• 과학교육연구지
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• 제34권1호
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• pp.23-32
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• 2010

이 연구의 목적은 과학 교수 학습 방법에 관한 국내 연구 동향을 분석하기 위한 것이다. 국내 학회의 학술지 6종에 게재된 논문들을 연도 방법 대상 주제에 따라 분석했다. 분석대상은 과학교육 분야 문헌들에서 공통적으로 제시하고 있는 방법들을 추출되었다. 분석 결과 과학 교수 학습 방법에 관한 연구들은 2000년 이후 지속적인 증가세를 보였다. 교수 학습 방법 중 실험, 협동학습, 토의가 많이 연구되었다. 이 세 가지 방법에 관한 연구들에서 가장 많이 채택하고 있는 연구방법은 실험연구로 나타났다. 다음으로는 내용분석, 조사연구, 사례연구 등의 순으로 나타났다. 세 가지 방법에 관한 실험연구는 중학생을 대상으로 한 것이 가장 많았다. 특히 실험연구를 통해 과학 성취도, 태도, 탐구능력, 자기효능감과 관련한 과학 교수 학습방법의 효과를 알아보는 것으로 나타났다. 이 연구의 결과를 근거로 볼 때 향후 과학 교수 학습 방법에 관한 연구는 다양한 방법들에 대한 적용 개발 연구 등이 이루어져야 할 것이다. 또한 질적 연구방법을 통해 교수 학습 방법이 실제 교육현장에 적용되는 맥락을 분석하도록 연구 방향이 확대될 필요가 있다고 생각된다. 그리고 다양한 학습자 집단을 대상으로 한 연구를 통해 교수 학습 방법의 효과를 종단적 횡단적으로 접근하려는 시도가 필요할 것이다.

• 공학교육연구
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• 제13권6호
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• pp.14-23
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• 2010

이러닝뿐만 아니라 모든 테크놀로지 활용은 교육목표의 달성을 위해 효과적으로 사용하는 방법을 파악하여야 하는데 이때에 중요한 것은 기술적 접근보다 교수학습적 접근이다. 공과대학에서 이러닝이 더욱 확대, 보급되는 현 시점에서 공대 이러닝의 활용 유형을 파악하고 교수학습 방법과 연관된 발전 방향을 제시하여야 한다. 본 연구는 공대 이러닝 콘텐츠와 운영의 유형을 조사하고 이를 교수학습 방법의 활용과 연계하여 이러닝 교수학습 전략을 도출한다. 이를 위해 공대에서 멀티미디어 콘텐츠를 활용하는 네 과목을 대상으로 활용 유형과 교수학습 방법과 관련된 특성을 조사하였다. 연구결과에 의하면 콘텐츠는 학습전에 개발되는 정형적 콘텐츠이며 운영은 학생 개인의 자율학습에 사용하는 콘텐츠 활용형으로 나타났다. 강의와 실습 외에 프로젝트가 학습활동의 하나로 사용되었지만 LMS와 웹 환경 등은 단순 기능의 활용에 국한되었다. 결론에서는 면대면 수업을 보강하는 주요한 방법으로 통합 활용형의 사용을 제안하면서 문제해결 유형 위주의 이러닝 활성화를 위한 조건 및 지원방안을 제시하였다.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• 한국수산과학회지
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• 제55권5호
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.195-202
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• 2023

This study is a comparative survey study conducted to explore the differences in learners' learning flow and learning satisfaction according to the non-face-to-face class operation methods implemented at universities. After implementing different class management methods for the same subject taught by the same instructor non-face-to-face for 15 weeks, each learning flow and learning satisfaction were compared and analyzed, and the collected data were analyzed with IBM SPSS 21.0. As a result of the study, learning flow was high in the order of lectures using real-time ZOOM and recorded lectures using self-studio(3.41±0.91, 3.28±1.01), and learning satisfaction was high in the order of lectures using real-time ZOOM and lectures using the automatic recording system of classes(3.40±0.80, 3.30±0.74). The item with the lowest score was the PPT audio recording lecture in both areas of learning flow and learning satisfaction(2.72±1.04, 1.73±1.04). Considering that system errors such as sound in the smart lecture environment operated for the first time in this study affected the research results, it is suggested that future research should be conducted by supplementing the corresponding part.

• 영재교육연구
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• 제7권2호
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• pp.47-67
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• 1997

Fourteen Special high schools for developing talents in Science, Foreign Languages, and Arts in Seoul Metropolitan city were analyzed in terms of their educational objectives, Students screening system, curriculum, teaching-learning methods, teachers, and consistency with higher education. Special high schools in talent area were more or less similar to each other in all the above aspects. However, special high schools in different talent areas were quite different in student screening, teaching-learning methods, teachers, and consistency with higher education. Public schools were more affluent than the private ones. Special high schools in Science, arts and Sports were providing individualized, activity oriented, process-oriented teaching-learning methods, while the Foreign languages high schools excercised teacher-centered, grammar oriented, lecture-focused teaching methods more. Special high schools in arts and sports could have consistency with higher education because the university focuses on talents in the specified field rather than academic scores. In conclusion, the schools have a great deal of rooms to improve in teaching-learning methods with which students can maximize their potential development. However, the special high schools were equipped with better learning environments than regular high schools in many aspects.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제25권4호
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• pp.162-172
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• 2021

This paper proposes stochastic methods to find an approximate solution for the L²-Wasserstein least squares problem of Gaussian measures. The variable for the problem is in a set of positive definite matrices. The first proposed stochastic method is a type of classical stochastic gradient methods combined with projection and the second one is a type of variance reduced methods with projection. Their global convergence are analyzed by using the framework of proximal stochastic gradient methods. The convergence of the classical stochastic gradient method combined with projection is established by using diminishing learning rate rule in which the learning rate decreases as the epoch increases but that of the variance reduced method with projection can be established by using constant learning rate. The numerical results show that the present algorithms with a proper learning rate outperforms a gradient projection method.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.

• ETRI Journal
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• 제44권4호
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.