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Institution Author: Türkmen, Güzin

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Now showing 1 - 3 of 3
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Online Learning Perceptions Amid Covid-19 Pandemic: the Engineering Undergraduates' Perspective
    (Tempus Publications, 2022) Eryilmaz, Meltem; Kalem, Guler; Kilic, Hurevren; Tirkes, Guzin; Topalli, Damla; Turhan, Cigdem; Yazici, Ali; Information Systems Engineering; Computer Engineering; Software Engineering
    The COVID-19 pandemic caused face-to-face education in just about all universities worldwide to shift to online education. For most students, this educational model was a compulsory first experience. In this study, the survey results are analyzed and discussed related to a group of students in the Engineering Faculty of a university in Turkey regarding their online education perceptions. Briefly summarized, the findings of the study indicate that: (a) most of the students still prefer face-to-face learning, which is also favored if accompanied by distance learning; (b) the concentration level of the students has dropped due to the concerns about the COVID-19 pandemic which affects their learning negatively; and (c) around half of the students participating in the study feel that the online exams conducted without a secure exam software, is considered unsafe. Additionally, the study's results were further extended to evaluate the questionnaire results and reported along with the suggestions of necessary actions in emergency online learning (EOL).
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    Article
    Citation - Scopus: 21
    Comparative Analysis of Programming Languages Utilized in Artificial Intelligence Applications: Features, Performance, and Suitability
    (Prof.Dr. İskender AKKURT, 2024) Sezen, Arda; Türkmen, Güzin; Şengül, Gökhan
    This study presents a detailed comparative analysis of the foremost programming languages employed in Artificial Intelligence (AI) applications: Python, R, Java, and Julia. These languages are analysed for their performance, features, ease of use, scalability, library support, and their applicability to various AI tasks such as machine learning, data analysis, and scientific computing. Each language is evaluated based on syntax and readability, execution speed, library ecosystem, and integration with external tools. The analysis incorporates a use case of code writing for a linear regression task. The aim of this research is to guide AI practitioners, researchers, and developers in choosing the most appropriate programming language for their specific needs, optimizing both the development process and the performance of AI applications. The findings also highlight the ongoing evolution and community support for these languages, influencing long-term sustainability and adaptability in the rapidly advancing field of AI. This comparative assessment contributes to a deeper understanding of how programming languages can enhance or constrain the development and implementation of AI technologies.
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    Article
    Citation - WoS: 27
    Citation - Scopus: 29
    Open Source Learning Management Systems in Distance Learning
    (Turkish online Journal Educational Tech-tojet, 2010) Aydin, Cansu Cigdem; Tirkes, Guzin; Computer Engineering
    This paper presents the major findings from evaluation the most widely used open source learning management systems and identify the most suitable open source e-learning platform. In this study, some analyses and comparisons were made about open source learning management systems the outcome of which was that Moodle was found to be outstanding with many features more than other LMS since it aims to improve the educational quality and include the tools that an e-learning system should have.