• 공학교육연구
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• 제26권6호
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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.

• The Journal of Asian Finance, Economics and Business
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• 제9권4호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.360-363
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• 2022

최근에 Kaggle ML & DS Survey에 따르면 기계 학습 및 데이터 과학을 위한 프레임워크에서 TensorFlow와 Keras의 비율이 각각 41.82%, 34.09%로 비중을 차지하고 있으며, 개발 프로그래밍의 경우 약 82%로 Python을 사용하는 것으로 나타났다. 상당수의 기계 학습 및 딥러닝의 구조가 Keras 프레임워크와 Python을 활용하고 있으나, Python의 경우에는 스크립트 언어인 관계로 인해 배포 및 실행을 Python 스크립트 환경에 제한되어 동작하므로 다양한 환경에서 동작하기 어려운 개연성이 있을 수 있다. 본 논문에서는 Visual Studio 2019에서 동작하는 C#과 Keras를 활용한 기계 학습 및 딥러닝 시스템을 구현하였으며, 세부적으로 Mnist 데이터셋을 활용하여 파이썬 3.8.2와 C# .NET 5.0 환경에서 20번의 테스트를 진행하였다. 테스트 수행 결과, Python은 최소 시간 1.86초, 최대 시간 2.38초, 평균 시간 1.98초 총 시간 39.53초가 소요되었으며, C#은 최소 시간 1.78초, 최대 시간 2.11초 평균 시간 1.85초 총 시간 37.02초가 소요되었다. 결론적으로 C#의 성능이 Python보다 6% 정도 향상되었음을 확인하였으며, 이를 통해 실행파일 추출이 가능하여 활용도가 높을 것으로 기대한다.

• 정보교육학회논문지
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• 제24권1호
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• pp.1-10
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• 2020

4차 산업 혁명에 따라 전 세계에서는 이에 맞는 새로운 인재를 양성하고자 SW 교육을 강조하고 있다. 이런 세계적 흐름에 맞추어 우리나라에서도 2015 개정교육과정에서 SW 교육을 필수화하였다. 하지만 우리나라 초등 SW교육은 블록 기반 프로그래밍 언어의 활용에 편중되어 있다. 또한 목표 설정 및 내용 구성에 있어 정의적 영역의 신장은 소홀하고 지식, 기능적 영역의 신장에만 집중되는 경향이 있다. 이에 본 연구에서는 텍스트 기반 프로그래밍 언어 중에서 최근 각광받고 있는 파이썬과 정의적 영역인 '공동체 역량'의 신장을 고려하여 팀 공유정신모형 개념을 활용한 SW교육 방법을 탐구하였다. 팀 공유정신모형 형성 정도가 유사한 두 집단에 t-검정을 수행해 본 결과, 본 연구에서 제시한 SW학습방법을 적용한 집단 내 학습자들의 팀 공유정신모형 형성에 효과가 있다는 것을 확인할 수 있었다.

• 공학교육연구
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• 제24권5호
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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.

• 한국인터넷방송통신학회논문지
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• 제23권4호
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• pp.79-84
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• 2023

최근 분산 및 병렬 처리 기술 중 빠른 처리 속도를 제공하는 Apache Spark는 실시간 기능 및 머신러닝 기능을 제공하고 있다. 이러한 기능에 대한 공식 문서 가이드가 제공되고 있지만, 기능들을 융합하여 실시간으로 특정 값을 예측하는 방안은 제공되고 있지 않다. 따라서 본 논문에서는 이러한 기능들을 융합하여 실시간으로 데이터의 값을 예측할 수 있는 연구를 진행했다. 전체적인 구성은 Python 프로그래밍 언어에서 제공하는 주가 데이터를 다운로드하여 수집한다. 그리고 머신러닝 기능을 통해 회귀분석의 모델을 생성하고, 실시간 스트리밍 기능을 머신러닝 기능과 융합하여 실시간으로 주가 데이터 중 조정종가를 예측한다.

• 디지털융복합연구
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• 제15권7호
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• pp.55-64
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• 2017

본 연구의 목적은 프로그래밍을 처음 접하는 학습자들의 어려움을 파악하여 비전공 대학생들을 위한 대학 교양기초 소프트웨어교육 운영 방안을 모색하는 데 있다. 이를 위해 다양한 전공자들로 구성된 H 대학의 파이썬 프로그래밍 수업에서 수업시간 전 온라인으로 제출한 수강생들의 질문과 수업 후 설문조사를 통하여 체감난이도와 체감이해도를 분석하였다. 비전공자들을 위한 효율적인 수업을 위해 플립드 수업으로 진행하였으며, 오프라인 수업에서는 사전질문을 활용한 학습자 맞춤형 피드백 방식 강의로 진행하였다. 분석결과 프로그래밍 수업을 처음 접하는 학습자들을 위해서는 컴퓨터 언어의 기본개념을 배우기 전에 교육과정 초반에 문제 파악을 통한 논리적인 추상화 과정을 배정하고, 코딩 실습 전에 단원마다 그에 대한 이해를 돕는 상향식(bottom-up) 문제풀이를 통한 충분한 연습이 필요하다. 또한, 학습자의 전공계열 및 수업 내용과 학습자의 진행 단계를 반영한 정밀한 교육과정 설계가 선행되어야 한다.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.347-351
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• 2022

Farmers E-Market is a website that allows agricultural workers to direct market their products to buyers without the use of a middleman. That theory is blockchain system will be used by authors to accomplish this. The system, which is built on a public blockchain system, supports sustainability, shippers, and consumers. Farmers can keep track of their farming activities. Customers can review the product's history and track its journey through carriers to delivery after making a purchase. Farmers are encouraged to get information about their interests promptly in a blockchain-enabled system like this. This functionality is being used by small-scale farmers to form groups based on their location to attract large-scale customers, renegotiate farming techniques or volumes, and enter into contracts with buyers. The analysis shows the use of blockchain technology with a farmer's portal that keeps the video of trading data of crops, taking into account the qualities of blockchain such as values and create or transaction data. The proposal merges python as a programming language with a blockchain system to benefit farmers, vendors, and individuals by preserving transactions.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.203-210
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• 2023

In this paper, we develope a stock trading system that automatically buy and sell stocks in Kiwoom Securities' HTS system. The system is made by using Kiwoom Open API+ with the Python programming language. A trading strategy is based on an enhanced system query method called a Condition-Search. The Condition-Search script is edited in Kiwoom Hero 4 HTS and the script is stored in the Kiwoom server. The Condition-Search script has the advantage of being easy to change the trading strategy because it can be modified and changed as needed. In the HTS system, up to ten Condition-Search scripts are supported, so it is possible to apply various trading methods. But there are some restrictions on transactions and Condition-Search in Kiwoom Open API+. To avoid one problem that has transaction number and frequency are restricted, a method of adjusting the time interval between transactions is applied and the other problem that do not support a threading technique is solved by an IPC(Inter-Process Communication) with multiple login IDs.

• ETRI Journal
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• 제46권3호
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.