• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.5
• /
• pp.1183-1197
• /
• 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
• /
• v.15 no.3
• /
• pp.32-38
• /
• 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.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.81-86
• /
• 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
• /
• v.18 no.2
• /
• pp.58-67
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.2
• /
• pp.107-114
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.292-293
• /
• 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.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.515-521
• /
• 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.

• Research in Mathematical Education
• /
• v.22 no.1
• /
• pp.1-17
• /
• 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
• /
• v.6 no.2
• /
• pp.131-151
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.297-304
• /
• 2011

This study was to analyze the learning effectiveness of multi-media based class by comparing with traditional classroom method. The "Distribution Working Subject" course that is one of the required courses of Vocational high school was selected and its contents were digitalized on MS Powerpoint for multi-media based class. The thirty students were sampled for each experimental and control groups. The homogeneity and learning achievement of sample groups were tested for experiment. Same teacher took the classes of two groups and delivered same contents of course. Only difference between two groups was the delivery method, one is traditional classroom teaching method and the other was the multi-media based class. The learning achievements and satisfaction of sample were post-tested in order to analyze the learning effectiveness by comparing two teaching methods. The results showed that there was a significant difference between experimental and control group in learning achievement after ANCOVA controlled pre-test as covariance(F=5.08, p<.05). It means that the learning achievement of multi-media based class was higher than that of traditional classroom group. The results also showed that a significant difference in students' satisfaction between two groups (t=5.57, p<.001). This study concluded that using multi-media in class could produce more learning achievements and satisfaction of students than traditional classroom method.