• 공학교육연구
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• 제25권6호
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• pp.69-80
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• 2022

In this study, the application of flipped learning methodology to thermodynamics in mechanical engineering was examined, and how university students view flipped learning and the effects of flipped learning were analyzed. To analyze the effects of flipped learning, pre-class survey, assessment on learning in pre-class, team activities during class, and post-class survey were conducted. The analysis was also conducted on 33 students who took the thermodynamics course in mechanical engineering, and the PARTNER flipped learning model was applied to the class. The results of this study are as follows; In the preliminary survey, the students expected that the flip-learning class with team activities and teaching between team members would be helpful in improving their learning. In addition, students recognized that cooperative learning through a team was helpful for learning. The case reflecting the result of pre-learning evaluation to the subject grades showed higher pre-learning evaluation results than the case not reflecting the result of the pre-learning evaluation to the subject grades, and it was found that the pre-learning evaluation was acting as a factor to promote learning in pre-class. In post-class survey, the satisfaction with the flipped learning class was high, indicating that the effectiveness of the flipped learning class applied to the thermodynamics class was excellent.

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

• 한국학교수학회논문집
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• 제25권3호
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• pp.261-278
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• 2022

최근 코로나19 바이러스의 팬데믹으로 인하여 기존의 사회 및 교육적 체계의 변화가 가속화되고 있으며, 특히 교육 격차로 인한 학습자 맞춤형 교육 체계와 같은 여러 가지 교육적 대응의 필요성이 제기되고 있다. 학습자 맞춤형 교육을 위해서는 학습자의 학습 단계별로 세밀한 진단을 통해 학습 과정에 대한 정보를 기반으로 피드백 및 보정 지원이 필요하다. 본 연구에서는 초등학교 학생들이 수학 학습 과정에서 겪는 어려움 및 오개념을 진단하기 위해 평가 문항을 개발하였다. 개발한 수학 학습 진단 평가 문항은 전국 초등학교 3~6학년 학생 675명에게 적용하였고, 그 결과를 분석하였다. 본고에서는 평가 문항 개발 과정, 평가 문항의 신뢰도 및 타당도 검사 과정, 현장 적용 과정 및 분석 결과를 공유하고, 연구 결과를 통해 도출한 학교 현장에서의 수학 교수·학습 지원 방안에 시사점을 제시하고자 한다. 또한 본 연구는 코로나19 감염증의 장기화 및 뉴노멀 시대의 비대면 학습 환경에서의 수학 학습 어려움 및 오개념 진단 평가 문항의 활용 방안 및 관련한 교육 정책에 제언을 주고 있다.

• 한국수학교육학회지시리즈A:수학교육
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• 제57권3호
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• pp.215-229
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• 2018

Despite the importance of learning to do mathematical proofs, researchers have reported that not only secondary school students but also undergraduate students have difficulties in learning proofs. In this study, we introduced a new toll for learning proofs and explored the reliability and the validity of peer assessment on proofs. In the course of a university in Seoul, students were given weekly proof assignments prior to class. After solving the proofs, each student had to assess other students' proofs. The inter-rater reliabilities of weekly peer assessment was higher than .9 over 90 percent of the observed cases. To examine the validity of peer assessment, we check whether students' assessments were similar to expert assessment. Analysis showed that the equivalence has been quite high throughout the semester and the validity was low in the middle of the semester but rose by the end of the semester. Based on these results, we believe instructors can consider the application of peer assessment on proving tasks as a tool to help students learn.

• 한국과학교육학회지
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• 제33권1호
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• pp.161-180
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• 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.

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

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

• 한국산업정보학회논문지
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• 제9권1호
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• pp.92-102
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• 2004

자기 주도적 학습능력의 수준별 교육을 위한 평가 방법으로서, 수행평가는 문제해결 능력과 높은 사고 능력평가에 대한 한계점을 극복하고 수업의 질과 학교 정상화를 위한 계획으로 선호되고 있다. 그러나 수행평가는 평가 오류, 채점 공정성 문제, 신뢰도 및 객관성 등의 확보에 어려움 있다. 이런 문제점을 해결하고 수행평가 만족을 높이기 위하여, 본 논문에서는 회계원리 과목을 대상으로 수행평가에 대한 각 영역에서 영향을 주는 인자를 분석하여 퍼지 소속 함수를 설계하고, 이를 적용한 객관적이고 신뢰성 높은 수행평가 방법을 제시하고자 한다.

• 한국과학교육학회지
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• 제28권7호
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• pp.730-748
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• 2008

Students often have tenacious physics misconceptions and many studies were conducted on engendering conceptual change. Correspondingly, there is much literature on alternative assessment and its role in student learning. This is a comparison study on using alternative assessments to improve common students' misconceptions in GCE Advanced Level Physics. This research also aims to affirm alternative assessment as a valid tool for learning and promote its use. This study involved two classes with 24 students each. For four weeks, electromagnetism was taught to students using the same classroom pedagogies but with different assignments. The control group completeda standard drill-and-practice assignment while the experimental group finished an alternative assessment. From the preliminary results, students who undertook the alternative assessment and the traditional assessment both improved, however, the treatment group did not perform statistically significantly better than the control group. The reasons will be discussed and commented and it is expected to have significant improvement on rectifying misconceptionsupon next batch of experimentation groups.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.126-129
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• 2020

We introduce a lifetime assessment technique using deep learning algorithm with complex electrical parameters such as resistivity, permittivity, impedance parameters as integrated indicators for predicting the degradation of the organic molecules. The evaluation system consists of fully automated in-situ measurement system and multiple layer perceptron learning system with five hidden layers and 1011 perceptra in each layer. Prediction accuracies are calculated and compared depending on the physical feature, learning hyperparameters. 62.5% of full time-series data are used for training and its prediction accuracy is estimated as r-square value of 0.99. Remaining 37.5% of the data are used for testing with prediction accuracy of 0.95. With k-fold cross-validation, the stability to the instantaneous changes in the measured data is also improved.