• Journal of Engineering Education Research
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• v.25 no.6
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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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• v.16 no.6
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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.

• 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.

• The Mathematical Education
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• v.57 no.3
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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.

• Journal of The Korean Association For Science Education
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• v.33 no.1
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• pp.161-180
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• 2013

The purpose of this paper is to introduce the current studies and research methods about Learning Progressions disseminated to several countries including the U.S. since 2006. It also provides a methodological base to investigate learning progressions in science by introducing a case study of learning progression conducted in Korea. For this study, we described several features of current studies on learning progressions in the U.S., and reported the common ways and sequences employed in examining learning progressions especially with respect to assessment for learning. Learning progressions are descriptions of developmental pathways of learning a topic, in which science knowledge is used in students' engaging in science practices. Each learning progression consists of upper anchor, lower anchor, and intermediate steps that connect both anchors. In investigating a learning progression, researchers usually utilize Wilson's four building blocks of assessment system based on the assessment triangle. This kind of method was also applied in investigating the learning progression for water cycle in this study. We discussed implication and consideration for the future research on learning progressions in science in Korea.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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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• v.25 no.1
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.92-102
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• 2004

Performance assessment was introduced to improve self-directed learning and method of assessment for differenced learning when the seventh educational curriculum was enforced. Written examinations often fail to properly assess students higher thinking abilities ad problem solving abilities. Performance assessment addresses this drawback and also allows normalization of class and school quality. However, performance assessment also has drawbacks that could lead to faulty assessment due to lack of fairness, reliability and validity of grading, ambiguity of grading standard etc. This study proposes a fuzzy performance assessment system to address the drawbacks of the conventional performance assessment. This paper presents in objective and reliable performance assesment method through fuzzy reasoning, design of fuzzy membership function. We define a fuzzy rule analyzing factor that influences in each sacred ground of performance assessment and accounts for the principle subject The proposed performance assessment method divides into three categories, namely, formation estimation subject estimation and design of membership function. Performance assessment result that is worked through fuzzy performance assessment system can reduce the burden of appraisal's fault and provide. We fair and reliable assessment results through grading that have correct standard mid consistency to students.

• Journal of The Korean Association For Science Education
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• v.28 no.7
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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.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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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.