• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.15-22
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• 2013

Applying iterative learning utilizing SNS & Community to the class for pre-kindergarten teachers, the effectiveness of teaching satisfaction, self-efficacy, and curriculum understanding was verified. A iterative learning model utilizing SNS & Community in teachers leading traditional off-line teaching at college education field was applied separately into thinking to one-self by advance organizer, thinking together by presentation in the beginning of the class, and sharing the thoughts by community activities after the class. Iterative learning begins by being sent SNS to students from teachers before the class, but learners for themselves subsequently start to proceed self-directed learning activities. As a result, class satisfaction and understanding of pedagogy have been increased, and it had a positive influence on self-efficacy. Thus, it is to suggest utilizable SNS of professors and a teaching method utilizing Community to college students who need basic learning skills.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.11-19
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• 2001

In this paper, we study the convergence property of iterative learning control (ILC). First, we present a new method to prove the convergence of ILC using sup-norm. Then, we propose a new type of ILC algorithm adopting intervalized learning scheme and show that the monotone convergence of the output error can be obtained for a given time interval when the proposed ILC algorithm is applied to a class of linear dynamic systems. We also show that the divided time interval is affected from the learning gain and that convergence speed of the proposed learning scheme can be increased by choosing the appropriate learning gain. To show the effectiveness of the proposed algorithm, two numerical examples are given.

• Journal of the Korean School Mathematics Society
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• v.24 no.1
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• pp.59-81
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• 2021

This study aimed to investigate how the learners' mathematics learning processes change with repeatedly reading mathematical text. As a way to teach and learn mathematics, we also wanted to examine the effect of repeated reading and to explore the implications for a more efficient teaching and learning strategy. To help us with this study, we mainly used eye tracking and heart rate (HR) measurement. There were four cycles in a cycle of repeated reading, and the number of repeated readings for all cycles was fixed to three times. Eight prospective mathematics teachers in the Department of Mathematics Education of a National University in South Korea participated. Data were analyzed in five aspects: (1) the total reading time per round, the total reading time per slide; (2) the change trends of total reading time per round and slide; (3) the order of slides read; (4) the change trends of HR per round. We found that most participants read in a similar pattern in the first reading, but the second and third reading patterns appeared more diverse for each learner. Also, the first reading required the most time regardless of the repeat cycle, and the time it took to repeatedly read afterward varied depending on the individual. Based on the findings of this study, the most primary conclusion is that self-directed mathematics learning by using repeated reading is effective regardless of cycle. In addition, we suggested four strategies to improve the efficiency of this teaching and learning method.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.669-672
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• 2002

인터넷 교육 시스템에서 학습자와 교사간의 상호작용을 위한 도구들이 다양하게 지원되고 있지만, 교과과정을 개설하고 이를 운영하는 교사의 입장에서 볼 때, 등록한 모든 학생들이 대면하게 되는 상황을 모두 접수하고, 그들의 학습 상태를 분석하여 학습자에게 가장 적합한 코스 구성 및 스케줄을 제공한다는 것은 어려운 일이다. 따라서, 이러한 웹기반 교육 시스템에서의 학습자에게 효과적인 학습 방법과 코스 구성, 그리고 코스 스케줄 등의 피드백을 제공할 수 있는 에이전트가 필요하게 되었다. 또한, 최근에 학습자의 요구에 맞는 코스웨어 주문이 증가되고 있는 추세이며 그에 따라 웹 기반 교육 시스템의 효율적이고 자동화된 교육 에이전트의 필요성이 인식되고 있다. 본 논문에서는 학습자의 학습 모니터링과 지속적인 학습 평가에 의하여 개인 학습자의 학습 성취도록 계산하여 학습자에게 적합한 코스 스케쥴을 제공해 주는 인터넷 반복 학습 시스템을 제안하고자 한다.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.247-250
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• 2009

본 실험에서는 단어회상이 영어어휘 학습에 미치는 영향을 살펴보기 위해 160개 단어 쌍에 대해 어휘학습을 실시하였다. 세 종류의 어휘 학습 방법(교대학습, 반복검사, 반복학습)을 채택하여 학습을 실시하였으며 학습 1주일 후 160개 단어 쌍에 대해 지연회상검사를 실행하였다. 그 결과 세 종류의 어휘 학습 방법 중 단어회상을 강조한 두 개의 어휘 학습 방법에서 그렇지 않은 조건에 비해 유의미하게 좋은 지연회상률을 보였다. 또한 실험 참가자를 대상으로 선호하는 학습 방법에 대해 설문조사를 실시한 결과 63.5%의 설문 응답자가 한 번 학습한 것에 대해 스스로 시행하는 회상 검사를 선호하였다. 그러나 자가검증을 통한 회상 검사 자체가 효과적인 학습 방법이라고는 생각하지 않는 것으로 나타났다.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.585-594
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• 2007

The back propagation algorithm took a long time to learn the input patterns and was difficult to train the additional or repeated learning patterns. So Aleksander proposed the binary neural network which could overcome the disadvantages of BP Network. But it had the limitation of repeated learning and was impossible to extract a generalized pattern. In this paper, we proposed a dynamic 3 dimensional Neuro System which was consisted of a learning network which was based on weightless neural network and a feedback module which could accumulate the characteristic. The proposed system was enable to train additional and repeated patterns. Also it could be produced a generalized pattern by putting a proper threshold into each learning-net's discriminator which was resulted from learning procedures. And then we reused the generalized pattern to elevate the recognition rate. In the last processing step to decide right category, we used maximum response detector. We experimented using the MNIST database of NIST and got 99.3% of right recognition rate for training data.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.40-47
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work the authors presented an iterative precision of linear decentralized learning control based on p-integrated teaming method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the loaming control field was learning in robots doing repetitive tasks such as on a]1 assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.46-52
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• 2012

In this study, middle school students, from 1st to 3rd grade, who already completed section of the 'force and motion' are targets. Based on the survey of the students following conclusions are obtained using by 'before and after' of repetition learning. First, through the pre-test results and measured the degree of concept, repetition learning was found to be effective. Second, through the post-survey after repetition learning science concerning was increased rather than before learning. Improved confidence through the repetition learning effect should contribute learning effect.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.71-74
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• 1996

본 연구에서는 반복 학습제어 이론을 기초로 하는 하이브리드 신경망 제어기를 제안한다. 신경망으로는 백프로퍼게이션(backpropagation) 신경망을 사용하고, 기존의 반복 학습 제어 이론의 단점을 보안한 제어 알고리즘을 제안한다. 백프로퍼게이션 신경망의 맵핑(mapping)의 특징으로 원하는 목표 패턴에 추종할 수 있는 출력 패턴을 생성하고 반복 학습에 소요되는 학습시간을 줄일 수 있다. 실험결과에서 보듯이 제안된 제어 알고리즘은 목표패턴에 수렴함을 알 수 있다. 제시한 알고리즘은 CD-ROM 드라이브와 같은 광디스크 드라이브류의 초점 제어 등에 응용할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.