• Educational Technology International
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• 제8권2호
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• pp.53-71
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• 2007

This study identified the effectiveness of e-learning by comparing learning outcome in conventional face-to-face lecture with the selected e-learning methods. Two e-learning contents (animation based and video based) were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which is http://www.enaged.co.kr. Fifty-four Korean agricultural high school students were randomly assigned into three groups (face-to-face lecture, animation based e-learning, and video based e-learning group). The students of the e-learning group logged on the LMS in school computer lab and completed each e-learning. All students were required to take a pretest and posttest before and after learning under the direction of the subject teacher. A one-way analysis of covariance was administered to verify whether there was any difference between face-to-face lecture and e-learning in terms of students' learning outcomes after controlling the covariate variable, pretest score. According to the results, no differences between animation based and video based e-learning as well as between face-to-face learning and e-learning were identified. Findings suggest that the use of well designed e-learning could be worthy even in agricultural education, which stresses hands-on experience and lab activities if e-learning was used appropriately in combination with conventional learning. Further research is also suggested, focusing on a preference of e-learning content type and its relationship with learning outcome.

• 한국지역사회생활과학회지
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• 제28권1호
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• pp.115-130
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• 2017

This study was conducted to examine the relationship between pre-service early childhood teacher's motivations of choosing a teaching profession, educational belief, and self-directed learning readiness. The sample included 308 early childhood education major students, and the data were collected using the Modified Orientation to Teach Survey (MOTS), Teaching-belief type scale, and self-directed learning readiness scale. A statistical analysis included correlation analysis and stepwise multiple regression analysis. The results were as follows: 1) analysis of the relationship between pre-service early childhood teacher's motivations of choosing a teaching profession, educational belief, and self-directed learning readiness conveys that intellectual stimulation and self-directed learning had the strongest relationships while nature of work had the weakest. For educational belief and self-directed learning readiness, maturationism and interactionism showed significantly positive correlations while behaviorism displayed a negative correlation. Behaviorism had a significantly negative correlation with openness for challenge, a sub-factor of self-directed learning. 2) Analysis of the effect of pre-service early childhood teacher's motivations of choosing a teaching profession and educational belief on self-directed learning readiness indicates that pre-service early childhood teacher's motivations of choosing a teaching profession had a stronger effect on self-directed learning. These results suggest the following: successful performance as an early childhood teacher not only requires receiving institutionalized education but also self-directed learning while working as an early childhood teacher.

• 한국수학교육학회지시리즈A:수학교육
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• 제42권4호
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• pp.541-559
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• 2003

This paper considers learning and teaching of mathematical analysis in teachers college. It concentrates on showing a way how learning and teaching of mathematical analysis should be considered for mathematical teachers training. It is composed of five chapters including Chapter I as an introduction and Chapter Vasa concluding remarks. Chapter II deals with goal and contents of global mathematical analysis. The main Chapter, named Chapter III, demonstrates exhibition of contents, way of operations, and contents of teaching and learning of mathematical real analysis. Chapter IV shows an example of learning and teaching of mathematical real analysis concerning to fixed points and approximate solutions.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• International Journal of Contents
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• 제16권3호
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• pp.18-32
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• 2020

The aim of this study was to perform a meta-analysis of the learning outcomes of immersive learning technologies in English language teaching (ELT). This study examined 12 articles, yielding a total of 20 effect sizes. The Comprehensive Meta-Analysis (CMA) program was employed for data analysis. The findings revealed that the overall effect size was 0.84, implying a large effect size. Additionally, the mean effect sizes of the dependent variables revealed a large effect size for both the cognitive and affective domains. Furthermore, the study analyzed the impact of moderator variables such as sample scale, technology type, tool type, work type, program type, duration (sessions), the degree of immersion, instructional technique, and augmented reality (AR) type. Among the moderators, the degree of immersion was found to be statistically significant. In conclusion, the study results suggested that immersive learning technologies had a positive impact on learning in ELT.

• 산업경영시스템학회지
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• 제5권6호
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제26권4호
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• pp.333-348
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• 2023

인공지능 기반의 수학 디지털교과서의 가장 핵심적인 기능으로 여겨지는 개별 맞춤형 교수·학습이 실현되기 위해서는 개별 학생의 여러 가지 특성 요인에 대한 명확한 분석과 진단이 가장 관건이다. 본 연구에서는 수학 AI 디지털교과서에서 개별 맞춤형 학습 진단을 위한 분석 요인과 도구, 데이터 수집·분석을 위한 구축 모델을 도출하였다. 이를 위하여 최근 교육부의 AI 디지털교과서 적용 계획에 따른 수학 AI 디지털교과서에 대한 요구, 개별화 맞춤형 학습과 이를 위한 데이터에 대한 선행 연구, 수학 디지털플랫폼에서 학습자 분석에 대한 요인 등이 검토되었다. 연구 결과, 연구자는 학생 개인별로 수집해야 할 데이터로 학습 분석을 위한 요인으로 학습 준비도, 과정 및 수행도, 성취도, 취약점, 성향 분석을 위한 요인으로 학습 지속 시간, 문제해결에 걸린 시간, 집중도, 수학학습 습관, 정서 분석을 위한 요인으로 자신감, 흥미, 불안, 학습의욕, 가치 인식, 태도 분석을 위한 요인으로 자기 관리, 학습 전략으로 정리하였다. 또한, 이러한 요인에 대한 데이터 수집 도구로, 문제에 대한 정오 데이터, 학습 진도율, 학생 활동에 대한 화면 녹화 자료, 이벤트 데이터, 시선 추적 장치, 자기 응답 설문 등을 제안하였다. 최종적으로 이러한 요인들을 학습 전, 중, 후로 시계열화한 데이터 수집 모델이 제안되었다.

• 예술인문사회 융합 멀티미디어 논문지
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• 제9권3호
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• pp.51-59
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• 2019

외국어 학습이론에서 자기조절학습, 교과흥미, 학습몰입, 자기효능감은 주요 구성개념들로 연구되었다. 이에 본 연구에서는 이 구성개념 간 상호 미치는 영향관계를 파악하기 위하여 상기 구성개념들을 토대로 연구모형을 설정하고 SEM분석을 통하여 그 내용을 파악하고자 하였다. 경남에 위치한 중학교 학생들을 대상으로 수집한 자료를 분석한 결과는 첫째, 교과흥미는 학습몰입에 긍정적 영향을 미치는 것으로 나타났다. 둘째, 교과흥미는 자기효능감에 긍정적 영향을 미치고 또한 자기조절학습에 긍정적 영향을 미쳤으며, 셋째, 학습몰입은 자기효능감에 긍정적 영향을 미치는 것으로 나타났고 넷째, 자기조절학습은 자기효능감에 긍정적 영향을 미쳤고 다섯째, 자기조절학습은 학습몰입에 긍정적 영향을 미쳤다. 상기 분석결과를 살펴보면 영어교육의 경우 학생들의 교과흥미는 학습몰입을 가져오고 자기효능감을 증진시키며 자기조절학습 능력을 증진하여 결국은 성과로 이어지게 된다.

• East Asian mathematical journal
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• 제38권2호
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• pp.187-214
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• 2022

This study compares the functions of the Learning Management Systems (LMS) in three widely used Edu-Tech platforms, that support students' individualized learning by using the learning characteristics of the students. The rapid advances in artificial intelligence (AI) are broadening their impacts in the education industry, and play a broad role in supporting student learning. In many countries, online classes have become a norm due to the COVID-19 crisis, and the demand for Edu-Tech in classes has increased rapidly. As a result, many countries, including South Korea, are now preparing and implementing various policy measures to adopt Edu-Tech in the class setting. Therefore, in this study, we analyze and compare the structures and characteristics of the three widely used Edu-Tech platforms that support individualized mathematics learning. In particular, we compare the LMSs of the three platforms by considering the elements such as learning design, learning management, learner analysis, learning result analysis, and student management functions. The results of this study give implications in the future directions to take on how to build Edu-Tech platform models that promote students' individualized mathematics learning in public education.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).