• Journal of Korean Elementary Science Education
• /
• v.33 no.1
• /
• pp.140-161
• /
• 2014

The purpose of this study was to analyze 181 papers about brain-based learning appeared in domestic scientific journals from 1989 to May of 2012 and suggest application conditions in elementary science education. The results of this study summarizes as follows; First, learning activity suggested by brain-based learning study is mainly explained by working of brain function. Learning activity explained by brain-based learning study are divided into 'learning according to specialized brain function, learning according to brain function integration and learning beyond specialization and integration of hemispheres'. Second, it searched how increased knowledge of brain structure and function affects learning. Analysis from this point of view suggests that brain-based learning study affects learning in many ways especially emotion, creativity and learning motivation. Third, brain-based learning study suggests various possibilities of learning activity reflecting brain plasticity. Plasticity which is one of most important characteristics of brain supports the validity of learning activity as learning disorder treatment and explains the possibility of selective increment of brain function by leaning activity and the need of whole-brain approach to learning activity. Fourth, brain-based learning brought paradigm shifts in education field. It supports learning sophistication on the understanding of student's learning activity, guides learning method that reflects the characteristics of subject and demands reconstruction of curriculum. Fifth, there are many conditions to apply brain-based learning in elementary science education field, learning environment that fits brain-based learning, change of perspectives on teaching and learning of science educators and development of brain-based learning curriculum are needed.

• Korean Journal of Cognitive Science
• /
• v.17 no.4
• /
• pp.375-398
• /
• 2006

Humans learn by observing, hearing, imitating, doing, and feeling. The brain(cortex) is the central tore of this process. The recent rapid progress of brain science and the active interdisciplinary collaboration between brain science and cognitive science opens a new possibility. That is a new research Held called 'Brain-Based learning Science', 'Edutational Neuroscienre', or 'NeuroEduration' This study reviews the nature and basic assumptions of brain-based learning science, current directions in educational neuroscience research, the neuro-myths, educational implications of neuroscience, and a possibility of making a meaningful connection between brain science and education. Also the future prospects and limitations of the brain-based learning science are discussed.

• Journal of Korean Elementary Science Education
• /
• v.34 no.2
• /
• pp.252-263
• /
• 2015

This study analyzed the effects of science outdoor activity applying a Brain-Based Evolutionary (ABC-DEF) approach on elementary school students' interest and academic achievement. Samples of the study were composed of 3 classes of 67 sixth graders in Seoul, Korea. Unit of 'Ecosystem and Environment' was selected as a object of the research. Textbook- and teachers' guidebook-based instruction was implemented in comparison group, brain-based evolutionary approach within classroom in experimental group A, and science outdoor learning by a brain-based evolutionary approach in experimental group B. In order to analyze the quantitative differences of students' interests and achievements, three tests of 'General Science Attitudes', 'Applied Unit-Related Interests', and 'Applied Unit-Related Achievement' were administered to the students. To find out the characteristics which would not be apparently revealed by quantitative tests, qualitative data such as portfolios, daily records of classroom work, and interview were also analyzed. The major results of the study are as follows. First, for post-test of interest, a statistically significant difference between comparison group and experimental group B was found. Especially, the 'interests about biology learning' factor, when analyzed by each item, was significant in two questions. Results of interviews the students showed that whether the presence or absence of outdoor learning experience influenced most on their interests about the topic. Second, for post-test of achievement, the difference among 3 groups according to high, middle, and low levels of post-interest was not statistically significant, but the groups of higher scores in post-interest tends to have higher scores in post-achievement. It can be inferred that outdoor learning by a brain-based evolutionary approach increases students' situational interests about leaning topic. On the basis of the results, the implications for the research in science education and the teaching and learning in school are discussed.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.225-234
• /
• 2010

To investigate the role of positive emotion in education, we have reviewed the previous studies on positive emotion, learning and motivation. In the present study, we examined the definition of positive emotion, and influences of positive emotion on cognition, creativity, social relationship, psychological resource such as life satisfaction, and interactive relationship among positive emotion, motivation and learning. To investigate the role of positive emotion on motivation and learning more scientifically, we examined the recent results of neuroscience. In other words, we have reviewed diverse research on positive emotion, learning and motivation based on brain-based learning. We also examined the research of autonomy-supportive environment as the specific example of improving positive emotion. As one of the most effective methods for emotional education, we discussed brain-based learning, the new research field. As the future prospects, we discussed the implications, possibilities and limitations of brain-based learning.

• Journal of Gifted/Talented Education
• /
• v.21 no.2
• /
• pp.255-268
• /
• 2011

The 21st century is called as the "Age of knowledge flood", and thus the importance of the ability which can use knowledge creatively is more emphasized. Also, not only individual problem solving but also solving problems through effective communication skills with group members are needed, and therefore, it is requested to train potential gifted learner working together with others to practice cooperation and eventually grown up as a competitive human resource to adapt successfully in future environment. In this paper, to show the effectiveness of cooperative learning in gifted learners, members for cooperative learning group has been selected using whole brain theory from the 42 gifted middle school students who participated in summer gifted learner vacation program. From the analysis of the learners' learning motivation and frequency of interactions whole brain based cooperative learning is effective for enhancing both learning motivation and interactions. Therefore, the whole brain based cooperative learning is an effective pedagogy for enhancing the motivation as well as facilitating interactions within gifted learners.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.154.1-154
• /
• 2001

The paper describes a new type of robot called "artificial liferobot" which is able to learn, make decisions, and behave by itself based on a brain-type computing technique called "artificial brain". The artificial liferobot has self-learning ability from the environment by the interactions between human being and it. The artificial brain makes the artificial liferobot to behave by itself with its intensions like living things as human being. We briefly introduce one attempt of our researches for developing cognitive and behavioral intelligent artificial liferobot in out laboratory. One of our purposes is the development of the artificial liferobot, which plays an Important role in taking care of elderly and infirm people in a rapidly aging society.

• Cartoon and Animation Studies
• /
• s.36
• /
• pp.237-257
• /
• 2014

This study is the process of performing the interdisciplinary fusion study between multiple fields by identifying the status on the previous artistic education considering the brain scientific mechanism of image creativity and brain-based learning principles. In recent years, producing the educational methods of each field as the fusion study activities are emerging as the trend and thanks to such, the results of brain-based educational fusion studies are being presented for each field. It includes artistic fields such as music, art and dance. In other words, the perspective is that by understanding the operating principles of the brain while creativity and learning is taking place, when applying various principles that can develop the corresponding functions as a teaching method, it can effectively increase the artistic performance ability and creativity. Since the animation drawing should be able to intuitively recognize the elements of movement and produce the communication with the target beyond the delineative perspective of simply drawing the objects to look the same, it requires the development of systematic educational method including the methods of communication, elements of higher cognitive senses as well as the cognitive perspective of form implementation. Therefore, this study proposes a literature study results on the artistic education applied with brain-based principles in order to design the educational model considering the professional characteristics of animation drawing. Therefore, the overseas and domestic trends of the cases of brain-based artistic education were extracted and analyzed. In addition, the cases of artistic education studies applied with brain-based principles and study results from cases of drawing related education were analyzed. According to the analyzed results, the brain-based learning related to the drawing has shown a common effect of promoting the creativity and changes of positive emotion related to the observation, concentration and image expression through the training of the right brain. In addition, there was a case of overseas educational application through the brain wave training where the timing ability and artistic expression have shown an enhancement effect through the HRV training, SMR, Beta 1 and neuro feedback training that strengthens the alpha/seta wave and it was proposing that slow brain wave neuro feedback training contributes significantly in overcoming the stress and enhancing the creative artistic performance ability. The meaning of this study result is significant in the fact that it was the case that have shown the successful application of neuro feedback training in the environment of artistic live education beyond the range of laboratory but the use of the machine was shown to have limitations for being applied to the teaching methods so its significance can be found in providing the analytical foundation for applying and designing the brain-based learning principles for future animation drawing teaching methods.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.124-132
• /
• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.5
• /
• pp.64-74
• /
• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Electronics and Telecommunications Trends
• /
• v.33 no.1
• /
• pp.1-10
• /
• 2018

Artificial intelligence, which is based on deep learning, is emerging as a fundamental technology that will bring about future social changes. Artificial intelligence technology in IT is an essential intelligent system, and will overcome the performance limit of computing systems, and is expected to be the foundation for the development of computing environment destructively. The development of artificial intelligence technology in developed countries is a direction toward convergence with brain science. In this article, we will look at the prospect of artificial intelligence as the manifestation of imagination, as well as the technology and policy trends of artificial intelligence both at home and abroad, and discuss the direction of future technology development in terms of fusion with brain science.