• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.487-495
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• 2021

Using self-regulation learning strategies that can cultivate the creative and critical thinking required in the era of the Fourth Industrial Revolution, it was applied to the exploration experimental class of the section 'quantitative relationship in chemical reactions' in high school chemistry and the effects on academic performance and scientific attitudes were analyzed. In case of academic achievement, although there was no meaningful difference between the two groups in the pre-test, the average value of the experimental group was significantly higher in the post-test. In the case of scientific attitudes, the difference in average points between the two groups was the greatest in readiness and curiosity. In the post-test of the experimental group, academic achievement showed the highest correlation with meta-cognition and scientific attitude with behavioral regulation, respectively. Considering the effectiveness of metacognition and scientific attitudes, self-regulation learning strategies are the most suitable teaching-learning forms for creativity and personality education in this era.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.70-78
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• 2021

Estimating 3D information from a single image is one of the essential problems in numerous applications. Since a 2D image inherently might originate from an infinite number of different 3D scenes, thus 3D reconstruction from a single image is notoriously challenging. This challenge has been overcame by the advent of recent deep convolutional neural networks (CNNs), by modeling the mapping function between 2D image and 3D information. However, to train such deep CNNs, a massive training data is demanded, but such data is difficult to achieve or even impossible to build. Recent trends thus aim to present deep learning techniques that can be trained in a weakly-supervised manner, with a meta-data without relying on the ground-truth depth data. In this article, we introduce recent developments of weakly-supervised deep learning technique, especially categorized as scene 3D reconstruction and object 3D reconstruction, and discuss limitations and further directions.

• Culinary science and hospitality research
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• v.16 no.3
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• pp.130-146
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• 2010

This research examines difference in learning motivation and self-regulated learning according to the general characteristics of the students in hotel and food service related departments at vocational colleges, and subsequently identifies correlation between the two attributes. The research conducted a survey for 300 students in hotel and food service related departments at three vocational colleges in Daegu region, and 267 copies were used for the final analysis. In terms of learning motivation, students in the age between 20 and 24 indicated 'employment after graduation' as the strongest motivation while relatively older students indicated 'joy of learning' as their motivation. It turned out that students who showed strong motivation in terms of 'employment after graduation' and 'fun of college life' acquired more professional certificates. Next, regarding self-regulated learning, female students showed higher ability than male students. Students in higher grade, with older age, and with field practice experience showed more strength in self-regulated learning. Students with higher levels of a cognitive strategy, meta cognition and achievement value acquired more professional certificates. Learning motivation and self-regulated learning showed positive correlation with an exception of 'amotivation' among learning motivations. 'Amotivation' demonstrated negative correlation with all the factors of self-regulated learning ability.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.89-102
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• 2023

This study was a one group, pre-post test design experimental study to identify the effects of problem-based learning applied to adult nursing subjects on meta-cognition, problem solving, professional self-concept and self-directed learning of nursing students. The participants were 60 fourth grade students who had registered for adult nursing class from a nursing university in D metropolitan city, the data were collected from September to December, 2022. The adult nursing class model was designed based on the ADDIE model suitable for PBL. The class period was conducted for 15 weeks, with 8 weeks of lectures, 2 weeks of exams, and 5 weeks of Barrow and Myers 5-step PBL learning. The collected data were analyzed using the SPSS/WIN 20.0 Program, and paired t-test was used to test the differences between variables before and after the intervention. There was a statistically significant difference in metacognition(t=-8.04, p<.001), problem solving(t=-4.08, p<.001), professional self-concept(t=-4.67, p<.001) and self-directed learning(t=-4.69, p<.001) between pre and post problem based learning. Therefore, our result recommend that to apply problem-based learning in various major subjects to strengthen nursing students' metacognition, problem-solving, professional self-concept, and self-directed learning skills.

• Journal of the Korean School Mathematics Society
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• v.17 no.4
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• pp.717-746
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• 2014

The study aimed to explore how to improve mathematical thinking through metacognitive learning by stressing metacognitive abilities as a core strategy to increase mathematical creativity and problem-solving abilities. Theoretical exploration was followed by an analysis of correlations between metacognitive abilities and various ways of mathematical thinking. Various metacognitive teaching and learning methods used by many teachers at school were integrated for sharing. Also, the methods of learning application and assessment of metacognitive thinking were explored. The results are as follows: First, metacognitive abilities were positively related to 'reasoning, communication, creative problem solving and commitment' with direct and indirect effects on mathematical thinking. Second, various megacognitive ability-applied teaching and learning methods had positive impacts on definitive areas such as 'anxiety over Mathematics, self-efficacy, learning habit, interest, confidence and trust' as well as cognitive areas such as 'learning performance, reasoning, problem solving, metacognitive ability, communication and expression', which is a result applicable to top, middle and low-performance students at primary and secondary education facilities. Third, 'metacognitive activities, metaproblem-solving process, personal strength and weakness management project, metacognitive notes, observation tables and metacognitive checklists' for metacognitive learning were suggested as alternatives to performance assessment covering problem-solving and thinking processes. Various metacognitive learning methods helped to improve creative and systemic problem solving and increase mathematical thinking. They did not only imitate uniform problem-solving methods suggested by a teacher but also induced direct experiences of mathematical thinking as well as adjustment and control of the thinking process. The study will help teachers recognize the importance of metacognition, devise and apply teaching or learning models for their teaching environments, improving students' metacognitive ability as well as mathematical and creative thinking.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.218-222
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• 2006

In making 3D animation with digital technology, it is necessary to increase productivity and reusability by managing production pipeline systematically through standardization of animation content. For this purpose, we try to develop the animation content management system that can manage all kind of information on the production pipeline, based on SCORM of e-teaming by considering production, publication and re-editing. A scene as the unit of visual semantics is standardize into an object that contains meta-data of place, cast, weather, season, time and viewpoint about the scene. The meta-data of content includes a lot of information of copyright, publication, description, etc, so that it plays an important role on the management and the publication. If an effective management system of meta-data such as ontology will be implemented, it is possible to search multimedia contents powerfully. Hence, it will bring on production and publication of UCC. Using the meta-data of content object, user and producer can easily search and reuse the contents. Hence, they can choose the contents object according to their preference and reproduce their own creative animation by reorganizing and packaging the selected objects.

• Journal of Korean Elementary Science Education
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• v.32 no.1
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• pp.104-112
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• 2013

We examined the strategies to stimulate the reflective thinking using science notebook for the improvement of problem solving ability which is one of the core skills for the future. The strategies we derived have four steps which are input, output, solving mission and reflection as my own mirror. We applied the strategies to the 6th grade class for autumn semester in order to examine the students learning process and the result. We could observe that students looked into their own learning and had a time to look back their activities in the class. We could also confirmed that science notebook would be effective to improve the problem solving as stimulating the reflective thinking. In addition, we could specify the strategy of using science notebook in the class. At a 'input' stage, students should be able to choose their own learning style as their preference and teacher need to give them proper feedback. Interaction with peers should be emphasized during the activities as 'question attack' and 'question defense' in 'output' stage and 'solving mission' stage. You should suggest the students various method to record their thought from looking back their classroom activities instead of mere writing. We also examine the students achievement from the students' notebook and Meta Cognitive Awareness test. As a result, students who had studied using science notebook showed statistically meaningful higher achievement than controlled students.

• School Mathematics
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• v.5 no.4
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• pp.477-506
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• 2003

In this paper, we analyze nine-grade students' conceptions of parameters, their relation to unknowns and variables and the process of understanding of letters in problem solving of equations and functions. The roles of letters become different according to the letters-used contexts and the meaning of letters Is changed in the process of being used. But, students do not understand the meaning of letters correctly, especially that of parameter. As a result, students operate letters in algebraic expressions according to the syntax without understanding the distinction between the roles. Therefore, the parameter of learning should focus on the dynamic change of roles and the flexible thinking of using letters. We develop a self-regulation model based on the monitoring working question in teaching-learning situations. We expect that this model helps students understand concepts of letters that enable to construct meaning in a concrete context.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.