Towards Giving Timely Formative Feedback and Hints to Novice Programmers
Open accessAbstract
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.
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There are cases where model solutions are useful: model solutions have been deliberately provided to students in some prior research, and they are also often provided in automated learning environments that are not focused on grading.
“model solutions have been deliberately provided to students in some prior research [62,63], and they are also often provided in automated learning environments that are not focused on grading [34].”
From Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests (2023) · cites this paper as [34]
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Cites
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · cited 2× · see citing works
“Hattie and Timperley [26] define four levels at which feedback operates: task, process, regulation, and self.”
BackgroundShute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
Self-Efficacy and Education and Instruction
Schunk, D. H. (1995) · The Plenum Series in Social/Clinical Psychology · doi:10.1007/978-1-4419-6868-5_10 · Cited by 720 · see citing works
Automatic feedback in online learning environments: A systematic literature review
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100027 · Cited by 440 · see citing works
Automatic test-based assessment of programming
Douce, C., Livingstone, D., & Orwell, J. (2005) · Journal on Educational Resources in Computing · doi:10.1145/1163405.1163409 · Cited by 416 · see citing works
A systematic literature review of automated feedback generation for programming exercises
Keuning, H., Jeuring, J., & Heeren, B. (2019) · ACM Transactions on Computing Education · doi:10.1145/3231711 · Cited by 370 · see citing works
Self-citationEducational Data Mining and Learning Analytics in Programming
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. (2015) · doi:10.1145/2858796.2858798 · Cited by 332 · see citing works
Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & Weerdt, J. D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104094 · Cited by 292 · see citing works
Automated Assessment in Computer Science Education: A State-of-the-Art Review
Paiva, J. C., Leal, J. P., & Figueira, Á. (2022) · ACM Transactions on Computing Education · doi:10.1145/3513140 · Cited by 275 · see citing works
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Rivers, K., & Koedinger, K. R. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0070-z · Cited by 258 · see citing works
Becker, B. A., Denny, P., Pettit, R., Bouchard, D., Bouvier, D. J., Harrington, B., Kamil, A., Karkare, A., McDonald, C., Osera, P.-M., et al. (2019) · Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3344429.3372508 · Cited by 199 · see citing works
Self-citationBrown, N. C. C., Kölling, M., McCall, D., & Utting, I. (2014) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2538862.2538924 · Cited by 179 · see citing works
Subgoals, Context, and Worked Examples in Learning Computing Problem Solving
Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015) · Proceedings of the eleventh annual International Conference on International Computing Education Research · doi:10.1145/2787622.2787733 · Cited by 174 · see citing works
Intelligent tutoring systems for programming education: a systematic review
Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018) · Proceedings of the 20th Australasian Computing Education Conference · doi:10.1145/3160489.3160492 · Cited by 166 · see citing works
Price, T., Dong, Y., & Lipovac, D. (2017) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3017680.3017762 · Cited by 140 · see citing works
Marwan, S., Gao, G., Fisk, S. R., Price, T., & Barnes, T. (2020) · International Computing Education Research Workshop · doi:10.1145/3372782.3406264 · Cited by 131 · see citing works
Self-citationDoes Help Help? Introducing the Bayesian Evaluation and Assessment Methodology
Beck, J. E., Chang, K.-M. K., Mostow, J., & Corbett, A. T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_42 · Cited by 125 · see citing works
Rist, R. S. (1991) · Human-Computer Interaction · doi:10.1207/s15327051hci0601_1 · Cited by 106 · see citing works
Ai, H. (2017) · ReCALL · doi:10.1017/s095834401700012x · Cited by 102 · see citing works
Effective compiler error message enhancement for novice programming students
Becker, B. A., Glanville, G., Iwashima, R., McDonnell, C., Goslin, K., & Mooney, C. (2016) · Computer Science Education · doi:10.1080/08993408.2016.1225464 · Cited by 101 · see citing works
A Pedagogical Analysis of Online Coding Tutorials
Kim, A. S., & Ko, A. J. (2017) · doi:10.1145/3017680.3017728 · Cited by 99 · see citing works
Ask-Elle: an Adaptable Programming Tutor for Haskell Giving Automated Feedback
Gerdes, A., Heeren, B. J., Jeuring, J. T., & van Binsbergen, L. T. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0080-x · Cited by 96 · see citing works
Self-citationA Systematic Literature Review of Assessment Tools for Programming Assignments
de Souza, D. M., Felizardo, K. R., & Barbosa, E. F. (2016) · doi:10.1109/cseet.2016.48 · Cited by 96 · see citing works
Krusche, S., & Seitz, A. (2018) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3159450.3159602 · Cited by 95 · see citing works
A Study of Code Design Skills in Novice Programmers using the SOLO taxonomy
Izu, C., Weerasinghe, A., & Pope, C. (2016) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2960310.2960324 · Cited by 81 · see citing works
Self-citationHao, Q., Smith, D. H., Ding, L., Ko, A. J., Ottaway, C., Wilson, J., Arakawa, K., Turcan, A., Poehlman, T., & Greer, T. (2021) · Computer Science Education · doi:10.1080/08993408.2020.1860408 · Cited by 81 · see citing works
Impact of interruption style on end-user debugging
Robertson, T. J., Prabhakararao, S., Burnett, M., Cook, C. R., Ruthruff, J. R., Beckwith, L., & Phalgune, A. (2004) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985729 · Cited by 73 · see citing works
The Effects of Enhanced Compiler Error Messages on a Syntax Error Debugging Test
Becker, B. A., Goslin, K., & Glanville, G. (2018) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3159450.3159461 · Cited by 62 · see citing works
Student perception and usage of an automated programming assessment tool
Rubio-Sánchez, M., Kinnunen, P., Pareja-Flores, C., & Velázquez‐Iturbide, J. Á. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.04.001 · Cited by 59 · see citing works
An Evaluation of the Impact of Automated Programming Hints on Performance and Learning
Marwan, S., Williams, J. J., & Price, T. (2019) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3291279.3339420 · Cited by 52 · see citing works
Self-citationAdaptive Immediate Feedback for Block-Based Programming: Design and Evaluation
Marwan, S., Akram, B., Barnes, T. M., & Price, T. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3180984 · Cited by 44 · see citing works
Self-citationA Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms
Price, T., Dong, Y., Zhi, R., Paaßen, B., Lytle, N., Cateté, V., & Barnes, T. M. (2019) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-019-00177-z · Cited by 42 · see citing works
Defining Tinkering Behavior in Open-ended Block-based Programming Assignments
Dong, Y., Marwan, S., Cateté, V., Price, T., & Barnes, T. M. (2019) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3287324.3287437 · Cited by 41 · see citing works
Self-citationLeite, A., & Blanco, S. A. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366921 · Cited by 39 · see citing works
ProgSnap2: A Flexible Format for Programming Process Data
Price, T., Hovemeyer, D., Rivers, K., Gao, G., Bart, A. C., Kazerouni, A. M., Becker, B. A., Petersen, A., Gusukuma, L., Edwards, S. H., et al. (2020) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3341525.3387373 · Cited by 34 · see citing works
Exploring the Assistance Dilemma: Comparing Instructional Support in Examples and Problems
McLaren, B. M., van Gog, T., Ganoe, C., Yaron, D., & Karabinos, M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-07221-0_44 · Cited by 34 · see citing works
The framework of a new online judge system for programming education
Zhou, W., Pan, Y., Zhou, Y., & Sun, G. (2018) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3210713.3210721 · Cited by 28 · see citing works
A feedback system to provide affective support to students
Jiménez, S., Juárez‐Ramírez, R., Castillo, V. H., Licea, G., Noriega, A. D. R., & Inzunza, S. (2018) · Computer Applications in Engineering Education · doi:10.1002/cae.21900 · Cited by 25 · see citing works
Online Tools to Support Novice Programming: A Systematic Review
Sim, T. Y., & Lau, S. L. (2018) · IEEE Conference on e-Learning, e-Management and e-Services (IC3e) · doi:10.1109/ic3e.2018.8632649 · Cited by 18 · see citing works
CodeProcess Charts: Visualizing the Process of Writing Code
Shrestha, R., Leinonen, J., Hellas, A., Ihantola, P., & Edwards, J. (2022) · Proceedings of the 24th Australasian Computing Education Conference · doi:10.1145/3511861.3511867 · Cited by 13 · see citing works
Kyrilov, A., & Noelle, D. C. (2016) · Journal of computing sciences in colleges · doi:10.5555/2904127.2904147 · Cited by 12 · see citing works
Feedbackstrategien für interaktive Lernaufgaben
Narciss, S. (2020) · Springer eBooks · doi:10.1007/978-3-662-54368-9_35 · Cited by 12 · see citing works
Dong, Y., Shabrina, P., Marwan, S., & Barnes, T. M. (2021) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3446871.3469764 · Cited by 9 · see citing works
Self-citationAn intelligent tutor to learn the evaluation of microcontroller I/O programming expressions
Arends, H. A., Keuning, H., Heeren, B. J., & Jeuring, J. T. (2017) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3141880.3141884 · Cited by 6 · see citing works
Self-citationRekursive Problemlösung in der Online Lernumgebung CodingBat durch Informatik-Studierende
Kiesler, N., Main, G.-U. F. A., & Fulda, H. (2022) · Research Data Center for Higher Education Research and Science Studies (FDZ-DZHW) · doi:10.21249/dzhw:studentsteps:1.0.0 · Cited by 6 · see citing works
Self-citationEnhancing Feedback to Students in Automated Diagram Assessment
Correia, H., Leal, J. P., & Paiva, J. C. (2017) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.slate.2017.11 · Cited by 5 · see citing works
An Inventory of Goals from CS1 Programs Processing a Data Series
Izu, C., Lonati, V., Morpurgo, A., & Sánchez, M. (2021) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637360 · Cited by 5 · see citing works
Self-citationAn Exploratory Analysis of Feedback Types Used in Online Coding ExercisesPreprint
Kiesler, N. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.03077 · Cited by 4 · see citing works
Self-citationLeveraging Community Software in CS Education to Avoid Reinventing the Wheel
Blanchard, J. J., Hott, J. R., Berry, V., Carroll, R., Edmison, B., Glassey, R. J., Karnalim, O., Plancher, B., & Russell, S. (2022) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3502717.3532169 · Cited by 4 · see citing works
Marwan, S. (2021) · NCSU Libraries Repository (North Carolina State University Libraries) · Cited by 1 · see citing works
Self-citationCatrambone, R. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_55
Paaßen, B. (2019) · PUB – Publications at Bielefeld University (Bielefeld University) · doi:10.4119/unibi/2941052
Edwards, J. (2022) · Harvard Dataverse · doi:10.7910/dvn/bvof7s
Flagged first, then by citations
Research works printed but not linked: 12
- Neil Christopher Charles Brown, Michael Kölling, Davin McCall, and Ian Utting. 2014. Blackbox: A Large Scale Repository of Novice Programmers' Activity. In Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE).
- Davide Fossati, Barbara Di Eugenio, Stellan Ohlsson, Christopher Brown, and Lin Chen. 2015. Data Driven Automatic Feedback Generation in the iList Intelligent Tutoring System. Technology, Instruction, Cognition and Learning 10, 1 (2015), 5-26.
- Andreas Giannakoulas and Stelios Xinogalos. 2020. A review of educational games for teaching programming to primary school students. Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education (2020).
- K.R. Koedinger, R.S.J.d. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper. 2010. A Data Repository for the EDM community: The PSLC DataShop. In Handbook of Educational Data Mining, C. Romero, S. Ventura, M. Pechenizkiy, and R.S.J.d. Baker (Eds.). CRC Press: Boca Raton, FL.
- Stephan Krusche and Andreas Seitz. 2018. Artemis: An automatic assessment management system for interactive learning. In Proceedings of the 49th ACM technical symposium on computer science education. 284-289.
- S. Marwan, N. Lytle, J. J. Williams, and T. W. Price. 2019. The Impact of Adding Textual Explanations to Next-step Hints in a Novice Programming Environment. In Proceedings of the 24th Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE19 (forthcoming).
- Thomas W. Price, Yihuan Dong, and Dragan Lipovac. 2017. iSnap: towards intelligent tutoring in novice programming environments. In Proceedings of the ACM SIGCSE Technical Symposium on computer science education.
- Sven Strickroth and Michael Striewe. 2022. Building a Corpus of Task-based Grading and Feedback Systems for Learning and Teaching Programming. The International Journal of Engineering Pedagogy (2022).
- Michael Striewe. 2015. Automated analysis of software artefacts-a use case in e-assessment. Ph.D. Dissertation. Duisburg, Essen, Universität Duisburg-Essen, Diss., 2014.
- Kurt VanLehn. 2006. The behavior of tutoring systems. International journal of artificial intelligence in education 16, 3 (2006), 227-265.
- Kurt VanLehn. 2011. The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educ. Psych. 46, 4 (2011).
- Burkhard C Wünsche, Edward Huang, Lindsay Shaw, Thomas Suselo, Kai-Cheung Leung, Davis Dimalen, Wannes van der Mark, Andrew Luxton-Reilly, and Richard Lobb. 2019. CodeRunnerGL-An interactive web-based tool for computer graphics teaching and assessment. In 2019 International Conference on Electronics, Infor- mation, and Communication (ICEIC). IEEE, 1-7.
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Sonawane, A. V., Garad, A. P., Puri, H. S. G., & Karande, A. P. (2026) · International Conference on Innovation & Growth in Next-Gen Intelligent Technology & Engineering (IGNITE) · doi:10.1109/ignite69592.2026.11706531
- Authors
- Jeuring, J., Keuning, H., Marwan, S., Bouvier, D., Izu, C., Kiesler, N., Lehtinen, T., Lohr, D., Peterson, A., & Sarsa, S.
- Affiliations
- Utrecht University (NL)
- University of Virginia (US)
- Southern Illinois University Edwardsville (US)
- The University of Adelaide (AU)
- DIPF | Leibniz Institute for Research and Information in Education (DE)
- Aalto University (FI)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University of Toronto (CA)
- ORCID
- Venue
- Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education
- Publisher
- ACM
- Published
- 2022-12-27
- Pages
- 95-115
- OpenAlex
- W4313288661
- Cited by
- 40
- References
- 48
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 13 · 2023: 8
- FWCI
- 10.43
- Type
- conference paper
- Language
- en
- Open access
- green
- research.aalto.fi
- Locations
- ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, cc-by-sa)
- Aaltodoc (Aalto University) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- formative feedback, hints, novice programmers, programming education, partial solutions, programming tasks
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