Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
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Published
Mar 15, 2023
John Edwards
Kaden Hart
Raj Shrestha
Abstract
Analysis of programming process data has become popular in computing education research and educational
data mining in the last decade. This type of data is quantitative, often of high temporal resolution,
and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity
can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs
being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially
at the keystroke level, is notable. There are several reasons for this failing, with the most prominent
being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present
the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of
both event and metadata richness. We describe our collection technique and properties of the data along
with deidentification techniques that, while not fully relieving researchers of significant effort, at least
reduce and streamline manual work in hopes that researchers will release similar datasets in the future.
How to Cite
Edwards, J., Hart, K., & Shrestha, R. (2023). Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets. Journal of Educational Data Mining, 15(1), 1–31. https://doi.org/10.5281/zenodo.7646659
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Keywords
datasets, keystrokes, CS1
References
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AHADI, A., LISTER, R., HAAPALA, H., AND VIHAVAINEN, A. 2015. Exploring machine learning methods to automatically identify students in need of assistance. In ICER ’15: Proceedings of the Eleventh Annual International Conference on International Computing Education Research, J. Sheard and Q. Cutts, Eds. Association for Computing Machinery, 121–130.
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ALLEN, L. K., MILLS, C., JACOVINA, M. E., CROSSLEY, S., D’MELLO, S., AND MCNAMARA, D. S. 2016. Investigating boredom and engagement during writing using multiple sources of information: the essay, the writer, and keystrokes. In LAK16: Proceedings of the sixth International Conference on Learning Analytics & Knowledge. Association for Computing Machinery, 114–123.
ALTADMRI, A. AND BROWN, N. C. 2015. 37 million compilations: Investigating novice programming mistakes in large-scale student data. In SIGCSE ’15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, 522–527.
BARNES, T. AND STAMPER, J. 2008. Toward automatic hint generation for logic proof tutoring using historical student data. In International Conference on Intelligent Tutoring Systems, B. P. Woolf, E. Aïmeur, R. Nkambou, and S. Lajoie, Eds. Springer, 373–382.
BECKER, B. A. 2016. A new metric to quantify repeated compiler errors for novice programmers. In ITiCSE ’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, 296–301.
BIXLER, R. AND D’MELLO, S. 2013. Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits. In IUI ’13: Proceedings of the 2013 International Conference on Intelligent User Interfaces. Association for Computing Machinery, 225–234.
BLIKSTEIN, P., WORSLEY, M., PIECH, C., SAHAMI, M., COOPER, S., AND KOLLER, D. 2014. Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences 23, 4, 561–599.
BRAUGHT, G. AND MIDKIFF, J. 2016. Tool design and student testing behavior in an introductory java course. In SIGCSE ’16: Proceedings of the 47th ACM Technical Symposium on Computing Science Education. Association for Computing Machinery, 449–454.
BROWN, N. C., ALTADMRI, A., SENTANCE, S., AND KÖLLING, M. 2018. Blackbox, five years on: An evaluation of a large-scale programming data collection project. In ICER ’18: Proceedings of the 2018 ACM Conference on International Computing Education Research. Association for Computing Machinery, 196–204.
BROWN, N. C. AND KÖLLING, M. 2020. Blackbox mini-getting started with blackbox data analysis. In SIGCSE ’20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, 1387–1387.
BROWN, N. C. C., KÖLLING, M., MCCALL, D., AND UTTING, I. 2014. Blackbox: A large scale repository of novice programmers’ activity. In SIGCSE ’14: Proceedings of the 45th ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, 223–228.
CARTER, A. S. 2012. Supporting the virtual design studio through social programming environments. In ICER ’13: Proceedings of the ninth annual International Conference on International Computing Education Research. Association for Computing Machinery, 157–158.
CARTER, A. S. AND HUNDHAUSEN, C. D. 2017. Using programming process data to detect differences in students’ patterns of programming. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. Association for Computing Machinery, 105–110.
CARTER, A. S., HUNDHAUSEN, C. D., AND ADESOPE, O. 2015. The normalized programming state model: Predicting student performance in computing courses based on programming behavior. In ICER’15: Proceedings of the Eleventh Annual International Conference on International Computing Education Research. Association for Computing Machinery, 141–150.
CARTER, A. S., HUNDHAUSEN, C. D., AND ADESOPE, O. 2017. Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE) 17, 3, 1–20.
CASTRO-WUNSCH, K., AHADI, A., AND PETERSEN, A. 2017. Evaluating neural networks as a method for identifying students in need of assistance. In SIGCSE’17: Proceedings of the 2017 ACM Technical Symposium on Computer Science Education. Association for Computing Machinery, 111–116.
COLLABORATION, O. S. 2015. Estimating the reproducibility of psychological science. Science 349, 6251, aac4716.
CROW, T., LUXTON-REILLY, A., AND WUENSCHE, B. 2018. Intelligent tutoring systems for programming education: a systematic review. In Proceedings of the 20th Australasian Computing Education Conference. Association for Computing Machinery, 53–62.
DWYL. 2022. List of English Words. https://raw.githubusercontent.com/dwyl/ english-words/master/words_alpha.txt (accessed 14 April, 2022).
EDWARDS, J., DITTON, J., SAINJU, B., AND DAWSON, J. 2020. Different assignments as different contexts: predictors across assignments and outcome measures in cs1. In 2020 Intermountain Engineering, Technology and Computing (IETC). IEEE, 1–6.
EDWARDS, J., DITTON, J., TRNINIC, D., SWANSON, H., SULLIVAN, S., AND MANO, C. 2020. Syntax exercises in CS1. In ICER ’20: Proceedings of the 2020 ACM Conference on International Computing Education Research. Association for Computing Machinery, 216–226.
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Special Issue on CSEDM: Educational Data Mining for Computing Education
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