• 한국컴퓨터정보학회논문지
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• 제23권4호
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• pp.41-49
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• 2018

The purpose of this study is to investigate the effect of computer pointing game on the visual perception ability of children with severe intellectual disability. Based on a literature review, we developed a computer pointing game to improve visual perception ability, which consisted of three stages; catching a hamburger, catching a hamburger and a soda, and catching various foods. At each stage, different instructional models were applied by difficulty level of the contents. Experiments were performed among four children with severe intellectual disabilities for three weeks. They belonged to H public school in Kyeonggi, Korea. Their visual perceptions were quantitatively measured four times by utilizing the Korean Developmental Test of Visual Perception tool (K-DTVP-2). For qualitative evaluation, an observation assessment diary was written and analyzed. All four children at the fourth test showed better visual perception ability, compared with the ability at the first test. As a result of the analysis of the observation assessment, they were considered successful in their learning and ordinary life related to visual perception. It can be concluded that the computer pointing game may play a role in helping children with severe intellectual disabilities improve their visual perception ability.

• 대한수학교육학회지:학교수학
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• 제5권3호
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• pp.275-295
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• 2003

본 논문에서는 교수·학습 방법을 중심으로 우리 나라 초등학교 수학교육의 실제를 이해하기 위하여, 선행 연구 고찰, 설문 조사, 수업 관찰 등을 실시하고 그 결과를 분석하였다. 본 연구의 결과, 초등학교 수학과에서 널리 활용하는 교수·학습 방법은 강의법(또는 발문 중심의 방법), 활동 중심의 방법, 소집단 협동 학습 방법, 공학적 도구 활용 방법인 것으로 나타났다. 본 연구에서 교수·학습 방법과 관련하여 가장 미흡한 것으로 분석된 점은, 교사들이 추구하고 있는 교수·학습 방법이 다소간은 외형적인 충실함에 치중하는 경향이 있다는 것이다. 그러므로, 어떤 특정한 교수·학습 방법을 활용하여 수업을 진행한다고 할 때, 그 방법의 절차적인 순서와 같은 외형적인 측면과 함께, 그 교수·학습 방법에서 본질적으로 목적으로 하고 있는 부분이 무엇인가를 파악하고 그것을 실현하기 위해 노력할 필요가 있다.

• 대한수학교육학회지:수학교육학연구
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• 제9권2호
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• pp.439-457
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• 1999

In this paper, we tried to find out what makes the students feel anxious to mathematics and the ways to decrease their anxiety by comparing two learning types, self-directed-learning and learning in a body, by means of continuous observation and interviews. To perform this study, two classes of self-directed-learning and other two classes of learning-in a body were chosen from the third year-students in Wando Middle School in Chollanamdo. In this study, we obtained the following results: 1. In high group in math grade, students in self-directed-learning are less anxious than students in learning-in a body. 2. In average group in math grade, students in self-directed-learning are much more anxious than students in learning-in a body. 3. In low group in math grade, both students in self-directed-learning and students in teaming-in a body feel anxious about math and there is no difference between them. 4. Anxiety about math hove positive influence on high group in math grade. 5. Anxiety about math have negative influence on average and low groups in math grade. Especially, low group students had no interests about mathematics because of their math anxiety. We observed that some students got over the math anxiety to some meaningful extent by means of interviews or appropriate advices, and became to have confidence and interests in mathematics.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1195-1202
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• 2018

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

• 한국초등과학교육학회지:초등과학교육
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• 제26권3호
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• pp.329-340
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• 2007

This study is to find out how well elementary school gardens work as places of observation learning. We compared the tree species planted in elementary school gardens with those which appeared in the science textbooks of the 7th Korean National Curriculum. The number of tree species are 60 throughout all the grades, specifically; 43 in the third grade, 22 in the fifth grade, 16 in the first grade, 15 in the second grade, 8 in the sixth grade, and 5 in the fourth grade, respectively. Their frequency of appearance (hereafter referred to as 'appearance frequency') throughout all the grades is 175, and the maximum frequency is 62 in the third grade. Of particular note is the fact that the appearance frequency in one grade was very high, meaning that a repeat study will not be conducted. The total number of tree species counted in the study was 13,028 and consisted of 167 species in 52 families. Only 23% of the total planted tree species, that is, 38 tree species appeared in the textbooks, so the ratio of the practical usage of school gardens was revealed to be low. In the school gardens, there are only an average of about 16 tree species per school. The fewest number of species in one school was 9 and the most was 22. The native species were 74 and the non-native species were 93. This means that almost all the planted species do not relate to observation learning in the textbooks. The 22 tree species among 60 species in the textbooks were not planted in the gardens. In conclusion, the degree of utilization of almost all the elementary school gardens examined during this investigation was very low.

• 한국지구과학회지
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• 제43권3호
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• pp.430-445
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• 2022

이 연구는 광물과 암석을 주제로 진행된 야외지질학습에 참여한 초등학생들의 학습 효과를 생소한 경험 공간(Novelty space) 개념을 중심으로 탐색하는 것을 목적으로 한다. 방과 후 자율 동아리 활동 형식으로 서울의 한 공립초등학교에서 진행된 본 프로그램에 6학년 학생 총 10명이 참여하였다. 학생들은 교실 학습 환경에서 광물과 암석 표본을, 야외 학습 환경에서 노두에 노출되었거나 정원석 등으로 쓰이고 있는 광물과 암석을 각각 관찰하였다. 저자들은 각 차시별 연구 참여자들이 작성한 활동지(글, 그림), 연구자 참여 노트, 연구 참여자의 활동이 담긴 영상 및 음성 자료와 사후 인터뷰 자료를 수집하였다. 인지적 영역에서 학생들의 학습 효과를 분석하기 위해 Remmen and Frøyland (2020)의 암석 분류를 위한 관찰 분석틀과 Oh (2020)의 암석 기술어 분석틀을 활용하였다. 또한 심리 및 지리적 영역의 학습 효과를 탐색하기 위해 학생들의 그림과 담화 및 면담 자료를 귀납적으로 분석하였다. 연구 결과 학생들은 교실 학습 환경에서 '일상적', '과도기적' 관찰 양상을 보였으며 야외 학습 환경에서(학교 운동장, 지역사회)는 '과도기적' 및 '과학적' 관찰 단계까지 발전하는 모습을 나타냈다. 덧붙여 과학적 관찰 단계로 갈수록 더 많은 종류의 암석 기술어가 사용되는 것 또한 확인되었다. 심리, 지리적 측면에서 학생들은 익숙한 야외 학습 환경으로의 답사 장소 선정, 야외지질학습에 대한 긍정적인 인식, 심미적 감상 등을 표현하였다. 끝으로 이 연구는 학생들의 학습 효과 분석을 위한 도구로써 생소한 경험 공간 개념이 유용한 도구가 될 수 있음을 강조하며 아울러 가상야외지질학습과 같은 새로운 학습환경을 고려하는 학술적인 접근이 필요함을 제안하는 바이다.

• 한국초등과학교육학회지:초등과학교육
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• 제35권2호
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• pp.253-264
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• 2016

The purpose of this study was to investigate implications of self-directed learning experiments in elementary science education through understanding elementary school students' awareness of their experiences in self-directed learning experiments. Twenty students joined the school science club voluntarily and conducted self-directed learning experiments. We collected data through observation of the experiments, interviews, and questionnaires. The students who participated in the club showed high satisfaction with self-directed learning experiments. The participants were aware that their scientific interest and knowledge, and the confidence in conducting experiments were increased. The students felt positive about the inquiry process of conducting self-directed learning experiments with their own subjects. They also felt a sense of achievement in attempting their experiments in defiance of several failures. The participants realized that the self-directed inquires led to increased declarative and procedural knowledge of science. The students stated that they had some difficulties in coping with the different results contrary to expectations and preparing laboratory materials and instruments. Nonetheless, they showed the promotion of their scientific literacy during overcoming those difficulties. We suggest that self-directed learning experiments can be a more effective way in science learning to make students experience the nature of science than existing school experiments. This can be implemented through a creative experience activities such as science clubs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권11호
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• pp.614-618
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• 2016

인터넷 상 데이터와 스마트 디바이스가 증가함에 따라 순차적으로 유입되는 스트림 형식의 데이터가 늘어나고 있다. 잠재적인 빅데이터인 스트림 데이터를 다루기 위해서는 온라인 학습이 가능해야 한다. 이에 본 고에서는 스트림 데이터 학습을 위한 새로운 모델인 예측적 컨볼루션 신경망과 온라인 학습방법을 제안한다. 이 모델은 탐지와 풀링을 반복하는 컨볼루션 연산을 통해 탐지 패턴을 계층화하여 상위 계층이 될수록 긴 길이의 패턴의 정보를 다루도록 한다. 본 모델의 기초적 검증을 위해 스마트폰으로 2달간 수집한 GPS 데이터를 이산화하여 관측데이터로 삼았다. 이를 제안모델을 통해 학습하여 계층을 따라 추상화된 정보로부터 복원한 데이터와 관측데이터를 비교하여, 긴 시간의 패턴을 다루면서도 관측 수준의 데이터를 복원하는 것을 확인하였다.

• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.