• 한국환경복원기술학회지
• /
• 제24권6호
• /
• pp.89-96
• /
• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• Educational Technology International
• /
• 제20권2호
• /
• pp.223-255
• /
• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• 대한가정학회지
• /
• 제51권2호
• /
• pp.253-261
• /
• 2013

The purpose of this study was to investigate the moderating effects of parental monitoring on the relationship between children's dependency on mobile phones and control of learning behavior. The data came from the 2010 Korean Children and Youth Panel (N = 1,609) conducted by the National Youth Policy Institute. The analysis method used was Structural Equation Modeling by using SPSS 17.0 and AMOS 7.0. To test the significant moderating effects, Ping's two-step technique, which is free from the requirement of nonlinear constraints, was used. Our results demonstrated that children's dependency on mobile phones had negative effects on control of learning behavior, and the interaction effects between such dependency and parental monitoring affected the control of learning behavior. Thus, these results proved the moderating effects of parental monitoring in the control of learning behavior. This study suggests that parental monitoring buffers against having difficulties to control and adjust one's behavior associated with control of learning behavior, which is affected by the dependency on mobile phones among children. We discussed that the risks of children's dependency on mobile phones and parental monitoring should be acknowledge as a significant protective factor.

• 한국과학교육학회지
• /
• 제17권1호
• /
• pp.103-113
• /
• 1997

The purpose of this study was to identify the preferable learning strategies in science learning and to find out the relationship between these strategies and scientific achievement of students in the secondary school. The learning strategies were tested with two categories, self-focused and work-focused learning. The four types of learning strategies in science learning were analyzed, and they were named to organization, monitoring, elaboration and memorization strategies, which were defined by GSSS test. In the self-focused learning, the organization and monitoring strategies were preferred to the elaboration and memorization strategies. Middle school students had a preference for memorization strategy (p=0.000), whereas high school students had a preference for monitoring strategy (p=0.015). In the case of organization strategy, female groups were preferable to male groups (p=0.027). In the second form of learning types, work-focused learning, the memorization strategy was the same preference as organization and monitoring strategies in the secondary school students, especially the male groups of high school students. The preference of elaboration strategy was relative lower compared with that of self-focused learning type. Middle school students had a preference for monitoring strategy (p=0.001), whereas high school students had a preference for elaboration strategy (p=0.001). The difference of each preference between male and female groups was not shown. From the analysis of correlation between learning strategy and scientific achievement, it showed that the monitoring strategy was commonly correlated with scientific achievement. In the self-focused learning, elaboration and organization strategies were correlated with scientific achievement in high school students (p<0.05). In the work-focused learning, memorization strategy was correlated with scientific achievement in middle school students, especially in male groups (p<0.05).

• Smart Structures and Systems
• /
• 제24권5호
• /
• pp.567-585
• /
• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• 산경연구논집
• /
• 제9권8호
• /
• pp.35-40
• /
• 2018

Purpose - The purpose of this study is to conduct a monitoring study on the learning process of e-learning contents. This study has two research objectives. First, by conducting monitoring research on the learning process, we aim to explore the implications for content development that reflects future student needs. Second, we want to collect empirical basic data on the estimation of appropriate amount of learning. Research design, data, and methodology - This study is a case study of learner's learning process in e-learning. After completion of the study, an in-depth interview was made after conducting a test to measure the total amount of cognitive load and the level of engagement that occurred during the learning process. The tool used to measure cognitive load is NASA-TLX, a subjective cognitive load measurement method. In the monitoring process, we observe external phenomena such as page movement and mouse movement path, and identify cognitive activities such as Think-Aloud technique. Results - In the total of three research subjects, the two courses showed excess learning time compared to the learning time, and one course showed less learning time than the learning time. This gives the following implications for content development. First, it is necessary to consider the importance of selecting the target and contents level according to the level of the subject. Second, it is necessary to design the learner participation activity that meets the learning goal level and to calculate the appropriate time accordingly. Third, it is necessary to design appropriate learning support strategy according to the learning task. This should be considered in designing lessons. Fourth, it is necessary to revitalize contents design centered on learning activities such as simulation. Conclusions - The implications of the examination system are as follows. First, it can be confirmed that there is difficulty in calculating the amount of learning centered on learning time and securing objective objectivity. Second, it can be seen that there are various variables affecting the actual learning time in addition to the content amount. Third, there is a need for reviewing the system of examination of learning amount centered on 'learning time'.

• 한국안전학회지
• /
• 제33권6호
• /
• pp.42-49
• /
• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• 한국가정과교육학회지
• /
• 제26권3호
• /
• pp.113-131
• /
• 2014

본 연구는 남녀 중학생의 영어 수학 학업성취도에 대한 부모의 사교육비 및 감독 애정, 자녀의 학습가치, 자기조절학습능력의 영향력을 알아보고자 수행되었다. 본 연구의 자료는 한국 아동 청소년 패널에 참여한 중학교 3학년 자료를 활용하여 수집되었으며, 현재 우리나라 사교육 참여율이 가장 높은 과목이 수학과 영어라는 현실을 고려하여 수학과 영어과목의 사교육 경험이 있는 중학생 1,123명과 그 부모가 연구대상으로 선정되었다. 조사도구는 영어 수학 학업성취도, 월평균 사교육비, 부모의 감독 애정, 중학교 자녀의 학습가치, 중학교 자녀의 자기조절학습능력을 측정하기 위한 자기기입식 설문지이었다. 수집된 자료는 기술통계와 상관분석, 위계적 회귀분석을 통해 분석되었다. 본 연구의 주요결과는 다음과 같다. 첫째, 중학생의 성별과 상관없이 부모의 월평균 사교육비 수준과 부모의 감독수준이 높을수록, 중학생이 학습에 대해 긍정적인 가치를 부여하는 정도와 자기조절학습능력 수준이 높을수록 영어 수학 학업성취도가 높아지는 경향을 보였다. 둘째, 남녀 중학생 모두 자기조절학습능력이 영어 수학 학업성취도를 가장 잘 예측하는 변인으로 제시된 반면 그 밖에 남자 중학생의 경우 학습가치 및 부모 감독의 순으로, 여자 중학생의 월평균 사교육비만이 영어 수학 학업성취도에 영향을 주는 것으로 나타났다. 또한 남녀 중학생 모두 사교육비가 영어 수학 학업성취도에 미치는 영향에 있어 부모의 감독 애정 및 중학생의 학습가치, 자기조절학습능력의 조절효과는 발견되지 않았다. 마지막으로, 본 연구결과를 토대로 중학생 자녀의 학업성취도에 대한 부모 및 '가정교과'의 역할을 제안하였다.

• Journal of Information Technology Applications and Management
• /
• 제13권4호
• /
• pp.197-214
• /
• 2006

This study explores factors to facilitate web-based collaborative learning and the effect of learning, based on the PBL(Problem Based Learning) from the constructivist approach in e-learning. A research model, using the key variables such as situations, interactions, and systems, was developed. In order to test this proposed model, experimental design and post-survey was conducted to the learners who took on-line and off-line course with team project. In the research model, situation category was divided into instructor's support, unstructured problem, and self-directed learning. Interaction category was divided into three factors; 'interaction between learners', 'interaction between learner and instructor', and 'interaction between learner and technology'. System category was divided into.monitoring and incentives. As a result, it was found that collaborative learning can be improved by situations, interactions, and systems, and the effectiveness of learning can be improved by situations and interactions in PBL.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.