• Journal of Fisheries and Marine Sciences Education
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• v.26 no.6
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• pp.1425-1434
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• 2014

The purpose of this study was tried to identify the effect of nursing students' learning motivation in microbiology through microbiology laboratory practice based on the Keller's ARCS model. In order to achieve this research, this study was designed a quasi-experimental pre-post tests control group. Experimental group received a microbiology theory and practice based on ARCS model and control group received microbiology theory only. To identify the microbiology learning motivation effect to nursing student, we measured learning motivation by Keller's ARCS model that consisted of attention, relevance, confidence, and satisfaction. The major results of the experimental group showed significantly higher level of total learning motivation and all four subcategories compared to control group. Based upon the above results, microbiology laboratory practice might be beneficial for the nursing students to motivate microbiology learning.

• Journal of ILASS-Korea
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• v.27 no.2
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.351-357
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• 2022

This study was conducted for the purpose of developing a flipped learning teaching and learning model for physical education and competitive activities and confirming the validity of the model. We used the model research method as a research method to achieve the purpose of this study. First, we developed a flipped learning model for physical education and competitive activities through model development research, and then confirmed the validity of the model through model validation research. Based on the teaching and learning model developed through this study, students can change from passive learners to active learners in physical education classes, and it is expected that they can achieve class goals based on interactions between learners different from existing physical education classes through cooperative activities.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.61-68
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• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.497-506
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• 2013

The purpose of this study is to explore and develop a new instructional approach to a technology-enhanced, collaborative learning environment called Smart technology-enhanced Team-Based Learning (S-TBL). We designed a novel instructional model that combines mobile technology, collaborative teamwork, a problem-solving process, and a variety of evaluation techniques from the viewpoint of a conventional team-based model. Based on the traditional TBL model, we have integrated smart learning technologies: 1) to provide a holistic learning environment that integrates learning resources, assessment tools, and problem solving spaces; and 2) to enhance collaboration and communication between team members and between an instructor and his or her students. The S-TBL instructional approach combines: 1) individual learning and collaborative team learning; 2) conceptual learning and problem-solving & critical thinking; 3) both individual and group assessment; 4) self-directed learning and teacher-led instruction; and 5) personal reflection and publication.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.36-45
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• 2011

An agent in collaborative e-learning has independent function for learners in any circumstance, status and task by the reasonable and general means for social learning. In order to perform it well, communication among agents requires standardized and regular information technology method. This study suggests data model as a communication tool for various agents. Therefore this study shows various agents types for collaborative learning, designation of rule for data model that enable to communicate among agents and data element of agent communication data model. A multi-agent e-learning system using like this standardized data model should able to exchange the message that is needed for communication among agents who can take charge of their independent tasks. This study should contribute to perform collaborative e-learning successfully by the application of communication data model among agents for social learning.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1029-1060
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• 2014

The purpose of this study are to figure out whether there are the meaningful differences between learner's learning achievements and contents preference in accordance with the delivery strategies (instructor-focused model, learner-focused model) of learning materials suggested by Bloom in web-based instruction, and to suggest the various options on the contents delivery strategies to improve the learner's learning achievements of each learning domains. Learning domains were divided by the cognitive domain, the affective domain, and the psychomotor domain. The result of research with 182 learners showed that learner-focused model in the cognitive domain caused higher learning achievements and preference than instructor-focused model. And instructor-focused model in the psychomotor domain compared with learner-focused model caused higher learning achievements and preference. However, there were less meaningful differences in the affective domain. In other words, learner-focused model is appropriate to the feature of the cognitive domain while instructor-focused model is appropriate to the feature of the psychomotor domain. The results suggest that delivery strategies should be chosen by domains of learning contents in order to improve learner's learning achievements in web-based instruction. Learner-focused delivery strategies in the cognitive domain and instructor-focused delivery strategies in the psychomotor domain need to be considered positively. Delivery strategies should be studied and developed in order to lead higher learning achievements and preference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.467-473
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• 2014

Adaptive learning system means a system that provides adaptively learning materials according to the learning needs of learners. It consists of expert model, instructional model and student model. Expert model is that stores information which is to be taught. Student model stores the data of learning history and learning information of students. Instructional model provides necessary learning materials for actual leaners. This paper has constructed student model through learner's profile information and instructional model through dynamic scenario construction. After that, We have developed adaptively to provide learning to learners by constructing suitable dynamic scenario based on learners profile information. In the end, satisfaction result about this system showed a high degree of satisfaction and 88%.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.