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Informatics 2018, 5(2), 17;

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Knowledge Management Center, University of Ciego de Ávila, Carretera a Morón Km 91/2, 65100 Ciego de Ávila, Cuba
Department of Computer Science, University of Camagüey, Circun. Km 51/2, 70100 Camagüey, Cuba
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Author to whom correspondence should be addressed.
Received: 19 February 2018 / Revised: 25 March 2018 / Accepted: 29 March 2018 / Published: 3 April 2018
(This article belongs to the Special Issue Advances in Recommender Systems)
PDF [886 KB, uploaded 3 May 2018]


Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists. View Full-Text
Keywords: programming online judges; fuzzy logic; problems recommendation programming online judges; fuzzy logic; problems recommendation

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Yera Toledo, R.; Caballero Mota, Y.; Martínez, L. A Recommender System for Programming Online Judges Using Fuzzy Information Modeling. Informatics 2018, 5, 17.

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