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Using Large Language Models to Automatically Identify Programming Concepts in Code Snippets

Published: 13 September 2023 Publication History

Abstract

Curating course material that aligns with students’ learning goals is a challenging and time-consuming task that instructors undergo when preparing their curricula. For instance, it is a challenge to find multiple-choice questions or example codes that demonstrate recursion in an unlabeled question bank or repository. Recently, Large Language Models (LLMs) have demonstrated the capability to generate high-quality learning materials at scale. In this poster, we use LLMs to identify programming concepts found within code snippets, allowing instructors to quickly curate their course materials. We compare programming concepts generated by LLMs with concepts generated by experts to see the extent to which they agree. The agreement was calculated using Cohen’s Kappa.

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Cited By

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  • (2024)"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students Using Large Language ModelsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653533(122-128)Online publication date: 3-Jul-2024
  • (2024)Experiences from Integrating Large Language Model Chatbots into the ClassroomProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690101(46-52)Online publication date: 5-Dec-2024
  • (2024)The Effects of Generative AI on Computing Students’ Help-Seeking PreferencesProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636248(39-48)Online publication date: 29-Jan-2024
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cover image ACM Conferences
ICER '23: Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 2
August 2023
140 pages
ISBN:9781450399753
DOI:10.1145/3568812
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 13 September 2023

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Author Tags

  1. computer science education
  2. explanations
  3. large language models

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ICER 2023
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Overall Acceptance Rate 189 of 803 submissions, 24%

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Cited By

View all
  • (2024)"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students Using Large Language ModelsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653533(122-128)Online publication date: 3-Jul-2024
  • (2024)Experiences from Integrating Large Language Model Chatbots into the ClassroomProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690101(46-52)Online publication date: 5-Dec-2024
  • (2024)The Effects of Generative AI on Computing Students’ Help-Seeking PreferencesProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636248(39-48)Online publication date: 29-Jan-2024
  • (2024)Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language ModelsProceedings of the 26th Australasian Computing Education Conference10.1145/3636243.3636245(11-18)Online publication date: 29-Jan-2024
  • (2024)LLM Generative AI and Students’ Exam Code Evaluation: Qualitative and Quantitative Analysis2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569820(1261-1266)Online publication date: 20-May-2024
  • (2023)Generative AI in Computing Education: Perspectives of Students and Instructors2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343467(1-9)Online publication date: 18-Oct-2023
  • (2023)Generating Multiple Choice Questions for Computing Courses Using Large Language Models2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342898(1-8)Online publication date: 18-Oct-2023

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