“Like a Nesting Doll”: Analyzing Recursion Analogies Generated by CS Students Using Large Language Models

EasyChair Preprint 12664

Abstract

Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is difficult even for experienced instructors. We investigate to what extent large language models (LLMs), specifically ChatGPT, can provide access to personally relevant analogies on demand. Focusing on recursion, a challenging threshold concept, we conducted an investigation analyzing the analogies generated by more than 350 first-year computing students. They were provided with a code snippet and tasked to generate their own recursion-based analogies using ChatGPT, optionally including personally relevant topics in their prompts. We observed a great deal of diversity in the analogies produced with student-prescribed topics, in contrast to the otherwise generic analogies, highlighting the value of student creativity when working with LLMs. Not only did students enjoy the activity and report an improved understanding of recursion, but they described more easily remembering analogies that were personally and culturally relevant.

Keyphrases: Code Explanations, Resource Generation, analogies, computing education, large language models

@booklet{EasyChair:12664,
  author    = {Seth Bernstein and Paul Denny and Juho Leinonen and Lauren Kan and Arto Hellas and Matt Littlefield and Sami Sarsa and Stephen MacNeil},
  title     = {“Like a Nesting Doll”: Analyzing Recursion Analogies Generated by CS Students Using Large Language Models},
  howpublished = {EasyChair Preprint 12664},
  year      = {EasyChair, 2024}}