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A Systematic Literature Review of Automated Feedback Generation for Programming Exercises
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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.
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“Findings showed that mostly automated feedback for programming exercises has been used to identify mistakes rather than fixing them.”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [76]
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Advancing data justice in education: some suggestions towards a deontological framework
Perrotta, C. (2022) · Learning Media and Technology · doi:10.1080/17439884.2022.2156536 · Cited by 16
Automated feedback for participants of hands-on cybersecurity training
Švábenský, V., Vykopal, J., Čeleda, P., & Dovjak, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12265-8 · Cited by 12
An Adaptive Approach to Provide Feedback for Students in Programming Problem Solving
Silva, P., Costa, E. D. B., & de Araújo, J. M. F. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22244-4_3 · Cited by 12
AI assistance in peer feedback provision: Pedagogically sound, but minimally adopted
Pozdniakov, S., Brazil, J., Banihashem, S. K., Noroozi, O., Gašević, D., Sadiq, S., & Khosravi, H. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105591 · Cited by 11
Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions
Koivisto, T., & Hellas, A. (2022) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie56618.2022.9962751 · Cited by 11
Zhang, Y., Paquette, L., & Hu, X. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105029 · Cited by 13
An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Hagerer, G., Lahesoo, L., Anschütz, M., Krusche, S., & Groh, G. (2021) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet50392.2021.9759809 · Cited by 11
Using Programming Autograder Formative Data to Understand Student Growth
Acuña, R., & Bansal, A. (2022) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie56618.2022.9962650 · Cited by 11
Kiesler, N. (2023) · Future of Educational Innovation-Workshop Series Data in Action · doi:10.1109/ieeeconf56852.2023.10104622 · Cited by 11
Skalka, J., & Drlík, M. (2023) · IEEE Access · doi:10.1109/access.2023.3317694 · Cited by 10
The Trees in the Forest: Characterizing Computing Students' Individual Help-Seeking Approaches
Ko, S.-H., & Stephens-Martinez, K. (2024) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3632620.3671099 · Cited by 11
PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming
Chrysafiadi, K., & Virvou, M. K. (2024) · Electronics · doi:10.3390/electronics13234827 · Cited by 11
Partnering with AI: A Pedagogical Feedback System for LLM Integration Into Programming Education
Scholz, N., Nguyen, M. H., Singla, A. K., & Nagashima, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03873-9_31 · Cited by 10
Liénardy, S., Leduc, L., Verpoorten, D., & Donnet, B. (2020) · Australasian Computing Education Conference · doi:10.1145/3373165.3373176 · Cited by 10
Model‐based automatic grading of object‐oriented programming assignments
Le, D. M. (2021) · Computer Applications in Engineering Education · doi:10.1002/cae.22464 · Cited by 9
Wang, F.-H. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100481 · Cited by 9
Learning UML database design and modeling with AutoER
Foss, S., Urazova, T., & Lawrence, R. (2022) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1145/3550356.3559091 · Cited by 9
Metadata
- Authors
- Keuning, H., Jeuring, J., & Heeren, B.
- Affiliations
- Windesheim University of Applied Sciences (NL)
- Utrecht University (NL)
- Open University of the Netherlands (NL)
- Venue
- ACM Transactions on Computing Education
- Published
- 2018-09-28
- Volume
- 19
- Issue
- 1
- Pages
- 1-43
- ISSN
- 1946-6226 (ISSN-L)
- OpenAlex
- W2894540915
- Cited by
- 370
- References
- 200
- Citations by year
- 2026: 72 · 2025: 71 · 2024: 60 · 2023: 58 · 2022: 46 · 2021: 29 · 2020: 22 · 2019: 12
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Locations
- ACM Transactions on Computing Education (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Netherlands Organisation for Scientific Research (023.005.063)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- automated feedback, programming exercises, formative feedback, programming education, adaptive feedback, tool evaluation
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