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Applied Intelligence

, Volume 47, Issue 2, pp 277–290 | Cite as

A recommendation approach for programming online judges supported by data preprocessing techniques

  • Raciel Yera
  • Luis Martínez
Article

Abstract

The use of programming online judges (POJ) to support students acquiring programming skills is common nowadays because this type of software contains a large collection of programming exercises to be solved by students. A POJ not only provides exercises but also automates the code compilation and its evaluation process. A common problem that students face when using POJ is information overload, as choosing the right problem to solve can be quite frustrating due to the large number of problems offered. The integration of current POJs into e-learning systems such as Intelligent Tutoring Systems (ITSs) is hard because of the lack of necessary information in ITSs. Hence, the aim of this paper is to support students with the information overload problem by using a collaborative filtering recommendation approach that filters out programming problems suitable for students’ programming skills. It uses an enriched user-problem matrix that implies a better student role representation, facilitating the computation of closer neighborhoods and hence a more accurate recommendation. Additionally a novel data preprocessing step that manages anomalous users’ behaviors that could affect the recommendation generation is also integrated in the recommendation process. A case study is carried out on a POJ real dataset showing that the proposal outperforms other previous approaches.

Keywords

Programming online judges Recommender systems Collaborative filtering Users’ inconsistencies 

Notes

Acknowledgments

This research work was partially supported by the Research Project TIN2015-66524-P.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of Ciego de ÁvilaCiego de ÁvilaCuba
  2. 2.Computer Science DepartmentUniversity of JaénJaénSpain

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