Abstract
Online judges are online repositories with hundreds or thousands of programming exercises or problems. They are very interesting tools for learning programming concepts, but novice users tend to feel overwhelmed by the large number of problems available. Traditional recommendation techniques based on content or collaborative filtering do not work well in these systems due to the lack of user ratings or semantic descriptions of the problems. In this work, we propose a recommendation approach based on learning itineraries, i.e., the sequences of problems that the users tried to solve. Our experiments reveal that interesting learning paths can emerge from previous user experiences and we can use those learning paths to recommend interesting problems to new users. We also show that the recommendation can be improved if we consider not only the problems but also the order in which they were solved.
Supported by UCM (Group 910494) and Spanish Committee of Economy and Competitiveness (TIN2014-55006-R).
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.
- 2.
- 3.
- 4.
https://www.aceptaelreto.com (Spanish only).
References
Alves, P., Amaral, L., Pires, J.: Case-based reasoning approach to adaptive web-based educational systems. In: Eighth IEEE International Conference on Advanced Learning Technologies, ICALT 2008, pp. 260–261. IEEE (2008)
Avancini, H., Straccia, U.: User recommendation for collaborative and personalised digital archives. Int. J. Web Based Communities 1(2), 163–175 (2005)
Dooms, S., Bellogín, A., De Pessemier, T., Martens, L.: A framework for dataset benchmarking and its application to a new movie rating dataset. ACM Trans. Intell. Syst. Technol. 7(3), 41:1–41:28 (2016)
Drachsler, H., Hummel, H.G.K., Van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A.J., Boers, N., Koper, R.: Effects of the ISIS recommender system for navigation support in self-organised learning networks. Educ. Technol. Soc. 12(3), 115–126 (2009)
Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, Heidelberg (2015). doi:10.1007/978-1-4899-7637-6_12
Goldman, R.P., Kuter, U.: Measuring plan diversity: pathologies in existing approaches and a new plan distance metric. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 3275–3282. AAAI Press (2015)
Hulpuş, I., Hayes, C., Fradinho, M.O.: A framework for personalised learning-plan recommendations in game-based learning. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C. (eds.) Recommender Systems for Technology Enhanced Learning. Springer, New York (2014). doi:10.1007/978-1-4939-0530-0_5
Jimenez-Diaz, G., Gómez Martín, P.P., Gómez Martín, M.A., Sánchez-Ruiz, A.A.: Similarity metrics from social network analysis for content recommender systems. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 203–217. Springer, Cham (2016). doi:10.1007/978-3-319-47096-2_14
Kurnia, A., Lim, A., Cheang, B.: Online judge. Comput. Educ. 36(4), 299–315 (2001)
Levandowsky, M., Winter, D.: Distance between sets. Nature 234(5323), 34–35 (1971)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. In: Soviet Physics Doklady, vol. 10, pp. 707 (1966)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)
Ruiz-Iniesta, A., Jimenez-Diaz, G., Gomez-Albarran, M.: A semantically enriched context-aware OER recommendation strategy and its application to a computer science OER repository. IEEE Trans. Educ. 57(4), 255–260 (2014)
Said, A., Bellogín, A.: Comparative recommender system evaluation. In: Proceedings of the 8th ACM Conference on Recommender systems - RecSys 2014, pp. 129–136 (2014)
Zaíane, O.R.: Building a recommender agent for e-learning systems. In: Proceedings of the International Conference on Computers in Education, pp. 55–59. IEEE (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sánchez-Ruiz, A.A., Jimenez-Diaz, G., Gómez-Martín, P.P., Gómez-Martín, M.A. (2017). Case-Based Recommendation for Online Judges Using Learning Itineraries. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-61030-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61029-0
Online ISBN: 978-3-319-61030-6
eBook Packages: Computer ScienceComputer Science (R0)