• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.145-152
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• 2009

Applications such as study guides and adaptive tutoring must rely on a fine grained student model to tailor their interaction with the user. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the information. I study how to design learner tailoring question process for recommendation. And this process can be applied the CAT and I use the formal language such as CSP in each process development for efficient process design. I use the item difficulty of item response theory for question recommendation process and learner can choice the difficulty step for learning change to control the difficulty of question in next learning. Finally, this method displayed the structural difference to compare between existent and this process.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.587-590
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• 2020

최근, 디지털 콘텐츠 산업이 폭발적으로 성장됨에 따라 고객 유치를 위한 개인화 추천 기술들이 많은 주목을 받고 있다. 개인화 추천 방식들을 큰 갈래로 나누어 본다면 협업 필터링 기술과 내용 기반 기술로 나눌 수 있다. 협업 필터링의 경우 개인화 추천에는 적합하지만 사용자 평가 데이터의 양이 방대해야 하며 초기에 평가자가 없는 콘텐츠에 대해 추천할 수 없는 초기 평가자 문제가 존재한다. 따라서 매일 방대한 양의 콘텐츠가 편입되는 분야에서 사용하기에 큰 결점이 될 수 있다. 본 논문에서는 영화들의 정보가 담긴 데이터 셋과 사용자 평가 데이터, 그리고 사용자의 선호 기준을 의미하는 메타 가중치를 활용한 내용 기반의 맞춤형 영화 추천 시스템을 제안한다. 논문에서는 먼저, 영화를 고를 때 일반적으로 중요시 보는 속성들을 활용하여 영화의 특징 벡터를 구성하고, 이를 사용자 평가와 결합하여 개인의 선호에 대한 특징 벡터를 구성하는 방법을 제안하며, 구성된 데이터와 코사인 유사도, 메타 가중치를 활용하여 사용자 선호와 유사한 영화들을 도출하는 방법을 제안한다. 또한, 평가데이터를 활용하여 구현된 추천시스템의 검증 프로세스를 구성하고, 검증 프로세스를 활용한 손실 함수를 설계하여 적합한 메타 가중치를 학습하는 방법을 제시한다. 본 논문에서 제안하는 시스템은 다수의 속성을 조합하여 활용하므로 추천 결과가 과도하게 특수화 되지 않을 수 있으며, 메타 가중치라는 요소를 통해 더욱 개인화 된 추천을 제공할 수 있다.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.26-31
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• 2010

This research does focus on realization of customizing recommendation service that all of formal, or informal learning is accomplished at real time according to worker's current situation or business context corresponding with the individual ability and the learning progress at industry or education field. For this, we designed the customizing intelligent recommendation system based on compound knowledge that workers can listen to coaching advices at real time and to retrieve and recommend multidimensional relation easily. Also, we constructed the repository based on compound knowledge and process engine for efficient management of compound knowledge. In specific industry, expert solution or coaching service will be created using the knowledge which is accumulated in long-term.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.487-494
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• 2010

One of the major issues in the field of corporate training and formal education is the support of personalized learning. Successful personalized learning needs the availability of the relevant learning contents at just-in-time for learners each. The competency is one of personal characteristics. So competency-based learning is one of the methods that fulfill the above need. Successful competency-based learning needs the method that recommends the relevant contents for the user's deficient competency based on the user's current competency and objectives. We assume that there exists a student information system that provides each user's competences and objectives as fields of a LIP/ePortfolio-compliant student information. This paper proposes an ontology-based system that, given the user's competences and objectives from the above student informaton system, recommends the relevant contents among a large number of educational contents using competency ontology and domain ontology. The advantage of this system can easily handle the change of competency map and terms related with competences in student information and education contents.