• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.464-470
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• 2018

With the introduction of the e-learning since 2000, the place of the education has not been limited to off-line, but the range of it has become broader in online. The e-learning market has evolved steadily over time. With the advent of the term "Edu-tech", which means a combination of education and technology, various IT technologies have incorporated education. Particularly, the Korean education market collects patterns by computerizing the learning history in classes taught according to curriculums. Because of that environment, various personalized learning services have been developed which maximize the effect of the learning. These services have qualitative differences depending on how many data is accumulated and algorithms are developed for the precise analysis. The purpose of this study is to recognize and data-ize OMR marking by the most suitable method to convert analog data into digital data without harming the Korean education system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.162-167
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• 2014

This study is to solve the public education's dilemma between the standardized education to maximize learning efficiency and the personalized education to maximize learning effectiveness, using the paradox management process. The process is based on combining the TOC (Theory Of Constraints) and TRIZ (Russian Theory of Inventive Problem Solving), which is a creative way of thinking to draw the synergic effect by pursuing simultaneously the conflicting elements. Through this research, a new concept of learning method can be suggested on a public course. Further research should be performed to develop a learning guideline based on the students' empirical study results.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.729-732
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• 2002

최근에 온라인 학습 시스템으로서 다양한 학습 컨텐츠를 갖고 있는 새로운 교수 모형이 제시되고 있다. 또한, 학습자의 요구에 따른 코스웨어의 주문이 증가되고 있는 추세이며 그에 따라 웹 기반 교육 시스템에 효율적이고 자동화된 교육 시스템의 필요성이 인식되고 있다. 본 논문은 이러한 웹기반 교육 시스템에서의 개별 학습자들에게 차별적으로 개인화된 학습 자료를 제공하므로서 보다 효과적이고 효율적으로 학습에 임할 수 있도록 개인화된 학습자료 생성 시스템을 제안한다. 이는 개별 학습자들의 학습 행위에 따른 이벤트를 검출하여 데이터베이스화 시킨 후 이를 이용하여 학습 자료 선호도를 계산함으로써 개인화된 학습 자료를 생성하는 시스템이다.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.271-272
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• 2013

오늘날, 컴퓨터와 통신기술의 급속한 발전은 교육의 질을 증진하는데 새로운 기회를 제공하고 있다. 그러나, 현존하는 코스 중심의 학습환경이 개인적인 학습을 적절한 방법으로 지원하지 못하고 있다. 캠퍼스 밖에서도 선도적인 기술로 지원하고 도움을 제공하는 개인별 학습지원이 필요하다. 이러한 새로운 환경에 부응하기 위하여 클라우드에 기반한 CPLE모델을 제안하고 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.70-76
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• 2010

In this paper, we propose a personalized Chinese character education system using RankBoost algorithm on a smartphone. In a typical Chinese character education scenario, a trainee is supplied with a finite number of Chinese characters as an input set in the beginning. And, as the training session repeats, the trainee will notice her/his difficult characters in the set which she/he hardly answers. Those characters reflect their personalized degrees of difficulty. Our proposed system constructs these personalized degrees of difficulty using RankBoost algorithm. In the beginning, the algorithm start with the set of Chinese characters, of which each is associated with the same weight values. As the training sessions are repeated, the algorithm increase the weights of Chinese characters that the trainee mistakes, thereby eventually constructs the personalized difficulty degrees of Chinese characters. The proposed algorithm maximizes the educational effects by having the trainee exposed to difficult characters more than easy ones.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.339-351
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• 2020

In response to changes in Fourth Industrial Revolution in recent years, the field of education has focused on development of the human resources in the areas of artificial intelligence (AI: Artificial Intelligence) and industrial robot. Due to particular interest in these areas, the importance of developing integrated human resources equipped with artificial intelligence technology is emphasized in higher education and vocational competence development. In regards to rapid changing environment, this study created a program "Fostering personalized AI integrated human resource" and established an operational model correspond to latest personalized education trend. The established operational model was conducted twice using Delphi survey with experts in AI and innovative education in order to verify the suitability of program's basic structure, training process, and the sub-components of the operational strategy. The final training model was applied to the online vocational training platform (STEP) and a plan was proposed to establish a personalized training model to foster an AI integrated competent individual.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Convergence Security Journal
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• v.8 no.4
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• pp.81-89
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• 2008

This paper introduces u-learning service model based on context awareness. Also, it concentrates on agent-based WPAN technology, OSGi based middleware design, and the application mechanism such as context manager/profile manager provided by agents/server. Especially, we'll introduce the meta structure and its management algorithm, which can be updated with learning experience dynamically. So, we can provide learner with personalized profile and dynamic context for seamless learning service. The OSGi middleware is applied to our meta structure as a conceptual infrastructure.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.248-256
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• 2000

This paper presents a personalized web agent that constructs user profile which consists of user preferences on the web and recommends his/her relevant information to the user. The personalized web agent consists of monitor agent, user profile construction agent, and user profile refinement agent. The monitor agent makes a user describe his/her preferences directly and it creates the database of preference document, finally performs several keyword extraction to increase the accuracy of the DB. The user profile construction agent transforms the extracted keywords into user profile that could be confirmed and edited by the user. and the refinement agent refines user profile by recursively learning and processing user feedback. In this paper, we describe the several keyword weighting and inductive learning techniques in detail. Finally, we describe the adaptive web retrieval and push agent that perform adaptive services to the user.