• 공학교육연구
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• 제15권4호
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• pp.109-117
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• 2012

The purpose of this study is to give the preliminary data and suggestion for introducing and spreading e-learning engineering education through analyzing learning behaviors, learning motivations, and satisfaction of e-learning engineering students. Especially, this comparative study analyzes each research domain according to majors and grades, thereby suggesting more specific and practical results. 2,745 students registered in 38 subjects of e-learning in 2 Universities were analyzed for this study. The study result shows that engineering students are attending around 2 e-learning subjects with a duration of about 30 minutes once a week. The main of learning motivation for e-learning was not easy test level and feasibility of acquiring credit but advantages of e-learning such as freedom of time and space, learning by repetition. The satisfaction scores of e-learning were lower compared to the aspects of system and contents Based on these results, first, an active spread of e-learning to engineering education is necessary because the demand from the engineering students is high enough and they have desirable learning behavior and learning motivation for it. Second, the characteristics of grades need to be taken into consideration on operation of e-learning. Third, a successful e-learning process needs more meticulous and active operation.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• 컴퓨터교육학회논문지
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• 제15권4호
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• pp.41-49
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• 2012

정보통신 기술의 발전과 스마트 디바이스의 대중화로 인한 관심과 열풍은 문화적인 이슈를 넘어 교육에까지 확산되고 있다. 이에 스마트교육의 학습 효과에 대한 많은 연구가 이루어지고 있으나 스마트교육에 대한 개념이 확립되지 않아 스마트 디바이스를 도구적으로 적용한 연구에만 머물러 있다. 본 논문은 효과적인 스마트교육을 위해 사용될 수 있는 교육환경, 학습자 특성, 스마트 교육 특징, 그리고 스마트교육 활동을 고려한 CTLA(Creation, Teaching, Learning and Assessment) 모델과 이를 적용한 스마트교육 시스템을 제안한다. 제안된 CTLA 모델은 스마트교육을 위해 고려되어야 하는 핵심적인 요소들을 반영하고 있으며 효과적인 스마트교육 시스템을 설계하는 기본 모델로 사용될 수 있다. 향후에는 제안된 모델을 기본으로 한 다양한 스마트교육 시스템을 개발하고, 개발된 스마트교육 시스템의 효과성과 효율성에 대한 과학적인 검증 연구가 지속되어야 할 것이다.

• 한국과학교육학회지
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• 제15권3호
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• pp.284-290
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• 1995

This study examined the effects of the learning cycle model by learner's characteristics such as I.Q., cognitive levels, inquiry skins, cognitive style, activity, reflectiveness. To see the effects of the learning cycle model, nonequivalent control group pretest-posttest multiple treatment designs was used in the study. 99 middle school second-graders(female) were divided into two groups. One group was selected as the experimental group (n=50), the other served at the comparison group(n=49). During the eight-month period, the students in the experimental group were instructed according to the learning cycle model, while the students in the comparison group were instructed according to the traditional instruction methods. Achievement data from science achievement test were analyzed by an ANOVA technique. The results of the study are as follows : 1. Science knowledge achievement. For the lower level students of activity, the learning cycle model is superior to the traditional approaches in science knowledge achievement. 2. Science inquiry skills. For the upper level students of I.Q., cognitive levels, inquiry skills, cognitive style and reflectiveness, the learning cycle model is superior to the traditional approaches in science inquiry skills. 3. Attitudes toward science. For the lower level students of I.Q., cognitive levels, inquiry skills, cognitive style, activity and reflectiveness, the learning cycle model is superior to the traditional approaches in attitudes toward science.

• 컴퓨터교육학회논문지
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• 제8권2호
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• pp.33-40
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• 2005

기존 CAI(Computer Assisted Instruction) 학습방법에서의 문제점은 학습자의 개인적 특성을 충분히 고려하지 못한 채 교수설계자가 정한 학습 경로에 따라 학습하도록 구현되었다는 점이다. 이런 점을 해결하기 위해서는 시스템 설계 시 누적된 개인자료를 통하여 개인차를 지능적으로 판단하고, 결손 된 부분을 처방할 수 있는 인공지능을 갖춘 ITS(Intelligent Tutoring System)가 필요하다. 본 연구에서는 향후 시스템 설계자가 전자계산기구조 학습을 위한 시스템 설계 시 학습자가 학습과정에서 범할 수 있는 오류와 성취능력수준을 파악하여 수준별 학습이 가능토록 할 수 있는 학습자 모델링을 설계하는데 있어 고려하여야 할 요소들을 제시하였다.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• 농촌지도와개발
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• 제22권2호
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• pp.233-243
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• 2015

This study is for understanding differences of satisfaction followed by learning and teaching presence perceived by learners and participation motivation on learning in a distance education. General characteristics of learner are classified as an entrance type, sex, age, new/transfer of distance university learners. Perceived presence is classified with learning presence and teaching presence. Participation motivation on learning is classified with the directivity of activity, goal, and study based on the reason of participating in classes in a distance university. And this research tried out to find the effects of learner's characteristics, perceived presence, and participation motivation on learning satisfaction. The results are as follows. First, there was no meaningful difference of general characteristic on satisfaction. It means sex, age, and entrance type did not have an effect on satisfaction. Second, with the level of presence perceived by learner, satisfaction has meaningful differences. It means that students who had higher learning presence perceived of distance university showed higher satisfaction, and so as in teaching presence perceived on satisfaction. Third, factors effecting satisfaction based on participation motivation on learning differ with types of motivation. There was no meaningful difference of the level of activity directivity study participant on satisfaction, but was a meaningful difference of goal directivity and of study directivity in participation motivation on learning satisfaction. It was the learning presence that had a significant effect on learning satisfaction of adult learning.

• 한국보건간호학회지
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• 제26권3호
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• pp.465-477
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• 2012

Purpose: The purpose of this study was to investigate the influence of breakfast characteristics of university students on their self-regulated learning, attentional control, and fatigue in order to provide-basic data for establishing desirable eating habits, self-regulated learning skills using attentional control, and advisable learning habits of university students. Method: The level of fatigue was estimated using the Visual analogue scale (VAS) and Critical flicker frequency (CFF). Attentional control was measured using the Attentional Control Questionnaire (ACQ) adapted by Yoon. Self-regulated learning was surveyed by the Self-Regulated Learning Test developed by Chung. Data from atotal of 142 university students were collected from November 30 to December 9, 2011. Result: 69% of the subjects skipped their breakfast. Attentional control has a negative correlation with fatigue (r=-.179, p=.033) and a positive correlation with self-regulated learning (r=.352, p<.001). The multiple regression model of self-regulated learning consists of attentional control (t=3.218, p=.002), commuting time (t=-3.076, p=.003), understanding the importance of breakfast (t=-2.413, p=.008), and skipping breakfast(t=-2.195, p=.030) and its R-square was 21.8%. Conclusion: Learning efficiency of university students should be improved by means of attentional control, which is related to self-regulated learning. Also, it is essential for university students to establish healthy lifestyles including regular eating habits and attentional control, in order to improve their self-regulated learning.

• 정보처리학회논문지A
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• 제19A권3호
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• pp.155-160
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• 2012

본 논문은 기능성 게임의 주요소인 재미와 학습 그리고 본인의 게임 점수 향상을 통한 성취감 배양을 통해 언제 어디에서나 자투리 시간을 활용하여 수열 문제 학습 능력을 향상시킬 수 있는 스마트폰 기반의 수열 퀴즈 어플리케이션을 설계하였다. 또한 최근 대중화된 유비쿼터스 환경에서 기능성 게임을 통한 수열 문제에 대한 학습 효과를 검증하기 위해 여러 종류의 수열 문제들로 구성된 기능성 게임을 아이폰과 안드로이드 환경 기반으로 구현하고, 수열 게임 사용자들의 점수 기록을 바탕으로 사회통계학적 분류에 따른 게임 사용자 특성 및 학습 효과를 분석하였다.