Profiling Programming Language Learning

TL;DR

This study used interactive quizzes embedded in the Rust programming language book over a year to analyze learning trajectories, question characteristics, and intervention effects, providing insights into improving programming education.

Contribution

It introduces a data-driven approach to profiling programming language learning using quizzes, revealing factors affecting dropout and question effectiveness.

Findings

Many readers drop out early when facing complex concepts.

Conceptual questions are more effective than factual ones.

Interventions increased quiz scores by approximately 20%.

Abstract

This paper documents a year-long experiment to "profile" the process of learning a programming language: gathering data to understand what makes a language hard to learn, and using that data to improve the learning process. We added interactive quizzes to The Rust Programming Language, the official textbook for learning Rust. Over 13 months, 62,526 readers answered questions 1,140,202 times. First, we analyze the trajectories of readers. We find that many readers drop-out of the book early when faced with difficult language concepts like Rust's ownership types. Second, we use classical test theory and item response theory to analyze the characteristics of quiz questions. We find that better questions are more conceptual in nature, such as asking why a program does not compile vs. whether a program compiles. Third, we performed 12 interventions into the book to help readers with difficult questions. We find that on average, interventions improved quiz scores on the targeted questions by +20%. Fourth, we show that our technique can likely generalize to languages with smaller user bases by simulating our statistical inferences on small N. These results demonstrate that quizzes are a simple and useful technique for understanding language learning at all scales.