• The Journal of Korean Association of Computer Education
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• v.11 no.4
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• pp.37-46
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• 2008

Using robots in the programming classes may help to induce learners' interest and motivation. However, simple introduction of new media, such as robots, may cause to increase learners' interest level temporarily, but also may give cognitive overload and offense against learning motivation. We developed a robot programming course to induce intrinsic motivation and to reduce cognitive load for learners in the programming education. And then, we implemented the developed course in college programming classes and analysed the educational effects of robot programming learning on novice learners' flow level. We found that robot programming course was helpful in enhancing novice learners' flow level. Especially, the element of 'autotelic experience', which explains an intrinsic motivation, was higher than conventional programming course group. It means that the developed strategies for robot programming course provides positive effects on learners' intrinsic motivation.

• The Journal of Korean Association of Computer Education
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• v.14 no.2
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• pp.61-73
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• 2011

The analysis on learners made through the study focuses on the intention of the participants in the learning activities of the robot programming. Therefore, for the analysis of the learners' intention, which is tried in the study, TAM, the analysis tool used for understanding buying acts or buying intention of buyers in the business sector, is basically utilized, and the Flow theory is additionally applied, trying to know, through the quantum analysis methods, the factors to give influence on the intention for learners to take part in the robot programming lesson. For this, a quantum analysis was made by PLS analysis, a kind of structural equations. As the result of the analysis, it is confirmed that such factors as 'recognized utility' and 'recognized readiness' and 'Flow' give significant influence on the intention of learners' participation in the lesson. As the result of the synthetic analysis and in regard with the value of the programming lesson, it is found that the following factors give actual influence to the intention of learners: the group where learners belong or teaching-learning organizations together with creating social rapport, learning tasks given for learners, etc.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.195-200
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• 1998

Since the neural network was introduced, significant progress has been made on data handling and learning algorithms. Currently, the most popular learning algorithm in neural network training is feed forward error back-propagation (FFEBP) algorithm. Aside from the success of the FFEBP algorithm, polynomial neural networks (PNN) learning has been proposed as a new learning method. The PNN learning is a self-organizing process designed to determine an appropriate set of Ivakhnenko polynomials that allow the activation of many neurons to achieve a desired state of activation that mimics a given set of sampled patterns. These neurons are interconnected in such a way that the knowledge is stored in Ivakhnenko coefficients. In this paper, the PNN model has been developed using the plasma enhanced chemical vapor deposition (PECVD) experimental data. To characterize the PECVD process using PNN, SiO$_2$films deposited under varying conditions were analyzed using fractional factorial experimental design with three center points. Parameters varied in these experiments included substrate temperature, pressure, RF power, silane flow rate and nitrous oxide flow rate. Approximately five microns of SiO$_2$were deposited on (100) silicon wafers in a Plasma-Therm 700 series PECVD system at 13.56 MHz.

• Cross-Cultural Studies
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• v.39
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• pp.407-432
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• 2015

This study focuses on the flow of story and content or related context when using movies as learning materials in a class. A great advantage of using movies is that they have a consistent story and detailed content development. Most teachers, however, tend to concentrate on practical expressions totally unrelated to the story or context of the movie they are using. This way might be efficient in the short run but it is certain that the expressions are unlikely to be retained in long-term memory. This study examines how a story-centered class influences learning of practical expressions and how efficient this approach to learning is. Learning and teaching with focus only on the expressions in a movie shades the meaning of the use of the movie a little. In this study the movie, Cars 2, was used in a course of general education with 150 students enrolled. Various group activities were suggested to immerse students into the story and contents of Cars 2. It was found that a story-centered class is helpful for students to acquire practical expressions and that students' satisfaction level with the class was high.

• Journal of Korea Game Society
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• v.12 no.1
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• pp.113-122
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• 2012

This study is focused on the effect and satisfaction of Education of Music Appreciation by utilizing music game. In order to come up with the result, the study has conducted an experimental comparison using 107 male and female 4th grade students at G-elementary school in Gyeonggi-do. As a result, the class that teaches with the game has relatively higher learning assessment, study involvement and in particular, has a significantly increased satisfaction level than the class that only uses music appreciation. In addition, it analyzes that study involvement and satisfaction has an effect on learning assessment. The results of this research deemed to contribute direction of planning on learning contents by applying Smart Devices in regular school classes.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1615-1622
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• 2022

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it's implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It's implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm.

• Korean Management Science Review
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• v.31 no.3
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• pp.77-94
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• 2014

This study empirically investigated the relationships among inter-organizational cost management (IOCM), cooperation with suppliers, information exchange between partners, inter-organizational learning, control integration, and the supply-chain performance of a firm. The results showed that the adoption of IOCM positively affects the collaboration between buyers and suppliers, which also leads to the increased information flow between them. According to the results of this study, it was found that inter-organizational information flow causes inter-organizational learning, and this learning contributes to the improved supply-chain performance. In this study, the positive effects of the cooperation with suppliers through IOCM on the control integration in supply-chains were not empirically confirmed. However, the impact of IOCM on control integration was significant and positive. Finally, the fact that the enhanced control integration can improve the supply-chain performance of a firm was empirically demonstrated.