• Hwankyungkyoyuk
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• v.15 no.1
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• pp.68-82
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• 2002

My responses to Kim Kyung-Ok's Critique on my critique on the Hungerford approach can be summarized as follows; First, it was argued that possible confusions and misunderstandings around the concept of behavior in REB were mainly caused by Hungerford himself who has used the word in several different ways, from a bunch of overt actions to almost all kinds of responses including cognitive skills, without any clear operational definition of it for more than 20 years. It seems to be needed for future users of the word, 'Behavior' to Prevent unnecessary confusions by providing their operational definition of it. Second, REB is too ambiguous to be a legitimate goal of environmental education and too outcome-oriented to be a meaningful measure for environmental education research. Anyone who accept REB as a goal of EE or a measure for research should clearly suggest procedures and criteria for judging the environmental responsibility of actions under consideration. Third, the Hungerford approach has begun by realizing the limit of a linear traditional behavior change system and has been evolving toward a complex model with dynamic interactions among/between cognitive variables and affective variables. However, it still has one-way structural orientation toward 'Behavior' with no feedbacks. Addition of some feedback processes would make the model more flexible and realistic. Finally, both the Hines model and the Hungeford model were established based on a series of behavioristic studies including three doctoral dissertations equiped with a list of actions which were prejudged to be environmentally responsible by the researchers, not by the learners. What they were primarily interested in was not how mind functions during the learning processes but how learners' behavior can be effectively changed. Considering uncertainty and complexity associated with environmental problems, a great deal of efforts ought to be made toward more context-based and less normative studies applying cognitive psychology and quantitative approaches.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.479-490
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• 2021

This study was attempted to identify the essence of the mixed practice experience that replaces the clinical practice of nursing students in the COVID-19 situation. From December 17, 2020 to December 31, 2020, eight nursing students were individually interviewed to collect data, and the data were analyzed by applying Colaizzi's phenomenological analysis methodology. The study's finding derived 6 categories. The specific categories were 'Accepting in anxiety', 'Autonomy at the fore', 'Straining non-face-to-face practice', 'Fill your knowledge', 'A variety of approaches to achieve goals', 'The best practice left with regret'. In the pendemic situation, nursing students accepted clinical practice substitution with anxiety and concern about mixed practice, and experienced a way to adapt to practice through self-directed learning and to achieve the goal of clinical practice.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• Journal of The Korean Association For Science Education
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• v.34 no.4
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• pp.335-347
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• 2014

Size/scale is a central idea in the science curriculum, providing explanations for various phenomena. However, few studies have been conducted to explore student understanding of this concept and to suggest instructional approaches in scientific contexts. In contrast, there have been more studies in mathematics, regarding the use of number lines to relate the nature of numbers to operation and representation of magnitude. In order to better understand variations in student conceptions of size/scale in scientific contexts and explain learning difficulties including alternative conceptions, this study suggests an approach that links mathematics with the analysis of student conceptions of size/scale, i.e. the analysis of mathematical structure and reasoning for a number line. In addition, data ranging from high school to college students facilitate the interpretation of conceptual complexity in terms of mathematical development of a number line. In this sense, findings from this study better explain the following by mathematical reasoning: (1) varied student conceptions, (2) key aspects of each conception, and (3) potential cognitive dimensions interpreting the size/scale concepts. Results of this study help us to understand the troublesomeness of learning size/scale and provide a direction for developing curriculum and instruction for better understanding.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• Journal of Science Education
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• v.47 no.2
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• pp.117-126
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• 2023

In an elementary school science teacher's guide, the guide in details that can help elementary school teachers with difficulties when preparing and conducting science classes are essential. To gain insights into the development of the 2022 revised curriculum elementary school science teacher's guide, it is necessary to investigate the perceptions and needs of elementary school teacher's guide in details. In this study, we developed a questionnaire by analyzing the components and design of an elementary school science teacher's guide in details to explore elementary school teachers' perceptions and needs. For this purpose, we first investigated elementary school teachers' perceptions of their needs, satisfaction, and the utilization of each component of the current guide in details. Next, we investigated teachers' needs regarding the specific components and design of a guide. The findings were as follows. First, elementary school teachers were delighted with the components that help them prepare and conduct lessons. Second, elementary school teachers wanted an easy-to-read design with a layout that allowed them to see the components they needed for their lessons at a glance. In conclusion, the elementary school science teacher's guide in details to be readable and organized to provide at-a-glance information on lesson preparation, lesson flow, and teaching and learning materials needed for science teaching-learning. Based on the results of this study, new approaches and attempts should be made to develop a textbook that elementary school teachers can utilize in the future.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.629-648
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• 2015

This study is a case study examining how research-based 'authentic' science education program contextually facilitates students' learning on NOS as a process. We developed 'Becoming a Scientist' mentor-mentee program and applied it to six Korean 7th graders for 8 months. A mentor, who is also a researcher, provided scaffolding and coaching, and her mentees were to perform the whole process of science research, including selecting the research subject and questions, planning research design, doing experiments, collecting and analysing data, writing research paper, and experiencing poster presentation at an academic conference. The research questions are 1) What would the students experience at every step of their research process?, and 2) Which perceptions would they construct NOS as a process? Data include classroom observations, interview, mentor's journal, and students' learning products. The results show that the mentees have experienced their views of NOS as a process in various ways such as role of research question and purpose, validity of measured value, researcher's subjectivity in interpreting data, experience of making public and peer review, and significance of academic conference. This study has shown that students' actual experience in scientific research enhanced their views about NOS as process without explicit and reflective approaches. We defined 'authenticity' associated with not only with its similarity to what scientists do but to learner's identity as scientific researcher. Based on the situated learning theory, this study sheds light on the necessity of reconsideration about the meaning of authenticity and embodying authentic context in science education for better NOS learning.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.3885-3890
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• 2014

Background: This study aimed to determine nursing student knowledge, behavior and beliefs for breast cancer and breast self-examination receiving courses with a traditional lecturing method (TLM) and the Six Thinking Hats method (STHM). Materials and Methods: The population of the study included a total of 69 second year nursing students, 34 of whom received courses with traditional lecturing and 35 of whom received training with the STHM, an active learning approach. The data of the study were collected pre-training and 15 days and 3 months post-training. The data collection tools were a questionnaire form questioning socio-demographic features, and breast cancer and breast self-examination (BSE) knowledge and the Champion's Health Belief Model Scale. The tests used in data analysis were chi-square, independent samples t-test and paired t-test. Results: The mean knowledge score following traditional lecturing method increased from $9.32{\pm}1.82$ to $14.41{\pm}1.94$ (P<0.001) and it increased from $9.20{\pm}2.33$ to $14.73{\pm}2.91$ after training with the Six Thinking Hats Method (P<0.001). It was determined that there was a significant increase in pre and post-training perceptions of perceived confidence in both groups. There was a statistically significant difference between pre-training, and 15 days and 3 months post-training frequency of BSE in the students trained according to STHM (p<0.05). On the other hand, there was a statistically significant difference between pre-training and 3 months post-training frequency of BSE in the students trained according to TLM. Conclusions: In both training groups, the knowledge of breast cancer and BSE, and the perception of confidence increased similarly. In order to raise nursing student awareness in breast cancer, either of the traditional lecturing method or the Six Thinking Hats Method can be chosen according to the suitability of the teaching material and resources.