• Cross-Cultural Studies
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• v.38
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• pp.461-500
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• 2015

This study aims to investigate the effective method to improve the competence of the Vocabulary by the image and listening towards the ELF. In the first part, we observed the problems and point improvement on learning vocabulary based on learner survey. In the second part, we analyzed two remarkable studies: - consistent and adapt method, communicational context - method based on the lexical, morphological semantical, notional and thematic field Then we proposed effective methods that are applicable to the vocabulary's learning in the class : - learning vocabulary by combining the words - learning vocabulary based on the meaning field - learning vocabulary as concrete characters - learning vocabulary by the descriptive character - learning vocabulary with the type "who am I?" - learning vocabulary by listening For teachers, one of the difficulties to the conduct of vocabulary course is that learners take passive position. Specifically, it is the teachers who play an important role because it runs in the direction of the course. However, learners do not show the active attitude for vocabulary lessons despite the course to take to improve their vocabulary skills. Therefore, teachers must prepare course materials that can both improve the competence of the vocabulary of learners and cause their interest or desire on the current vocabulary. This is why teachers should exploit various materials depending on the skill level of the learner vocabulary.

• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.966-978
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• 2002

There are two important problems in improving text classification systems based on machine learning approach. The first one, called "selection problem", is how to select a minimum number of informative documents from a given document collection. The second one, called "composition problem", is how to reorganize selected training documents so that they can fit an adopted learning method. The former problem is addressed in "active learning" algorithms, and the latter is discussed in "boosting" algorithms. This paper proposes a new learning method, called AdaBUS, which proactively solves the above problems in the context of Naive Bayes classification systems. The proposed method constructs more accurate classification hypothesis by increasing the valiance in "weak" hypotheses that determine the final classification hypothesis. Consequently, the proposed algorithm yields perturbation effect makes the boosting algorithm work properly. Through the empirical experiment using the Routers-21578 document collection, we show that the AdaBUS algorithm more significantly improves the Naive Bayes-based classification system than other conventional learning methodson system than other conventional learning methods

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.100-105
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• 2021

This study examines the effectiveness of the study through a case of PBL(problem-based-learning) class conducted in a balanced culture course called at 00- University in the second semester of 2020. The effects of learning are as follows: First, PBL(problem-based-learning) has sufficient active interaction between the teacher and the learner. In the face of prolonged non-face-to-face learning, the PBL teaching method has sufficient interaction between the professors-learner and the learner. Second, PBL learning can actively utilize various problems that fit the characteristics of the subject and actively utilize the process of role sharing and collaboration. By presenting various problem situations suitable for the subject, students will be able to share roles individually or as a team, and fully experience the process of collaboration and discussion in the process of investigating the data. Third, critical perceptions of problem situations can be extended. In modern times, a variety of problem situations arise and critical perceptions of them must be fully learned. In a mass production and mass consumption society, students should develop the ability to blindly recognize and distinguish between real and fake information in a flood of information. The limitations identified in this class case are, first, the nature of the subject, "Understanding Culture and Philosophy," which makes it possible to discuss the global cultural phenomenon, but it should be discussed in terms of philosophy. Second, it is not easy to work as a team on non-face-to-face online. Nevertheless, PBL is a very effective method of learning in which active interactions and learning activities take place between professors and students, whether face-to-face or face-to-face online learning.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.859-868
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• 2004

We propose a method of selecting initial training examples for active learning so that it can reach high accuracy faster with fewer further queries. Our method is based on the assumption that an active learner can reach higher performance when given an initial training set consisting of diverse and typical examples rather than similar and special ones. To obtain a good initial training set, we first cluster examples by using k-means clustering algorithm to find groups of similar examples. Then, a representative example, which is the closest example to the cluster's centroid, is selected from each cluster. After these representative examples are labeled by querying to the user for their categories, they can be used as initial training examples. We also suggest a method of using the centroids as initial training examples by labeling them with categories of corresponding representative examples. Experiments with various text data sets have shown that the active learner starting from the initial training set selected by our method reaches higher accuracy faster than that starting from randomly generated initial training set.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.45-52
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• 1999

In this raper, an enhanced learning method is proposed for improving the learning speed of the error back propagation learning algorithm. In order to cope with the premature saturation phenomenon at the initial learning stage, a variation scheme of active functions is introduced by using higher order functions, which does not need much increase of computation load. It naturally changes the learning rate of inter-connection weights to a large value as the derivative of sigmoid function abnormally decrease to a small value during the learning epoch. Also, we suggest the hybrid learning method incorporated the proposed method with the momentum training algorithm. Computer simulation results show that the proposed learning algorithm outperforms the conventional methods such as momentum and delta-bar-delta algorithms.

• Journal of Engineering Education Research
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• v.20 no.1
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• pp.53-62
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• 2017

This paper explored how university students viewed flipped learning from their own perspectives. Using qualitative research methods, 5 students from a Computer Graphics course at a mid-scale university in Seoul were interviewed for this purpose. Researchers collected data about their learning experiences, emotions, and reflections about flipped learning in general and its components such as online materials, in-class activities, and instructor guidance. Research findings indicated that students were not so much conscious about the unfamiliarity of the class, the increased work load, nor the online lectures. They rather prioritized 'what they could actually learn' from the course, and thus defined flipped learning as a method which enabled students to constantly check and fill in the gaps in their learning through team-based activities and prompt feedback from the professor. A combination of students' positive attitude and active participation in team-based activities, the overall atmosphere of the department which supported interactivity and collaboration, the professor's emphasis on learning-by-doing and student-centered learning appeared to form their notions of flipped learning. The use of technology did not appear to heavily impact students' conceptions of flipped learning. Researchers suggest that pedagogical beliefs of the professor, culture surrounding the learner, and the good match between the course content and instructional strategies are central for designing a successful flipped learning class.

• 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 Science Education
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• v.41 no.2
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• pp.213-225
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• 2017

Most students have difficulties and negative perceptions about physics learning. Especially, it is difficult to understand the whole context by learning based on logical-scientific thinking which excludes narrative thinking. This study aims to develop a storytelling teaching-learning method using the narrative thinking in physics lessons for improving the difficulty of students of physics learning, For this purpose, a storytelling teaching-learning method that can improve scientific attitude and understand and change the concepts was developed through literature research. The following results were confirmed its effects to apply high school students and middle school students. First, the teaching-learning method using the storytelling for high school students with low interest in learning had a significant effect in science-related occupation, interest in science and science-related activities, criticism, openness, cooperation, and spontaneity. Second, the middle school students who are active in learning recognized that teaching and learning methods using storytelling helped to understand physics concepts. The storytelling teaching-learning method developed through this study is expected to stimulate students' interest and motivation in physics and to be useful for learning concepts by improving their scientific thinking skills.