• Title/Summary/Keyword: Judgment of Learning

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Implementation of Smart E-learning based on Blended Learning (혼합형 학습 기반 스마트 이러닝 구현)

  • Hong, YouSik
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.2
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    • pp.171-178
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    • 2020
  • Many countries are establishing and operating blended learning that combines the advantages of online and offline education. However, online education lecture-based Mooc courses have a very low level, with a graduation rate of less than 5-10%. Therefore, in order to increase the graduation rate of students taking online Mooc distance education lectures that anyone can easily take lectures anytime, anywhere on the web-based basis, it is necessary to introduce automatic analysis of students' understanding level of lectures and an automatic academic warning system. Moreover, in order to enter an advanced education country, it is necessary to develop an automatic judgment SW for wrong answer rate, automatic summary SW for lectures, and automatic analysis SW education for lecture-based weak subjects based on mixed learning levels. In order to improve this problem, in this paper, we proposed and simulated an automatic summarization system for lecture contents, an automatic warning system for incorrect answers, and an automatic judgment algorithm for weak subjects.

Machine Learning-based Concrete Crack Detection Framework for Facility Maintenance (시설물의 유지관리를 위한 기계학습 기반 콘크리트 균열 감지 프레임워크)

  • Ji, Bongjun
    • Journal of the Korean GEO-environmental Society
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    • v.22 no.10
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    • pp.5-12
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    • 2021
  • The deterioration of facilities is an unavoidable phenomenon. For the management of aging facilities, cracks can be detected and tracked, and the condition of the facilities can be indirectly inferred. Therefore, crack detection plays a crucial role in the management of aged facilities. Conventional maintenances are conducted using the crack detection results. For example, maintenance activities to prevent further deterioration can be performed. However, currently, most crack detection relies only on human judgment, so if the area of the facility is large, cost and time are excessively used, and different judgment results may occur depending on the expert's competence, it causes reliability problems. This paper proposes a concrete crack detection framework based on machine learning to overcome these limitations. Fully automated concrete crack detection was possible through the proposed framework, which showed a high accuracy of 96%. It is expected that effective and efficient management will be possible through the proposed framework in this paper.

Deep learning-based product image classification system and its usability evaluation for the O2O shopping mall platform (딥 러닝 기반 쇼핑몰 플랫폼용 상품 이미지 자동 분류 시스템 및 사용성 평가)

  • Sung, Jae-Kyung;Park, Sang-Min;Sin, Sang-Yun;Kim, Yung-Bok;Kim, Yong-Guk
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.3
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    • pp.227-234
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    • 2017
  • In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

A Way to Develop Contents for Officials Ethics Education using Value Clarification (가치명료화 기법을 활용한 공직윤리 교육 콘텐츠 개발 방향)

  • Park, Gyun-Yeol
    • The Journal of the Korea Contents Association
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    • v.19 no.11
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    • pp.415-423
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    • 2019
  • This study focused on establishing the basic direction for the development of officials ethics education contents using value clarification. The value clarification is well known as an effective teaching and learning method to develop moral judgment among others as follows: moral sensitivity, moral motivation, moral practice. This study outlines the teaching-learning method and the evaluation method for the actual officials ethics education. This waits for the further empirical works.

A Study on the Meaning of Learning in Adult Learners (성인학습자의 배움 의미에 관한 연구)

  • Bae, Na-Rae
    • Journal of the Korea Convergence Society
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    • v.13 no.3
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    • pp.185-190
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    • 2022
  • The study of the meaning of learning began with the question of what causes people to start learning. Learning is humanization and personification. Learning is a basic human instinct, and the essence of learning is to understand other people and my life, learn community, and learn social capital. Learning gives humans nomadic judgment and provides an opportunity for a productive life for mankind, who must live in constant harmony with the social environment. Learning provides opportunities for self-management, communication with various generations, and self-actualization.

A study of creative human judgment through the application of machine learning algorithms and feature selection algorithms

  • Kim, Yong Jun;Park, Jung Min
    • International journal of advanced smart convergence
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    • v.11 no.2
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    • pp.38-43
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    • 2022
  • In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

An Effective Anomaly Detection Approach based on Hybrid Unsupervised Learning Technologies in NIDS

  • Kangseok Kim
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.2
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    • pp.494-510
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    • 2024
  • Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

Investigation of Topographic Characteristics of Parcels Using UAV and Machine Learning

  • Lee, Chang Han;Hong, Il Young
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.35 no.5
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    • pp.349-356
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    • 2017
  • In this study, we propose a method to investigate topographic characteristics by applying machine learning which is an artificial intelligence analysis method based on the spatial data constructed using UAV and the training data created through spatial analysis. This method provides an alternative to the subjective judgment and accuracy of spatial data, which is a problem of existing topographic characteristics survey for officially assessed land price. The analysis method of this study is expected to improve the problems of topographic characteristics survey method of existing field researchers and contribute to more accurate decision of officially assessed land price by providing more objective land survey method.

Learning data analysis strategy in intelligent learning system (지능형 학습 시스템에서의 학습데이터 분석 전략)

  • Shin, Soo-Bum
    • 한국정보교육학회:학술대회논문집
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    • 2021.08a
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    • pp.37-44
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    • 2021
  • This study is about a strategy to analyze learning activities in an intelligent learning system. To this end, the conceptual definition of the intelligent learning system and the type of learning using the intelligent learning system were analyzed. The learning types were presented as individual, adaptive, competency-based, and blended learning, and although there are some differences, most of them have similar characteristics. In addition, learning activity analysis is based on data such as mouse clicks, keyboarding, and uploads generated by the system. Through this, basic analysis such as viewing time and number of uploads can be performed. However, more diverse learning analysis is needed for personalization and adaptation. It can judge not only learning attitude and achievement level, but also metacognitive level and creativity level. However, since the level of metacognition includes complex human cognitive activities, the teacher's intervention is required in the judgment of the intelligent learning system.

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A Case Study on Application of Web-based PBL to Practical Health Administrative Affairs (웹 기반 PBL을 적용한 원무관리실무 수업에 관한 사례연구)

  • Kim, Minkyung;Shin, Kyeongae
    • Journal of The Korean Society of Integrative Medicine
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    • v.2 no.3
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    • pp.15-22
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    • 2014
  • Backround : The paradigm of recent education has been shifting from existing style of professor-oriented, passive and rote teaching to learner-centered education. Rather than mere delivery of knowledge, today's idea of education uses various audiovisual media to let learners gain more problem-solving skills, judgment, cognitive thinking ability, and creativity to apply to real practice. Also, while current trends and change in policy ask for related industry to require practice-centered teaching learning model, Problem-Based Learning (PBL) is quite effective that it activates problem-solving skills as well as application of National Competency Standards (NCS). Purpose : The purpose of this study was to suggest a teaching learning model article as an approach to apply web-based PBL for patient & medical charge management practices. Discussion & Conclusion : This paper the cases on PBL and presents the teaching learning model on web-based PBL as an approach to applying web-based PBL, which fits Medical Information System Department that combines health-medical treatment and computer applications, to practical health administrative affairs.