• Journal of The Korean Association For Science Education
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• v.24 no.4
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• pp.687-708
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• 2004

The purpose of this study is to investigate the relationships between cognitive conflict and students' causal attributions and to find out what kinds of attributions affect successful resolution of cognitive conflict in learning physics. Twenty-nine college students who attended a base general physics course took an attribution test and a conceptual pretest related to action and reaction concept. Of these, twenty students who revealed alternative conceptions were selected. They were confronted with a discrepant demonstration and took part in the cognitive conflict level test, a posttest, and delayed posttest. Those students who experienced high levels of cognitive conflict were selected and interviewed to find out what kinds of attributions affect resolving the conflict. When confronted with the discrepant event, the students who attributed success outcomes to "effort" experienced higher levels of cognitive conflict than those to "task difficulty." However, those students who revealed high levels of cognitive conflict and attributed success outcomes to effort did not always produce conceptual change. They had different perspectives on effort and conducted different effort activities to resolve the cognitive conflict. In addition, these effort activities appeared to include their motivational beliefs, metacognitive and volitional strategies. The results of this study indicate that in order for the conflicts to lead to change, students need to have the perspective on effort implying the use of the self-regulated learning strategy and to conduct effort activities based on them. Beyond cold conceptual change, this article suggests that there is a management strategy of cognitive conflict in the classroom context.

• Journal of Engineering Education Research
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• v.25 no.4
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• pp.35-41
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• 2022

In order to enhance basic competencies for freshmen at engineering college, whose learning ability is gradually declining, a new course was developed to review basic mathematics and physics through a process of collecting opinions from fellow professors. Tests in six fields of math and physics with the same problems showed the correct answer rate rose from 24.8% at the beginning of the semester to 59.0% at the end of the semester after operating the course developed. According to the survey, the students' self-evaluated confidence on the basic competencies in 16 fields of math and physics showed a significant increase. Students with high confidence in basic competencies also received high actual grades. General high school graduates' confidence point in basic competencies improved from 54.7 at the beginning to 75.3 points at the end of the semester, while specialized high school graduates' enhanced from 38.3 to 64.0 which is higher than that of general high school graduates at the beginning of the semester.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.114-125
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• 2023

The physics-informed neural network (PINN) has been proposed to overcome the limitations of various numerical methods used to solve partial differential equations (PDEs) and the drawbacks of purely data-driven machine learning. The PINN directly applies PDEs to the construction of the loss function, introducing physical constraints to machine learning training. This technique can also be applied to wave equation modeling. However, to solve the wave equation using the PINN, second-order differentiations with respect to input data must be performed during neural network training, and the resulting wavefields contain complex dynamical phenomena, requiring careful strategies. This tutorial elucidates the fundamental concepts of the PINN and discusses considerations for wave equation modeling using the PINN approach. These considerations include spatial coordinate normalization, the selection of activation functions, and strategies for incorporating physics loss. Our experimental results demonstrated that normalizing the spatial coordinates of the training data leads to a more accurate reflection of initial conditions in neural network training for wave equation modeling. Furthermore, the characteristics of various functions were compared to select an appropriate activation function for wavefield prediction using neural networks. These comparisons focused on their differentiation with respect to input data and their convergence properties. Finally, the results of two scenarios for incorporating physics loss into the loss function during neural network training were compared. Through numerical experiments, a curriculum-based learning strategy, applying physics loss after the initial training steps, was more effective than utilizing physics loss from the early training steps. In addition, the effectiveness of the PINN technique was confirmed by comparing these results with those of training without any use of physics loss.

• Journal of The Korean Association For Science Education
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• v.8 no.1
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• pp.73-79
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• 1988

According to the previous studies, the science education departments in the college of education should develop better curricula to teach future secondary school teachers in a more professional way As one example of such curricula developments. one important topics of modem physics was integrated to teach the future high school physics teachers In the physics education departments. The title is "The Physics of Solar Constant Measurement The surrounding core physics for this measurements were pulled together with these important points in minds(1) clear goal of learning In the teachers college physics(2) Clear explanation of physics and visualization of important technologies for the high school students(3) these teachings should encourage for the students to use the knowledge and technologies learned through the class toward the industrial applications Korea will move toward one of the heavily industrialized countries in the world where the physics education can become key player to manufacture physics based products. Therefore developments of physics curricula which relates teachers college, high school, and industry will become more and more Important.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.1
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• pp.25-33
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• 2012

Though science and technology are evolving rapidly in recent years, the traditional science education has limits for students to be satisfied their interests and needs because they couldn't follow these speeds. STEAM as a education integrating science, technology, engineering, arts and mathematics has strengths of increasing interests and understandings in science and technology and improving integrated thinking and problem solving ability for leaners. In this study we analyze the elementary school curriculum and construct physics learning based on STEAM and develop a android application to increase interests in science and improve problem solving ability. In the future, we need to analyze and develop the curriculum and contents for the STEAM education.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• Journal of Engineering Education Research
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• v.23 no.6
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• pp.68-76
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• 2020

This study presents a teaching model to increase the participation and interest, and to improve their understanding of physical concepts of first-year engineering students taking physics(2) course at a three-year college. In the class, a team task solution based on teamwork and a peer learning method through questions and answers between participants in each team were applied so that learners could actively participate in the class to discuss and present. We examined how the activities of each team affected students' interest in subjects, motivation to learn, and the degree of understanding of physical concepts. In the team activity, students were able to actively participate through emotional sharing between learners and free questions and explanations, and it was confirmed that academic achievement was improved by comparing the final exam evaluation results with the evaluation results of the previous three years.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.729-755
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• 2012

As an effort to understand alienated gifted students, we investigated learning characteristics and learning tactics of a scientifically gifted student with economic difficulty and physical disadvantage. The student we studied is attending the Saturday Physics Class which is an after school science activity offered by our university. We adopted techniques of qualitative case study. Participant observation was carried out at the field and the interview was done with the participant, his mother, and his teacher of 5th grade. Field documents and self-reports were used to understand the student synthetically. As a result, learning characteristics of the participant could be summarized as a spontaneous learning which originated from the internal motivation and struggle for learning to overcome the sense of inferiority and isolation from the peers. The participant adopted a strategic method for learning to satisfy his learning desire given the circumstance of socioeconomic and physical disadvantage: the three tactics we found were various learning routes, meta-cognitive ability and fervent response.

• 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 The Korean Association For Science Education
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• v.22 no.2
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• pp.276-285
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• 2002

In this study the concepts for science learning of physics and earth science presented in the seventh grade science textbooks for the seventh national curriculum of Korea approved by the ministry of education were analyzed in terms of the concrete and formal concept level. The parts of textbook analyzed for science learning consist of three sections in physics such as light, force, and waves, and three sections in earth science such as the structure of the earth, the substance of crust, and the movement and composition of the ocean. The analyzed results showed that the number of scientific concepts were differed from 54 to 74 in physics and from 86 to 120 in earth science depending upon publishers. In general, the concepts for science learning in the physics were found to be more in the formal level than the concrete level. However, the concepts for science learning in earth science were found to be more in the concrete level than the formal level. The analyzed results suggest that the concepts of science learning should be considered the learner's cognitive level and the sections should be disposed depending on the degree of difficulty for writing the science textbook. Therefore, it seems to be important to review carefully whether the textbook meets the object of the seventh curriculum of Korea during the process of the investigation for the science textbook.