Non-restricted Access to Model Solutions: A Good Idea?
Open accessAbstract
In this article, we report an experiment where students in an introductory programming course were given the opportunity to view model solutions to programming assignments whenever they wished, without the need to complete the assignments beforehand or to wait for the deadline to pass. Our experiment was motivated by the observation that some students may spend hours stuck with an assignment, leading to non-productive study time. At the same time, we considered the possibility of students using the sample solutions as worked examples, which could help students to improve the design of their own programs. Our experiment suggests that many of the students use the model solutions sensibly, indicating that they can control their own work. At the same time, a minority of students used the model solutions as a way to proceed in the course, leading to poor exam performance.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 [62]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 28 · Cited by 7
Research works printed but not linked: 3
- Allan Collins, John Seely Brown, and Susan E Newman. 1989. Cognitive appren- ticeship: Teaching the crafts of reading, writing, and mathematics. Knowing, learning, and instruction: Essays in honor of Robert Glaser 18 (1989), 32-42.
- Vanessa P Dennen and Kerry J Burner. 2008. The cognitive apprenticeship model in educational practice. Handbook of research on educational communications and technology 3 (2008), 425-439.
- J Gregory Trafton and Brian J Reiser. 1993. Studying examples and solving problems: Contributions to skill acquisition. In Proceedings of the 15th conference of the Cognitive Science Society. Citeseer, 1017-1022.
- Authors
- Nygren, H., Leinonen, J., & Hellas, A.
- Affiliations
- University of Helsinki (FI)
- Venue
- Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education
- Publisher
- ACM
- Published
- 2019-07-02
- Pages
- 44-50
- OpenAlex
- W2964119902
- Cited by
- 7
- References
- 28
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2019: 1
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- model solutions, worked examples, introductory programming, programming exercises, exam performance, programming education
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