Towards Giving Timely Formative Feedback and Hints to Novice Programmers

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 * co-leader †

Exploring the Potential of Large Language Models to Generate Formative Programming Feedback

“…Textual explanation of errors and fixes along with improved code are key characteristics of ChatGPT's feedback compared to other learning environments. The majority of recent tools provide simple feedback, report failed test cases, or compiler errors [4], [5].…”

AI-Enhanced Auto-Correction of Programming Exercises: How Effective is GPT-3.5?

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