• Journal of The Korean Association For Science Education
• /
• v.25 no.3
• /
• pp.371-381
• /
• 2005

In this study recent trends of research about beliefs in physics education were discussed and cultural approaches were suggested. Cultural aspects in the contexts of science education were discussed and diverse aspects of beliefs in physics education-beliefs about nature, physics, learning physics, value and expectation, and learning physics-were analyzed considerating cultural aspects. Finally, directions for future studies about beliefs and cultural approaches in physics education were suggested.

• Journal of Engineering Education Research
• /
• v.16 no.2
• /
• pp.50-57
• /
• 2013

The study to improve engineering students' performance in studying physics lacked despite of the importance of studying physics in engineering education. The cognitive belief and the motivational belief in studying physics had a strong effect on studying physics. The purpose of this study was to seek the educational way through comparing professors' expectations with students' beliefs about the cognitive belief and the motivational belief in studying physics. The cognitive belief in studying physics was considered as variables like 'knowledge', 'learning' and 'relation'. The motivational belief in studying physics was considered as variables like 'expectancy' and 'value'. It was the 'expectancy' that was the most different dimension between professors' expectations and students' beliefs. It means that students have little confidence in their abilities to study physics, though professors expect their students to be confident. Professor who teaches physics to engineering students recognize these differences, need to have interest in affective domains of beliefs to teach. In addition, there is need to teaching and learning strategies that can lead engineering students' beliefs about ability to perform the task, the purpose, importance, interesting for physics.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.54 no.3
• /
• pp.81-89
• /
• 2012

This paper presents a set of controlled simulated statical and engineering mechanical experiments accessible via the virtual world environment (VWE) and virtual physics lab S/W. Online courses of the university offering courses and/or programs online are growing and the number of students want education in ways which fit their personal places, e-learning is becoming more important and ubiquitous each year. In this study, first of all, question is rather 'How do we execute the learning effectiveness of e-learning courses?' than 'Why does they need e-learnig or VW-learning?'. In particular, is it possible to effectively teach mechanical engineering courses online? The answer was 'No'. So, there is little research on many of these questions. And another important question is 'Is e-learning cost effective?'. For the answer, This research provided that an instructional design model is used to 'How to think and apply the Newtonian forces' in the virtual physics lab S/W. Collected data from student are administered in the spring semester when students studied 'Introduction to Bio-resources and Systems Engineering'. Results show that a cadre of students can take highly interactively physical properties of mechanical engineering in the virtual laboratory environment. Those show that VWE is greater than that of a similar real world presentation or experimental lab, since most of students are delighted to modify and retry modeling works in the VWE.

• Journal of The Korean Association For Science Education
• /
• v.34 no.7
• /
• pp.693-701
• /
• 2014

Based on social cognitive theory, self-efficacy in the context of learning has been steadily emphasized as an indicator of students' motivation and performance. The premise for developing such an instrument was that a specific measure of Physics self-efficacy was deemed to be an important predictor of the change processes necessary to improve students' physics understanding. In this study we described the process of developing and validating an instrument to measure students' beliefs in their abilities to perform essential tasks in physics and then investigated high school students' self-efficacy about physics learning and performance. Validity and reliability of PSEI were tested using various statistical techniques including the Cronbach alpha coefficient, exploratory factor analysis. The result of factor analysis supported the contention that the Physics Self-Efficacy Inventory (PSEI) was a multidimensional construct consisting of at least four dimensions: understanding and application of Physics concepts, achievement motivation, confidence for physics laboratory, confidence for Mathematics. The result showed that Kroean high schools students have low Physics self-efficacy for the all four dimensions. Therefore, researchers should focus on development of students' Physics self-efficacy. In addition, the instrument may lead to further understanding of student behavior, which in turn can facilitate the development of strategies that may increase students' aspiration to understand and study Physics. More specifically, by using the PSEI as a pre- and post-test indicator, instructors can gain insight into whether students' confidence levels increase as they engage in learning Physics, and, in addition, what type of teaching strategies are most effective in building deeper understanding of Physics concepts.where they freely exchanged opinions and feedback for constructing better collective ideas.

• Journal of Engineering Education Research
• /
• v.22 no.4
• /
• pp.22-31
• /
• 2019

This study aims to elucidate the relationship between physics and mathematics to predict achievement for the college level of engineering courses. For the last 4 years, more than 3,000 engineering college freshmen of this study took the diagnostic tests on three subjects, which were physics, mathematics, and chemistry before enrollment. We studied how strongly these diagnostic scores can predict each general college course grades. The correlation between the physics diagnostic scores and the course grades in physics was .264, which was significantly lower than the correlation between the mathematics scores and the physics grades, .311. This stronger prediction of the mathematical diagnostic scores for the general course grades was not found when predicting the grades in chemistry. We therefore conclude that mathematical preparation can unexpectedly predict future achievement in physics better than physics preparation due to the academic interrelationships between mathematics and physics.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.420-437
• /
• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• Journal of The Korean Association For Science Education
• /
• v.18 no.2
• /
• pp.245-256
• /
• 1998

In this study, we discussed about the implications of the idealization, which take an important role in physics, to the physics education. First, understanding of the idealization help the physics learning itself. This is because that various types of idealizations are included in the physics terms and concepts, derivation processes of physics laws and formulas, and explanation of natural phenomena and problem solving activities. Second, understanding of the idealization can help the application of the physics world to the real world. That is, by understanding the extent and the limit of idealization used in physics world, physics students can understand the discrepancies between the real world and the physics world. And also, by modifying or eliminating the idealization, students can extend the extent of understanding about how predictions based on the idealization used in the physics world will change. To do this, we suggested the application of computer simulation program in physics laboratories. Third, idealization take an important role in the inquiry learning for students' originality. The activities of identifying or controlling the variables, as one of the principal factors of scientific inquiry, need the appropriate establishment of the ideal conditions. And to analyze the limiting case or practice the thought experiments for understanding the impossible situation in the real world, ideal conditions also are needed. This study discussed above three aspects with various concrete examples and, with Park et al.'s study (Park et al., 1998), present the theoretical basis for the study of students' and teachers' understanding the idealization.

• Journal of Science Education
• /
• v.42 no.2
• /
• pp.242-255
• /
• 2018

Recently, flipped learning has been paid much attention as one of the improvement methods of teaching and learning at university level. Few studies investigated the effects of flipped learning in general physics classes. However, in order to be successfully established and spread new attempts such as flipped learning, it is necessary to investigate in detail the effect of flipped learning and the way it is perceived by students in accordance with other variables such as student's background and characteristic. In this study, we investigated differences in students' perception on the flipped learning and their achievement according to their background and characteristic in flipped-learning-based introductory physics course in which class hours are divided into lecture and group problem solving equally. Students' achievement was more influenced by their readiness before the beginning of the semester than their time consuming for learning during the semester. Students generally had a very positive perception of the new way of flipped-learning-based physics teaching. However, students of insufficient prior learning, or relatively not-hard learner agreed with careful selection of subjects rather than the overall expansion of flipped learning.

• Journal of Applied and Pure Mathematics
• /
• v.5 no.1_2
• /
• pp.9-22
• /
• 2023

In this paper, the connection and convergence between mathematics and science (physics) concepts were investigated. In addition, methods to closely analyze the degree of mathematics and science (physics) learning were looked into. Furthermore, methods to express and analyze the learning states of individual learners were investigated and a plan to organize educational programs was sought.

• Progress in Medical Physics
• /
• v.30 no.2
• /
• pp.39-48
• /
• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.