• Educational Technology International
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• v.11 no.2
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• pp.25-41
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• 2010

Challenges brought by applications of advanced technologies in education call for new approaches that can best ensure the provision of quality e-learning experiences. This paper presents an ecological approach as one of such approaches to quality assurance in e-learning that can monitor, assess and improve the effectiveness and the links between the various elements of e-learning. The ecological model for QA in e-learning emphasizes interrelation transactions between elements (e.g. providers, learners, cultures and policies) and systemic integration of those elements, and stresses that all these elements within a QA system play an equal role in maintaining balance of the whole. The model focuses attention both on individual and societal/cultural environmental factors as cornerstones for QA efforts in e-learning. It addresses the importance of QA efforts directed at changing QA transactions from provider-centered to 'all stakeholder-oriented', from one-size-fits-all model to 'globally oriented, locally adaptive model' and from control framework to 'culture creation framework'.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.49-60
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• 2009

The aim of this study was to develop a model for teaching-teaming by applying Ubiquitous at a learning experience field, in which connect characteristics of both ubiquitous application learning and experience teaming, making use of them. A literature survey of concepts was conducted, with the main areas to find out relationships between ubiquitous application learning and experience learning. Experience learning by applying ubiquitous learning methods maximizes its efficiency of experience learning in considering ubiquitous learning methods's characteristics of dynamic, interaction, sharing. Also it makes communications through positive participation and active interaction, and leads to a process of internal examination. The research data suggests that critical factors of experiencing learning applying ubiquitous are acquiring information and memory, information integration and exquisiteness, emotional and social activity, producing activity, help activity.

• Research in Mathematical Education
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• v.1 no.2
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• pp.107-116
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• 1997

The purpose of this study is to develop a teaching/learning model for the mathematical enculturation of elementary and secondary school students. It is clear that the development of teaching and learning in the classroom is essential for the realization of global innovations in mathematics education. Research questions for this purpose are as follow: (1) What can be learned from literatures reviews of the socio-cultural perspective on mathematics education, and of ethnomathematics as a mathematics intrinsic to cultural activities? (2) What is the direction of teaching and learning from the perspective of mathematical enculturation? (3) What is the teaching /learning model for mathematical enculturation? (4) What is the instructional exemplification based on the developed model? This study promotes the establishment of mathematics education theory from the review of literatures on the socio-cultural perspective, the development of a teaching/learning model, and the instructional exemplification based on the developed model.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.23-31
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• 2009

Supporting of the user-wanted teaching&learning functions is an important factor to improve the learning effects in a e-learning system. However, most methods are not enough to refer a model for supporting a teaching&learning function in a planning, development, operation, and evaluation. Accordingly, we propose the specified reference model for supporting a teaching&learning function in the web-based e-learning system. To verify the validity of the proposed system, we consulted the students experienced in e-learning system. As a results, The proposed specified reference model can be expected more $11%{\sim}23%$ effectiveness improvement than that of their experienced in the previous system. Also, as the pre-evaluated results using the teaching&learning services supporting degree by the proposed reference model, those measurements are very similar to the services requirement degree of their experienced in e-learning system.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.527-545
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• 2008

This paper suggested a 4E&E Learning Cycle Model using learning motivation for students in science education. The model has been developed on the basis of motivational and instructional design. The 4E&E Learning Cycle Model has four phases such as engage, explore, explain and expand, and two subsidiary phases such as evaluate, and feedback provided with at each phase. The model has gone a process of instruction with learning effects evaluation and providing feedback in science classroom, which facilitate to increase the effectiveness of learning activities. Especially, the 4E&E Learning Cycle Model using motivational learning strategies makes the learners be attractive to and immersed in instruction. This model has potentials in educating students in science education.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.271-281
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• 2010

In an industrial Era, OJT(On-the-Job Training) has been accepted as the field learning. But in a breaking up era, traditional field training needs to change and make an evolutionary model. Also, we need to make evolutionary model for various changing ways and means and need means to maximize the transformation of learning by operating learning organization. In knowledge based society, as people work and learn new knowledge in order to pass the experience knowledge and capabilities, they are not the traditional relationship between trainer and trainee but maximize work and learning, development and performance through several different ways. So, the study about new learning model is needed because the learning is creating the value and makes low cost and high efficiency about the elements of cost and time. We study the evolutionary model, OJF(On-the-Job Facilitating) - new learning methodology - through operating learning organization in S Electronics and its application practices.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.569-575
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• 2021

Deep learning shows differences in prediction performance depending on data quality and model. This study uses various input data and multiple deep learning models to build an optimal deep learning model for predicting solar radiation, which has the most influence on power generation prediction. did. As the input data, the weather data of the Korea Meteorological Administration and the clairvoyant meteorological image were used by segmenting the image of the Korea Meteorological Agency. , comparative evaluation, and predicting solar radiation by constructing multiple deep learning models connecting the models with the best error rate in each model. As an experimental result, the RMSE of model A, which is a multiple deep learning model, was 0.0637, the RMSE of model B was 0.07062, and the RMSE of model C was 0.06052, so the error rate of model A and model C was better than that of a single model. In this study, the model that connected two or more models through experiments showed improved prediction rates and stable learning results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.72-74
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• 2021

The deep learning process currently utilized in various fields consists of data preparation, data preprocessing, model generation, model learning, and model evaluation. In the process of model learning, the loss function compares the value of the model with the actual value and outputs the difference. In this paper, we analyze various loss functions used in the deep learning model for biomarker extraction, which measure the degree of loss of neural network output values, and try to find the best loss function through experiments.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.28-37
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• 2020

This study developed a deep learning model that predicts rental demand for public bicycles. For this, public bicycle rental data, weather data, and subway usage data were collected. After building an exponential smoothing model, ARIMA model and LSTM-based deep learning model, forecasting errors were compared and evaluated using MSE and MAE evaluation indicators. Based on the analysis results, MSE 348.74 and MAE 14.15 were calculated using the exponential smoothing model. The ARIMA model produced MSE 170.10 and MAE 9.30 values. In addition, MSE 120.22 and MAE 6.76 values were calculated using the deep learning model. Compared to the value of the exponential smoothing model, the MSE of the ARIMA model decreased by 51% and the MAE by 34%. In addition, the MSE of the deep learning model decreased by 66% and the MAE by 52%, which was found to have the least error in the deep learning model. These results show that the prediction error in public bicycle rental demand forecasting can be greatly reduced by applying the deep learning model.