• 정보교육학회논문지
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• 제17권4호
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• pp.497-506
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• 2013

본 연구의 목적은 스마트 테크놀로지를 활용한 팀 기반 학습(Smart technology-enhanced Team-Based Learning, S-TBL) 모형 설계를 위한 디자인 원칙과 S-TBL의 개요, 절차 및 활동을 개념화하는 것이다. 이를 위해 기존의 팀 기반 학습(Team-Based Learning, TBL) 모형을 기반으로 모바일 테크놀로지, 협력학습, 문제해결학습과 다양한 평가 모형들을 종합한 학습 모형을 설계하였다. 기존의 TBL 모형을 기반으로 스마트 테크놀로지 학습 환경에서 적용 가능한 학습 모형을 설계함에 있어 1) 학습 자원, 평가 도구, 문제해결상황과 문제해결과정을 통합하는 총체적인 학습 환경을 제공하고, 2) 팀 구성원 간 및 교수자와 학습자 간 협력과 커뮤니케이션을 증대시킬 수 있는 환경 개발에 중점을 두었다. 이러한 S-TBL 모형은 1) 개별 학습과 협력적인 팀 학습을 통합하고, 2) 개념 학습과 문제해결 및 비판적 사고력 신장을 위한 학습을 통합하며, 3) 개별 평가와 그룹 평가를 통합하고, 4) 자기주도적 학습과 강의식 설명 학습을 통합하고, 5) 개인적 성찰과 산출물의 공유를 통합할 수 있도록 설계되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• 정보교육학회논문지
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• 제9권1호
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• pp.79-86
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• 2005

컴퓨터와 통신 기술이 발전함에 따라 네트워크를 통한 일반 사용자들의 컴퓨터 활용 빈도와 요구하는 데이터의 양이 급격히 증가되었다. 이에 따라 최근의 교육 시스템들은 정보의 활용성을 향상시키기 위하여 이질적인 시스템들을 의미상으로 연결하고 있다. 따라서 최근의 웹 기반 교수-학습은 학습자 스스로 학습 내용, 학습 시간 및 학습 순서를 선택하고 조직하는 유비쿼터스 학습방향으로 나아가고 있다. 즉, 학습자 개개인의 특성(선수 지식, 학습 양식, 흥미, 관심)에 맞는 적응적인 교수-학습 환경을 제공하는 방향으로 변화되고 있다. 본 논문은 유비쿼터스 학습 환경에서 다양한 분산정보의 통합을 위하여 사용자들이 요구하는 학습내용을 각 지역서버의 자치성을 유지하면서 효과적으로 학습하기 위한 미디에이터내의 처리방법에 대해 제안한다. 또한 과거와 최근의 학습내용의 활용형태가 다양하게 변할 수 있으므로 시간에 따른 감쇄율을 활용빈도에 적용하여 최근의 활용빈도의 변화에 민감하게 반응하고 활용형태의 변화에 따라 적응적으로 학습내용을 사용할 수 있는 방법을 제안한다.

• 컴퓨터교육학회논문지
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• 제7권6호
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• pp.69-76
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• 2004

e러닝을 교육과 학습을 위하여 e비즈니스 기술 및 서비스를 사용하는 응용프로그램이다. 이는 원격지 자원과 서비스에 접근을 수월하게 함으로서 교육의 질을 높이기 위한 새로운 멀티미디어 및 인터넷 기술을 사용한다. 본 논문은 실제 TOEFL CBT에 기반을 두어 신중하게 설계되고 구현된 인터넷기반의 시험 시스템에 대하여 기술한다. 본 시스템은 콘텐츠 전달 기술, 컴퓨터 적응형 시험 알고리즘, 리뷰엔진으로 구성되어 있다. 본 논문에서는 컴퓨터기반 시험 시스템을 설계 및 구현 시 고려사항들에 대하여 서술한다.

• 한국IT서비스학회지
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• 제14권4호
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• pp.237-256
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• 2015

The purpose of this study is to develop the eTextbook and to explore the usability of the eTextbook for accelerating mobile learning in university. This study was primarily based on needs analysis of students, and eTextbook was created based on digital publishing through Adobe DPS. The major design principles of the eTextbook based on previous studies were as follow. First, eTextbook should deliver learning contents in a simple and systematic way. Second, eTextbook should induce student's flows by providing segmented learning contents and various learning resources. Third, eTextbook should expand information accessibility by providing a wide variety of multimedia functions. The development principles of eTextbook have been developed based on cognitive psychology. Location and function of the link or icon used for eTextbook have been developed on the basis of the principles of cognitive psychology. The main development principles were Coherence, Signaling, Redundancy, Segmentation, Multimedia principle, and so on. In order to examine usability of the developed eTextbook, experts' and learners' reactions were evaluated. The primary responses of learners are that the eTextbook increased the learning accessibility and provided various multimedia factors, and thus increased the learning flows in the class.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2101-2121
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• 2020

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos' repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users' interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users' interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.

• 한국콘텐츠학회논문지
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• 제7권1호
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• pp.155-160
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• 2007

SCORM에서 학습 자원은 공유 가능 콘텐츠 객체 또는 하나 이상의 애셋(asset)으로 구성된다. 이러닝 환경에서 애셋을 신속하게 검색하고 재사용할 수 있는 저장 방법이 필요하지만 아직 관련된 연구가 거의 없다. 본 논문에서는 클러스터에 기반을 둔 애셋의 저장 방법을 제안하고 수학적으로 정형화하여 정의하였다. 또한, 애셋을 평가하는 기준과 각 애셋을 평가하는 절차를 제시하였다. 실험을 통하여 제안한 클러스터저장 방법에 기반을 둔 검색이 텍스트 카테고리화에 기반한 검색보다 처리시간과 정확도 측면에서 성능이 우수함을 보였다.

• 패션비즈니스
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• 제16권2호
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• pp.1-11
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• 2012

The purpose of this study was to examine the effects of UBL (Ubiquitous basedlearning) on fashion and consumer behavior course. Thirty-one undergraduate university students completed a 15-week capstone course in a clothing and textiles department. About sixteen percent students were majoring in liberal arts and sixty-three percent of the participants were majoring in the clothing and textiles. Mainly, the participants were junior and senior undergraduate students. The participants demonstrated positive attitude toward the UBL (Ubiquitous based-learning) on fashion and consumer behavior course. The results showed that seventy-seven percent of the participants have more opportunities to handle multi-media resources using social network and social media. Eighty percent of the participants have been developed of communication skills. Seventy-one percent of the participants were helped to learn foreign language skills. Overall, most of the participants were satisfied that their presentation skill was improved in class and they had willing to recommend the class to other students for the future.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.