Experimenting with Model Solutions as a Support Mechanism
Open accessAbstract
We describe an experiment from an introductory programming course where we provided students an opportunity to access model solutions of programming assignments they have not yet completed. Access to model solutions was controlled with coins, which students collected by completing programming assignments. The quantity of coins was limited so that students could buy solutions to at most one tenth of the course assignments. When compared to the traditional approach where access to model solutions is limited to only after the assignment is completed or the assignment deadline has passed, students seemed to enjoy the opportunity more and collecting coins motivated some students to complete more assignments. Collected coins were mostly used close to deadlines and on more difficult assignments. Overall, the use of coins and model solutions may be a viable option to providing students additional support. Data from the use of coins and model solutions could also be used to identify students who could benefit from additional guidance.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [63]
Cites
Motivational processes affecting learning.
Dweck, C. S. (1986) · American Psychologist · doi:10.1037/0003-066x.41.10.1040 · Cited by 6,971 · see citing works
Gamification. using game-design elements in non-gaming contexts
Deterding, S., Sicart, M., Nacke, L., O'Hara, K., & Dixon, D. (2011) · CHI '11 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1979742.1979575 · Cited by 2,413 · see citing works
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_4 · Cited by 1,927 · see citing works
COGNITIVE APPRENTICESHIP: MAKING THINKING VISIBLE
Collins, A., Brown, J. S., & Holum, A. (1991) · Cited by 1,516 · see citing works
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010) · Psychological Bulletin · doi:10.1037/a0018947 · Cited by 1,308 · see citing works
The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra
Sweller, J., & Cooper, G. A. (1985) · Cognition and Instruction · doi:10.1207/s1532690xci0201_3 · Cited by 1,214 · see citing works
Achievement Goal Theory at the Crossroads: Old Controversies, Current Challenges, and New Directions
Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.538646 · Cited by 931 · see citing works
Rethinking achievement goals: When are they adaptive for college students and why?
Harackiewicz, J. M., Barron, K. E., & Elliot, A. J. (1998) · Educational Psychologist · doi:10.1207/s15326985ep3301_1 · Cited by 772 · see citing works
Failure rates in introductory programming revisited
Watson, C., & Li, F. W. B. (2014) · Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14 · doi:10.1145/2591708.2591749 · Cited by 639 · see citing works
Failure rates in introductory programming
Bennedsen, J., & Caspersen, M. E. (2007) · ACM SIGCSE Bulletin · doi:10.1145/1272848.1272879 · Cited by 634 · see citing works
Introductory programming: a systematic literature review
Luxton-Reilly, A., Simon, Al-Bluwi, I., Becker, B. A., Giannakos, M. N., Kumar, A. N., Ott, L. M., Paterson, J. H., Scott, M., Sheard, J., et al. (2018) · doi:10.1145/3293881.3295779 · Cited by 511 · see citing works
Gamification of Education: A Review of Literature
Nah, F. F., Zeng, Q. H., Telaprolu, V. R., Ayyappa, A. P., & Eschenbrenner, B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07293-7_39 · Cited by 496 · see citing works
Gibson, D., Ostashewski, N., Flintoff, K., Grant, S., & Knight, E. B. (2013) · Education and Information Technologies · doi:10.1007/s10639-013-9291-7 · Cited by 472 · see citing works
Engaging Engineering Students with Gamification
Barata, G., Gama, S. P., Jorge, J. A., & Gonçalves, D. J. V. (2013) · International Conference on Games and Virtual Worlds for Serious Applications · doi:10.1109/vs-games.2013.6624228 · Cited by 327 · see citing works
A case study in the gamification of a university-level games development course
O'Donovan, S., Gain, J. E., & Marais, P. (2013) · South African Institute of Computer Scientists and Information Technologists · doi:10.1145/2513456.2513469 · Cited by 287 · see citing works
Help seeking, learning and contingent tutoring
Wood, H., & Wood, D. (1999) · Computers & Education · doi:10.1016/s0360-1315(99)00030-5 · Cited by 275 · see citing works
Comparing three models of achievement goals: Goal orientations, goal standards, and goal complexes.
Senko, C., & Tropiano, K. L. (2016) · Journal of Educational Psychology · doi:10.1037/edu0000114 · Cited by 228 · see citing works
Vihavainen, A., Airaksinen, J., & Watson, C. (2014) · doi:10.1145/2632320.2632349 · Cited by 224 · see citing works
Extreme apprenticeship method in teaching programming for beginners
Vihavainen, A., Paksula, M., & Luukkainen, M. (2011) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1953163.1953196 · Cited by 177 · see citing works
Gamification and Web-based Homework
Goehle, G. (2013) · PRIMUS · doi:10.1080/10511970.2012.736451 · Cited by 170 · see citing works
How (not) to introduce badges to online exercises
Haaranen, L., Ihantola, P., Hakulinen, L., & Korhonen, A. (2014) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2538862.2538921 · Cited by 133 · see citing works
Analogy Events: How Examples are Used During Problem Solving
VanLehn, K. A. (1998) · Cognitive Science · doi:10.1207/s15516709cog2203_4 · Cited by 122 · see citing works
Design Team Convergence: The Influence of Example Solution Quality
Fu, K. K., Cagan, J., & Kotovsky, K. (2010) · Journal of Mechanical Design · doi:10.1115/1.4002202 · Cited by 84 · see citing works
Eleftheria, C. A., Charikleia, P., Iason, C. G., et al. (2013) · doi:10.1109/iisa.2013.6623724 · Cited by 77 · see citing works
Examining Interest and Grades in Computer Science 1
Zingaro, D. (2015) · ACM Transactions on Computing Education · doi:10.1145/2802752 · Cited by 72 · see citing works
Automatically detectable indicators of programming assignment difficulty
Ihantola, P., Sorva, J., & Vihavainen, A. (2014) · Conference on Information Technology Education · doi:10.1145/2656450.2656476 · Cited by 58 · see citing works
Impact of Student Achievement Goals on CS1 Outcomes
Zingaro, D., & Porter, L. (2016) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2839509.2844553 · Cited by 58 · see citing works
The influence of achievement goal orientation on plagiarism
Koul, R., Clariana, R. B., Jitgarun, K., & Songsriwittaya, A. (2009) · Learning and Individual Differences · doi:10.1016/j.lindif.2009.05.005 · Cited by 57 · see citing works
Classroom Live: a software-assisted gamification tool
de Freitas, A. A., & de Freitas, M. (2013) · Computer Science Education · doi:10.1080/08993408.2013.780449 · Cited by 56 · see citing works
Comparison of Time Metrics in Programming
Leinonen, J., Leppänen, L., Ihantola, P., & Hellas, A. (2017) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3105726.3106181 · Cited by 41 · see citing works
Self-citationVihavainen, A., Vikberg, T., Luukkainen, M., et al. (2013) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/2462476.2462508 · Cited by 34 · see citing works
Hint systems may negatively impact performance in educational games
O'Rourke, E., Ballweber, C., & Popovi, Z. (2014) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2556325.2566248 · Cited by 25 · see citing works
Spacco, J., Pugh, W., Ayewah, N., et al. (2006) · doi:10.1145/1176617.1176665 · Cited by 21 · see citing works
Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor
Mostafavi, B., Zhou, G., Lynch, C. F., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19773-9_102 · Cited by 15 · see citing works
Stephens, K., & Fox, A. (2018) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3197091.3197102 · Cited by 10 · see citing works
Non-restricted Access to Model Solutions: A Good Idea?
Nygren, H., Leinonen, J., & Hellas, A. (2019) · Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3304221.3319768 · Cited by 7 · see citing works
Self-citationIllusion of Progress is Moar Addictive than Cat Pictures
Leppänen, L., Vapaakallio, L., & Vihavainen, A. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2893388 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3501385.3543957 · Cited by 497 · see citing works
Self-citationComparing Code Explanations Created by Students and Large Language Models
Leinonen, J., Denny, P., MacNeil, S., Sarsa, S., Bernstein, S., Kim, J., Tran, A., & Hellas, A. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588785 · Cited by 210 · see citing works
Self-citationExploring the Responses of Large Language Models to Beginner Programmers’ Help Requests
Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanpää, L., & Sorva, J. (2023) · Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 · doi:10.1145/3568813.3600139 · Cited by 145 · see citing works
Self-citation
- Authors
- Nygren, H., Leinonen, J., Pirttinen, N., Leinonen, A., & Hellas, A.
- Affiliations
- University of Helsinki (FI)
- Venue
- Proceedings of the 1st UK & Ireland Computing Education Research Conference on - UKICER
- Publisher
- ACM Press
- Published
- 2019
- Pages
- 1-7
- OpenAlex
- W2970608058
- Cited by
- 4
- References
- 37
- Citations by year
- 2023: 2 · 2022: 1 · 2020: 1
- FWCI
- 0.58
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Työväentutkimus Vuosikirja (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- model solutions, introductory programming, student support, gamification, coins, programming exercises, student motivation, assignment completion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
