• 공학교육연구
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• 제22권6호
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• pp.12-20
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• 2019

The purpose of this study is to development of a teaching-learning model for active learning in engineering education. For this, the adequacy between educational objectives and active learning activities is verified and furthermore an "active learning teaching-learning model" is suggested. This suggested teaching-learning model is expected to supplement weakness of traditional lecture-type teaching-learning activity. Based on the literature review, first, the representative activities of active learning were derived. there are twenty active learning activities, which compose of five of individual learning activity, five of pair-learning activity and five of group-learning activity, and five of alternative- learning activity. In addition, a survey on adequacy between designed active learning activities and learning outcomes were conducted to ten educational experts. Lawshe's content validity calculation method was applied to analyze the validity of this study. Second, five teaching-learning principles, such as thinking, interaction, expression, reflection, and evaluation were derived to develop an "active learning teaching-learning model" which supplements lecture-type classes and then the "TIERA teaching-learning model" which consists of five stages was designed. Finally, based on the survey on educational experts, adequate active learning activities were proposed to apply in each stage of the "TIERA teaching-learning model" and as a result the TIERA model's active learning activities were developed. The result of this study shows that some activities of active learning are appropriate to induce high cognitive learning skills from the learners even in traditional lecture-type classrooms and therefore this study suggests meaningful direction to new paradigm of teaching-learning for engineering education. This study also suggests that instructors of engineering education can turn their traditional teaching-learning activities into dynamic learning activities by utilizing "active learning teaching-learning model".

• ETRI Journal
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• 제32권5호
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• pp.766-773
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• 2010

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권10호
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• pp.2650-2662
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• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• 한국응급구조학회지
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• 제23권2호
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• pp.61-73
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• 2019

Purpose: This study aimed to introduce active learning methods, including flipped, case-based, and team-based learning in an electrocardiography (ECG) course and to investigate outcomes and satisfaction with these methods. Methods: To identify the learning effect of active learning, pre-and post-academic self-efficacy was compared between the experimental and control groups. In the experimental group, pre-and post-knowledge and clinical performance regarding ECG were also assessed. In addition, class satisfaction was investigated after application of active learning methods in the experimental group. Data were collected from 84 paramedic students and analyzed using SPSS 22.0 (IBM, Armonk, NY, USA). Results: The experimental group showed significant improvement in post-academic self-efficacy and knowledge. The experimental group also showed high clinical performance (9.83 out of 10 in ECG checking ability and 9.63 out of 10 in ECG reading ability). The mean satisfaction score was 4.23 out of 5 (responses based on a Likert scale) in the experimental group. Conclusion: Active learning in an ECG course was found to be highly effective and satisfactory. Furthermore, paramedic students can enhance their accountability and judgement with team-based learning through free engagement in discussion.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• 실천공학교육논문지
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• 제14권2호
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• pp.295-303
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• 2022

본 연구는 고등 공학교육에 플립러닝을 Active Learning Classroom(ALC)에 적용한 사례와 고정식 강의실에 적용한 사례를 비교하는 것을 목적으로 하였다. 이를 위하여 ALC 플립러닝과 고정식 강의실 플립러닝 사례 간에 사전학습, 학업성취, 수업만족도가 어떻게 다른지 비교하였다. 연구결과, ALC 플립러닝이 고정식 강의실 플립러닝에 비해 사전학습 영상강의 시청을 더 많이 하였고, 중간시험 점수는 낮으나 기말시험 점수는 더 높았다. 또한 수업 요인, 교수자 요인, 전반적 만족도 문항으로 수업만족도를 확인한 결과, ALC 플립러닝이 고정식 강의실 플립러닝에 비해 모든 요인에서 높은 만족도를 보였다. 본 사례연구는 플립러닝 강의실 환경으로서 학습자중심의 수업에 용이하도록 구축한 학습공간인 ALC 환경이 강의실에서 학생 중심의 활발한 상호작용을 필요로 하는 플립러닝에 더 효과적임을 시사한다.

• 한국공간구조학회논문집
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• 제21권4호
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• pp.73-80
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• 2021

A mid-story isolation system was proposed for seismic response reduction of high-rise buildings and presented good control performance. Control performance of a mid-story isolation system was enhanced by introducing semi-active control devices into isolation systems. Seismic response reduction capacity of a semi-active mid-story isolation system mainly depends on effect of control algorithm. AI(Artificial Intelligence)-based control algorithm was developed for control of a semi-active mid-story isolation system in this study. For this research, an practical structure of Shiodome Sumitomo building in Japan which has a mid-story isolation system was used as an example structure. An MR (magnetorheological) damper was used to make a semi-active mid-story isolation system in example model. In numerical simulation, seismic response prediction model was generated by one of supervised learning model, i.e. an RNN (Recurrent Neural Network). Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm The numerical simulation results presented that the DQN algorithm can effectively control a semi-active mid-story isolation system resulting in successful reduction of seismic responses.

• 한국인터넷방송통신학회논문지
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• 제17권2호
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• pp.165-171
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• 2017

사람의 얼굴 표정을 실제 환경에서 인식하는 데에는 여러 가지 난이한 점이 존재한다. 그래서 학습에 사용된 데이터베이스와 실험 데이터가 여러 가지 조건이 비슷할 때에만 그 성능이 높게 나온다. 이러한 문제점을 해결하려면 수많은 얼굴 표정 데이터가 필요하다. 본 논문에서는 능동준지도 학습을 통해 다양한 조건의 얼굴 표정 데이터를 쉽게 모으고 보다 빠르게 성능을 확보할 수 있는 방법을 제안한다. 제안하는 알고리즘은 딥러닝 네트워크와 능동 학습 (Active Learning)을 통해 초기 모델을 학습하고, 이후로는 준지도 학습(Semi-Supervised Learning)을 통해 라벨이 없는 추가 데이터를 확보하며, 성능이 확보될 때까지 이러한 과정을 반복한다. 위와 같은 능동준지도 학습(Active Semi-Supervised Learning)을 통해서 보다 적은 노동력으로 다양한 환경에 적합한 데이터를 확보하여 성능을 확보할 수 있다.

• 한국간호교육학회지
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• 제20권4호
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• pp.577-586
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• 2014

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.

• Structural Engineering and Mechanics
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• 제83권2호
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.