Abstract
Every year, millions of students learn how to write programs. Learning activities for beginners almost always include programming tasks that require a student to write a program to solve a particular problem. When learning how to solve such a task, many students need feedback on their previous actions, and hints on how to proceed. For tasks such as programming, which are most often solved stepwise, the feedback should take the steps a student has taken towards implementing a solution into account, and the hints should help a student to complete or improve a possibly partial solution.
This paper investigates how previous research on feedback is translated to when and how to give feedback and hints on steps a student takes when solving a programming task. We have selected datasets consisting of sequences of steps students take when working on a programming problem, and annotated these datasets at those places at which experts would intervene, and how they would intervene. We have used these datasets to compare expert feedback and hints to feedback and hints given by learning environments for programming.
Although we have constructed extensive guidelines on when and how to give feedback, we observed plenty of disagreement between experts. We also found several differences between feedback given by experts and learning environments. Experts intervene at specific moments, while in learning environments students have to ask for feedback themselves. The contents of feedback is also different; experts often give (positive) feedback on subgoals, which is not supported by most environments.
This paper investigates how previous research on feedback is translated to when and how to give feedback and hints on steps a student takes when solving a programming task. We have selected datasets consisting of sequences of steps students take when working on a programming problem, and annotated these datasets at those places at which experts would intervene, and how they would intervene. We have used these datasets to compare expert feedback and hints to feedback and hints given by learning environments for programming.
Although we have constructed extensive guidelines on when and how to give feedback, we observed plenty of disagreement between experts. We also found several differences between feedback given by experts and learning environments. Experts intervene at specific moments, while in learning environments students have to ask for feedback themselves. The contents of feedback is also different; experts often give (positive) feedback on subgoals, which is not supported by most environments.
Original language | English |
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Title of host publication | ITiCSE-WGR '22: Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education |
Editors | Brett Becker, Keith Quille |
Publisher | ACM |
Pages | 95–115 |
Number of pages | 21 |
ISBN (Electronic) | 979-8-4007-0010-1 |
DOIs | |
Publication status | Published - 27 Dec 2022 |
MoE publication type | A4 Article in a conference publication |
Event | Annual Conference on Innovation and Technology in Computer Science Education - Dublin, Ireland Duration: 8 Jul 2022 → 13 Jul 2022 Conference number: 27 |
Conference
Conference | Annual Conference on Innovation and Technology in Computer Science Education |
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Abbreviated title | ITiCSE |
Country/Territory | Ireland |
City | Dublin |
Period | 08/07/2022 → 13/07/2022 |