• Steel and Composite Structures
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• 제49권3호
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Korean journal of medical education
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• 제30권4호
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• pp.295-308
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• 2018

Purpose: Although medical curricula are now better structured for integration of biomedical sciences and clinical training, most teaching and learning activities still follow the older teacher-centric discipline-specific formats. A newer pedagogical approach, known as Collaborative Learning Cases (CLCs), was adopted in the medical school to facilitate integration and collaborative learning. Before incorporating CLCs into the curriculum of year 1 students, two pilot runs using the action research method was carried out to improve the design of CLCs. Methods: We employed the four-phase Kemmis and McTaggart's action research spiral in two cycles to improve the design of CLCs. A class of 300 first-year medical students (for both cycles), 11 tutors (first cycle), and 16 tutors (second cycle) were involved in this research. Data was collected using the 5-points Likert scale survey, open-ended questionnaire, and observation. Results: From the data collected, we learned that more effort was required to train the tutors to understand the principles of CLCs and their role in the CLCs sessions. Although action research enables the faculty to improve the design of CLCs, finding the right technology tools to support collaboration and enhance learning during the CLCs remains a challenge. Conclusion: The two cycles of action research was effective in helping us design a better learning environment during the CLCs by clarifying tutors' roles, improving group and time management, and meaningful use of technology.

• Educational Technology International
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• 제6권1호
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• pp.121-137
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• 2005

In today's information-rich society, the need for information literacy has urgency. Three tasks of information processing are filtering, meaning-matching, meaning-construction that could be strengthened through inquiry-based learning. The cycles of reflection and practice develop the habit of mind, or conscious information processing that allow the learners to acquire higher level of information literacy. An on-line inquiry-based learning environment designed for information literacy may help learners to perform their lifelong learning better with the ability to appreciate, locate, evaluate, and use information effectively.

• 한국주조공학회지
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• 제41권6호
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• pp.521-527
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• 2021

본 연구는 고압 다이캐스팅 공정에서 제품 결함을 사전에 예측하기 위한 기계 학습 기반의 공정 관리 모델 개발에 관한 연구이다. 모델은 이전 사이클에서의 온도를 입력받고, 사이클에 걸쳐서 나타나는 특징을 인식하여 다음 사이클의 결함 발생 여부를 예측한다. 기어 박스 형상에 대하여 제안된 알고리즘을 적용하여, 3 사이클의 정보를 통해서 98 .9%의 정확도와 96.8 %의 재현율로 제품 수축 결함을 사전에 예측하였다.

• 한국지구과학회지
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• 제39권4호
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• pp.378-387
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• 2018

In this research, the change of Korean middle-school science education environments is investigated through analyzing eighth graders' survey data collected over the past 20 years of TIMSS. We extracted educational context variables that provide meaningful information on changes of Korean science education, and have been surveyed more than 3 study cycles up to TIMSS 2015. The selected educational context variables include school resources and school climate from the school principal's questionnaires, and teacher characteristics and instructional activities from the teacher's questionnaires. For each context variable, we analyzed its trend over TIMSS cycles, and discussed its implications in light of Korean educational policy and curriculum changes. Based on the results, we recommended several ways that help to improve science teaching and learning in light of lab assistants, computer availability, teacher learning community, and middle school Earth science curriculum.

• Geomechanics and Engineering
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• 제32권6호
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• 한국과학교육학회지
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• 제12권3호
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• pp.61-76
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• 1992

The purpose of this study was to develop the instructional model and teaching material to change the middle school students'conceptions of electric current into the scientific ones and to investigate the effects of the model in actual classrooms. We identified the students' ideas and their misunderstanding about the concept of eIectic current through reviewing the literatures and our in this study. Based on the above results, we developed the instructional model and designed the teaching sequence and prepare the learning materials about the unit of the electric current in middle school Our instructional model was based on 'learning cycle' developed by Lawson, but the new stage called "exploration through qualitative questions" to elicit the students' own conceptions was inserted to it. To investigate the effects or the new teaching model, the pre- and post-test using the POE type were administered to experimental group(52 students) taught with learning cycles and control group(52 students) taught with traditional styles. The results are as follows; 1) The rates of correct. predictions was varying according to the kinds of problems. And the rates of the correct. reasons of their predictions were lower than those of the predictions. 2) The mean scores of the post-test of both groups were significantly higher than those of the pre-test. We could not find statistically significant difference in theme an score between experimental group and control group after implementation of the model. But the experimental group gained higher scores than those of the control group on two problem. Therefore, although we cannot show the prominent effects of our teaching model based on learning cycles, there are some effects of our model on changing the middle school students' conceptions of electric current.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제27권4호
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• pp.473-486
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• 2013

본 연구는 자연대학과 공과대학 신입생들의 학습유형을 살펴보고, 학습유형과 대학수학교과의 학업성취도 간 관계를 탐색하여 학습자의 학습유형에 보다 적합한 대학수학교과 수업에 대한 기초자료를 제공하는데 목적이 있다. 이를 위하여, Kolb의 LSI에 대한 신뢰도분석을 한 후 수도권 중규모 대학교의 자연대학과 공과대학 신입생 282명을 대상으로 LSI를 실시하여 그 결과를 분석하였다. 연구결과, 첫째, 학습자 유형은 수렴형, 동화형, 조절형, 확산형의 순으로 나타났다. 둘째, 추상적개념화(AC)와 학업성취도 간에는 정적상관관계가, 구체적경험(CE)과는 부적상관관계가 나타났다. 셋째, 수렴형이 조절형과 확산형보다 학업성취도가 높았다. 넷째, 학습유형과 학업성취 도간 상관관계에 있어서 연구대상자의 특성에 따라 차이가 있었다. 연구결과를 바탕으로, 수학의 학문적 특성과 학습자의 학습유형을 고려하는 맞춤형의 다양한 교수-학습전략의 필요성과 함께, 4단계의 학습사이클을 개별 학습자가 효과적으로 개발할 수 있도록 하는 수업방법이 필요함을 제안하였다.

• 한국수소및신에너지학회논문집
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• 제32권6호
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• 한국과학교육학회지
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• 제37권4호
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• pp.553-564
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• 2017

이 연구에서는 초등 예비교사 8명을 대상으로 과학 수업 비디오에 기초한 개인적, 협동적 반성 활동이 예비교사의 전문적 시각을 어떻게 변화시키는지 그 과정을 탐색하였다. 본 연구의 데이터로는 예비교사와의 개별 면담 녹음 자료, 예비교사의 과학 수업 지도안, 과학수업 비디오, 연구자가 수업 참관 시 작성한 현장 노트, 연구 일지, 예비교사가 작성한 이벤트 맵 등이 활용되었다. 예비교사의 전문적 시각은 '선택적 주목'과 '교육적 추론'의 두 가지 범주로 분석하였다. 선택적 주목은 1) 수업 운영과 통제, 2) 교사의 지도, 3) 학생의 사고와 학습 4) 내용 지식 5) 평가의 다섯 측면으로 구분하였고 교육적 추론수준은 증거의 수, 증거의 영역, 유형에 따라 6 수준으로 구분하였다. 연구 결과 수업 비디오에 기초한 개인적 반성은 '수업 운영과 통제'에 대한 주목을 줄이고 '교사의 지도'에 대해 좀 더 주목하도록 하는 효과가 있었다. '학생의 사고나 학습'에 대한 주목을 증진시키기 위해서는 수업 비디오에 대한 개인적 반성 활동만으로는 그 효과가 충분하지 않고 협동적 반성 활동이 필요한 것으로 나타났다. 교육적 추론수준은 개인적 반성과 협동적 반성을 거치며 점진적으로 증가하였다. 학생의 사고나 학습에 대한 주장에서 하나의 증거보다는 여러 증거를 사용하게 되었고 증거의 영역과 유형도 좀 더 다양해졌다. 그러나 증거 형태는 직접적인 관찰에 의한 것이 대부분이었고, 교육이론에 기초한 증거는 거의 나타나지 않았다. 교사교육 과정에서 수업 비디오의 활용에 대한 시사점을 논의하였다.