• 한국보건간호학회지
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• 제34권3호
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• pp.405-415
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• 2020

Purpose: This study was undertaken to identify the mediating effect of family strengths in the relationship between e-learning readiness and learning management system-based online education learning outcomes. Our results provide basic data for proposing strategies to increase online education learning outcomes of nursing students. Methods: A self-report questionnaire was surveyed by 133 nursing students who took online education using a learning management system at three nursing colleges in Daejeon, Jeonbuk, and Gyeongbuk. The mediating effect of family strengths in the relationship between the e-learning readiness of the subject and online education learning outcomes, were analyzed by hierarchical multiple regression. Sobel test was performed to verify effectiveness of the pathway. Results: In the relationship between e-learning readiness and online education learning outcomes of nursing students, family strengths were determined to exert absolute mediating effect. Conclusions: Our results indicate that in order to improve e-learning readiness, the basic curriculum for nursing students should include web-based communication, cooperation, and the use of information technology, including interaction for online education. Improvements in family strengths can be achieved through home study activities, such as frequent conversations with members, monitoring achievements of the students, and sharing family leisure activities.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• 영재교육연구
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• 제20권3호
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• pp.885-899
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• 2010

본 연구의 목적은 학업 성취가 매우 뛰어난 우리나라 고등학생들이 이공계 진로를 선택하거나 또는 선택하지 않는 이유를 설명하며 성별차를 탐구하는 것이다. 과학고등학교와 국제고등학교에 재학 중인 1학년 학생들이 참여하여 과학 활동, 과학 학습을 위한 자신의 강점, 과학 전공을 선택하거나 선택하지 않는 이유를 묻는 개방형 문항에 응답하였다. 학업성취가 뛰어난 고등학생들의 과학 전공 선택 이유로서는 내적 흥미가 가장 비중이 크며 다음으로 자기효능감의 비중이 큰 것으로 드러났다. 그러나 과학을 전공으로 선택하지 않으려는 것에는 흥미의 부족보다는 자기효능감의 부족이 더 큰 관련이 있는 것으로 드러났다. 이 외에 과학 학습에 대한 자아개념과 정규 수업 외의 과학 활동을 분석하고 비교하였으며 교육적, 정책적 시사점들을 논의하였다.

• 수산해양교육연구
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• 제24권1호
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• pp.25-33
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• 2012

Though science and technology are evolving rapidly in recent years, the traditional science education has limits for students to be satisfied their interests and needs because they couldn't follow these speeds. STEAM as a education integrating science, technology, engineering, arts and mathematics has strengths of increasing interests and understandings in science and technology and improving integrated thinking and problem solving ability for leaners. In this study we analyze the elementary school curriculum and construct physics learning based on STEAM and develop a android application to increase interests in science and improve problem solving ability. In the future, we need to analyze and develop the curriculum and contents for the STEAM education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• 한국과학교육학회지
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• 제31권6호
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• pp.920-930
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• 2011

본 연구에서는 한국과학영재학교 학생들의 학습관련 어려움의 원인과 해결 방법에 대해 조사하여 과학 영재 고등학생들의 학습 실태를 알아보고자 하였다. 연구에는 한국과학영재학교 전 학년 284명의 학생들이 참여하였다. 학습관련 어려움의 실태를 조사하기 위하여 창의성 4P 이론에 근거한 과학영재의 학습관련 어려움 설문지를 개발하여 사용하였으며, 신뢰도(Cronbach's ${\alpha}$)는 .88이었다. 결과 분석에는 SPSS 12.0 통계프로그램을 이용하여 빈도분석과 중다변량 분석을 실시하였으며, 유의 수준은 5%미만으로 설정하였다. 과학영재 고등학생들은 학년별로 어려움을 겪고 있는 요인에서 차이를 보였으며, 그러한 차이는 대인관계의 어려움에서 주로 나타났다. 성별에 따른 차이 또한 유의하게 나타났는데, 여학생은 남학생에 비해 일반적 학습능력에서 어려움을 보였으며, 남학생은 여학생에 비해 학습전략 사용에서 더 많은 어려움을 겪는 것으로 나타났다. 과학영재학생들은 학습관련 어려움을 주로 동료들과 함께 해결하는 것으로 나타난 반면, 가장 어려움을 잘 해결해 주는 사람은 928 윤소정.배새벽 기타의 경우 다음으로 AA교사로 나타났다.

• 대한물리치료과학회지
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• 제31권2호
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• pp.90-104
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• 2024

Background: Hybrid learning is an educational approach that combines the teaching methods of online and lecture-style classes to compensate for each method's strengths and weaknesses. Compared to lecture-style classes, flipped learning improves overall class satisfaction and self-directed learning but is associated with lower learning motivation. It is necessary to determine whether hybrid flipped learning can solve the learning motivation problem of flipped learning by incorporating flipped learning into hybrid learning. The purpose of this study is to compare the effects of hybrid flipped learning and flipped learning on students' learning ability. Design: Cross-sectional study Methods: For students in the Department of Physical Therapy, classes were conducted using both flipped learning and hybrid flipped learning. In both learning methods, students took online classes first and participated in them every week. Flipped learning classes was conducted offline at school every week, while hybrid flipped learning alternated between live classes on YouTube and offline classes at school every other week. Results: Hybrid flipped learning resulted in significantly lower learning satisfaction and course evaluation than flipped learning, with no significant difference in grades. Conclusion: Hybrid flipped learning was able to cope with the situation well with the non-face-to-face teaching method caused by COVID-19, but it was difficult to improve learning ability because there were restrictions on activities that could interact with students. Flipped learning is a smooth offline activity that enables two-way activities between professors and students to improve learning ability, but the effect of improving test scores is still unclear.

• 한국경영과학회지
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• 제27권3호
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• pp.93-114
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• 2002

This paper suggests a dynamic balanced scorecard (DBSC) model employing the concept of system dynamics (SD), which could overcome the limitations inherent in the conventional balanced scorecard (BSC) and facilitate strategic learning process in organizations. The BSC has been a successful framework for measuring an organization＇s performance in various Perspectives through translating an organization＇s vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance Indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, we employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC we suggest in this paper would serve as a useful strategic learning tool for facilitating an organization＇s communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, we apply the DBSC model to Korea Coal Corporation (KoCoal ) BSC case.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.419-422
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• 2003

To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9％. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25％.