• Journal of Korea Game Society
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• v.15 no.6
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• pp.89-96
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• 2015

The purpose of this paper is to analyze quantitative learning motivation on G learning. For the purpose the paper has analyzed the learning motivation factors through students' review on G learning which had been done at La Ballona Elementary School in Culver City, USA in 2010. On the basis of contents analysis method, it showed what factors of G learning influenced students and raised their academic motivation. Students used the positive, active words such as good, fun, learn, accomplish, easy, quest in terms of learning process, interest and achievement. They also showed future G learning intention describing terms such as love and miss. Team Quest has been especially developed for G learning class this time. Students had to help each other to solve the team quests which is different from traditional textbook. The system raised students' academic motivation.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.67-74
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• 2018

The purpose of this study was to examine the difference between communicative competence and problem solving ability by applying project - based instruction in the community dental hygienic subject. For this purpose, we measured 30 students' communication ability and problem solving ability using the same questionnaire before and after experiencing project learning. The data were Collected from 30 undergraduate students and analyzed by paired t-test with SPSS. The result demonstrated that project-based learning method has an significant effect on improvement of communication skill and problem solving ability of undergraduate students. Based on the results of this study, in order to improve the practical ability, it is necessary to activate the self-directed active learning method such as Project-based learning for community dental hygiene major study.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.1-11
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• 2023

The purpose of this study is to develop a discussion-centered teaching and learning model for nurturing creative and convergence talents. Regarding the research method, a draft model on discussion-centered teaching and learning was devised, and the model was completed through expert validation. The final draft was revised and supplemented by verifying how valid the model was when applied in class by using the derived final draft. Compared with the draft on discussion-centered teaching and learning model, the final model focused on text-reading emphasis, methods of questioning, and question generation strategies, excluding jigsaw discussions. The discussion-centered teaching and learning model developed in this study is expected to help instructors foster creative and convergence talents. Three suggestions have been provided to effectively apply this model to the field. First, an attitude of listening and respect is required during a discussion. Second, a plan should be considered on how to induce active participation of learners participating in the discussion. Third, the importance of managing discussion time was emphasized.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.1-9
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• 2018

The purpose of this study is to examine the concepts and types of cross-cultural collaborative online learning that enhance the utilization of advanced ICT in education and contribute to the promotion of educational exchanges between countries, and suggest exchange learning design models necessary for the active introduction. For this study, previous studies related to cross-cultural collaborative online learning were examined. As a result, cross-cultural collaborative online learning is an educational method based on constructivism that explore and construct knowledge by interacting and collaborating with students, teachers, and field experts who are linguistically and culturally heterogeneous based on advanced ICT. The type of cross-cultural collaborative online learning could be divided into synchronous exchange learning centered on remote video classes and asynchronous exchange learning centered on website based tasks. A PPIE learning design model considering the characteristics of each type is presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.420-424
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• 2016

Recently, with the development of Internet technology and multi-media technology, the Internet is going to develop in many areas, a new application areas. In particular, in the area of education, has made a Epoch-making development in the Internet applications, it has presented the instructional methods of the new paradigm. Study using the online learning, instructional method of a conventional traditional new proposal that deviates from the off-line teaching, Unlike the existing off-line learning, without being bound by time and space, in terms of anytime, anywhere it is possible to attend the lecture, is a very efficient learning. Online lectures Despite many advantages, and containing a number of problems. In terms of space of the learning is performed on-line, there is a disadvantage that the student management and learning, the reliability of evaluation missing number. In this study, out of such a variety of problems, concentration to induce an active learning attitude of learners, learners of learning who attempt to increase the reliability and using the face detection system of attendance learning It proposed a degree system.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.227-238
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• 2022

One of the main roles of the science teacher is to help students become active agents in their learning of science. This study described how an elementary science teacher used students' emotional resources to conduct emotionally responsive teaching and what were the learning outcomes of this approach. The participants of the study included the teacher himself and his 6^th grade students, and emotionally responsive teaching was applied in the science unit of 'Various Living Things and Our Human Lives'. Multiple types of data, including the teacher's teaching logs, video recordings of the lessons in the unit, and interviews with the students, were collected. The data were analyzed qualitatively, and the results were described using a self-study method. The teacher took advantage of three kinds of students' emotional resources (i.e., character drawing, t-shirt design, and raps) to organize his emotionally responsive teaching. The learning outcome included the students' positive emotions and active participation in science lessons, their epistemic practices such as explorations and justifications of knowledge, and the students' reconstruction of knowledge in consideration of their everyday lives. It was suggested that emotionally responsive teaching can promote elementary school students' active participation in science learning, resulting in meaningful learning outcomes in emotional, cognitive, and conceptual aspects. Further studies should thus be conducted to understand the characteristics of emotionally responsive teaching and its detailed teaching strategies.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.45-58
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• 2002

In this paper, we propose the new approach with which user is able to retrieve the massive volume of learning contents in the multimedia learning system. In order to secure the compatibility of learning contents. we apply the SMIL on the basis of XML, so that the integration and the synchronization of multimedia components can be available to realize in the mode of standardization. We also implement the multimedia learning contents represented by the RDF on the IEEE LOM. We present the two step-retrieval method to get precise results. In the first step, user can find with high speed and ease whatever contents user wants to take a look through metadata in the system. The second step is followed that by using the time information of SMIL, user can retrieve the interest synchronous parts in the result of the first step. This innovative retrieval approach applied in the multimedia learning system is highly expected to make a meaningful contribution to implement the principles of self-directed learning in the learning environments, where user can use and revise the retrieval results for their own learning purpose and make further the active knowledge-reconstruction.

• Smart Structures and Systems
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• v.29 no.4
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.