• The Mathematical Education
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• v.60 no.4
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• pp.555-580
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• 2021

The purpose of this study is to explore the factors of situational interest in math learning, and based on the results, to reveal the factors of situational interest included in teaching and learning methods, teaching and learning activities in mathematics class, and extracurricular activities outside of class. As a result of conducting a questionnaire to high school students, the factors of situational interest in learning mathematics were divided into 10 detail-domain(Enjoy, Curiosity, Competence / Real life, Other subjects, Career / Prior knowledge, Accumulation knowledge / Transformation, Analysis), 4 general-domain(Emotion, Attitude / Knowledge, Understanding), 2 higher-domain(Affective / Cognitive) were extracted. In addition, it was revealed that various factors of situational interest were included teaching and learning methods, teaching and learning activities and extracurricular activities. When examining the meaning of 10 situational interest factors, it can be expected that the factors for developing individual interest are included, so it can be expected to serve as a basis for expanding the study on the development of individual interest in mathematics learning. In addition, in order to maintain individual interest continuously, it is necessary to maintain situational interest by seeking continuous changes in teaching and learning methods in the school field. Therefore, it can be seen that the process of exploring the contextual interest factors included in teacher-centered teaching and learning methods and student-centered teaching and learning activities and extracurricular activities is meaningful.

• Proceedings of the Zoological Society Korea Conference
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• 2000.10a
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• pp.290.2-290
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• 2000

• Proceedings of the Zoological Society Korea Conference
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• 2000.10a
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• pp.290.3-290
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• 2000

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• Journal of the Korean earth science society
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• v.23 no.8
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• pp.640-648
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• 2002

The purposes of this study is to examine the effects of cooperative teaming through student team-achievement division (STAD) model on high school students’ leaming achievements and scientific attitudes in the field of astronomy. It is another aim to compare effects of cooperative learning based on improvement scores with traditional teaching method done only by teachers in astronomy field. This study was conducted on two tenth grade classes in a boy’s high school in Incheon. Students had four classes a week in cooperative learning way for four weeks. During cooperative learning classes, formative evaluation was given to students every week oil Stars and Exploring the Solar System. The results show that these two approaches have great different effects on students’ astronomical knowledge and that students adopt more positive scientific attitude toward cooperative learning classes than traditional ones. In conclusion, the cooperative learning is more effective and positive than traditional one in learning astronomical knowledge and in students scientific attitude for science classes.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.355-363
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• 2007

One of major factors for learning achievement is the student's learning preference according to his character type. In course of learning, if a student studies e-learning contents opposed to his preference, then he would be under stress and his blood pressure and heart beat be changed. For measuring unwillingness, we used spectral components in frequency domain known as stress measure. For 13 children attending kindergarten we examined S(sensing)/ N(intuition) of MBTI and presented same learning contents during 10 minutes. During learning we gathered ECG signals, changed into HRV(heart rate variability), transformed time-varying HRV signal into spectral density in frequency domain. And then, we divided it into three areas of low(LF), middle(MF), and high-frequency(HF) and calculated stress measures by rates of those frequency area. We compared estimated stress measures of S group with them of N group whether students in different group preferred different contents or not. Experimental shows that students according to MBTI type prefer different contents.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.375-383
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• 2010

The purpose of this study is to develop and apply the Blog-Based Co-op Co-op learning model to improve elementary student's Information Ethics Consciousness. For this goal, we have develope the Blog-Based Co-op Co-op learning model and verified its effectiveness. The developed model was applied to 5th and 6th elementary school students. Test was done for 6 sessions during 3 weeks. The developed model was applied to the treatment group and ordinary Co-op Co-op learning model was applied to the comparison group. We applied independent sampling t-test that can compare averages of two groups. As a result, the Bolg-Based Co-op Co-op learning model was significantly enhance student's Information Ethics Consciousness. We've analyzed student's Information Ethics divided into area, were significant in all areas.

• The Journal of Korean Association of Computer Education
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• v.13 no.2
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• pp.15-23
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• 2010

Computational Literacy education is being required in current digital age, but the educational strategy of it is lacking. In traditional education, instructors have been teaching by considering learners' characteristics for effective learning. It is necessary to investigate their characteristics for applying this method to computational literacy education. Therefore, we taught programming that is main area on computational literacy, and analyzed learners' characteristics focused on Felder's learning style and multiple intelligence. That is, we taught 194 university students computational literacy with scratch that was one of the popular educational programming languages, and analyzed the relation among learning style, multiple intelligence and the students' programming performance. Also, we found considerations through comparing students' characteristics with experts' ones.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.60-62
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• 2023

인공지능의 학습 작업은 연산량이 많아 고성능 연산 장치인 GPU(Graphics Processing Unit)를 필요로 하며, GPU 장치의 성능은 학습 작업의 실행 성능에 직접적으로 영향을 미치는 요소 중 하나로 작용한다. 인공지능 작업을 처리하기 위해 많이 사용되는 텐서플로의 경우 GPU를 사용해 연산을 수행할 때 기본적으로 거의 모든 GPU 메모리 영역을 단일 학습 작업이 점유하도록 GPU 메모리를 관리한다. 이 방법은 컴퓨팅 자원 중 확장성이 가장 낮은 GPU 메모리의 단편화를 방지하기 위해 사용되는 방법이지만, 하나의 학습 작업이 GPU를 점유하게 되면, 실제 GPU 메모리 사용량과 상관없이 다른 프로세스는 GPU를 사용할 수 없는 문제를 유발한다. 특히, 전이학습, 소규모 학습과 같이 상대적으로 작업 규모가 작은 경우에는 전체 GPU 메모리 용량 중 대부분의 영역이 낭비된다. 본 논문에서는 컨테이너 환경에서 텐서플로의 기본 GPU 메모리 사용 방식으로 인해 다수의 학습 작업을 동시 실행하는 것이 불가능한 문제를 확인하고 GPU 메모리 사용량을 제한한 경우와 하지 않은 경우에 실제 GPU 메모리 사용량과 학습 작업의 실행 시간에 대한 성능 비교를 통해 GPU 메모리의 단편화 방지가 성능에 유의미한 요소인지 검증한다.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1061-1068
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• 2012

This paper deals with a noise reduction algorithm which uses the binary masking approach in the time-frequency domain to improve speech intelligibility. In the binary masking approach, the noise-corrupted speech is decomposed into time-frequency units. Noise-dominant time-frequency units are removed by setting the corresponding binary masks as "0"s and target-dominant units are retained untouched by assigning mask "1"s. We propose a binary mask estimation by comparing the local signal-to-noise ratio (SNR) to a threshold. The local SNR is estimated by a training-based approach. An optimal threshold is proposed, which is obtained from observing the distribution of the training database. The proposed method is evaluated by normal-hearing subjects and the intelligibility scores are computed by counting the number of words correctly recognized.