative, abstract and divergent thinker and she showed a strong aspiration to be a world-famous biologist with breakthrough contribution. The five students with highest levels of self-regulated learning ability showed a common preference in science learning: they dislike memory-oriented and theory-centered lecture with note-taking from teacher's writings on chalkboard; they prefer science learning with inquiry-oriented laboratory work, discussion among students as well as teachers. However, reasons to prefer discussion were diverse as one student wants to listen other students' opinions while the other student want to present his opinion to other students. The most favorable science teachers appeared to be who ask questions frequently, increase student interests, behave friendly with students, and is a active person. In conclusion, science teaching for the gifted should employ individualized teaching strategies appropriate for individual personality and preferred learning styles as well as meeting with individual interests in science themes.