• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.310-313
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• 2006

지능형 학습 시스템(ITS, Intelligent Tutoring System)은 학습자의 학습 스타일을 인지하여 학습자에 맞는 학습전략을 세우고 적절한 학습 서비스를 제공하는 시스템이다. 기존의 학습시스템은 학습자의 학습 스타일 보다는 학습 컨텐츠에 중심을 두어 학습자에게 맞는 학습 전략을 적절히 세우는 과정이 부족했다. 이에 본 논문에서는 학습자의 학습과정에서 발생한 데이터를 기반으로 학습자의 학습 스타일을 파악하는 방법을 제안한다. 이를 위해 서양 건축양식 학습을 위한 교육 컨텐츠를 이용하였으며, 수집된 데이터를 분석하여 Folder & Silverman 이 제시한 학습 스타일에 근거한 학습자의 학습 스타일을 추출하였다. 실험에서는 70명의 데이터를 수집하였고, 학습자가 교육 컨텐츠를 학습한 순서에 대한 시계열 데이터를 기반으로 학습자 성향을 알아보기 위하여 은닉 마코프 모델(Hidden Markov Model)을 사용하였다. 은닉 마코프 모델을 적용하여 얻은 분석 결과를 가지고 각 학습자에게 맞는 학습 스타일을 진단하였다. 은닉 마코프 모델에서 얻은 학습 스타일 진단 모델은 향후에 학습자 학습 스타일을 파악하는데 사용할 수 있으며, ITS에 있어 학습자 성향 분석 모듈로 고려해볼 수 있다.

• Journal of Gifted/Talented Education
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• v.20 no.2
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• pp.571-594
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• 2010

Learning styles affect how students access and handle their task, so it is very important to understand how they study, when planning teaching-learning process, to enhance their potential to the maximum. In addition, in order to improve the quality of gifted education, there is a need to examine the curriculum and teaching-learning process which reflect learner characteristics. In this study, gifted student's preferred learning styles are investigated using questionnaires and learning style inventory. Also by analyzing the characteristics of the learners, it is hoped to get parents and teachers to understand the gifted who have various talents, and to support teaching programs for the gifted in order to develop their potential. This study shows that there are differences in the studying style between the gifted child and the average child. Namely, learner's physical and psychological environment can affect learning styles. Also there is a difference between the studying style which the gifted students prefer and the teaching style which teachers use most frequently. Programs for the gifted should be planned through better understanding of the characteristics of the learners.

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.145-153
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• 2016

The purpose of this study is to suggest a suitable learning style for 300 college students who are attending Duniversity in Jeonnam providence according to QSSC(Questionnaire of Sasang Contribution Classification), major and brain dominance after finding out the difference of learning style according to Sasang constitution and major and brain dominance according to major. First, Soyangin's learning style was found out extroversion -type, but soeumin's learning style was found out introvert type. There was difference on physical type on learning style according to major. Third, there were found difference for a few questionnaire of the dominance of brain. According to above findings, The results of study suggest that we need to develop a unique teaching method which based on learners constitution and major instead of uniform teaching method.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.383-391
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• 2013

This study developed an adaptive instruction system for individual learning. We applied Gregorc's learning style to support the adaption. This proposed Instruction system provides instruction content based on a more effective instruction method that takes into consideration the learner's individual learning style. We applied Gregorc's learning style to create a clear treatment of differing learning styles. Proposed adaptive instruction system was applied to college students studying computer learning. A t-test indicated that our proposed adaptive instruction system resulted in positive effects in an overall style comparison. In addition, our Abstract-Sequential learning style indicated the most effective results while the Abstract-Random learning style indicated no difference between the experimental and comparative groups in each style analysis for Gregorc's learning style.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.432-434
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• 2022

우리는 전이 학습을 이용하여 원하는 특정 패션 스타일 분류기를 학습하였다. 패션 스타일 검색 결과물을 온라인 쇼핑몰과 연결하는 웹 서비스를 사용자에게 제공한다. 패션 스타일 분류기는 구글에서 이미지 검색을 통해 수집된 데이터를 이용하여 ResNet34[1]에 전이 학습하였다. 학습된 분류 모델을 이용하여 사용자 이미지로부터 패션 스타일을 17가지 클래스로 분류하였고 F1 스코어는 평균 65.5%를 얻었다. 패션 스타일 분류 결과를 네이버 쇼핑몰과 연결하여 사용자가 원하는 패션 상품을 구매할 수 있는 서비스를 제공한다.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.81-89
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• 2011

Cyber-home Learning aims to extend learning space from the classroom to real-life situations, and teachers of research schools on Cyber-home learning have indicated the importance of connection with school instruction in order to improve students' performance. The goal of this study is to evaluate the influence of the instructional method( blended-learning vs just cyber learning) and the learning styles by Kolb's LSI on Cyber-home learning. We carried out the experiment using two similar classes in the social and math subjects for 1 semester. The results statistically shows the instructional method is the most influence on learning score and the next element is the learning styles. Therefor this study proved again Cyber-home learning is effective when connecting with school instruction and also the more various contents on learning styles could be supportive to students. In other words teacher's role and the adaptive learning contents by learning styles are essential for Cyber-home Learning's success.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.64-68
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• 2020

텍스트 스타일 변환은 문장 내 컨텐츠는 유지하면서 문장의 스타일을 변경하는 것이다. 스타일의 정의가 모호하기 때문에 텍스트 스타일 변환에 대한 연구는 대부분 지도 학습으로 진행되어왔다. 본 논문에서는 병렬 데이터 구축이 되지 않은 데이터를 학습하기 위해 비병렬 데이터를 이용하여 스타일 변환을 시도한다. 트랜스포머 기반의 문장 생성기를 이용하여 문장을 생성하고, 해당 스타일을 분류하는 판별기로 이루어진 모델을 제안한다. 제안 모델을 통해, 감정 변환의 성능은 정확도(Accuracy) 56.9%, self-BLEU 0.393(긍정→부정), 0.366(부정→긍정), 유창성(fluency) 798.23(긍정→부정), 1381.05(부정→긍정)을 보였다. 본 연구는 비병렬 데이터에 대해 스타일 변환을 적용함으로써, 병렬 데이터가 없는 다양한 도메인에도 적용가능 할 것이다.

• Journal of The Korean Association of Information Education
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• v.13 no.3
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• pp.351-358
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• 2009

This study shows how to impact on programming learning using Scratch by the learning style. Firstly, students were classified by learning styles test using the VARK questionnaire. After one semester of programming education, correlation with learning styles and various instructional factors was analyzed. Scratch-based programming education improves the cognitive effectiveness and learning satisfaction for elementary school students. Especially students with visual preference performed better on programming education based on Scratch statistically. As a result, the process of programming must be mainly considered in programming education.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.437-443
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• 2024

Adhering to coding style guidelines is crucial for both companies and developers as it improves code readability and reduces the costs associated with testing and maintenance. However, teaching coding style in programming courses poses challenges. Setting up an environment for learning coding styles is hard, and there are no predefined coding style rules for beginners. From the learners' perspective, since adherence to coding styles does not affect their grades, they do not feel a strong need to learn them. This paper introduces a coding style checking system for an online evaluation system. The proposed system is implemented to check and evaluate coding styles in C, Java, and Python. Additionally, we applied 234 out of the 1,023 rules provided by the language-specific tools, which is 23.08%, allowing for the application of coding style rules according to the course progression. Moreover, we motivated learners to improve their coding style by adding quality scores to their basic scores. After introducing the coding style education system, the number of students scoring over 25 points on their initial submissions increased by 149.47%, from 18 students in the first week to 44 students in the sixth week. Learners used the coding style checking system to learn how to apply coding style rules and subsequently implemented their code in adherence to the specified coding styles.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1339-1342
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• 2022

예술적 스타일 전이는 예술 작품이 지닌 특징을 다른 이미지에 적용하는 이미지 처리의 오랜 화두 중 하나로, 최근에는 StyleGAN 과 같이 미리 학습된 GAN(생성적 적대 신경망)을 통해 제한된 데이터로도 고해상도의 예술적 초상화를 생성하도록 학습하는 연구가 다양한 방면에서 성과를 내고 있다. 본 논문에서는 2 가지 경로의 StyleGAN과 Facial Destylization 을 통해 고해상도의 예시 기반 스타일 전이를 달성한 DualStyleGAN 연구에 대해 소개하고, 기존 연구에서 사용된 Facial Destylization 방법이 지닌 한계점을 분석한 뒤, 이를 개선한 새로운 방법, Re-Destyle을 제안한다. 새로운 Re-Destyle 방법으로 Facial Destylization 을 적용할 경우 학습 시간을 기존 연구의 방법보다 20 배 이상 개선할 수 있으며 그 결과 1000 개 이하의 적은 데이터와 1~2 시간의 추가 학습만으로도 원하는 타겟 초상화 스타일에 대해 1024×1024 수준의 고해상도의 예시 기반 초상화 스타일 전이 및 이미지 생성 모델을 학습할 수 있다.