• 공학교육연구
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• 제22권6호
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• pp.12-20
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• 2019

The purpose of this study is to development of a teaching-learning model for active learning in engineering education. For this, the adequacy between educational objectives and active learning activities is verified and furthermore an "active learning teaching-learning model" is suggested. This suggested teaching-learning model is expected to supplement weakness of traditional lecture-type teaching-learning activity. Based on the literature review, first, the representative activities of active learning were derived. there are twenty active learning activities, which compose of five of individual learning activity, five of pair-learning activity and five of group-learning activity, and five of alternative- learning activity. In addition, a survey on adequacy between designed active learning activities and learning outcomes were conducted to ten educational experts. Lawshe's content validity calculation method was applied to analyze the validity of this study. Second, five teaching-learning principles, such as thinking, interaction, expression, reflection, and evaluation were derived to develop an "active learning teaching-learning model" which supplements lecture-type classes and then the "TIERA teaching-learning model" which consists of five stages was designed. Finally, based on the survey on educational experts, adequate active learning activities were proposed to apply in each stage of the "TIERA teaching-learning model" and as a result the TIERA model's active learning activities were developed. The result of this study shows that some activities of active learning are appropriate to induce high cognitive learning skills from the learners even in traditional lecture-type classrooms and therefore this study suggests meaningful direction to new paradigm of teaching-learning for engineering education. This study also suggests that instructors of engineering education can turn their traditional teaching-learning activities into dynamic learning activities by utilizing "active learning teaching-learning model".

• 정보교육학회논문지
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• 제21권4호
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• pp.393-401
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• 2017

본 연구의 목적은 학습자의 데이터 분석적 사고력 향상을 위한 딥러닝 기반 학습 시스템 개발 연구이다. 연구의 내용은 다음과 같다. 첫째, 데이터 분석적 사고력 향상을 위해 발견학습 모형에 딥러닝 기법을 적용하였다. 이는 데이터의 관계를 나타내주는 모델을 딥러닝 기법을 사용하여 생성하고, 새로운 데이터를 이 모델에 적용하여 데이터를 분석하는 과정을 경험할 수 있는 학습 방법이다. 둘째, 이 학습 방법에 따른 수업을 위한 딥러닝 기반 학습 시스템을 개발하였다. 딥러닝 기법을 사용하여 학습자가 입력한 데이터의 모델을 생성하고 적용할 수 있는 시스템을 개발하였다. 딥러닝을 적용한 발견학습 및 시스템 설계 연구는 데이터의 중요성이 더욱 커지는 미래 사회에서 학습자의 데이터 분석적 사고력을 향상시킬 수 있는 새로운 접근이 될 것으로 기대한다.

• ETRI Journal
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• 제39권3호
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• 반도체디스플레이기술학회지
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• 제21권1호
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• pp.108-113
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• 2022

The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.

• 감성과학
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• 제17권1호
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• pp.3-12
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• 2014

다양한 전자제품의 조작법을 빠르고 정확하게 학습하는 것은 일상적이고 중요한 과제가 되었다. 특히 소프트웨어는 여러 제품들의 통제 및 조작에서 핵심적인 지위를 차지하고 있다. 본 연구는 기존 학습연구에서 중요한 변인으로 연구되어온 학습방법, 학습계획, 과제난이도가 소프트웨어 학습에 미치는 영향을 검증하였다. 실험1에서는 2 (학습방법: 경험적 vs. 언어적) ${\times}$ 2 (학습계획: 간격 vs. 덩이진) ${\times}$ 2 (난이도: 쉬움 vs. 어려운)의 피험자간 요인설계를 사용하여 각 조건에서 참가자들이 윈도우 무비메이커를 사용하여 파일을 조작하는 방법을 학습하는 실험을 실시하였다. 그 결과 학습계획에 따른 수행의 차이는 발견할 수 없었으나, 언어적 학습보다 경험적 학습에서 참가자들은 더 빠르게 평가과제를 완료하였다. 특히 과제난이도가 높아질 경우 참가들은 언어적 조건에서 경험적 조건보다 두드러진 수행저하를 보였는데, 이는 과제가 어려워질수록 경험적 학습이 효과적인 학습방법이 라는 것을 시사한다. 즉 소프트웨어 학습에서 간단한 조작의 경우에는 매뉴얼 혹은 지시문의 형태로 구성된 언어적 학습으로 충분하지만 어려운 과제의 경우에는 체험 프로그램이나 투토리얼 모드를 통해 학습하는 것이 효과적일 것이다. 추가실험에서 난이도 증가에 따른 언어적 학습의 선형적 이득을 확인하기 위해 난이도를 3단계로 세분화하여 검증하였으며 (실험 2) 학습계획의 효과를 확인하기 위해 학습시행간 간격을 증가시켰으나 (실험3) 유의미한 결과를 발견하지는 못했다.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• 대한물리치료과학회지
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• 제31권2호
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• pp.90-104
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• 2024

Background: Hybrid learning is an educational approach that combines the teaching methods of online and lecture-style classes to compensate for each method's strengths and weaknesses. Compared to lecture-style classes, flipped learning improves overall class satisfaction and self-directed learning but is associated with lower learning motivation. It is necessary to determine whether hybrid flipped learning can solve the learning motivation problem of flipped learning by incorporating flipped learning into hybrid learning. The purpose of this study is to compare the effects of hybrid flipped learning and flipped learning on students' learning ability. Design: Cross-sectional study Methods: For students in the Department of Physical Therapy, classes were conducted using both flipped learning and hybrid flipped learning. In both learning methods, students took online classes first and participated in them every week. Flipped learning classes was conducted offline at school every week, while hybrid flipped learning alternated between live classes on YouTube and offline classes at school every other week. Results: Hybrid flipped learning resulted in significantly lower learning satisfaction and course evaluation than flipped learning, with no significant difference in grades. Conclusion: Hybrid flipped learning was able to cope with the situation well with the non-face-to-face teaching method caused by COVID-19, but it was difficult to improve learning ability because there were restrictions on activities that could interact with students. Flipped learning is a smooth offline activity that enables two-way activities between professors and students to improve learning ability, but the effect of improving test scores is still unclear.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.245-250
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• 2005

인터넷의 급격한 발전으로 교육환경$\cdot$방법에 대한 새로운 패러다임 창출요구가 증가하고 있으며 전통적인 교육산업도 교육의 전 분야에서 이론 활용한 e-teaming이 많은 분야에서 도입되었고, 빠른 속도로 그 영역이 확장되고 있다. 이러한 e-learning 확산 노력에 힘입어 그동안 e-learning의 학습자 만족도에 대한 연구도 많이 진행되어 왔지만 기업체를 대상으로 한 연구가 거의 대부분이었고 고등학교를 대상으로 한 연구는 거의 없는 실정이다. 따라서, 본 연구에서는 이러한 배경을 바탕으로 고등학생을 대상으로 한 e-learning 교육만족도 평가를 위한 모델을 제안하고, 제안한 모델을 대상으로 퍼지구조 모델링법을 이용하여 고등학생의 e-learning 교육 만족도에 관한 의식구조를 분석하였다. 또한, 의식구조분석의 결과가 고려된 평가모델을 구축하여 e-learning 교육 만족도를 평가하고, 민감도분석을 통하여 e-learning 교육만족도 향상 방안을 제시 하였다.

• 한국지능시스템학회논문지
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• 제15권1호
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• pp.1-5
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• 2005

강화학습(Reinforcement-Learning)의 목적은 환경으로부터 주어지는 보상(reward)을 최대화하는 것이며, 강화학습 에이전트는 외부에 존재하는 환경과 시행착오를 통하여 상호작용하면서 학습한다 대표적인 강화학습 알고리즘인 Q-Learning은 시간 변화에 따른 적합도의 차이를 학습에 이용하는 TD-Learning의 한 종류로서 상태공간의 모든 상태-행동 쌍에 대한 평가 값을 반복 경험하여 최적의 전략을 얻는 방법이다. 본 논문에서는 강화학습을 적용하기 위한 예를 n-Queen 문제로 정하고, 문제풀이 알고리즘으로 Q-Learning을 사용하였다. n-Queen 문제를 해결하는 기존의 방법들과 제안한 방법을 비교 실험한 격과, 강화학습을 이용한 방법이 목표에 도달하기 위한 상태전이의 수를 줄여줌으로써 최적 해에 수련하는 속도가 더욱 빠름을 알 수 있었다.

• 한국학교수학회논문집
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• 제4권1호
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• pp.91-101
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• 2001

Nowadays the number of students that is losing their interest as well as learning desire in mathematics is increasing because of lack of logical thought creative power and abstract expression that present-day mathematics requires by reason of discrepancy of extreme scholastic ability by speciality of mathematics. In these conditions, we reduce the number of learning depression by bringing about learning desire or learning interest on mathematics, and students learn effective learning methods to be voluntary learning of discovery themselves that studies basic concepts, principles, rules through logical thought of students to solve difference of scholastic ability, thus we assumed that debate studies through small group activities in ability group would be one of ways to improve learning power, so the results of our research are as follows; 1. Debate studies through small group activities were very effective because of reinforcing the achivement level of students. 2. By this learning method, an individual or cooperrative learning was fostered, and lively discussions were accomplished. And learning attitudes of students were changed by the extension of cooperative learning abilities through advices or by themselves. 3. A personal opinion is payed regard by accepting an individual idea in the process of making questions. Learners can correct wrong concepts in the process of correcting wrong answers. So if we apply above-mentioned studies with easy contents from the lower grades, the effectiveness would increase as learners go to the higher grade. According to the results of various researches as follows; "The teaching-learning method oriented coopperative debate studies is effective to find solutions to mathematical problems." If small group activities are applied in the educational situation to search the course of a desirable cooperation learning through small group activities to improve scholastic abilities for a discoverable problem-solving power. I think that the teaching-learning method oriented cooperative debate studies is one of the most desirable methods to increase the problem-solving ability.