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Recommending Tasks in Online Judges

2019; Springer Nature; Linguagem: Inglês

10.1007/978-3-030-23990-9_16

2194-5357

Giorgio Audrito, Tania Di Mascio, Paolo Fantozzi, Luigi Laura, Gemma Martini, Umberto Nanni, Marco Temperini,

Reinforcement Learning in Robotics

Online Judges are e-learning tools used to improve the programming skills, typically for programming contests such as International Olympiads in Informatics and ACM International Collegiate Programming Contest. In this context, due to the nowadays broad list of programming tasks available in Online Judges, it is crucial to help the learner by recommending a challenging but not unsolvable task. So far, in the literature, few authors focused on Recommender Systems (RSs) for Online Judges; in this paper we discuss some peculiarities of this problem, that prevent the use of standard RSs, and address a first building brick: the assessment of (relative) tasks hardness. We also present the results of a preliminary experimental evaluation of our approach, that proved to be effective against the available dataset, consisting in all the submissions made in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.