• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.174-176
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• 2022

본 논문에서는인공지능(AI; Artificial Intelligence)알고리즘을 활용한 조음 장애 아동들의 '개인화된 맞춤형 학습' 모바일 애플리케이션을 제시한다. 조음과 관련된 빅데이터(Big Data)를 수집-정제-가공한 데이터 셋(Data Set)으로 학습자의 조음 상황 및 정도를 분석, 판단, 예측한다. 특히, 인공지능 활용 시 기존 애플리케이션에 비해 어떻게 개선되고 고도화할수 있는지를 UX/UI(GUI) 측면에서 바라보고 프로토타입 모델을 설계해 보았다. 지금까지 시각적 경험에 많이 치중해 있었다면, 이제는 데이터를 어떻게 가공하여 사용자에게 UX/UI(GUI) 경험을 제공할 수 있는지가 중요한 시점이다. 제시한 모바일 애플리케이션의 UX/UI(GUI)는 딥러닝(Deep Learning)의 CRNN(Convolution Recurrent Neural Network)과 Auto Encoder GPT-3 (Generative Pretrained Transformer)를 활용하여 학습자의 조음 정도와 상황에 맞게 제공하고자 하였다. 인공지능 알고리즘의 활용은 조음 장애 아동들에게 완성도 높은 학습환경을 제공하여 학습효과를 높일 수 있를 것이다. '개인화된 맞춤형 학습'으로 조음의 완성도를 높여서, 대화에 대한 두려움이나 불편함을 갖지 않길 바란다.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.49-54
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• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

• 한국컴퓨터정보학회논문지
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• 제17권11호
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• pp.197-206
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• 2012

본 연구는 21세기에 필요한 핵심적인 학습 능력을 연구하고 미래사회 국가 발전에 필요한 핵심 인력을 양성하는데 있다. '21세기 학습 능력 프로젝트'는 학습자의 학습 능력과 개인 사회적으로 가치 있는 주제를 중심으로 기존 지식과 학문을 다학문적, 통합적으로 접근하는 것이다. 국내에서도 학습 능력 배양을 위한 다양한 교과 간 통합 교육과정의 시도가 있었으나 각 교과의 내용과 특성의 차이를 충분히 이해하여 효과적으로 교수할 수 있는 교사가 부족하고 현장에서 쉽게 적용하는데 어려움이 있다. 이에 본 연구는 21세기에 필요한 학습 능력 신장을 위해 학문의 지식을 통합하는 다학문적 맞춤형 교육과정을 개발하고 이를 효율적으로 지원하기 위해 교육과정 실태를 분석 연구하고 그 결과로 초 중등학교에 적용할 수 있는 다학문 맞춤형 학문 통합 모형을 제안한다.

• 공학교육연구
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• 제20권3호
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• pp.3-12
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• 2017

This research points out the necessity of customized communication education for engineering students in the era of the Fourth Industrial Revolution. This paper also points out such problems of current communication education as presentation and discussion-focused 'Public Speech' exercises, absence of interests about social issues, and lack of interactive communication learning. In general, as the characteristics of their major education, engineering students are not aggressive in self-questioning and active communication rather than their sensitive reaction to the changes of the new era. Considering these characteristics of engineering students, this research emphasizes that future communication education should be deployed from the major-focused thinking to the development of convergent thinking, from the problem-solving to the problem-finding, and from the contentious thinking to the cooperative thinking. In addition, as a class design reflecting future trends, this research emphasizes, firstly the development of cooperative communication education model, secondly active utilization of SMART technology, and lastly the importance of customized-coaching for each student considering their own characteristics and requirements.

• 한국의류학회지
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• 제43권3호
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• 디지털산업정보학회논문지
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• 제14권3호
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• pp.103-113
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• 2018

The purpose of this study supports the establishment of national e-learning policy by analyzing e-learning status and current status of higher education. Enhance the competitiveness of higher education through sharing information between universities. And to improve e-learning quality management. We surveyed the current status of e-learning in 341 universities and questionnaires about e-learning content, e-learning application form, e-learning platform status was surveyed through each school's learning management system. As a result, the infrastructure of e-learning, the rate of platforms secured, and the contents are increasing gradually each year; however, still, not all students can receive the services equally. Dedicated servers and learning management systems were secured by more than 70% of general universities. In the current development status of e-learning content, multimedia, animation, and text forms are gradually decreasing, but video contents are increasing every year. Most of the online contents were used in the e-learning contents by application type, and blended learning, flipped learning, and mooc is not yet actively used since they are still in the beginning stage. Learning analysis techniques should be supported in order to easily use online learning contents such as flipped learning and mooc. We suggest that the effectiveness of e-learning should be measured and the current state of learning analysis for customized learning should be done. This study aims to contribute to the improvement of competitiveness of higher education by sharing information about e-learning among universities as a basis for improvement of e-learning policy. Future tasks are to improve the customized learning environment by adding whether the system environment for learning analysis is provided at the time of the survey.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권1호
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• pp.106-110
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• 2008

웹 2.0 기술의 발전에 따라 사용자가 임의로 서비스 공간을 재구성할 수 있는 개인화 서비스의 수요가 증가하고 있다. 이러닝 분야에서도 점차 개인화 서비스를 적용하여 사용자에게 학습 공간의 재구성을 위한 다양한 기능들이 제공되고 있다. 그러나 기존의 개인화 서비스인 컴포넌트 단위의 레이아웃 재배치는 학습자에게 제한된 요소의 변경만을 허용하기 때문에 세부적인 구성 변경이 이루어질 수 없다. 또한 이러한 재배치 정보를 다른 용도로 활용하지 못하고 있다. 본 논문에서는 학습 공간의 컨트롤 단위 재구성을 위한 e-Space Manager와 동적 컨트롤 배치 기법을 제안하고 이를 통해 사용자가 배치하는 컨트롤이 콘텐츠의 특성에 최적화되어 구성됨을 확인한다. 제안하는 컨트롤 배치 기법은 사용자가 학습 공간의 구성을 컨트롤 단위로 조정할 수 있다는 장점이 있다. 이는 콘텐츠의 배치뿐만 아니라 시스템 개발자가 허용한 범위 내에서 컨트롤의 변경을 통해 콘텐츠의 입 출력 형태를 학습자가 임의로 구성할 수 있게 된다. 그리고 컨트롤 배치를 통해 생성되는 재구성 정보와 사용 기록을 바탕으로 사용자 선호도에 따른 컴피턴시 모델에 사용될 수 있는 자료를 생성한다.

• 한국수학교육학회지시리즈A:수학교육
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• 제59권4호
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• pp.373-387
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• 2020

본 연구의 목적은 학습 빅데이터 분석을 통해 추천 알고리즘을 스스로 고도화하는 머신러닝 추천모듈이 적용된 개인 맞춤형 학습 플랫폼이 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 미치는 영향과 이들 사이의 구조적 관계를 검증하는 것이다. 연구 결과 개인 맞춤형 학습은 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 대해 긍정적인 영향을 미치고 있었다. 또한, 맞춤형 학습과 수학에 대한 태도와 수학학업성취도의 관계에서 학습시간과 자기주도적 학습능력의 매개효과가 유의하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.1-15
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• 2023

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning. To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics. As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments. The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress. In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed. On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students. It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.