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***: Empowering SQL Learning and SocialChange Through IT-Enabled Smart Tutoring in computer science and engineering

***: Empowering SQL Learning and SocialChange Through IT-Ena...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dixit, Rashmi Gajjam, Nikhil Walchand Institute of Technology Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798331517953

In order to support query-based learning, this research focuses on the applications of an AI tool named *** in the instruction of computer science and engineering students in Structured Query Language (SQL). As the foundation for obtaining data from databases and manipulating vast volumes of data in a variety of vocations, SQL is essential to the field of data management. For computer science students, database knowledge and skills - especially SQL - are essential because they provide them with insights into the querying, altering, and analyzing of data - a crucial component of software engineering, data administration, and database management. Appropriate and effective techniques, including tutoring, which provide the student with individualised support, are particularly beneficial for learning SQL. It was noted that both situations allowed for the temporary usage of ***, as well as feedback to encourage selfdirected product immersion. In an effort to provide *** in a learning environment, this article reports on variations in student performance, practical SQL skills, and engagement levels. The article compares the efficacy of ***'s case studies and effectiveness studies to other traditional tutoring methods, describing how the platform encouraged students to practise SQL outside of the classroom. The findings demonstrate that nearly all students made progress in both writing and composing SQL queries. © 2024 IEEE.

关键词： Structured Query Language

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Effective Supervision for Enhancing Quality of Doctoral Research in computer science and engineering

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SN computer science 2023年第5期4卷 678页

作者： Malekian, Reza Department of Computer Science and Media Technology Malmö University Malmö 205 06 Sweden Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria 0083 South Africa

This article reflects on effective supervision and possible guidance for enhancing quality of doctoral research in the computer science and engineering field. The aims of this study are (1) to understand supervision and the role of supervisors in the quality of doctoral research, (2) to elaborate on effective supervision in the computer science and engineering field and challenges in effective supervision, and (3) to identify key indicators for evaluating effective supervision with a view to improving the quality of doctoral research. After studying various pieces of literature and conducting interviews with experienced supervisors and doctoral students, the article concludes by describing important characteristics in effective supervision. Some of the features for effective supervision are common to other areas of research;however, in computer science and engineering and similar fields, it is important that a supervisor takes the role of a team member by giving proper advice on the reports, algorithm and mathematical modeling developed in the research, and demonstrating the ability to provide advice on complex problems with practical approaches. © 2023, The Author(s).

关键词： computer science and engineering Doctoral research Doctoral students Effective supervision Quality Supervisor

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WIP: Beyond Code: Evaluating ChatGPT, Gemini, Claude, and Meta AI as AI Tutors in computer science and engineering Education 54

WIP: Beyond Code: Evaluating ChatGPT, Gemini, Claude, and Me...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Nath, Sagnik Yoon, So Yoon University of California Santa Cruz Dept. of Computer Science and Engineering Santa Cruz United States University of Cincinnati Dept. of Engineering and Computing Education Cincinnati United States

ISBN: (纸本)9798350351507

This Work-in-Progress research paper evaluates the validity of Large Language Models (LLMs) as conversational AI tutors for computer science learning. While current engineering education literature has predominantly emphasized the rapid evolution of LLMs as conversational AI tutors for programming languages, the exploration into their effectiveness within general STEM topics remains comparatively scarce. This WIP study thus centers on evaluating the potential of LLMs to facilitate understanding of core hardware design concepts critical to computer science and engineering (CSE) education. By cross-checking the responses from generative AI chatbots to an openended CSE-based question, we aimed to uncover how LLMs, such as ChatGPT-3.5, Claude, Gemini, and Meta AI, can contribute to teaching and learning of general CSE courses instead of a specifically coding-based one. Our method involved simulating a student query on the popular debate between CISC vs. RISC related to computer architecture and analyzing the chatbots' responses. This initial collection of data served as the foundation for a continual comparative analysis aimed at determining the inherent instructional value of each LLM and its validity and reliability. To systematically assess the responses, we introduced an evaluation framework focusing on metrics, such as response accuracy, persuasiveness, and depth of explanation. The current work anticipates not only enriching our understanding of how these advanced LLMs can support general CSE education but also identifying areas where further development is needed for a more holistic integration of LLM-based chatbots in assisting student comprehension in the overarching engineering education. © 2024 IEEE.

关键词： engineering education

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Work-in-Progress: Integrating Generative AI with Evidence-based Learning Strategies in computer science and engineering Education 53

Work-in-Progress: Integrating Generative AI with Evidence-ba...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Lauren, Paula Watta, Paul Lawrence Technological University Department of Mathematics and Computer Science SouthfieldMI United States University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI United States

ISBN: (纸本)9798350336429

Generative AI assistants are AI-powered applications that can provide personalized responses to user queries or prompts. A variety of AI assistants have recently been released, and among the most popular is OpenAI's ChatGPT. In this work-in-progress in innovative practice, we explore evidence-based learning strategies and the integration of Generative AI for computer science and engineering education. We expect this research will lead to innovative pedagogical approaches to enhance undergraduate computer science and engineering education. In particular, we describe how ChatGPT was used in two computing-based courses: a Junior-level course in database systems and a Senior-level class in mobile application development. We identify four evidence-based learning strategies: well-defined learning goals, authentic learning experiences, structured learning progression, and strategic assessment. We align these strategies with the two aforementioned courses and evaluate the usefulness of ChatGPT specifically in achieving the learning goals. Combining Generative AI with evidence-based learning has the potential to transform modern education into a more personalized learning experience. © 2023 IEEE.

关键词： Artificial Intelligence in Education (AIEd) Generative AI pedagogy

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***: Empowering SQL Learning and SocialChange Through IT-Enabled Smart Tutoring in computer science and engineering

***: Empowering SQL Learning and SocialChange Through IT-Ena...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Rashmi Dixit Nikhil Gajjam Computer Science and Engineering Walchand Institute of Technology Solapur Maharashtra India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

In order to support query-based learning, this research focuses on the applications of an AI tool named *** in the instruction of computer science and engineering students in Structured Query Language (SQL). As the foundation for obtaining data from databases and manipulating vast volumes of data in a variety of vocations, SQL is essential to the field of data management. For computer science students, database knowledge and skills—especially SQL—are essential because they provide them with insights into the querying, altering, and analyzing of data—a crucial component of software engineering, data administration, and database management. Appropriate and effective techniques, including tutoring, which provide the student with individualised support, are particularly beneficial for learning SQL. It was noted that both situations allowed for the temporary usage of ***, as well as feedback to encourage selfdirected product immersion. In an effort to provide *** in a learning environment, this article reports on variations in student performance, practical SQL skills, and engagement levels. The article compares the efficacy of ***'s case studies and effectiveness studies to other traditional tutoring methods, describing how the platform encouraged students to practise SQL outside of the classroom. The findings demonstrate that nearly all students made progress in both writing and composing SQL queries.

关键词： computer science Training Knowledge engineering Structured Query Language Quantum computing Databases Learning (artificial intelligence) Writing Pupils Software engineering

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The AI Companion in Education: Analyzing the Pedagogical Potential of ChatGPT in computer science and engineering

The AI Companion in Education: Analyzing the Pedagogical Pot...

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IEEE Education engineering (EDUCON)

作者： Zhangying He Thomas Nguyen Tahereh Miari Mehrdad Aliasgari Setareh Rafatirad Hossein Sayadi Departmentof Computer Engineering and Computer Science California State University Long Beach CA USA Center for Information Systems and Technology Claremont Graduate University CA USA Department of Computer Science University of California Davis CA USA

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in computer science and engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objective of this work is to comprehensively analyze the pedagogical potential of ChatGPT in CSE education, understanding its strengths and limitations from the perspectives of educators and learners. We employ a systematic approach, creating a diverse range of educational practice problems within CSE field, focusing on various subjects such as data science, programming, AI, machine learning, networks, and more. According to our examinations, certain question types, like conceptual knowledge queries, typically do not pose significant challenges to ChatGPT, and thus, are excluded from our analysis. Alternatively, we focus our efforts on developing more in-depth and personalized questions and project-based tasks. These questions are presented to ChatGPT, followed by interactions to assess its effectiveness in delivering complete and meaningful responses. To this end, we propose a comprehensive five-factor reliability analysis framework to evaluate the responses. This assessment aims to identify when ChatGPT excels and when it faces challenges. Our study concludes with a correlation analysis, delving into the relationships among subjects, task types, and limiting factors. This analysis offers valuable insights to enhance ChatGPT's utility in CSE education, providing guidance to educators and students regarding its reliability and efficacy.

关键词： Systematics Limiting Focusing Machine learning Chatbots Reliability Task analysis

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Work-in-Progress: Integrating Generative AI with Evidence-based Learning Strategies in computer science and engineering Education

Work-in-Progress: Integrating Generative AI with Evidence-ba...

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Frontiers in Education (FIE) Conference

作者： Paula Lauren Paul Watta Department of Mathematics and Computer Science Lawrence Technological University Southfield Michigan USA Department of Electrical and Computer Engineering University of Michigan-Dearborn Dearborn MI USA

关键词：

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WIP: Beyond Code: Evaluating ChatGPT, Gemini, Claude, and Meta AI as AI Tutors in computer science and engineering Education

WIP: Beyond Code: Evaluating ChatGPT, Gemini, Claude, and Me...

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Frontiers in Education (FIE) Conference

作者： Sagnik Nath So Yoon Yoon Dept. of Computer Science and Engineering University of California Santa Cruz Santa Cruz U.S.A. Dept. of Engineering and Computing Education University of Cincinnati Cincinnati U.S.A.

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

关键词： computer science Measurement Reduced instruction set computing Large language models computer architecture Chatbots Hardware Reliability engineering education STEM

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Multiplex Collaboration Network of the Faculty of computer science and engineering in Skopje 15th

Multiplex Collaboration Network of the Faculty of Compute...

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15th International Conference on ICT Innovations, ICT Innovations 2023

作者： Ivanoska, Ilinka Trivodaliev, Kire Ilijoski, Bojan Faculty of Computer Science and Engineering Ss Cyril and Methodiuos University Skopje Macedonia

ISBN: (纸本)9783031543203

Multiplex collaboration networks facilitate intricate connections among individuals, enabling multidimensional collaborations across various domains and fostering synergistic knowledge exchange. This study focuses on the construction and basic analysis of a multiplex collaboration network among employees at the Faculty of computer science and engineering (FCSE), Ss. Cyril and Methodius University in Skopje. The multiplex network is built with three layers based on: scientific collaborations resulting from joint project participations by FCSE employees, joint employees participations in the FCSE graduation thesis committees, and scientific FCSE employees collaborations defined by co-authorships in Google Scholar papers. The network’s structure plays a vital role in determining the information accessibility and cooperative opportunities for individuals within FCSE institution. The aim here is to investigate the FCSE multiplex collaboration network’s internal structure for discovering its latent knowledge and understand its implications. We perform identification of key individuals within the network, by computing various centrality and hubs detection network metrics. Additionally, we employ a community detection algorithm to reveal the underlying modular structure of the network. By comprehensively analyzing the acquired multiplex collaboration network model, we contribute to a better understanding of the collaboration patterns among FCSE employees. The findings can potentially inform decision-making processes and foster strategic planning aimed at enhancing collaboration and knowledge sharing within the institution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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