• Asian Pacific Journal of Cancer Prevention
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• 제13권9호
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• pp.4593-4595
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• 2012

Introduction: Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. Methods: We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. Results: The mobile learning method had a significantly better effect on learning and created more interest in the subject. Conclusion: Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

• 한국컴퓨터정보학회논문지
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• 제18권12호
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• pp.217-223
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• 2013

본 논문은 한 대학에서 3년에 걸쳐 실시한 토익 모의고사를 바탕으로 4주간의 집중코스와 한 학기에 걸쳐 진행되는 상시적 토익코스의 점수 분포를 비교하고, 두 프로그램에 참여한 학생 그룹의 능력 뿐만 아니라 사용된 교수용 교재 및 장비를 살펴본다. 해당 대학은 여름방학과 겨울방학을 이용하여 4주간의 토익 집중코스를 개설하고 있으며, 참여 학생들은 오전 9시부터 오후 4시까지 수업에 참가한다. 또한 해당 대학은 매 학기동안 15주간의 상시적인 토익코스를 개설해서 월요일부터 금요일까지 매일 한 시간의 수업을 진행한다. 본 논문은 EFL/ESL 교육에서 교사가 교수 자료, 교수법 및 e-학습을 효과적으로 운용하는 것이 얼마나 중요한지를 보여준다. 상시적으로 개설되는 토익코스는 한 학기동안 75시간 수업이 진행되며, 4주간의 집중코스는 120시간 수업이 진행된다. 이 두 코스의 제한된 시간들은 학생들이 일상생활에서 의사소통을 가능하게 하는데 필요한 충분한 시간이라고 볼 수는 없다. 미래에 학생들이 영어 학습에서 성공적인 향상을 보이기 위해서는 효과적이고 질적으로 우수한 EFL/ESL 집중코스의 개발이 필수적이다.

• 디지털콘텐츠학회 논문지
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• 제11권4호
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• pp.561-569
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• 2010

본 논문은 학습자의 학습목적과 학습상황에 대한 정보를 제공하거나 사례를 검색하여 학습자에게 가장 적합한 학습정보를 제공하는 것을 목적으로 하였다. 현재 다양한 웹을 통한 학습시스템이 제공되어 지고 있으나 학습을 위한 이전 단계, 학습자에게 적합한 학습이 무엇인지에 대한 추론과정이 이루어지지 않고 있다. 따라서 본 논문에서는 일괄적인 학습 콘텐츠의 제공보다는 학습 콘텐츠, 학습 템플릿, 풍부한 학습콘텐츠의 재사용 및 학습자의 학습 능력에 따른 개별 학습을 위해 효과적인 맞춤형 학습 콘텐츠를 제공하기 위한 시스템을 설계하고자 한다.

• Journal of Information Science Theory and Practice
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• 제7권3호
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• pp.6-20
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• 2019

Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.

• 한국정보컨버전스학회논문지
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• 제5권2호
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• pp.59-65
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• 2012

국내 전문대학 우수교수학습센터의 홈페이지를 분석하였다. 우수교수학습센터란 한국전문대학교육협의회에서 2010년부터 3년간 선정한 9개의 교수학습센터를 말한다. 대체로 홈페이지의 구성은 센터소개, 교수지원, 학습지원, 이러닝지원, 서비스, 매체지원, 자료실, 커뮤니티 등으로 구성되어있으며, 전문대학 우수교수학습센터 홈페이지의 유사점과 차이점 및 특이점을 홈페이지 메뉴 및 활성화 정도에 따라 확인하고 분석한다. 현재 활성화를 위해 노력하고 있는 전문대학 교수학습센터들의 홈페이지가 가져야 할 기능들과 방향을 제시하고자 한다.

• Laboraroty Animal Research
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• 제34권4호
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• pp.317-328
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• 2018

Cognitive impairment responses are important research topics in the study of degenerative brain diseases as well as in understanding of human mental activities. To compare response to scopolamine (SPL)-induced cognitive impairment, we measured altered parameters for learning and memory ability, inflammatory response, oxidative stress, cholinergic dysfunction and neuronal cell damages, in Korl:ICR stock and two commercial breeder stocks (A:ICR and B:ICR) after relevant SPL exposure. In the water maze test, Korl:ICR showed no significant difference in SPL-induced learning and memory impairment compared to the two different ICRs, although escape latency was increased after SPL exposure. Although behavioral assessment using the manual avoidance test revealed reduced latency in all ICR mice after SPL treatment as compared to Vehicle, no differences were observed between the three ICR stocks. To determine cholinergic dysfunction induction by SPL exposure, activity of acetylcholinesterase (AChE) assessed in the three ICR stocks revealed no difference of acetylcholinesterase activity. Furthermore, low levels of superoxide dismutase (SOD) activity and high levels of inflammatory cytokines in SPL-treated group were maintained in all three ICR stocks, although some variations were observed between the SPL-treated groups. Neuronal cell damages induced by SPL showed similar response in all three ICR stocks, as assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, Nissl staining analysis and expression analyses of apoptosis-related proteins. Thus, the results of this study provide strong evidence that Korl:ICR is similar to the other two ICR. Stocks in response to learning and memory capacity.

• 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.

• 지능정보연구
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• 제25권4호
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• pp.53-65
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• 2019

영상 기반의 보안 시스템의 증가함에 따라 각 용도마다 다른 다양한 객체들에 대한 처리들이 중요해지고 있다. 객체 추적은 객체 인식, 검출과 같은 작업들과 함께 필수적인 작업으로 다뤄진다. 이 객체 추적을 달성하기 위해서 다양한 머신러닝이 적용될 수 있다. 성공적인 분류기로써 전체 에러율 최소화(total-error-rate minimization) 기반의 방법론이 사용될 수 있다. 이 전체 에러율 최소화 기반의 방법론은 오프라인 학습을 기반으로 하고 있다. 객체 추적은 실시간으로 처리하며 갱신해야하는 것이 필수적이므로 온라인 학습(online learning)을 기반으로 하는 것이 적합하다. 온라인 전체 에러율 최소화 방법론이 개발되었지만 점근적으로 재가중되는(approximately reweighted) 작업이 포함되어 에러를 누적시킬 수 있다는 단점이 있다. 본 논문에서는 정확하게 재가중되는(exactly reweighted) 방법론을 제안하면서 온라인 전체 에러율 최소화가 달성되었다. 이 제안된 온라인 학습 방법론을 객체 추적에 적용하여 총 8개의 데이터베이스에서 다른 추적 방법론들 보다 좋은 성능이 달성되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2527-2545
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• 2016

Measuring the similarity of given samples is a key problem of recognition, clustering, retrieval and related applications. A number of works, e.g. kernel method and metric learning, have been contributed to this problem. The challenge of similarity learning is to find a similarity robust to intra-class variance and simultaneously selective to inter-class characteristic. We observed that, the similarity measure can be improved if the data distribution and hidden semantic information are exploited in a more sophisticated way. In this paper, we propose a similarity learning approach for retrieval and recognition. The approach, termed as LDA-FEK, derives free energy kernel (FEK) from Latent Dirichlet Allocation (LDA). First, it trains LDA and constructs kernel using the parameters and variables of the trained model. Then, the unknown kernel parameters are learned by a discriminative learning approach. The main contributions of the proposed method are twofold: (1) the method is computationally efficient and scalable since the parameters in kernel are determined in a staged way; (2) the method exploits data distribution and semantic level hidden information by means of LDA. To evaluate the performance of LDA-FEK, we apply it for image retrieval over two data sets and for text categorization on four popular data sets. The results show the competitive performance of our method.

• 공학교육연구
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• 제13권1호
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• pp.78-86
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• 2010

훈련과정종합정보망은 훈련과 관련된 모든 훈련 과정을 검색할 수 있을 뿐만 아니라, 수강 신청, 학습, 훈련 성과 분석, 훈련 이력 관리까지를 원스톱으로 처리할 수 있는 통합 학습시스템이다. 이와 같은 훈련과정종합정보망을 개발하고 운영함으로써 훈련생의 자기 주도적 훈련 선택권의 강화, 훈련 과정의 다양화 및 경쟁을 통한 훈련의 질 제고를 할 수 있다. 훈련과정종합정보망의 운영을 위하여서는 적극적인 홍보와 정확하고 신뢰도 높은 정보 제공 서비스, 그리고 풍부한 콘텐츠와 이용자 개인과 기관에 대한 관리가 필요하며, 안정적인 재정지원과 개인정보보호, 훈련과정에 대한 저작권보호 등이 이루어져야 한다.