• Journal of Engineering Education Research
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• v.24 no.3
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.370-373
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• 2022

Recently, various deep learning framework models such as Tensorflow, Pytorch, Keras, etc. have appeared. In addition, CNN (Convolutional Neural Network) is applied to image recognition using frameworks such as Tensorflow, Pytorch, and Keras, and the optimization model in image classification is mainly used. In this paper, based on the results of training the CNN model with the Paitotchi and tensor flow frameworks most often used in the field of deep learning image recognition, the two frameworks are compared and analyzed for image analysis. Derived an optimized framework.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1811-1817
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• 2018

The purpose of this study is to elucidate the effects of a new clinical training simulator for dental radiography using augmented reality (AR) on user learning context. To accomplish this purpose, we divided 217 dental hygiene students into two groups. The experimental group was presented with the new clinical training simulator for dental radiography using AR, and the control group was presented with task information using a textbook. The results showed that the experimental group presented the new clinical training simulator for dental radiography using AR had a higher level of self-efficacy, interest in learning, flow, and practice satisfaction compared with the control group shown the task information using a textbook. Therefore, the AR-based radiography simulator can be utilized in dental radiology practice education as an effective educational device.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1208-1213
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• 2018

The study investigates how the structured debriefing method affects the learning flow, critical thinking disposition, and clinical performance of nursing students, using the Lasater Clinical Judgment Rubric (LCJR). Nursing students in the 4th grade of P University were divided into three groups, each trying out a different structured debriefing method: the experimental group - structured video debriefing using the LCJR question, the comparative group - structured oral debriefing, and the control group - structured group discussion debriefing. There was no significant difference between the three groups in learning flow (p=.640), critical thinking disposition (p=.420) and clinical performance ability (p=.360). Planning and intervention among the areas of clinical performance were significantly improved in the experimental group compared to the other two groups (p=.005). Structured debriefing when used with LCJR improves the learning flow and critical thinking disposition of students, while structured video debriefing improves clinical performance.

• Journal of the Korean Society of Earth Science Education
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• v.3 no.1
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• pp.47-54
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• 2010

The purpose of this study is finding examine the Thinking Maps and how to use Thinking Maps effectively in Science Education. The result of this study were as follows: First, There are 8 type Maps, Circle Map, Tree Maps, Bubble Map, Double Bubble Map, Flow Map, Multi Flow Map, Brace Map, Bridge Map. Each Maps are useful in the following activities ; Circle Map-Express their thoughts. Tree Map-Activities as like determine the structure, classification, information organization. Bubble Maps-Construction. Double Bubble Map-Comparison of similarities and differences. Flow Map-Set goals, determine the result of changes in time or place. Multi Flow Map-Analysis cause and effect, expectation and reasoning. Brace Map-Analysis whole and part. Bridge Map-Activities need analogies. Second, each element of inquiry has 1~2 appropriate type of Thinking Maps. So student can choose the desired map. Third, the result of analysing of Science Curriculum Subjects, depending on the subject variety maps can be used. Therefore the Thinking Maps can be used for a variety on activities and subject. And student can be selected according to their learning style. So Thinking Maps are effective to improve student's Self-Directed Learning.

• The Journal of Information Systems
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• v.17 no.4
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• pp.31-49
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• 2008

As the development of information technology, the biggest change in educational paradigm is apparent in the shift that the emphasis of education is layed on from teachers to learners. E-learning education service through the internet is less restricted in the respect of time and places in comparison with off-line education. Therefore e-Learning is spreaded rapidly and the educational effectiveness of that is needed to be investigated. In this study theoretical research was performed firstly and framework of the study was constructed. After establishment of hypotheses the survey data were collected by the learners of e-Learning and the hypotheses were verified by the SPSS version 12.0. The results are as follows : First, the quality of e-Learning service influences significantly to the technology acceptance of users. Secondly, perceived usability and perceived easiness of technology acceptance model influences significantly to the intention of reuse of users of e-Learning services. Lastly, the playfulness of the Flow theory influences significantly to the intention of reuse of users of e-Learning services. Although there are some limitations in the respect of the numbers of variables, parameters, or samples, this study will contribute for enhancing the effectiveness of education in e-Learning service by providing the acceptance factors of e-learners.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.221-229
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• 2018

Machine learning and artificial intelligence are core technologies for the 4th industrial revolution. However, it is difficult for the general public to get familiar with those technologies because most people lack programming ability. Thus, we developed a Graphic User Interface(GUI) to overcome this obstacle. We adopted TensorFlow and used .Net of Microsoft for the develop. With this new GUI, users can manage data, apply algorithms, and run machine learning without coding ability. We hope that this development will be used as a basis for developing artificial intelligence in various fields.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.401-411
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• 2022

The purpose of this study was to identify the convergence factors influencing learning satisfaction of nursing students in the COVID-19 pandemic after applying non-face-to-face mixed classes consisted of both real-time and non-real time distance educations. The participants were 109 nursing students who attended in a university and completed the self-report questionnaire. Data were analyzed using the SPSS 23.0 program. The results showed that the learning flow was 3.41, self-regulated learning ability was 3.75, and learning satisfaction was 3.98. Learning satisfaction showed a positive correlation with learning flow (r=.42, p<.001) and self-regulated learning ability (r=.75, p<.001). In addition, the factors influencing the learning satisfaction of the subjects of this study were self-regulated learning ability (𝛽=.662) followed by 60.6% (F=25.63, p<.001). Therefore, to enhance learning satisfaction of nursing students, it is necessary to increase their self-regulated learning abilities and to develop and apply training program considering the needs of the educational environment change in the post-COVID-19 era.