CS1-LLM: Integrating LLMs into CS1 Instruction

• URL: http://arxiv.org/abs/2406.15379v1
• Date: Wed, 17 Apr 2024 14:44:28 GMT
• Title: CS1-LLM: Integrating LLMs into CS1 Instruction
• Authors: Annapurna Vadaparty, Daniel Zingaro, David H. Smith IV, Mounika Padala, Christine Alvarado, Jamie Gorson Benario, Leo Porter,
• Abstract summary: This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of Large Language Models. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Students were given three large, open-ended projects in three separate domains that allowed them to showcase their creativity.
• Score: 0.6282171844772422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.

Related papers

• MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series [86.31735321970481]
  We open-source MAP-Neo, a bilingual language model with 7B parameters trained from scratch on 4.5T high-quality tokens. Our MAP-Neo is the first fully open-sourced bilingual LLM with comparable performance compared to existing state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2024-05-29T17:57:16Z)
• An Exploratory Study on Upper-Level Computing Students' Use of Large Language Models as Tools in a Semester-Long Project [2.7325338323814328]
  The purpose of this study is to explore computing students' experiences and approaches to using LLMs during a semester-long software engineering project. We collected data from a senior-level software engineering course at Purdue University. We analyzed the data to identify themes related to students' usage patterns and learning outcomes.
  arXiv  Detail & Related papers  (2024-03-27T15:21:58Z)
• An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project [1.433758865948252]
  Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s) In this paper, we analyze the AI-generated code, prompts used for code generation, and the human intervention levels to integrate the code into the code base. Our findings suggest that LLMs can play a crucial role in the early stages of software development.
  arXiv  Detail & Related papers  (2024-01-29T14:32:32Z)
• "Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students [2.6043678412433713]
  This study evaluates the effectiveness of large language models (LLMs) in performing tasks common among undergraduate computer science students. Our research systematically assesses some of the publicly available LLMs such as Google Bard, ChatGPT(3.5), GitHub Copilot Chat, and Microsoft Copilot Chat.
  arXiv  Detail & Related papers  (2024-01-22T15:11:36Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback [61.83548032416181]
  We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
  arXiv  Detail & Related papers  (2023-07-29T18:01:46Z)
• Calculating Originality of LLM Assisted Source Code [0.0]
  We propose a neural network-based tool to determine the original effort (and LLM's contribution) put by students in writing source codes. Our tool is motivated by minimum description length measures like Kolmogorov complexity.
  arXiv  Detail & Related papers  (2023-07-10T11:30:46Z)
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [48.70557995528463]
  This guide aims to provide researchers and practitioners with valuable insights and best practices for working with Large Language Models. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios.
  arXiv  Detail & Related papers  (2023-04-26T17:52:30Z)
• Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
  This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
  arXiv  Detail & Related papers  (2023-04-17T09:27:40Z)
• Automatically Generating CS Learning Materials with Large Language Models [4.526618922750769]
  Large Language Models (LLMs) enable software developers to generate code based on a natural language prompt. LLMs may enable students to interact with code in new ways while helping instructors scale their learning materials. LLMs also introduce new implications for academic integrity, curriculum design, and software engineering careers.
  arXiv  Detail & Related papers  (2022-12-09T20:37:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.