Abstract
Writing programs is essential to learning programming. Most programming courses encourage students to practice with lab and homework assignments. By analyzing solutions to these exercises teachers can discover mistakes and concepts students are struggling with, and use that knowledge to improve the course. Students however tend to submit many different programs even for simple exercises, making such analysis difficult. We propose using tree regular expressions to encode common patterns in programs. Based on these patterns we induce rules describing common approaches and mistakes for a given assignment. In this paper we present a case study of rule-based analysis for an introductory Python exercise. We show that our rules are easy to interpret, and can be learned from a relatively small set of programs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Available at https://github.com/krivers/ITAP-django.
References
Jin, W., Barnes, T., Stamper, J., Eagle, M.J., Johnson, M.W., Lehmann, L.: Program representation for automatic hint generation for a data-driven novice programming tutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 304–309. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_40
Rivers, K., Koedinger, K.R.: Data-driven hint generation in vast solution spaces: a self-improving Python programming tutor. Int. J. Artif. Intell. Educ. 1–28 (2015)
Nguyen, A., Piech, C., Huang, J., Guibas, L.: Codewebs: scalable homework search for massive open online programming courses. In: Proceedings of the 23rd International World Wide Web Conference (WWW 2014), pp. 491–502 (2014)
Hovemeyer, D., Hellas, A., Petersen, A., Spacco, J.: Control-flow-only abstract syntax trees for analyzing students’ programming progress. In: Proceedings of the 2016 ACM Conference on International Computing Education Research, pp. 63–72. ACM (2016)
Glassman, E.L., Scott, J., Singh, R., Guo, P.J., Miller, R.C.: OverCode: visualizing variation in student solutions to programming problems at scale. ACM Trans. Comput. Hum. Interact. (TOCHI) 22(2), 7 (2015)
Lazar, T., Možina, M., Bratko, I.: Automatic extraction of AST patterns for debugging student programs. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 162–174. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_14
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Možina, M., Lazar, T. (2018). Syntax-Based Analysis of Programming Concepts in Python. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-93846-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93845-5
Online ISBN: 978-3-319-93846-2
eBook Packages: Computer ScienceComputer Science (R0)