• 스마트미디어저널
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• 제8권1호
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• pp.67-73
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• 2019

본 연구는 4차 산업혁명 시대 직업교육에 대응하기 위하여 핵심역량 중 직무능력을 향상 시킬 수 있는 액티브러닝과 퍼실리테이션(Active Learning&Facilitation) 융합 교육프로그램을 연구하였다. 연구방법으로는 '문제해결 능력'을 키워주는 Active Learning의 장점과 창의적 아이디어 발상을 위한 Facilitation의 장점을 융합하여 앱 디자인 교과목에 적용하고 학생들의 만족도 조사를 통해 교육의 효과성을 검증하였다. 모바일 환경에 익숙한 학생의 특성을 반영하여 앱 콘텐츠를 기획하고, 데이터 시각화를 위한 UI 디자인을 수행하여 S/W분야의 스킬을 강화할 수 있도록 하였다. 모든 수행과정은 팀 프로젝트(PBL)로 운영되었다. 연구결과 액티브러닝과 퍼실리테이션 융합 교육프로그램은 전문대학교육이 요구하는 foundation skills, competencies을 향상할 수 있었다. 더불어 산업현장과 대학 간 교육격차를 줄일 수 있을 것으로 기대한다.

• 산경연구논집
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• 제10권2호
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• pp.25-37
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• 2019

Purpose - The goal of this study is to analyze the differences in education performances between students of the government's financial support program and those who do not receive support at a local university in Korea. Research design, data, and methodology - The questionnaire used was NASEL. NASEL is considered a highly suitable survey tool for professors, courses, and performances in Korean universities. The 290 students who participated and 44 students do not participate in the financial support program were surveyed for 10 days. The characteristics of students were investigated by frequency analysis and technical statistics. The analysis of student collective characteristics used independent t and f-tests,and one-way ANOVA with IBM SPSS Statistics 22.0 for statistical purposes. Results - The p-value of the group receiving financial support and the group without financial support in active-collaborative learning is 0.167. The p-value of the economically supported group and the non-supported group of the faculty-student interaction is 0.281. The confidence coefficient of the active-collaborative learning questionnaire is 0.861. The reliability coefficient of the questionnaire for the faculty-student interaction questionnaire is 0.871. Conclusions - There are no clear differences in active-collaborative learning and faculty-student interaction between participating and non-participating students in the economic program.

• Educational Technology International
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• 제12권1호
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• pp.67-94
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• 2011

The major concern of e-learning environment design is to create and improve artifacts that support human learning. To facilitate effective and efficient learning, e-learning environment designers focused on the contemporary information technologies. Web 2.0 services, which empower users and allow the inter-transforming interactions between users and information technologies, have been increasingly changing the way that people learn. By adapting these Web 2.0 technologies in learning environment, educational technology can facilitate learners' abilities to personalize learning environment. The main purpose of this study is to conceptualize comprehensively constructs for understanding the inter-transforming relationships between learner and learning environment and mutable learning environments' impact on the process through which learners learn and strive to shape their learning environment. As results, this study confirms conceptualization of four constructs by incorporating aspects of design that occur in e-learning environments with Web 2.0 technologies. First, learner-designer refers to active and intentional designer who is tailoring an e-learning environment in the changing context of use. Second, learner's secondary design refers to learner's design based on the primary designs by design experts. Third, transactional interaction refers to learner's inter-changeable, inter-transformative, co-evolutionary interaction with technological environment. Fourth, trans-active learning environment refers to mutable learning environment enacted by users.

• 대한음성학회지:말소리
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• 제66호
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• pp.87-103
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• 2008

We describe a new active learning method in conditional random fields (CRFs) framework for keyphrase extraction. To save elaboration in annotation, we use diversity and representative measure. We select high diversity training candidates by sentence confidence value. We also select high representative candidates by clustering the part-of-speech patterns of contexts. In the experiments using dialog corpus, our method achieves 86.80% and saves 88% training corpus compared with those of supervised method. From the results of experiment, we can see that the proposed method shows improved performance over the previous methods. Additionally, the proposed method can be applied to other applications easily since its implementation is independent on applications.

• International Journal of Contents
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• 제12권4호
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• Educational Technology International
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• 제18권2호
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• pp.193-213
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• 2017

This study aimed to explore online learning profiles of in-service teachers in South Korea, focusing on video lecture and discussion activities. A total of 269 teachers took an online professional development course for 14 days, using an online learning platform from which web log data were collected. The data showed the frequency of participation and the initial participation time, which was closely related to procrastinating behaviors. A cluster analysis revealed three online learning profiles of in-service teachers: procrastinating (n=42), passive interaction (n=136), and active learning (n=91) clusters. The active learning cluster showed high-level participation in both video lecture and discussion activities from the beginning of the online course, whereas the procrastinating cluster was seldom engaged in learning activities for the first half of the learning period. The passive interaction cluster was actively engaged in watching video lectures from the beginning of the online course but passively participated in discussion activities. As a result, the active learning cluster outperformed the passive interaction cluster in learning achievements. The findings were discussed in regard to how to improve online learning environments through considering online learning profiles of in-service teachers.

• 한국공간구조학회논문집
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• 제21권1호
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• 융합정보논문지
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• 제10권8호
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• pp.161-172
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• 2020

본 연구는 학습자의 능동적인 학습활동을 촉진하여 학습의 질을 제고하기 위해 설계된 ALC(Active learning classroom) 수업에 대한 학습자의 인식을 살펴보는 것을 목적으로 한다. 본 연구의 목적을 달성하기 위해 수도권 소재 A대학 71명의 학생(ALC 수업 43명, 일반교실 수업 28명)을 대상으로 설문조사를 실시하고, 교실 구성요소 간의 관계, 교수자의 수업전문성, 교실의 사회·문화적 환경, 심리·정서적 환경에 대한 인식을 비교하였다. 주요 연구 결과로는 첫째, ALC 수업 학생들은 교수·학습과 물리적 학습환경의 관계를 보다 긍정적으로 인식하는 것으로 나타났다. 둘째, ACL 수업 학생들은 ALC 환경을 능숙하게 다루는 측면에서의 교수자의 전문성을 긍정적으로 인식하는 것으로 나타났다. 셋째, ALC 수업 학생들은 교수자와의 비형식적 관계가 보다 촉진되었다고 인식하였으나 심리·정서적 측면에서의 만족도 및 몰입은 차이가 없는 것으로 나타났다. 본 연구는 새로운 학습공간 설계에 있어 교수·학습활동 운영을 위한 실제적인 시사점을 제공할 것으로 기대한다.

• Smart Structures and Systems
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• 제29권1호
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.