• English Language & Literature Teaching
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• v.17 no.4
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• pp.375-393
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• 2011

Many aspects of e-러닝 and m-러닝 have been conducted in language learning settings while few studies have examined learners'psychological attitudes in both Internet-based languages learning environment. Althoughe-Learning and m-Learningin the content of language learningshares many common aspects, the study that particularly examinesEnglish learners' psychological attitudes from both learning environments has not been conducted. Thus, the purpose of this study is to investigate group difference between e-러닝 and m-러닝 in terms of characteristics of both learning environments, including Contextual Offer, Interactivity, Enjoyment, Usefulness, Easiness, Variety, Connectivity, Satisfaction, and Learning Performance. Results showed that even if there was little difference within and among groups in English learners' feelings, learners have different attitude on Enjoyment, Easiness, and Connectivity.

• Journal of The Korean Association of Information Education
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• v.17 no.2
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• pp.115-123
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• 2013

With the rapid growth of mobile technologies, the mobile learning has been gradually considered as a efficient and effective learning form because it breaks the limitations of learning time and space occurring in the traditional classroom learning. Therefore, this research aims how the learners' m-learning efficacy, ubiquity, perceived usefulness, and ease of use predict perceived learning achievement and satisfaction Participants were 144 11th-grade students in A high school in Kyungnam area, Korea. After studying science class using mobile devices, they responded the following surveys: m-learning efficacy, ubiquity, perceived usefulness, ease of use, and satisfaction. Multiple regression analyses with correlation were applied to this study as a data analysis method. Findings of this study include: (a) m-learning efficacy and perceived usefulness predicted learning satisfaction, (b) perceived usefulness and ubiquity predicted perceived learning achievement. These findings imply that m-learning efficacy, perceived usefulness, ubiquity should be valued to enhance learning outcomes in mobile learning class.

• Digital Contents
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• no.7 s.146
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• pp.59-61
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• 2005

정보통신 분야는 물론, 문화, 교육 등 생활 속 모든 분야에서 유비쿼터스라는 수식어가 따라다니고 있는 것을 많이 볼 수 있다. 관련 전문가들은 2010년경에는 유비쿼터스가 우리 생활에서 대중화가 될 것이며 이에 따른 부가가치 규모도 80조원에 이를 것으로 전망하고 있다. 교육 분야도 아날로그 환경 하에서 주변 환경 변화에 더디게 반응해 왔던 과거와 달리 최근 조금은 걱정스러울 정도로 IT의 신기술에 발 빠르게 적응하면서 e러닝, T러닝, M러닝, U러닝 등의 새로운 신조어들이 생겨나고 있다. 이에 진정 살아 있는 e러닝의 최종 모습이라고 불려지고 있는 유비쿼터스 학습(U러닝)에 대해 살펴보고, U러닝이 성공하기 위해서는 어떠한 요소들이 필요한가에 대해 살펴봤다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.317-317
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• 2021

효율적인 물관리를 위한 댐 유입량 대한 연구는 필수적이다. 본 연구에서는 다양한 머신러닝 알고리즘을 통해 40년동안의 기상 및 댐 유입량 데이터를 이용하여 소양강댐 유입량을 예측하였으며, 그 중 고유량과 저유량예측에 적합한 알고리즘을 각각 선정하여 머신러닝 알고리즘을 결합한 CombML을 개발하였다. 의사 결정 트리 (DT), 멀티 레이어 퍼셉트론 (MLP), 랜덤 포레스트(RF), 그래디언트 부스팅 (GB), RNN-LSTM 및 CNN-LSTM 알고리즘이 사용되었으며, 그 중 가장 정확도가 높은 모형과 고유량이 아닌 경우에서 특별히 예측 정확도가 높은 모형을 결합하여 결합 머신러닝 알고리즘 (CombML)을 개발 및 평가하였다. 사용된 알고리즘 중 MLP가 NSE 0.812, RMSE 77.218 m3/s, MAE 29.034 m3/s, R 0.924, R2 0.817로 댐 유입량 예측에서 최상의 결과를 보여주었으며, 댐 유입량이 100 m3/s 이하인 경우 앙상블 모델 (RF, GB) 이 댐 유입 예측에서 MLP보다 더 나은 성능을 보였다. 따라서, 유입량이 100 m3/s 이상 시의 평균 일일 강수량인 16 mm를 기준으로 강수가 16mm 이하인 경우 앙상블 방법 (RF 및 GB)을 사용하고 강수가 16 mm 이상인 경우 MLP를 사용하여 댐 유입을 예측하기 위해 두 가지 복합 머신러닝(CombML) 모델 (RF_MLP 및 GB_MLP)을 개발하였다. 그 결과 RF_MLP에서 NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, R2 0.859, GB_MLP의 경우 NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, R2 0.831로 CombML이 댐 유입을 가장 정확하게 예측하는 것으로 평가되었다. 본 연구를 통해 하천 유황을 고려한 여러 머신러닝 알고리즘의 결합을 통한 유입량 예측 결과, 알고리즘 결합 시 예측 모형의 정확도가 개선되는 것이 확인되었으며, 이는 추후 효율적인 물관리에 이용될 수 있을 것으로 판단된다.

• Journal of the Korea Convergence Society
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• v.1 no.1
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• pp.1-15
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• 2010

The Education has responsibility for predicting the social changes and cultivating global talent which the society needs. The ministry of education, science and technology in govern ment has been the concerns on social educational changes and thus built the '5 31 educational reform policy' in 1995 by the educational reform committee. As a solution of a social change, this paper reviews the three-phase educational information policies, and e-learning and u-learning which are the main technologies in educational information. Also, the technologies of e-learning can be divided into m-learning, t-learning, u-learning, r-learning, game-based learning according to the contents mass media. Among them, this paper introduces the concept of robot-learning, called r-learning, and compares it with u-learning.

• The Journal of Korean Association of Computer Education
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• v.15 no.6
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• pp.53-64
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• 2012

This study was to compare and analyze actual conditions and awareness of high school students' mobile learning. Survey was used as a research method and percentile, t-test and F-test were conducted for the statistical analyses. Results revealed that in the case of actual conditions on mobile device and mobile learning, slight differences were shown in various sub-variables, depending on independent variables (gender, grade, track); year 2010 can be the most important year for the mobile learning; high school students seldom utilize mobile learning in a small piece of time; mobile learning using Apps was not widespread yet. In the case of awareness of mobile learning, statistically significant differences were found in the use capacity of mobile devices, the increase of learning performance, and the continual interests of mobile devices.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.211-217
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• 2005

This study was about design and development of the Mobile Learning Evaluation Announced Systems, which could operate the e-learning and send the result of that to the parents. From this, teachers and parent can communicate with each other dynamically. Especially, this system does not simply present results of student' learning but provide the extra learning which can make up for student' wrong questions in the base of the evaluation standard. this paper suggests this total learning solution so that parents can perceive the state of their children's learning under the mobile learning environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1108-1111
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• 2011

최근 E-러닝 발전과 함께 U-러닝에 대한 관심이 집중되고 있으며 이와 관련된 다양한 관련 표준들이 채택되고 있다. 기존의 E-러닝 관련 기술들이 웹에 기반한 학습을 지향하였다면 U-러닝에서는 다양한 환경에서 학습자가 학습의 맥락을 이어가며 학습을 하는 것으로서 언제, 어디서나, 누구나 학습을 진행하여 생활의 학습화를 지원할 기술을 필요로 한다. 즉, U-러닝에서는 다양한 사용 환경에서 학습이 이루어지므로 사용 환경에 적합한 학습이 이루어져야 하고, 이를 위해 사용 환경 맞춤형 콘텐츠 적응화 기술이 필요하다. 크게 사용 환경 맞춤형 콘텐츠 적응화 기술은 다양한 단말기 정보를 포함한 사용 환경 정보를 표현할 수 있는 기술과 사용자의 사용 환경 정보를 분석하는 기술, 사용 환경에 적합한 콘텐츠를 구성하는 기술로 구성된다. 이에 본 연구에서는 지식서비스 USN 산업원천 기술개발 과제의 세부과제인 'U-러닝 환경 표준 및 표준 명세 개발 및 검증' 과제에서 콘텐츠 적응화를 위해 연구 개발된 사용 환경 정보를 표현하는 U-러닝 프로파일에 대하여 소개한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.517-523
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• 2019

PM₁₀ concentration is a spatiotemporal phenomenta and capturing data for such continuous phenomena is a difficult task. This study designed a model that enhances spatiotemporal resolution of PM₁₀ concentration levels using satellite imagery, atmospheric and meteorological sensor data, and multiple deep learning models. The designed deep learning model was trained using input data whose factors may affect concentration of PM₁₀ such as meteorological conditions and land-use. Using this model, PM₁₀ images having 15 minute temporal resolution and 30m×30m spatial resolution were produced with only atmospheric and meteorological data.