Combining latent profile analysis and programming traces to understand novices’ differences in debugging
Author:
Funder
National Science Foundation
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Link
https://link.springer.com/content/pdf/10.1007/s10639-022-11343-7.pdf
Reference61 articles.
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2. Alqadi, B. S., & Maletic, J. I. (2017). An empirical study of debugging patterns among novices programmers. In Proceedings of the 48th ACM Technical Symposium on Computer Science EducationSIGCSE ’17. (pp. 15–20). http://doi.org/10/ghqjp4
3. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181
4. Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 20–31. https://doi.org/10.1080/10705511.2014.955104
5. Baumstark, L., & Orsega, M. (2016). Quantifying introductory CS students’ iterative software process by mining version control system repositories. Journal of Computing Sciences in Colleges, 31(6), 97–104. https://doi.org/10.5555/2904446.2904470
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