A Systematic Literature Review of Automated Feedback Generation for Programming Exercises

By Hieke Keuning, Johan Jeuring, Bastiaan Heeren. ACM Transactions on Computing Education, 2018

369 citations recorded by the source corpus.

Formative feedback, aimed at helping students to improve their work, is an important factor in learning. Many tools that offer programming exercises provide automated feedback on student solutions. We have performed a systematic literature review to find out what kind of feedback is provided, which techniques are used to generate the feedback, how adaptable the feedback is, and how these tools are evaluated. We have designed a labelling to classify the tools, and use Narciss’ feedback content categories to classify feedback messages. We report on the results of coding a total of 101 tools. We have found that feedback mostly focuses on identifying mistakes and less on fixing problems and taking a next step. Furthermore, teachers cannot easily adapt tools to their own needs. However, the diversity of feedback types has increased over the past decades and new techniques are being applied to generate feedback that is increasingly helpful for students.

Subfields: Computer Science Applications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/8bb1f141632860792c2762a83c74ef6d59f8a89a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

In Information Systems

In Software

Citations in the corpus

Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models (2022, background): “In general, formative feedback – feedback given as a part of the learning process – is preferred over summative feedback, i.e. feedback given after the learning process [40, 77].”