Profiling Programming Language Learning
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...
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| Vydáno v: | Proceedings of ACM on programming languages Ročník 8; číslo OOPSLA1; s. 29 - 54 |
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| Jazyk: | angličtina |
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New York, NY, USA
ACM
29.04.2024
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| ISSN: | 2475-1421, 2475-1421 |
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| 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. |
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| AbstractList | 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 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. |
| ArticleNumber | 95 |
| Author | Crichton, Will Krishnamurthi, Shriram |
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| Cites_doi | 10.1145/3291279.3339404 10.1145/3328778.3366950 10.1111/j.1745-3984.1988.tb00288.x 10.1111/j.1745-3984.1988.tb00288.x 10.1037/h0024117 10.1111/j.2517-6161.1995.tb02031.x 10.1111/j.2517-6161.1995.tb02031.x 10.1145/3287324.3287370 10.3102/00346543049002280 10.1207/S1532690XCI2103_01 10.1145/3478431.3499354 10.4230/OASIcs.PLATEAU.2018.5 10.5281/zenodo.10798571 10.19173/irrodl.v15i1.1651 10.1145/2509136.2509515 10.18653/v1/D19-1434 10.1145/3510003.3510164 10.1145/3133850.3133863 10.1016/j.compedu.2022.104562 10.1145/3622841 10.1079/searchRxiv.2022.00005 10.1111/j.2517-6161.1995.tb02031.x 10.1111/j.1745-3984.1988.tb00288.x 10.18653/v1 |
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| Title | Profiling Programming Language Learning |
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