• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.679-681
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• 2004

본 논문에서는 산업 기사 시험을 대비하는 학생들을 위한 웹 기반 학습 시스템을 설계함에 있어 기사 시험에 맞는 새로운 문제 출제 방식을 제시하고, 학습 결과에 따라 각 문제에 대한 새로운 자동 난이도 조정 방법을 제시한다. 이를 위해 문제 출제에 있어 단원과 난이도에 따라 골고루 문제를 출제할 수 있는 알고리즘을 제시하고, 각 문제의 난이도를 조정함에 있어 학습자 개인 또는 단체의 학습 능력을 고려한 새로운 자동 난이도 조정 방법을 제시한다. 또한 제시된 시스템은 학습자가 기사 시험을 준비함에 있어 스스로 학습, 평가할 수 있으며, 평가 결과를 즉시 확인하고 재학습을 할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.11-24
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• 2022

For the questions asked for LMS-based online evaluation the professor directly set exam questions, or use the automatic question-taking method according to the level of difficulty using the question bank divided by category. Among them, it is important to manage the difficulty of questions in an objective and efficient way, above all, in the automatic question-taking method according to difficulty. Because the questions presented to the evaluators may be different. In this paper, we propose an difficulty re-adjustment algorithm that considers not only the correct rate of a problem but also the time taken to solve the problem. For this, a logistic regression classification algorithm was used of machine learning, and a reference threshold was set based on the predicted probability value of the learning model and used to readjust the difficulty of each item. As a result, it was confirmed that there were many changes in the difficulty of each item that depended only on the existing correct rate. Also, as a result of performing group evaluation using the adjustment difficulty problem, it was confirmed that the average score improved in most groups compared to the difficulty problem based on the percentage of correct answers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.225-234
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• 2019

Bitcoin is a cryptocurrency with characteristics such as de-centralization and distributed ledger, and these features are maintained through a mining system called "proof of work". In the mining system, mining difficulty is adjusted to keep the block generation time constant. However, Bitcoin's current method to update mining difficulty does not reflect the future hash power, so the block generation time can not be kept constant and the error occurs between designed time and real time. This increases the inconsistency between block generation and real world and causes problems such as not meeting deadlines of transaction and exposing the vulnerability to coin-hopping attack. Previous studies to keep the block generation time constant still have the error. In this paper, we propose a machine-learning based method to reduce the error. By training with the previous hash power, we predict the future hash power and adjust the mining difficulty. Our experimental result shows that the error rate can be reduced by about 36% compared with the current method.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.600-602
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• 2023

사전 학습된 언어 모델은 최근 다양한 도메인 및 응용태스크에 활용되고 있다. 하지만 언어 모델을 활용한 문장 난이도 측정 태스크에 대해서는 연구가 수행된 바 없다. 이에 본 논문에서는 교과서 데이터를 활용해 문장 난이도 데이터 셋을 구축하고, 일반 말뭉치로 훈련된 BERT 모델과 교과서 텍스트를 활용해 적응 학습한 BERT 모델을 문장 난이도 측정 태스크에 대해 미세 조정하여 성능을 비교했다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.43-54
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• 2024

Recently, there has been a lot of research on utilizing Language Models, and Large Language Models have achieved innovative results in various tasks. However, the practical application faces limitations due to the constrained resources and costs required to utilize Large Language Models. Consequently, there has been recent attention towards methods to effectively utilize models within given resources. Curriculum Learning, a methodology that categorizes training data according to difficulty and learns sequentially, has been attracting attention, but it has the limitation that the method of measuring difficulty is complex or not universal. Therefore, in this study, we propose a methodology based on data heterogeneity-based Curriculum Learning that measures the difficulty of data using reliable prior information and facilitates easy utilization across various tasks. To evaluate the performance of the proposed methodology, experiments were conducted using 5,000 specialized documents in the field of information communication technology and 4,917 documents in the field of healthcare. The results confirm that the proposed methodology outperforms traditional fine-tuning in terms of classification accuracy in both LoRA fine-tuning and full fine-tuning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.160-164
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degree of difficulty. This methods is the kernel of a question selection that degree of difficulty as make test questions, and then needs continuous management for degree of difficulty. This paper present improved algorithms for dynamically adjustment of degree of difficulty based on examination result that is more efficient set of question. We identified this algorithms is more effective as compared with previously algorithms on web-based education system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2232-2238
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degrees of difficulty. This method is the kernel of a question selection that degrees of difficulty as make test questions, and then needs continuous management for degrees of difficulty. This paper presents improved algorithms for dynamically adjustment of degrees of difficulty based on examination result that is more efficient sot of questions. We identified this algorithm is more effective as compared with previous algorithms on web-based education systems.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.471-480
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• 2005

Most questions made for remote examinations on web-based education system use methods of making questions using fixed questions or randomly using item pools or automatically using degree of difficulty. Particularly, automatically selection methods using degree of difficulty is the kernel of a question that objectivity of the first degree of difficulty for questions and an effective questions selection using degree of difficulty and mediateness degree of difficulty based result of examination. This paper is use automatically selection methods for examination on web-based education system. Firstly, we present new question selection algorithms as regards degree of difficulty and distribution between all units. Secondly, we present new algorithms of mediateness degree of difficulty as regards education ability of students for adjust the degree of difficulty. We identified this algorithms is more effective as compared with previously algorithms on web-based education system.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1404-1407
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• 2007

본 논문에서는 객관식 문항을 난이도에 따라 자동으로 생성하는 방법을 고안하여, 학습자 수준에 적합하도록 다양하고 동적인 형태로 문항 제시를 할 수 있는 시스템을 제안하였다. 이를 위해서는 주어진 문장에서 형태소 분석을 통해 키워드를 추출하고, 각 키워드에 대하여 워드넷의 계층적 특성에 따라 의미가 유사한 후보 단어를 제시한다. 의미 유사 후보 단어를 제시할 때, 워드넷에서의 어휘간 유사도 측정 방법을 사용함으로써 생성된 문항의 난이도를 사용자가 원하는 수준으로 조정할 수 있도록 하였다. 단어의 의미 유사도는 동의어를 의미하는 수준 0에서 거의 유사도를 찾을 수 없는 수준 9 까지 다양하게 제시할 수 있으며, 이를 조절함으로써 문항의 전체 난이도를 조절할 수 있다. 후보 어휘들의 의미 유사도 측정을 위해서, 본 논문에서는 두 가지 방법을 사용하여 구현하였다. 첫째는 단순히 두 어휘의 워드넷 상에서의 거리만을 고려한 것이고 둘째는 두 어휘가 워드넷에서 차지하는 비중까지 추가적으로 고려한 것이다. 이러한 방법을 통하여 실제 출제자가 기존에 출제된 문제를 토대로 보다 다양한 내용과 난이도를 가진 문제 또는 문항을 보다 쉽게 출제하게 함으로써 출제에 소요되는 비용을 줄일 수 있었다.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.113-122
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• 1998

1999년 대학입학 수학능력고사(이하 수능)부터 새롭게 선택과목제와 표준점수제가 도입된다. 선택과목제는 수리탐구II 영역에서 공통과목외 한 개의 과목을 수험생 개인이 선택해서 보는 것을 의미하고, 표준점수제는 영역별 난이도를 조정하기 위해 각 영역의 원점수를 평균 50, 표준편차 10인 점수로 표준화시키는 것을 뜻한다. 선택과목이 있는 영역의 경우는 난이도차뿐만 아니라 각 선택과목 집단별로 일반적인 학업능력의 차이가 존재할 수 있다. 따라서 점수를 표준화시킬 때 과목별 난이도뿐만 아니라 그룹별 학업능력의 차이도 고려해야 한다. 지금까지 발표된 등화방법은 대표적으로 모수적 방법인 선형등화와 비모수적 방법인 백분위수등화가 있는데 이 두 가지 방법은 모두 각 그룹의 학업능력이 동일하다는 가정 하에 전개되어왔다. 따라서 본 논문에서는 우리 나라 입시상황에 적절한 그룹별 능력차이를 보정한 선형등화와 분위수 등화 방법을 비교해 보았다.