• Journal of the Korean School Mathematics Society
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• v.25 no.3
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• pp.261-278
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• 2022

The purpose of this study is to develop an assessment to diagnose difficulties in learning mathematics and misconstructions that elementary students have. With thorough theoretical background and analysis of mathematics curriculum documents, we established learning trajectories for the following content areas in grades 3 to 6: number and operation, regularity, data and chance, geometry, and measurement. Then, the research team created the assessment items targeting a specific stage in the learning trajectories and including item options to identify possible misconceptions. Based on the unified validity theory, we reported the detailed procedure of the assessment development and the evidence for the content, substance, and structural validity of the assessment. We collected the data of 675 elementary students. Rasch measurement modeling was applied, and Cronbach's alpha was estimated. We considered how to report students' assessment results to teachers appropriately and immediately, which suggested important implications for supporting teaching and learning mathematics in elementary schools. We also suggested how to use the assessment developed in this study in online and distance learning environments due to the COVID-19 pandemic.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.327-329
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• 2022

본 논문에서는 AI Hub의 개방 데이터인 '유형별 두피 이미지'를 사용하여 두피 상태에 대한 신경망을 학습한다. 이 두피 상태에는 6가지 상태가 있는데, 각각의 상태들에 대한 평가를 양호(0)부터 심각(3)까지 분류하여 학습한 신경망 모델로 실제 어플리케이션으로 구현하여 사람들의 두피 사진을 찍어서 두피 상태를 진단한다. 이 과정에서 기존 개방 데이터에서 사용했던 값 비싼 두피 진단기를 사용하는 것이 아닌 값싸게 구할 수 있는 스마트폰용 현미경을 사용하여 좀 더 효율적으로 두피 상태를 진단 할 수 있는 어플리케이션을 만들었다. 몇백만 원 상당의 비싼 두피 진단기로 촬영한 사진과 비교하였을 시 평균적으로 65%의 정확도를 보여주고 있으며 데이터가 많은 유형은 77%의 정확도까지도 보여주었다.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.53-59
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• 2002

This paper presents a model for diagnosing students'learning misconceptions in the domain of heat and temperature using fuzzy cognitive maps(FCM) and fuzzy associative memory(FAM). In a model for diagnosing learning misconceptions. an FCM can represent with cause and effect between preconceptions and misconceptions that students have about scientific phenomenon. An FAM which represents a neurallike memory for memorizing causal relationships is used to diagnose causes of misconceptions in learning. This study will present a new method for more autonomous and intelligent system than a model to diagnose misconceptions that was being done with classical methods in learning and may contribute as an intelligent tutoring system for learning diagnosis within various educational contexts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.310-313
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• 2006

지능형 학습 시스템(ITS, Intelligent Tutoring System)은 학습자의 학습 스타일을 인지하여 학습자에 맞는 학습전략을 세우고 적절한 학습 서비스를 제공하는 시스템이다. 기존의 학습시스템은 학습자의 학습 스타일 보다는 학습 컨텐츠에 중심을 두어 학습자에게 맞는 학습 전략을 적절히 세우는 과정이 부족했다. 이에 본 논문에서는 학습자의 학습과정에서 발생한 데이터를 기반으로 학습자의 학습 스타일을 파악하는 방법을 제안한다. 이를 위해 서양 건축양식 학습을 위한 교육 컨텐츠를 이용하였으며, 수집된 데이터를 분석하여 Folder & Silverman 이 제시한 학습 스타일에 근거한 학습자의 학습 스타일을 추출하였다. 실험에서는 70명의 데이터를 수집하였고, 학습자가 교육 컨텐츠를 학습한 순서에 대한 시계열 데이터를 기반으로 학습자 성향을 알아보기 위하여 은닉 마코프 모델(Hidden Markov Model)을 사용하였다. 은닉 마코프 모델을 적용하여 얻은 분석 결과를 가지고 각 학습자에게 맞는 학습 스타일을 진단하였다. 은닉 마코프 모델에서 얻은 학습 스타일 진단 모델은 향후에 학습자 학습 스타일을 파악하는데 사용할 수 있으며, ITS에 있어 학습자 성향 분석 모듈로 고려해볼 수 있다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.226-230
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• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.3
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• pp.60-67
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• 2008

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideration of the performance deterioration consist of the compressor, the gas generation turbine and the power turbine. Compared to the on-design point, the teaming data has been increased 200 times in case off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimal division has been proposed for learning time decrease as well as the high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been confirmed under 5 %.

• The Journal of Korean Association of Computer Education
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• v.13 no.1
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• pp.37-44
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• 2010

Recently, the scientific approach to brain engineering is actively being made for effective foreign language learning and diagnosis. In order to supplement the problem of preexistence paper exam, the study aimed to develop a tool for foreign language fluency diagnosis which based on brain engineering. The proposed tools in the paper indirectly measure the aspects of brain information processing by testing learners' 3 abilities of linguistic memory, comprehension, and language production in 5 different ways.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.377-382
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• 2007

본 논문에서는 애견 질병에 대한 전문적인 지식이 부족한 일반인들을 대상으로 자신의 애견 건강상태를 파악 할 수 있는 진단 시스템을 제안한다. 제안된 진단 시스템은 105가지 질병과 각 질병의 증상을 데이터베이스에 구축하여 입력된 증상을 통해서 애견의 질병을 도출한다. 본 논문에서는 신경망의 자율 학습 방법인 ART2 알고리즘을 적용하여 질병을 클러스터링 하고 그 결과 값인 클러스터의 출력값과 연결강도를 데이터베이스에 저장한다. 각 질병의 증상과 관련된 질의 결과를 입력 벡터로 제시하여 학습된 질병 정보와 비교하여 애견의 건강 상태를 진단한다. 애견의 건강 상태를 진단하는데 있어서 질병과 증상의 정확한 정보는 매우 중요하다. 따라서 본 논문에서는 질병과 증상의 정보를 데이터베이스로 구축하고 질병과 증상 정보를 효율적으로 관리할 수 있도록 하였다. 제안된 진단 시스템을 구현하여 수의학 전문의가 분석한 결과, 본 논문에서 제안한 시스템이 애견 질병의 보조 진단 시스템으로서의 가능성을 확인하였다.

• 대학교육
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• s.185
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• pp.58-64
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• 2014

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.