• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.1
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• pp.140-150
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• 2006

Purpose: The purpose of this study was to determine learning styles and preferred learning methods of clinical nurses. Method: Data were collected from 735 nurses at one university hospital in Seoul. Learning style inventory, a self-report questionnaire was completed by the subjects. Result: Learning styles of nurses were accommodator 35.9%, diverger 30.4%, converger 18.2%, assimilator 15.5%. Learning styles varied significantly with clinical practice area and academic background. Furthermore, RO(reflective observation) learning mode varied significantly according to the clinical practice area. AC(abstractive conceptualization) learning mode varied significantly with job position. AC and AE(active experimentation) learning modes varied significantly according to the academic background and preferred learning method. Preferred learning methods were lecture 24.8%, clinical practice 23.1%, self-directed learning 21.5%, audiovisual education 16.7%, and group discussion 13.9%. Preferred learning methods varied significantly with learning styles and career. Lecture was preferred in diverger and self-directed learning was preferred in assimilator. Clinical practice was preferred in accommodator and converger. Conclusions: This study suggested that clinical education should be applied to nurses after examining learning styles and preferred learning methods. In conclusion, to identify the nurses' learning styles could be helpful for developing the effective educational skill.

• The Journal of Korean Academic Society of Nursing Education
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• v.13 no.1
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• pp.13-22
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• 2007

Purpose: The purpose of this study was to determine learning styles and preferred learning methods of undergraduate nursing students in Korea. Method: Data was collected from 724 nursing students at five universities in Seoul, Busan, Daegu, Daejeon, and Gwangju. Kolb's Learning Style Inventory, a self-report questionnaire was completed. Result: Learning styles of nursing students were diverger 43.5%, accommodator 36.7%, assimilator 10.8%, or converger 9.0% Learning styles were significantly different related to preferred future clinical practice area and grade. Furthermore, active experimentation(AE) learning mode was significantly different by grade. Concrete experience(CE), conceptualization(AC), and active experimentation(AE) learning modes were significantly different preferred future clinical practice area. preferred learning methods were lecture 40.7%, clinical practice 37.2%, self-directed learning 8.7%, laboratory practice 8.0%, and group discussion 5.4%. Preferred learning methods were significantly different by learning styles and grade. Lecture was preferred in diverger and assimilator. Clinical practice was preferred in accommodator and converger. Styles Conclusion: This study suggested that nursing education should be applied to nursing students after examining learning styles and preferred learning methods. In conclusion, nursing educators should help to develop various learning modes for student's balanced learning capabilities.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• The Korean Journal of Emergency Medical Services
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• v.23 no.2
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• pp.61-73
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• 2019

Purpose: This study aimed to introduce active learning methods, including flipped, case-based, and team-based learning in an electrocardiography (ECG) course and to investigate outcomes and satisfaction with these methods. Methods: To identify the learning effect of active learning, pre-and post-academic self-efficacy was compared between the experimental and control groups. In the experimental group, pre-and post-knowledge and clinical performance regarding ECG were also assessed. In addition, class satisfaction was investigated after application of active learning methods in the experimental group. Data were collected from 84 paramedic students and analyzed using SPSS 22.0 (IBM, Armonk, NY, USA). Results: The experimental group showed significant improvement in post-academic self-efficacy and knowledge. The experimental group also showed high clinical performance (9.83 out of 10 in ECG checking ability and 9.63 out of 10 in ECG reading ability). The mean satisfaction score was 4.23 out of 5 (responses based on a Likert scale) in the experimental group. Conclusion: Active learning in an ECG course was found to be highly effective and satisfactory. Furthermore, paramedic students can enhance their accountability and judgement with team-based learning through free engagement in discussion.

• Journal of Engineering Education Research
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• v.20 no.2
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• pp.31-38
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• 2017

In this study, specific teaching methods of lecturing and improved discussion methods (combining discussion and problem-based learning) were selected and applied for each major subject and learning content area in the fields of engineering, language, and social sciences. Then, the selected teaching methods were examined to determine the most effective learning contents. Finally, in order to determine the most effective teaching methods, a survey on student satisfaction was analyzed statistically. The results showed that students preferred teaching methods that combine lectures and improved discussion methods to the traditional method of only lectures. Therefore, this research proposes the combined teaching method for each major subject and learning content area.

• Journal of Korean society of Dental Hygiene
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• v.19 no.6
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• pp.1047-1058
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• 2019

Objectives: The aim of this study is to investigate extracurricular program needs according to the learning styles of dental hygiene students, and to develop and organize non-subject programs that strengthen student competencies. Methods: The subjects in this study were dental hygiene students from three colleges located in Chungbuk, Chungnam, and Ulsan, respectively. The survey tools were composed of learning style, a non-subject field, and non-subject teaching and learning methods. Lastly, 313 data points were analyzed. Results: Learning styles of subjects were as follows: assimilators, divergers, convergers, and accommodators, at 44.6%, 33.0%, 16.0%, and 6.4%, respectively. Preference of the non-subject field, according to learning style, showed that accommodators were higher than divergers on startup, and the difference was found to be statistically significant (p<0.05). Preference of non-subject teaching and learning methods, according to learning style, shows that both divergers and convergers prefer special lectures, while assimilators prefer tours, and convergers prefer experience/exercise. The results had achieved statistical significance (p<0.05). Conclusions: This study shows that dental hygiene students had different learning styles, and their learning methods varied depending on learning style. Therefore, a method should be identified to develop and run non-subject programs suitable for each learning style.

• Fourth Industrial Review
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• v.3 no.2
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.

• Educational Technology International
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• v.11 no.1
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• pp.145-169
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• 2010

Many universities are searching for educational methods to cultivate problem-solving ability and cooperative learning ability or already trying to implement them. Problem Based Learning(PBL) and Action Learning(AL) are effective teaching and learning methods to cultivate men of talent qualified for problem-solving and cooperative learning abilities that universities are seeking after. PBL and AL have something in common in that learning is accomplished while learners are solving the authentic problem. But, in spite of this similarity, PBL and AL have differences. However, most literatures and cases on these two models introduce only the outline of commons and differences and do not provide teachers with actual helping aids to select a model appropriate for the actual design or operation of classes. Accordingly, many teachers usually select and utilize a familiar model rather than select a proper model to the nature of a subject and the educational goal. Teaching and learning methods or learning environment should be selected appropriately to the educational goal. This study indicates the characteristics of PBL and AL that are being introduced and utilized as a principal teaching and learning method of college education and then shows how this method can be realized in the university by comparing the cases of classes applied in two methods.