• 수산해양교육연구
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• 제28권5호
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• pp.1183-1197
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• 2016

The purpose of this study is to develop a Puzzle-Based Computer Science Instruction Model and Learning Program and to confirm the effects. To do so, we selected 2 classes with a similar level of pre-computational thinking in elementary schools in the Jeju Province. After that, from 2 classes, we designated the 5th grade students in 'D' elementary school as group A and designated students of the same grade in 'J' elementary school as group B. In a total of 28 sessions during an 18 week period, a Puzzle-Based Computer Science Learning Program was used with 31 students in group A, and the traditional computer science course was used with 25 students in group B. The results showed that there were significant improvements in computational thinking, which is computational cognition and its creativity, of the students in group A compared to students in group B. Also, this study proved that the Puzzle-Based program correlated with positive changes group A students' Science-Related Affective Domain. In this paper, on the basis of proven effectiveness, we introduce the Puzzle-Based Computer Science Instruction Model and Learning Program as an alternative to traditional, computer science education.

• International Journal of Contents
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• 제15권3호
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• 로봇학회논문지
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• 제14권2호
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• pp.81-86
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• 2019

Robots are used in various industrial sites, but traditional methods of operating a robot are limited at some kind of tasks. In order for a robot to accomplish a task, it is needed to find and solve accurate formula between a robot and environment and that is complicated work. Accordingly, reinforcement learning of robots is actively studied to overcome this difficulties. This study describes the process and results of learning and solving which applied reinforcement learning. The mission that the robot is going to learn is bottle flipping. Bottle flipping is an activity that involves throwing a plastic bottle in an attempt to land it upright on its bottom. Complexity of movement of liquid in the bottle when it thrown in the air, makes this task difficult to solve in traditional ways. Reinforcement learning process makes it easier. After 3-DOF robotic arm being instructed how to throwing the bottle, the robot find the better motion that make successful with the task. Two reward functions are designed and compared the result of learning. Finite difference method is used to obtain policy gradient. This paper focuses on the process of designing an efficient reward function to improve bottle flipping motion.

• International Journal of Contents
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• 제18권2호
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• pp.58-67
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• 2022

As the Internet rapidly evolves, online learning has emerged as the third largest scenario in the field of education. Online education, different from the two traditional learning scenarios of the school and society, is characterized with broader learning types and higher freedom. In today's post-pandemic era, art education, which relies on face-to-face teaching, is of particular significance to expand online education methods. Based on the UTAUT model, this paper posits seven hypotheses about the willingness to continue learning in online art education. After collecting valid data through a questionnaire, a detailed empirical analysis was conducted via SPSS and AMOS. The results of empirical analysis show that less than half of the respondents had experienced the online art education, mirroring that this is a market worth developing. Based on the findings, learning habit does not significantly impact art learners' willingness to continue learning online. This result and other verified hypotheses are detailed in the discussion part of this paper. This study proves that UTAUT can better explain user behavior than the traditional information system model prior to the improvement, and also has strong explanatory power in the field of art education. The conclusion also posits some operational suggestions from the perspective of practitioners in this field, thereby providing a theoretical basis for art education practitioners.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.

• 한국산업융합학회 논문집
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• 제23권3호
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• pp.515-521
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• 2020

Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, image processing and computer vision. One of the most frequently used deep learning methods in image processing is the convolutional neural networks. Compared to the traditional artificial neural networks, convolutional neural networks do not use the predefined kernels, but instead they learn data specific kernels. This property makes them to be used as feature extractors as well. In this study, we compared the quality of CNN features for traditional texture feature extraction methods. Experimental results demonstrate the superiority of the CNN features. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제22권1호
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• pp.1-17
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• 2019

This study aims to examine changes in teaching and learning conceptions and sense of efficacy as well as relationships between them. Data were collected from 121 Korean preservice teachers before and after a 4-week teaching practicum. The results indicated that constructivist conceptions of teaching and learning increased over the practicum period and teacher efficacy shifted as well. In addition, correlations among the constructs were strengthened over the practicum period. Interestingly, constructivist conceptions related to differentiated education were not significant, while traditional conceptions related to teacher-guided lessons were significant after the practicum. These results imply that Korean preservice teachers still place value on the traditional perspective, even though constructivism dominates the current educational policies of Korea.

• Educational Technology International
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• 제6권2호
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• pp.131-151
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• 2005

As student-centered learning grows, formative peer assessment practices have been universally used in various fields. According to the review of traditional peer assessment practices, the formative peer assessment has five common stages: planning, assessing (giving feedback), receiving feedback, reflection, and revising. As the each stage of traditional formative peer assessment has some weaknesses, the study discusses solutions that are recommended for dealing with the problems by introducing the potential benefits of web-based peer assessment. Then, desirable future trends of web-based peer assessment are suggested. The author hopes that understanding the potential benefits of web-based formative peer assessment will promote the proper use of peer assessment and render positive effect on student learning.

• 한국컴퓨터정보학회논문지
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• 제16권12호
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• pp.297-304
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• 2011

본 논문은 멀티미디어 교육자료와 전통적 수업방식의 효과성을 실질적으로 검증하고자 전문계 고등학교의 수업 방법을 비교분석하였다. 실업계의 전문교과 중 유통관리실무과목을 학습내용으로 선정하고, 멀티미디어 교육자료를 MS파워포인트로 디지털화하였다. 이를 위하여 30명의 학생들을 각각 실험집단과 통제집단의 표본집단으로 하고 동질성과 학업성취도를 고려하여 구성하였다. 동일한 교사가 두 집단을 같은 자료로 가르쳤다. 차이점은 두 집단간의 전달방법인데 두 유형의 교수방법으로 학습효과성과 만족도를 분석하기 위하여 사후검증을 실시하였다. 방법은 사전검사 점수를 공변인으로 통제한 후, 학생들의 학업성취도 사후검사 점수를 종속변인으로 공변량분석을 수행하였으며, 결과는 집단 간에 통계적으로 유의미한 차이가 있었다.(F=5.06, p<.05). 그것은 멀티미디어 교육자료를 활용한 수업의 학업성취도가 교과서 위주의 전통적 설명식 수업보다 더 높은 것으로 나타났다. 수업방식에 대한 만족도는 실험 통제집단 검증에서 통계적으로 유의미한 차이가 나타났다(t=5.65, p<.001). 따라서 멀티미디어 교육자료를 활용한 실험집단의 수업방식이 전통적 수업방식을 적용한 통제집단 보다 수업성취도와 만족도 측면에서 더 높은 것으로 나타났다.