• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.239-250
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• 2022

This study examined the level of capital structure and its determinants of publicly traded companies in Malaysia before and after the COVID-19 pandemic. The data for this study was examined using Python Programming Language and time-series financial data from 2,784 quarterly observations in 2019 and 2020. The maximum debt is larger before the COVID-19 period, according to the findings. During the COVID-19 period, short-term debts and total debts have both decreased slightly. However, long-term debts have increased marginally. As a result, this research demonstrates that the capital structure has changed slightly during the COVID-19 period. The findings imply that independent of the capital structure proxies, tangibility, liquidity, and business size had an impact on capital structure in both periods. It was found that profitability had a significant impact on total debts both before and after the COVID-19 crisis. While higher-profit enterprises appear to have lesser short-term debts before the COVID-19 periods, they are also more likely to have lower long-term debts during the COVID-19 periods. Even though growing companies tend to have higher short-term debts and thus total debts during those periods, longterm debts are unaffected by potential growth.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.319-327
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• 2022

Robots for construction sites, although not deeply widespread, are finding applications in the duties of project monitoring, material movement, documentation, security, and simple repetitive construction-related tasks. A significant shortcoming in the use of robots is the complexity involved in programming and re-programming an automation routine. Robotic programming is not an expected skill set of the traditional construction industry professional. Therefore, this research seeks to deliver a low-cost approach toward re-programming that does not involve a programmer's skill set. The researchers in this study examined an approach toward programming a terrestrial-based drone so that it follows a taped path. By doing so, if an alternative path is required, programmers would not be needed to re-program any part of the automated routine. Changing the path of the drone simply requires removing the tape and placing a different path - ideally simplifying the process and quickly allowing practitioners to implement a new automated routine. Python programming scripts were used with a DJI Robomaster EP Core drone, and a terrain navigation assessment was conducted. The study examined the pass/fail rates for a series of trial run over different terrains. The analysis of this data along with video recording for each trial run allowed the researchers to conclude that the accuracy of the tape follow technique was predictable on each of the terrain surfaces. The accuracy and predictability inform a non-coding construction practitioner of the optimal placement of the taped path. This paper further presents limitations and suggestions for some possible extended research options for this study.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.1-10
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• 2020

According to the Fourth Industrial Revolution, SW education is emphasized around the world to educate student with new abilities. Following to these global trends, SW education has become mandatory in Korea's 2015 revised curriculum. However, Korean elementary SW education is focused on the use of block-based programming languages. In addition, the point of view of selecting goals and organizing content of SW Education, the affective domain is ignored and focused only on the cognitive and psychomotor domains. So, this study explored method of SW education using the concept of Team-Shared Mental Model for develop of community capacity and Python, which is textual programming language gaining popularity recently. As a result of performing the post test t-test on two groups with similar Team-Shared Mental Model formation, we found that it was effective in forming a Team-Shared Mental Model of the group applying the SW teaching method suggested in the study.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.3-14
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• 2021

This study deals with the case of online-based project learning, which was designed for the purpose of university educational innovation and enhancing learners' competencies required by society, operated during the COVID-19 pandemic. The course was applied Python programming language, team-based project learning, and intensive course system, which is required by our society and companies in the era of the 4th industrial revolution. Also it was operated as a non-face-to-face online class, which would have been operated in an offline class if it had not been for Covid 19 pandemic, to explore the possibilities and educational effects of online learning. To do this, 32 university students participated in online-based project learning during 8 weeks, and then conducted a survey. The survey results were analyzed in terms of i) non-face-to-face online learning, ii) team-based project learning, and iii) application of the intensive course system. Results say that most of the learners were satisfied with the online learning, team-based project learning, and the intensive semester system applied in this course at a high level, and also they clearly presented the reasons. Thereby, it has been confirmed that the learners were already well aware of the pros and cons of each learning method. Based on these results, the implications were discussed.

• The Journal of Information Systems
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• v.28 no.4
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• pp.403-424
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• 2019

Purpose The objective of this study is to determine the programming language for improving algorithmic thinking of basic software education for non-majors, which has recently been receiving attention to nurture talents needed in the era of the Fourth Industrial Revolution. Design/methodology/approach In this study, Delphi method was used to select the suitable programming language for the features of each of five departments for basic software education for non-majors in order to develop the capability of algorithmic thinking. The survey was conducted three times to 21 experts, and the results were analyzed using quantitative analysis (CVR) values and stability. Findings For the most suitable programming language for each department determined in this study, App Inventor was selected for humanities department, RUR-PLE for natural science department, App Inventor for social science department, Python for engineering department, and Scratch for fine arts department. This is expected to be used as the basis for determining the direction of curriculum and operation of universities starting basic software education through programming language by department proposed in this study.

• East Asian mathematical journal
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• v.39 no.2
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• pp.93-117
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• 2023

The aim of this study is to explore the pre-service math teachers' characteristics of education to develop their AI literacy and SW competency, and to derive some implications. We conducted a 14-hours AI and SW education program for pre-service teachers with theory and practice, and an analysis on class observation data, video frames of classes and interview, Python programming assignments and papers. The results of this case study for 3 pre-service teachers are as follows. First, two students understood artificial neural network and deep learning system accurately, furthermore, all students conducted a couple of explorations related with performance improvement of deep learning system with interest. Second, coding and exploration activities using Python improved students' computational thinking as well as SW competency, which help them give convergence education in the future. Third, they responded positively to the necessity of AI literacy and SW competency development, and to applying coding to math class. Lastly, it's necessary to endeavor to give a coding education to the student's eye level according to his or her prerequisite and to ease the burden of student's studying AI technology.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.71-78
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• 2023

ChatGPT significantly broadens the application of artificial intelligence (AI) services across various domains, with one of its primary functions being assistance in programming and coding. Nevertheless, due to the short history of ChatGPT, there have been few studies analyzing its coding capabilities in Korean higher education. In this paper, we evaluate it using exam questions from three foundational programming courses at S University. According to the experimental results, ChatGPT successfully generated Python, C, and JAVA programs, and the code quality is on par with that of high-achieving students. The powerful coding capabilities of ChatGPT imply the need for a strict prohibition of its usage in coding tests; however, it also suggests significant potential for enhancing practical exercises in the educational aspect.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.35-44
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• 2021

As the execution speed of Python is slower than those of other programming languages (e.g., C, C++, and FORTRAN), Python is not considered to be efficient for writing numerical geodynamic code that requires numerous iterations. Recently, many computational techniques, such as the Just-In-Time (JIT) compiler, have been developed to enhance the calculation speed of Python. Here, we developed two-dimensional (2D) numerical geodynamic code that was optimized for the JIT compiler, based on Python. Our code simulates mantle convection by combining the Particle-In-Cell (PIC) scheme and the finite element method (FEM), which are both commonly used in geodynamic modeling. We benchmarked well-known mantle convection problems to evaluate the reliability of our code, which confirmed that the root mean square velocity and Nusselt number obtained from our numerical modeling were consistent with those of the mantle convection problems. The matrix assembly and PIC processes in our code, when run with the JIT compiler, successfully achieved a speed-up 30× and 258× faster than without the JIT compiler, respectively. Our Python-based FEM-PIC code shows the high potential of Python for geodynamic modeling cases that require complex computations.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.203-211
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• 2024

Utilizing programming languages for data visualization can enhance the efficiency and effectiveness in handling data volume, processing time, and flexibility. However, practice is required to become proficient in programming. Therefore public data-based the problem bank was developed to practice data visualization in a programming automatic assessment system. Public data were collected based on topics suggested in the curriculum and were preprocessed to make it suitable for users to visualize. The problem bank was associated with the mathematics curriculum to learn various data visualization methods. The developed problems were reviewed to expert and pilot testing, which validated the level of the questions and the potential of integrating data visualization in math education. However, feedback indicated a lack of student interest in the topics, leading us to develop additional questions using student-center data. The developed problem bank is expected to be used when students who have learned Python in primary school information gifted or middle school or higher learn data visualization.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.31-34
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• 2022

The concept of locating chromatic number of graph is a development of the concept of vertex coloring and partition dimension of graph. The locating-chromatic number of G, denoted by χ_L(G) is the smallest number such that G has a locating k-coloring. In this paper we will discussed about the procedure for determine the locating chromatic number of Origami graph using Python Programming.