- Recommending Tasks in Online Judges
- Learning Analytics in Competitive Programming Training Systems
Giorgio Audrito, Tania Di Mascio, Paolo Fantozzi, Luigi Laura, Gemma Martini, Umberto Nanni, Marco Temperini
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.
William Di Luigi, Paolo Fantozzi, Luigi Laura, Gemma Martini, Edoardo Morassutto, Dario Ostuni, Giorgio Piccardo and Luca Versari
In this paper we discuss the use of Analytics in oii-web, an online programming contest training system. We first provide an overview of the challenges in training for programming contests. Then we discuss the data collected in these years using oii-web, a platform devoted to the training of students for the Italian Olympiads in Informatics (Olimpiadi Italiane di Informatica -OII), and analyze them comparing two distinct group of users in two distinct platform built on oii-web, one devoted to students and one to their teachers. Most notably, the two groups are more similar than one would expect when dealing with programming contest training.