• Journal of the Korean Society for Library and Information Science
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• v.32 no.2
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• pp.259-276
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• 1998

This study is to contribute to develop adaptive information systems meeting inside information needs as well as represented information needs, and dealing with every levels of users and user's preferences. Also, this study is to present a method of developing adaptive information system through developing user profile using machine teaming and decision tree, applying relevance feedback using merged vector, and applying user feedback.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.451-452
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• 2021

본 논문에서는 머신러닝의 원리를 쉽게 이해할 수 있는 블록 기반 코딩 플랫폼을 내장한 LMS를 제안한다. 해당 LMS는 Moodle이라는 LMS 플랫폼을 기반으로 사이트가 구축되었으며, LTI를 통해 LMS 내부에 DIY라는 코딩 툴을 내장 시켰다. 또한, 사용자의 모든 로그데이터를 통해 추천시스템을 구상하였으며, DIY를 통해 실행되는 코드를 Python Pedal라이브러리를 백엔드에서 실행 시켜 사용자가 작성한 코드에 대해 즉각적인 피드백을 제공하게 구성되어 있다.

• Annual Conference of KIPS
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• 2021.05a
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• pp.310-311
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• 2021

추천시스템은 사용자가 아이템들에 남긴 과거 피드백을 바탕으로 사용자가 선호할 법할 아이템을 추천한다. 추천시스템에서 사용자의 선호도는 단일클래스 세팅과 다중클래스 세팅 두 가지로 표현 할 수 있다. 우리는 추천시스템을 위해 제안된 지식증류기법인 Ranking Distillation 을 다중클래스 세팅에서 실험하여, 증류된 지식을 통한 작은 모델 학습이 효과적인지에 대해 알아보고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.597-598
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• 2023

본 논문에서는 상용 소프트웨어나 웹, 앱, 클라우드 서비스 등 다양한 IT 서비스에서 취약점이 발생하는 근본적인 원인을 알아보고 그에 대한 효과적이고 미래지향적인 대응 방안을 제안한다. 이 대응 방안은 공개된 취약점들을 학습한 인공지능 모듈을 기존의 개발환경에 도입하는 것을 통해 개발 중인 서비스의 설계 문제에 대해 즉각적인 피드백을 줌으로서 작업 효율을 높이고 피드백한 취약점의 위험도를 함께 알려줌으로 혹여 미흡했을 수 있는 개발자의 기존 보안 의식 수준을 높여서 IT 시장에 전체적으로 긍정적인 영향을 끼칠 수 있을 것이라 보여진다. 이 과정을 통해 IT 보안 관점에서 인공지능의 양면성을 바라보고 점점 발전해 가는 인공지능 기술 앞에 우리가 각추어야할 자세를 제안하고자 한다.

• Annual Conference of KIPS
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• 2007.11a
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• pp.591-594
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• 2007

프로그래밍 과제는 자료구조 및 알고리즘에 관련된 이론을 습득하고, 문제해결 능력을 기르는 효과적인 교육방법 중 하나이다. 최근 교수-학습 과정에서 컴퓨터의 활용성을 고려할 때, 과제를 출제하고 평가, 피드백을 제공하는 과정이 자동화 된다면, 일련의 과정을 수행하는데 드는 시간과 비용, 그리고 노력의 절감이 가능할 것이다. 따라서 본 논문에서는 과제의 출제와 제출, 평가, 피드백을 제공할 수 있는 일련의 시스템을 설계하고 개발 하였다.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.181-190
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• 2005

The development of web-based distance education surroundings is steadily achieving. However the studying on the learning effect of learners is still weak against development of educational media itself. At present, most of the distance education system is showing limits in the evaluation of the learning effect because the teaming evaluation depends on mid or final-term exams by on-line or off-line. There is a strong point that the distance education is free from space and time. One of present weak points is limitation of evaluating learner's attitude which can estimate learning effects. Earnest teaming attitude at distance education is important factor for improving the learning effects. In this paper, we propose a model for improving the teaming effects by forcing real-time evaluation and returning feedback to learners at the cyber lectures. The developed experimental system is verified its possibilities.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.335-344
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• 2021

The purpose of this paper is the development of course data models and learning achievement computation algorithms for enabling the course-embedded assessment(CEA), which is essential of competency-based education in higher education. The previous works related CEA have weakness in the development of the systematic solution for CEA computation. In this paper, we propose data models and algorithms to implement competency-based assessment system. Our data models are composed of a layered architecture of learning outcomes, learning modules and activities, and an associative matrix of learning outcomes and activities. The proposed methods can be applied to the development of the course-embedded assessment system as core modules. We evaluated the effectiveness of our proposed models through applying the models to a practical course, Java Programing. From the result of the experiments we found that our models can be used in the assessment system as a core module.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.19-29
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• 2020

The study aims to examine the effectiveness of flipped learning teaching methods by using learning analytics to enable effective programming learning for non-major students. After designing a flipped learning programming class model applied with the ADDIE model, learning-related data of the lecture support system operated by the school was processed with crawling. By providing data processed with crawling through a dashboard so that the instructor can understand it easily, the instructor can design classes more efficiently and provide individually tailored learning based on this. As a result of analysis based on the learning-related data collected through one semester class, it was found that the department, academic year, attendance, assignment submission, and preliminary/review attendance had an effect on academic achievement. As a result of survey analysis, they responded that the individualized feedback of instructors through learning analysis was very helpful in self-directed learning. It is expected that it will serve as an opportunity for instructors to provide a foundation for enhancing teaching activities. In the future, the contents of social network services related to learners' learning will be processed with crawling to analyze learners' learning situations.

• Journal of Engineering Education Research
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• v.11 no.2
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• pp.15-24
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• 2008

Teaching is communication between instructor and students. The learning outcomes can be enhanced by active learning of students. However, there are many obstacles to effective learning below, such as lecture notes authored by instructor, passive student participation, and paper-based homework. In this paper, we propose an effective method for enhancing learning effect through constructing learner ontologies in which knowledge discovered by students is conceptualized and organized. The learning ontology is composed of a teacher ontology and many learner ontologies. The learning ontology is used in discussion, visual presentation, and knowledge sharing between instructor and students. We used the learning ontology in two lectures in practice and learned that the learning ontology enhances learning effect through analysis of feedbacks of students.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.595-600
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• 2001

Recently, WBI model which is based on web has been proposed in the part of the new activity model of teaching-learning. The demand for the customized coursewares which is required from the learners is increased, the needs of the efficient and automated education agents in the web-based instruction are recognized. But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the learning weakness which is observed in the continuous feedback of the course. In this paper we propose design of multi-agent system for course scheduling of learner-oriented using weakness analysis algorithm. First, proposed system monitors learner's behaviors constantly, evaluates them, and calculates his accomplishment. From this accomplishment, the multi-agent schedules the suitable course for the learner. The learner achieves an active and complete learning from the repeated and suitable course.