In this paper we focus on the problem of finding personalized learning paths in presence of a large number of available learning components. In particular, we model the relationships holding between learning activities and the related (needed/achieved) competence, based on directed hypergraphs. We show as the complexity of optimizing learning paths depends dramatically on the adopted metrics; in particular, we prove that finding a learning path with minimum timespan can be done in quasi-linear time, whilst finding one with minimum total effort (apparently, a very similar problem) is NP-hard. Therefore in some cases, it is possible to use simple and fast algorithms for computing personalized elearning paths, while in other cases the developers must rely on approximated heuristics, or adequate computational resources. We are implementing this modeling and the related algorithms in the framework provided by the LECOMPS system for personalized e-learning. The final aim is to apply the modeling in large repositories, or in wider web-based e-learning environments.

The organization of large-scale repositories of learning objects with directed hypergraphs / Laura, Luigi; Nanni, Umberto; Temperini, Marco. - STAMPA. - 8699(2014), pp. 23-33. - LECTURE NOTES IN COMPUTER SCIENCE. [10.1007/978-3-319-13296-9_3].

The organization of large-scale repositories of learning objects with directed hypergraphs

LAURA, Luigi;NANNI, Umberto;TEMPERINI, Marco
2014

Abstract

In this paper we focus on the problem of finding personalized learning paths in presence of a large number of available learning components. In particular, we model the relationships holding between learning activities and the related (needed/achieved) competence, based on directed hypergraphs. We show as the complexity of optimizing learning paths depends dramatically on the adopted metrics; in particular, we prove that finding a learning path with minimum timespan can be done in quasi-linear time, whilst finding one with minimum total effort (apparently, a very similar problem) is NP-hard. Therefore in some cases, it is possible to use simple and fast algorithms for computing personalized elearning paths, while in other cases the developers must rely on approximated heuristics, or adequate computational resources. We are implementing this modeling and the related algorithms in the framework provided by the LECOMPS system for personalized e-learning. The final aim is to apply the modeling in large repositories, or in wider web-based e-learning environments.
2014
New Horizons in Web Based Learning
978-3-319-09634-6
personalized learning paths; reusability of elearning repositories; directed hypergraphs
02 Pubblicazione su volume::02a Capitolo o Articolo
The organization of large-scale repositories of learning objects with directed hypergraphs / Laura, Luigi; Nanni, Umberto; Temperini, Marco. - STAMPA. - 8699(2014), pp. 23-33. - LECTURE NOTES IN COMPUTER SCIENCE. [10.1007/978-3-319-13296-9_3].
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