• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.608-611
• /
• 2003

Performance assessment was introduced to improvement of self-directed learning and method of assessment for differenced learning as the seventh educational curriculum is enforced. Performance assessment is overcoming limitation about problem solving ability and higher thinking abilities assessment that is problem of a written examination and get into the spotlight by way for quality of class and school normalization. But performance assessment has problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy performance assessment system to solve problem of the conventional performance assessment. This paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.

• 공학교육연구
• /
• 제15권6호
• /
• pp.98-102
• /
• 2012

This paper diagnose subjects and measures the learning ability of students based on the goal of developing an assessment tool for education productivity based on multi-aspect evaluation conducted by ICEE at Honam University. Furthermore, develop an assessment tool for education productivity that provides a motive to bring detailed improvements in teaching methods through the diagnosis. In addition, a method for compensating the issues and improving the quality of subject is suggested to develop learning ability of students through applying the assessment tool. An integrated operated system of CQI is desired to be built along with quality improvement of education through measuring academic quality by studying the methods for enhancing academic and learning ability achievement from analysis of the curriculum provided in the "ABEEK program". Through this study the current state of education productivity is presented through analyzing the difference between students who participated in the "ABEEK program" and who did not participate, and operating a comparison between the student's comprehension on their majors and liberal arts by the multi-aspect evaluation that has been conducted for 2 years.

• 한국가정과교육학회지
• /
• 제31권3호
• /
• pp.23-39
• /
• 2019

본 연구의 목적은 2015 개정 실과(기술·가정) 교육과정에 제시된 생활자립역량을 기를 수 있는 교수학습-평가 연계 방안에 대한 가정교과 전문가와 교사들의 인식을 분석하고, 이 결과에 따라 교수학습-평가 설계안을 구상하고 이를 적용할 수 있는 사례를 제시하는 것에 있다. 연구를 통해 얻어진 결과는 다음과 같다. 첫째, 가정교과 전문가 10명의 2차에 걸친 델파이조사와 가정과교사 422명의 설문조사에서 생활자립 역량 함양을 위한 교수학습-평가 연계 방식으로 프로젝트 학습-프로젝트 평가, 프로젝트 학습-포트폴리오 평가, 문제해결학습-포트폴리오 평가, 문제해결학습-프로젝트 평가 순으로 적합하다는 결론을 도출하였다. 둘째, 생활자립역량 함양할 수 있음을 보여주는 '생애주기별 발달 과업과 생애 설계' 단원을 중심으로 프로젝트 학습-프로젝트 평가 방법을 연계하여 교수학습-평가 과정 예시안 개발 및 적용한 수업설계안을 제시하였다. 이 연구는 교과 역량을 함양하기 위한 교수학습-평가 활동의 일관성, 방법의 통일성, 평가의 환류성에 초점을 둔 교수학습-평가 연계 방안의 적용 가능성을 확인한 것에 의의가 있다. 향후 교과역량별 교수학습-평가 연계 방안에 대한 다양한 방안에 대한 연구가 이루어질 필요가 있다.

• 한국융합학회논문지
• /
• 제12권11호
• /
• pp.467-473
• /
• 2021

본 연구는 플립러닝을 적용한 건강사정수업이 간호대학생의 자기주도 학습능력, 비판적 사고성향, 학업적 자기효능감에 미치는 효과를 확인하기 위한 연구이다. 경상북도 K시 일개 간호대학에서 플립러닝을 적용하여 건강사정을 수강하는 2학년 학생을 대상으로 하였으며, 단일군 전후 비교 연구이다. 최종분석은 104명을 대상으로 하였고, 사전 사후 차이는 대응표본검정으로 분석하였다. 그 결과 자기주도 학습능력(t=-3.23, p<.01), 비판적 사고성향(t=6.381, p<.001), 학업적 자기효능감(t=-4.62, p<.001) 모두 통계적으로 유의하게 증가하였다. 이 연구결과를 바탕으로 플립러닝 학습법이 간호대학생의 자기주도 학습능력, 비판적 사고능력, 학업적 자기효능감을 증진시키는 효과적인 프로그램임이 확인되었다. 플립러닝 학습범의 적용은 장기적으로는 교육환경을 개선하고 학생들의 능력을 강화시키는 역할을 할 것이다.

• 반도체디스플레이기술학회지
• /
• 제14권3호
• /
• pp.73-77
• /
• 2015

This paper proposes an object tracking system according to reliability assessment of learning in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information that has the best reliability of learning. The standard object information is used for evaluating and learning the object that is successful tracking in tracking module. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track the reliable objects with reliability assessment of learning for the use of mobile platform.

• Communications for Statistical Applications and Methods
• /
• 제16권6호
• /
• pp.979-987
• /
• 2009

In this study, we compare the assessments made by self, peer and instructor in a statistics course. The goal is to investigate the following two questions: (1) Is it reasonable or fair to expect students to be responsible for assessing the work of their colleagues and themselves? (2) What are students' opinions about the learning effect after they participate in the assessment process? As part of the study investigating these questions, we designed a prototype for a Web-based assessment tool and a procedure to apply the assessment techniques in a statistics course. In addition, we collected and analyzed the data produced in the assessment processes from students and the instructor. The analysis results are summarized as follows: First, self assessment was not accord with instructor assessment, but peer assessment was similar to the assessment by instructor. This result reflected that it is reasonable or fair to expect students to be responsible for assessing the work of their colleagues. Second, peer assessment of their colleagues successfully helped students increase their understanding of the course, and the students increased their skills in the actual assessment process by assessing the work of their colleagues. Finally, many students indicated a high interest level on the assessments.

• 한국과학교육학회지
• /
• 제33권1호
• /
• pp.161-180
• /
• 2013

이 논문은 2006년 이후 미국을 비롯한 세계 여러나라로 점차 확산되고 있는 학습 발달과정(Learning Progressions) 연구의 현황과 연구 방법을 소개하고, 최근에 국내에서 수행된 연구 사례를 중심으로 학습발달과정 연구의 실제를 제시하여 학습 발달과정을 조사하기 위한 방법론적 기초를 제공하기 위한 것이다. 이를 위해 미국을 중심으로 진행되어 온 학습 발달과정 연구의 현황을 소개하고, 특별히 학습을 위한 평가의 관점에서 학습 발달과정을 조사하는 방법과 절차를 정리하였다. 과학의 학습 발달과정은 과학의 주제를 학습할 때 형성되는 발달의 경로를 기술한 것으로서, 발달의 경로를 따라 학생들은 과학 지식을 활용하여 과학의 탐구실행에 참여하게 된다. 각각의 학습 발달과정은 상위 정착점과 하위 정착점, 그리고 두 정착점을 연결해 주는 중간 단계들로 구성되었다. 과학의 학습 발달과정을 조사할 때, 연구자들은 평가의 삼각형에 기반하여 구성된 Wilson의 4단계의 평가 시스템 구성단위를 주로 사용하였다. 논문에서는 학습 발달과정의 조사 방법과 절차를 물의 순환에 대한 학습 발달과정 조사에 적용한 사례 연구를 소개하고, 한국에서 수행될 학습 발달과정에 대한 후속 연구를 위한 함의점과 고려할 점을 논의하였다.

• Geomechanics and Engineering
• /
• 제25권1호
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 한국학교수학회논문집
• /
• 제7권2호
• /
• pp.1-20
• /
• 2004

본 연구는 포트폴리오를 이용하여 학습자의 수학학습능력을 다방면에 걸쳐 종합 분석하여 평가하고 학생들의 수학에 대한 태도 그리고 효과적인 수행능력을 제고한다. 특히, 수학 학습에서 수학의 개념과 그 역사에 대한 포트폴리오를 구성하여 효과적인 수학 학습과 학생들의 학습태도를 점검한다. 이렇게 함으로서 수학학습의 지도에 있어서 미치는 영향과 그 효과를 분석한다. 또한 이러한 것들은 학생들의 잠재성을 발견하도록 도와주고, 나아가서는 수학교육과정을 분석한다. 또한 학습자들이 수동적인 입장이 아니라 능동적 입장에서 학습의 주체가 되어 수학학습을 수행하도록 격려한다.

• Nuclear Engineering and Technology
• /
• 제52권4호
• /
• pp.784-790
• /
• 2020

The accelerated corrosion by Flow Accelerated Corrosion (FAC) has caused unexpected rupture of piping, hindering the safety of nuclear power plants (NPPs) and sometimes causing personal injury. For the safety, it may be necessary to select some pipes in terms of condition monitoring and to measure the change in thickness of pipes in real time. Direct current potential drop (DCPD) method has advantages in on-line monitoring of pipe wall thinning. However, it has a disadvantage in that it is difficult to quantify thinning due to various thinning shapes and thus there is a limitation in application. The machine learning approach has advantages in that it can be easily applied because the machine can learn the signals of various thinning shapes and can identify the thinning using these. In this paper, finite element analysis (FEA) was performed by applying direct current to a carbon steel pipe and measuring the potential drop. The fundamental machine learning was carried out and the piping thinning model was developed. In this process, the features of DCPD to thinning were proposed.