• 정보교육학회논문지
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• 제11권2호
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• pp.231-241
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• 2007

본 연구는 창의적 문제해결 능력 향상을 위한 알고리즘 학습도구로서 로봇을 이용한 프로그래밍 학습방법을 제안하는데 목적이 있다. 이를 위해 30차시 분량의 로봇 프로그래밍 교육과정과 교재를 개발하였으며, 초등학생 6학년을 대상으로 30차시를 학습시킨 후 평가하였다. 각 차시별 학습결과 산출물 중심으로 성취수준을 평가한 결과, 학습자들이 교육과정 내용을 대부분 이해한 수준으로 분석되었다. 이러한 결과는 개발한 교육과정과 교재가 초등학생들에게 충분히 공감하고 실천 가능하도록 구성되었다고 판단된다. 본 연구에서의 실행 경험을 통해 초등학교에서 로봇 프로그램 학습이 창의적 알고리즘 학습도구로 성공할 수 있는 가능성을 확인하게 되었다.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• Asia Marketing Journal
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• 제21권4호
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• pp.77-89
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• 2020

Population-level learning has traditionally been used to provide an explanation for the underlying mechanism of industry change. But it has yet to examine the impact on strategic decisions such as market entry. This conceptual paper aims to provide an insight into how population-level learning affects entry likelihood by acting as a tool for interpreting population-level changes. We study this in the context of the fast-paced mobile gaming industry where population-level information is salient and develop a set of propositions with regard to the likelihood of entry.

• 디지털콘텐츠학회 논문지
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• 제10권1호
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• pp.73-85
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• 2009

본 논문은 학습관리시스템(LMS), 학습콘텐츠관리시스템(LCMS), 학습지원도구를 포함한 기업 e-Learning 시스템과 운영을 연계한 통합적 형태의 가이드라인을 제시하고 있다. 본 연구의 목적은 국내 e-Learning 시스템 현황 및 특성을 실태조사를 통해 파악하고, 관련된 국내 e-Learning 시스템의 다양한 질 관리 방법과 운영자, 교.강사, 학습자에게 적합한 실행 전략을 제시하고자 한다. 이를 통해 e-Learning을 새롭게 도입하거나 업그레이드 하고자 하는 교육훈련기관에게 e-Learning 시스템을 구축하고 운영체계를 수립하는데 도움을 줄 수 있을 것이다.

• 한국콘텐츠학회논문지
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• 제15권6호
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• pp.616-631
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• 2015

본 연구는 3세~5세를 대상으로 하는 모바일 AR(Augmented Reality, 증강현실) 기반의 "낱말카드" 교구를 설계 제안하고 있다. 본 연구에서는 동기유발과 몰입도 향상을 목적으로 학습구조를 도출하고, 도출된 학습구조를 적용한 사용자 인터페이스를 설계해 이를 구현한다. 그리고 콘텐츠 제작을 지원하는 콘텐츠 관리도구를 설계하여, 교수자가 여러 학습자의 콘텐츠를 관리하기 용이하도록 제안하고 있다. 이를 위해 관련 연구 분석, 학습자용 AR 유아 언어교육 콘텐츠 설계, 교수자용 AR 유아 언어교육 콘텐츠 관리도구 설계, AR기반 언어학습도구의 효과검증의 4단계로 연구를 진행하였다. 본 연구에서 제안하고 있는 AR 유아 언어 교육 콘텐츠는 몰입도 향상과 동기유발을 위한 학습구조로 설계되었고, AR 교육 콘텐츠 갱신의 어려움을 극복하기 위하여 일반 교수자가 손쉽게 AR 타겟과 교육 내용을 등록 관리할 수 있는 교수자용 AR 콘텐츠 관리 도구를 포함하고 있다. 제안 콘텐츠를 통한 학습 몰입도 향상 검증 및 콘텐츠 설계 확장을 위한 사전 실험으로 3~4세 유아 6명을 대상으로 한 실험 수업을 진행하였다. 실험결과 몰입도 향상 효과를 검증할 수 있었고, 교수자의 설문의견을 통하여 유아를 위한 차별화된 인터랙션의 설계의 필요성을 도출하였다.

• 한국콘텐츠학회논문지
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• 제16권2호
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• pp.546-557
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• 2016

치의학 분야에 입문한 학습자가 치아 형태를 이해하고 임상의 기반 기술을 익히기 위해서는 치아카빙 실습이 필요하다. 치아카빙 실습은 단계별 모델을 필요할 때마다 관찰할 수 있을 때 더 효과적이다. 본 연구의 목적은 치아카빙 실습모형을 제안하고, 카빙 단계에 대한 3D모델링 정보를 제공하는 튜토리얼 방식의 모바일 실습지원도구를 개발하는 것이다. 연구결과 치아카빙 실습 모형은 치아형태학 강의 및 실습을 포함하는 강의실 활동과 학습자의 실습과 일상적 학습을 모바일 실습지원도구로 연계하는 모바일 심리스 러닝 형태로 도출하였다. 모바일 실습지원도구는 치아형태학 사전, 치아카빙 실습 튜토리얼, 3D 치아 모델링으로 구현되었다. 개발된 콘텐츠에 대한 전문가 평가 결과 내용 및 기능이 타당하게 설계된 것으로 분석되었다(내용 타당도: 5.0, 인터페이스 타당도: 4.53). 따라서 개발된 모바일 실습지원도구도구는 치아카빙 실습의 모바일 심리스 러닝을 지원하는 데 적합한 학습도구로 판단된다. 본 연구를 기반으로 치의학 분야에 ICT를 활용하는 수업모형과 지원도구인 모바일 콘텐츠 개발 연구가 촉진되기를 기대한다.

• 한국간호교육학회지
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• 제14권1호
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• pp.63-72
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• 2008

Purpose: This study focused on two aspects: 1) how to design and implement a mobile learning course which is facilitated by a PDA with a web-based class homepage as a tool for mobile learning; 2) how to increase and enhance interactive activities among and between the students and the faculty members by utilizing a PDA as a tool for communication as well as collaboration. Method: To analyze the results of the m-Learning course, data was collected from interviews with the involved two faculty members and a survey from 27 students. Result: The results showed a positive outcome of the m-Learning approach in terms of a more collaborative learning environment in a clinical course where the students practice their clinical activities out of the classroom, far from their faculty members. On the other hand, the problems of the m-Learning approach were that more thorough preparation was needed for the new tools from both the students and the faculty members in preparation in social, cultural, and mental aspects, not withstanding the assumed technical limits of a PDA. Conclusion: m-Learning must be more actively implemented in classes, even though several problems were noticed in terms of both technical aspects of the tools, and social and cultural aspects from the users.

• 산업경영시스템학회지
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• 제44권3호
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• Educational Technology International
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• 제18권2호
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• pp.73-99
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• 2017

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.