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关键词： Students  

摘要： Large language models (LLMs) such as ChatGPT and Google Bard have garnered significant attention in the academic community. Previous research has evaluated these LLMs for various applications such as generating programming exercises and solutions. However, these evaluations have predominantly been conducted by instructors and researchers, not considering the actual usage of LLMs by students. This study adopts a student-first approach to comprehensively understand how undergraduate computer science students utilize ChatGPT, a popular LLM, released by OpenAI. We employ a combination of student surveys and interviews to obtain valuable insights into the benefits, challenges, and suggested improvements related to ChatGPT. Our findings suggest that a majority of students (over 57%) have a convincingly positive outlook towards adopting ChatGPT as an aid in coursework-related tasks. However, our research also highlights various challenges that must be resolved for long-term acceptance of ChatGPT amongst students. The findings from this investigation have broader implications and may be applicable to other LLMs and their role in computing education. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer science   Education   Machine learning   Mathematics and computing  

摘要： Researchers, educators and companies are experimenting with ways to turn flawed but famous large language models into trustworthy, accurate ‘thought partners’ for learning.

关键词： Computer science   Education   Machine learning   Society  

摘要： Many students now use AI chatbots to help with their assignments. Educators need to study how to include these tools in teaching and learning — and minimize pitfalls.

摘要： Nowadays, computer science (CS) has emerged as a dominant force in numerous research areas both within and beyond its own discipline. However, despite its significant impact on scholarly space, only a limited number of studies have been conducted to analyze the research trends and relationships within computer science. In this study, we collected information on fields and subfields from over 2,000 research articles published in the 2022 proceedings of the top Association for Computing Machinery (ACM) conferences spanning various research fields. Through a network approach, we investigated the interconnections between CS fields and subfields to evaluate their interdisciplinarity and multidisciplinarity. Our findings indicate that computing methodologies and privacy and security stand out as the most interdisciplinary fields, while human-centered computing exhibits the highest frequency among the papers. Furthermore, we discovered that machine learning emerges as the most interdisciplinary and multidisciplinary subfield within computer science. These results offer valuable insights for universities seeking to foster interdisciplinary research opportunities for their students. © 2023, CC BY.

摘要： Preface: Proceedings of 2023 International Conference on Computer Science and Mechatronics, AIP Conference Proceedings, Volume 3017, Issue 1, 15 November 2023,

摘要： The recent pandemic attack COVID-19 has caused a great health issue for all people, mainly the workers in the industries. However, health monitoring systems are exceedingly perceptive and it becomes a prompting to recognize the health of each employee. In the current system, it takes a long time to recognize the health of an employee, which causes some security issues for the worker’s database. To overcome this, we have implemented an IoT-based health monitoring system using machine learning. It mainly accelerates and monitors the workers’ health based on four different parameters using a predictive algorithm. The database of workers’ health is stored in the Thingspeak cloud. Even if one of the four parameters exceeds the average value of normal health, a prescription is sent directly to the workers through e-mail. Thus, our work is highly efficient in terms of security and sending prescriptions compared to the existing system.

关键词： Software engineering  

摘要： HighlightsWe provide a literature review to analyze some of the main causes of the lack of women professionals in software engineering, some of which are underlooked in state-of-the-art studies on the gender gap in ICT;We build on previous projects to combat the gender gap in ICT - which often include mentoring programs, the use of role models, and gaming activities - by proposing a teaching methodology aimed both at highlighting the role of ICT technologies in addressing societal issues, and at strengthening soft *** present a strategy, that we call "greed(y)" because it has been conceived as a grid of vertical (hard) and horizontal (soft) skills, intertwining topics to which girls are traditionally sensitive, such as environmental sustainability, health, etc., with digital skills and soft skills that the education system more rarely considers - such as team-working, public speaking, social networking, and competition - with visible consequences more for girls than for boys. © 2023, The Authors. All rights reserved.

关键词： Application programs  

摘要： The field of computer science (CS) is facing a crucial challenge in broadening participation and embracing diversity, especially among underrepresented gender groups. The presented interdisciplinary educational program is an efficient response to this challenge, designed to catalyze diversity in CS through engagement with complex, interest-driven problems. This paper outlines the program's structure, elucidates the pedagogical underpinnings, and reflects on the emergent challenges and opportunities. We delve into how the fusion of CS with other academic disciplines can allure a more varied demographic, emphasizing the engagement of female high school students — a demographic pivotally positioned yet significantly untapped in CS. Through a systematic survey analysis, we measure the program's efficacy in increasing interest in CS and in cultivating an appreciation for its application in addressing real-world, cross-disciplinary challenges. Our findings affirm the program’s success in bridging the engagement gap by leveraging students’ intrinsic interests, thus charting alternative pathways into the CS field. These insights underscore the critical role of interdisciplinary approaches, establishing a new standard for transformative CS educational methods. © 2023, The Authors. All rights reserved.

关键词： Motivational climate   Engagement   Effort   Computer science   MUSIC model of motivation  

摘要： BackgroundThe motivational climate within a course has been shown to be an important predictor of students' engagement and course ratings. Because little is known about how students' perceptions of the motivational climate in a computer science (CS) course vary by sex, race/ethnicity, and academic major, we investigated these questions: (1) To what extent do students' achievement and perceptions of motivational climate, cost, ease, and effort vary by sex, race/ethnicity, or major? and (2) To what extent do the relationships between students' achievement and perceptions of motivational climate, cost, and effort vary by sex, race/ethnicity, and major? Participants were enrolled in a large CS course at a large public university in the southeastern U.S. A survey was administered to 981 students in the course over three years. Path analyses and one-way MANOVAs and ANOVAs were conducted to examine differences between ***' perceptions of empowerment, usefulness, interest, and caring were similar across sexes and races/ethnicities. However, women and Asian students reported lower success expectancies. Students in the same academic major as the course topic (i.e., CS) generally reported higher perceptions of the motivational climate than students who did not major or minor in the course topic. Final grades in the course did not vary by sex or race/ethnicity, except that the White and Asian students obtained higher grades than the Black students. Across sex, race/ethnicity, and major, students' perceptions of the motivational climate were positively related to effort, which was positively related to *** implication is that females, Asian students, and non-CS students may need more support, or different types of support, to help them believe that they can succeed in computer science courses. On average, these students were less confident in their abilities to succeed in the course and were more likely to report that they did not have t