The DOI matches its Crossref record.
Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers
Abstract
Since the early 1960's, researchers have built a number of programming languages and environments with the intention of making programming accessible to a larger number of people. This article presents a taxonomy of languages and environments designed to make programming more accessible to novice programmers of all ages. The systems are organized by their primary goal, either to teach programming or to use programming to empower their users, and then, by each system's authors' approach, to making learning to program easier for novice programmers. The article explains all categories in the taxonomy, provides a brief description of the systems in each category, and suggests some avenues for future work in novice programming environments and languages.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., et al. (2009) · Communications of the ACM · doi:10.1145/1592761.1592779 · Cited by 3,333
Lye, S. Y., & Koh, J. H. L. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.012 · Cited by 1,421
The state of the art in end-user software engineering
Ko, A. J., Abraham, R., Beckwith, L., Blackwell, A., Burnett, M., Erwig, M., Scaffidi, C., Lawrance, J., Lieberman, H., Myers, B., et al. (2011) · ACM Computing Surveys · doi:10.1145/1922649.1922658 · Cited by 633
Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529
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
An Empirical Investigation into Programming Language Syntax
Stefik, A., & Siebert, S. (2013) · ACM Transactions on Computing Education · doi:10.1145/2534973 · Cited by 295
Noh, J., & Lee, J. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-019-09708-w · Cited by 207
A Tool for Introducing Computer Science with Automatic Formative Assessment
Benotti, L., Martinez, M. C., & Schapachnik, F. (2018) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2017.2682084 · Cited by 141
Cheng, Y.-P., Lai, C.-F., Chen, Y.-T., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104794 · Cited by 130
Personifying programming tool feedback improves novice programmers' learning
Lee, M. J., & Ko, A. J. (2011) · Proceedings of the seventh international workshop on Computing education research · doi:10.1145/2016911.2016934 · Cited by 125
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
A Tangible Programming Tool for Children to Cultivate Computational Thinking
Wang, D., Wang, T., & Liu, Z. (2014) · The Scientific World Journal · doi:10.1155/2014/428080 · Cited by 82
Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v22i4.5474 · Cited by 65
End-user debugging strategies: A sensemaking perspective
Grigoreanu, V., Burnett, M., Wiedenbeck, S., Cao, J., Rector, K., & Kwan, I. (2012) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2147783.2147788 · Cited by 60
Fields, D., & Enyedy, N. (2013) · Mind, Culture, and Activity · doi:10.1080/10749039.2012.691199 · Cited by 51
Franklin, D., Coenraad, M., Palmer, J., Eatinger, D., Zipp, A., Anaya, M., White, M., Pham, H., Gökdemir, O., & Weintrop, D. (2020) · Proceedings of the 2020 ACM Conference on International Computing Education Research · doi:10.1145/3372782.3406256 · Cited by 43
Qian, Y., & Lehman, J. D. (2019) · Sage Open · doi:10.1177/2158244019885136 · Cited by 42
(2013) · Computer Science Education Research Conference · doi:10.5555/2541917.2541927 · Cited by 34
FalconCode: A Multiyear Dataset of Python Code Samples from an Introductory Computer Science Course
de Freitas, A., Coffman, J., de Freitas, M., Wilson, J., & Weingart, T. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569822 · Cited by 15
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7
Metadata
- Authors
- Kelleher, C., & Pausch, R.
- Venue
- ACM Computing Surveys
- Published
- 2005-06
- Volume
- 37
- Issue
- 2
- Pages
- 83-137
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2012761058
- Cited by
- 886
- References
- 0
- FWCI
- 76.19
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
