• 유통과학연구
• /
• 제22권4호
• /
• pp.1-10
• /
• 2024

Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results:social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentionsto engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.

• Child Health Nursing Research
• /
• 제25권4호
• /
• pp.507-517
• /
• 2019

Purpose: This study was conducted to examine the effects of team-based problem-based learning combined with smart education among nursing students. Methods: A quasi-experimental non-equivalent control group, pre-posttest design was used. The experimental group (n=36) received problem-based learning combined with smart education and lectures 7 times over the course of 7 weeks (100 minutes weekly). Control group (n=34) only received instructor-centered lectures 7 times over the course of 7 weeks (100 minutes weekly). Data were analyzed using the $x^2$ test, the Fisher exact test, and the independent t-test with SPSS for Windows version 21.0. Results: After the intervention, the experimental group reported increased learning motivation (t=2.70, p=.009), problem-solving ability (t=2.25, p=.028), academic self-efficacy (t=4.76, p<.001), self-learning ability (t=2.78, p<.001), and leadership (t=2.78, p=.007) relative to the control group. Conclusion: Team-based problem-based learning combined with smart education and lectures was found to be an effective approach for increasing the learning motivation, problem-solving ability, academic self-efficacy, self-learning ability, and leadership of nursing students.

• Journal of Computing Science and Engineering
• /
• 제10권4호
• /
• pp.111-117
• /
• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• 영어어문교육
• /
• 제12권4호
• /
• pp.185-210
• /
• 2006

This study has two purposes. First, it aims to suggest an appropriate approach to English writing education for Korean high school students in a cooperative learning situation. It also aims to suggest what type of learner grouping, either homogeneous or heterogeneous, is appropriate by comparing the learners' writing abilities and the changes of their affective factors after being exposed to cooperative EFL writing instruction. Two homogeneous classes were selected and instructed to write in English for 11 weeks. One was composed of homogeneous small groups based on the students' writing scores, and the other was composed of heterogeneous small groups, again based on the students' writing scores. The results showed that the improvement between the two class types was quite different across different proficiency levels. For example, although there is little difference between the homogeneous and the heterogeneous classes of low and intermediate-level learners in writing ability improvement, high-level students showed a significant difference between the classes. In addition, it was found that class participation correlated significantly to writing ability improvement. Cooperative learning was proved to be an effective writing instructional approach to encourage learners' interest and increase their self-confidence; however, the results did not show any significant differences in learners' affective domain between the homogeneous and the heterogeneous classes. Similarly, the learners' grouping preference was not affected by the grouping method.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.169.3-169
• /
• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...

• 영어어문교육
• /
• 제9권1호
• /
• pp.1-21
• /
• 2003

Learner-centered approach has been a recent research focus in the second language acquisition, but few studies have dealt with how to develop learner autonomy, particularly in a computer-assisted language learning environment. The paper first illustrates the importance of promoting learner autonomy in the EFL context and elaborates the three main factors contributing to its development. Then it focuses on how the CALL research project promotes autonomous learning through a small-scale study in Gyeongju University. Both quantitative and qualitative methods have been employed to examine whether in the CALL project learners exercise control over their own learning and evaluate the outcome. The results indicate that due to a flexible syllabus, highly motivating research topics and the network-assisted environment, learners do take responsibility for most aspects of learning and thus the CALL project proves to be a promising approach for autonomous learning.

• 공학교육연구
• /
• 제14권1호
• /
• pp.55-63
• /
• 2011

본 논문은 문제기반학습모형(Problem-based learning model)접근방법을 공학회계라는 다분히 학제 간 성격(공학과 회계학)을 갖는 교과에 어떻게 적용할 지를 다룬다. PBL 접근방법은 급변하는 기업 환경에서 공학회계와 관련한 기업 문제를 제기하고 다룰 수 있을 것이며, 또한 이를 시스템적 관점해서 해결하려는 공학 측면에서도 적절할 것으로 판단된다. PBL모형의 현실적인 적용을 위해서 학생들이 해결해야 할 문제를 생성하고, 해결하는 과정을 수행할 수 있도록 웹 기반 실습 시스템을 개발, 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2831-2845
• /
• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.959-979
• /
• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Journal of information and communication convergence engineering
• /
• 제14권2호
• /
• pp.97-105
• /
• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.