In the teaching process of colleges and universities, there is a general lack of early-warning technology for failing grades, and teachers cannot identify the students at risk of failing grades in advance. Thus, the t...
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作者：
Kumar, M PremaAshfaq Ahmed, K.Subash, K.Aeron, Anurag
Department of Ece Andhra Pradesh Bhimavaram India
Department of Emerging Technologies in Computer Science Andhra Pradesh Kurnool India
Department of Data Science Trichy India Miet Meerut
Department of Computer Science and Engineering Meerut India
Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set ...
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Background: Large Language Models (LLMs) have begun to influence software engineering practice since the public release of GitHub's Copilot and OpenAI's ChatGPT in 2022. Tools built on LLM technology could rev...
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Background: Large Language Models (LLMs) have begun to influence software engineering practice since the public release of GitHub's Copilot and OpenAI's ChatGPT in 2022. Tools built on LLM technology could revolutionize the way software engineering is practiced, offering interactive "assistants" that can answer questions and prototype software. It falls to software engineering educators to teach future software engineers how to use such tools well, by incorporating them into their pedagogy. While some institutions have banned ChatGPT, other institutions have opted to issue guidelines for its use. Additionally, researchers have proposed strategies to address potential issues in the educational and professional use of LLMs. As of yet, there have been few studies that report on the use of LLMs in the classroom. It is, therefore, important to evaluate students' perception of LLMs and possible ways of adapting the computing curriculum to these shifting paradigms. Purpose: The purpose of this study is to explore computing students' experiences and approaches to using LLMs during a semester-long software engineering project. We investigated the impacts of a low-cost intervention. While there have been studies on the use of LLMs in the classroom, there have been limited works on the use within a project-based course in the computing classroom. Our study helps fill this knowledge gap. Design/Method: We collected data from a senior-level software engineering course at Purdue University, a large public R1 university in the Midwest. This course uses a project-based learning (PBL) design with a semester-long team project. In Fall 2023, the students were required to use LLMs such as ChatGPT and Copilot as they completed their projects. A sample of these student teams were interviewed in the middle and at the end of the semester to understand: (1) how they used LLMs in their projects;and (2) whether and how their perspectives on LLMs changed over the course of the semester. We ana
Assessing the interdisciplinary learning quality of student learning processes is significant but complex. While some research has experimented with ChatGPT for qualitative analysis of text data through crafting promp...
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ISBN:
(纸本)9783031643118;9783031643125
Assessing the interdisciplinary learning quality of student learning processes is significant but complex. While some research has experimented with ChatGPT for qualitative analysis of text data through crafting prompts for tasks, the in-depth consideration of task-specific knowledge, like context and rules, is still limited. The study examined whether considering such knowledge can improve ChatGPT's labeling accuracy for interdisciplinary learning quality. The data for this research consists of 252 online posts collected during class discussions. This study utilized prompt engineering, fine-tuning, and knowledge-empowered approaches to evaluate student interdisciplinary learning and compare their accuracy. The results indicated that unmodified GPT-3.5 lacks the capability for analyzing interdisciplinary learning. Fine-tuning significantly improved the models, doubling the accuracy compared to using GPT-3.5 with prompts alone. Knowledge-empowered approaches enhanced both the prompt-based and fine-tuned models, surpassing the researchers' inter-rater reliability in assessing all dimensions of student posts. This study showcased the effectiveness of combining fine-tuning and knowledge-empowered approaches with advanced language models in assessing interdisciplinary learning, indicating the potential of applying this method for qualitative analysis in educational settings.
This research study explores the development of an innovative method for identifying stress in IT professionals through the analysis of facial images, utilizing advanced image processing and machine learning technique...
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Finance is a compulsory basic course for finance students. At present, students have the following problems when studying Finance: insufficient knowledge transfer ability, insufficient attention to practical problems,...
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The amplification function of hearing aids is normally prescribed based on population-based prescriptions, often overlooking individual hearing needs. Previous studies have shown that personalization or individualizat...
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This research examines the issues and solutions of machine learning with sparse, heterogeneous data. Heterogeneous data includes a variety of data kinds and architectures, such as structured, semi-structured, and unst...
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Welding is a commonly used industrial process. In this process two or more metal pieces or thermoplastics components are securely joined together by applying heat and pressure. Though the welding process depends on mu...
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The paper explores the application of machine learning algorithms for network traffic intrusion detection with the aim of enhancing the security of information systems. More specifically, it provides a comprehensive i...
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