Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course

TL;DR

This study investigates how social perceptions influence undergraduate students' use of large language models in programming education and how this usage relates to their self-efficacy and performance.

Contribution

It applies social shaping theory to understand LLM adoption in education and links perceptions and usage patterns to student outcomes.

Findings

Students' LLM use linked to career expectations and peer perceptions.

Early LLM use associated with lower self-efficacy and midterm scores.

Perceived over-reliance on LLMs correlates with decreased self-efficacy.

Abstract

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM's technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students' social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students' self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students' use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students' perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.