• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.880-883
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• 2003

본 연구에서는 효율적인 e-teaming 학습을 학습자에게 적합한 학습 환경의 학습 순서로 제공하기 위한 튜터(코치) 기능의 에이전트 시스템을 제안하고자 한다. 본 연구에서 제안하는 튜터 기능의 에이전트는 다양한 학습자 환경과 학습 수준에 따른 학습자의 학습파일과 선수 학습 자료를 데이터베이스로 저장하여 학습자에게 적합한 학습 순서의 제공을 목적으로 한다. 학습자와 에이전트의 지속적인 상호작용으로 효율적인 e-teaming 학습이 유지될 수 있도록 한다. 본 연구에서는 학습자의 선수(기초) 학습이나 학습 진행 상황, 결과 등의 정보를 저장하고 이를 이용하여 학습자에게 최적의 학습 순서를 제공할 수 있도록 튜터(코치) 기능의 에이전트와 협력 학습이 이루어질 수 있도록 한다. 그 방법의 하나로 학습자의 학습 진행 상황을 저장하고 학습자들의 학습 순서와 시스템에서 제안하는 학습 순서를 비교하여 학습자에게 보다 적합한 학습 순서(courseware)를 제안할 수 있도록 한다. 본 연구의 결과로 학습 순서를 제안하는 튜터 에이전트 시스템은 학습 시스템이 제안하는 학습 순서와 학습자가 학습하고자 하는 학습 순서를 학습자의 학습 진행에 따라 학습 순서를 재구성하고 평가 전에 학습자의 학습 순서 경로를 다시 한번 반복 학습하게 함으로써 학습자가 최대의 학습 효과를 얻을 수 있도록 하는 효과를 나타낼 수 있다.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.723-735
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• 2010

Evaluating the level of achievement and providing the knowledge which is appropriate at the evaluated level have great influence in studying of the human beings. This shows the importance of the order of training and the training order should be considered in machine learning. In this research, to assess the influence of the order of training, we propose a method of controlling the order of training samples utilizing the experience of supervisor in the training of MLP. The supervisor finds out the current state of MLP using teaching experience and student evaluation, and then selects the most instructive sample for MLP in that state. We use CRF to represent and utilize the experience of supervisor. While the proposed method is similar to active learning in selecting samples, it is basically different in that selection is not to reduce the number of samples to be used but to assist the learning progress. The result from classification problem shows that the method is usually effective in terms of time taken in training in contrast to random selection.

• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.489-502
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• 2012

Today computer education puts an emphasis on algorithm education. There are little researches about how to express the given problem in algorithm and how to interpret the expressed algorithm. In this study play-based learning methods dealing with flow chart which is one of the expressing tools of algorithm are developed for lower graders of elementary school. Then we diagnosed the learning possibility of the tool after applying the methods in a classroom environment. There are four types of learning game activities; sequential play, selective play, repetitive play and puzzle play. Puzzle play is a game that students need to reconstruct the learned content to a real flow chart by using flow chart cards. The result of an achievement test after teaching students flow chart showed that the group who took the play-based lesson got their average score with about 7.5% higher than the group who took the ICT-based lesson. Both the groups got their average scroe of more than 9 out of 10 after the lesson. This result shows that flow chart lessons are adaptable for the lower graders of elementary school. It also shows that play-based education can be exceptionally effective.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• Korean Journal of Cognitive Science
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• v.17 no.3
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• pp.231-253
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• 2006

The interaction effects of the cognitive style and the presentation order of learning material was explored in the study. Visualizers performed better when the graphic information was presented prior to the verbal information, whereas verbalizers did better when the verbal information was presented prior to the graphic information. The results of the present research have practical implication of personalized multimedia design based on the learner's cognitive style. The results also have suggested that the cognitive load of a multimedia material can be varied depending on the compatibility of the cognitive style and the material.

• Journal of Creative Information Culture
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• v.6 no.2
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• pp.65-75
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• 2020

The necessaries of Flowchart learning for software education have been discussed but most studies were conducted on learning methods. In this study, Unplugged Flowchart Learning programs for fifth grade students were developed and taught, and their effectiveness were analyzed. The programs were made of 8 themes(16 periods) based on the learner's levels. The effectiveness of the programs were qualitatively analyzed based on classwork sheets, as well as observation and interview. Computational Thinking tests were pre-tested and post-tested for qualitative analyses. This study found that all sub-areas of CT of the students who took the Unplugged flowchart learning program were significantly improved as well as the overall scores of CT. In particular, students' improvements in the area of abstraction and automation was notable. Various interactions between teacher-learners and learners-learners were observed during class, and were found to have positive effects on changes in learners' attitudes and perceptions.

• Journal of the Korean Chemical Society
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• v.43 no.4
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• pp.475-484
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• 1999

This study was to investigate the effective order of instruction for students learning the concepts of chemical change. Chemical change was considered as the important area in 8th grade chemistry part. The study consisted of 168 8th grade students, two classes of boys and girls each, from a middle school in Seoul. They were divided into two groups, the experimental group and the control group. The control group was taught in the order, which was presented in the science textbook; chemical change, atom, and molecule (CAM). For the experimental group, the order was molecule, atom, and chemical change (MAC). From the results of the study, there was a statistically significant difference between the control group and the experimental group. But the interviews indicated that the students were confused with the MAC method in spite of the effective learning. Therefore, for more effective concepts learning without a confusion, we need to provide our students with various learning sequences of science textbooks rather than fixed learning sequences.

• The Journal of Korean Association of Computer Education
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• v.15 no.6
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• pp.33-41
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• 2012

This paper presents the online assessment questions management system and method. The proposed system consists of a database to store learner information and zone-specific items grouped by difficulty and item bank. This database includes: an item selection department and authoring assessment to select questions about a particular learner or specific learning item. In this paper, we propose: an item bank database which stores online output assessments; and an online test department to collect and sort learner evaluation data and answer selection order for online tests, click statistics, response time, and analysis unit response patterns department by analyzing the data collected by the online learners' test assessment, learners' level and ability, the diagnosis and assessment of report propensity. The proposed system will diagnose and effectively evaluate the learner's learning levels and learning ability by: answer selection order, number of clicks, and response time reflected in the results of the learners' evaluations.

• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.55-61
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• 1998

The learning order of teaching materials to be a learning data in CAI is arranged from an easy item to a difficult one A learning in not necessary to be learned arranged this order. Actually the learning is done by the rules of trial and error on the sequences of an arrangement among items. In this papers, the constructed is modelled by the fuzzy inference after leaning the understanding on items by the intelligent CAI through the rile of trial and error of fuzziness. Given the difference of leaning and understanding, the leaning model is quantified by the order relationship among items and by the rules of fuzzy inference. The rule of trial and error of learning is restricted to the treatment of CAL system minimizing the rules of inference.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.