A machine learning approach to identify dependencies among learning objects

Selecting and sequencing a set of Learning Objects (LOs) to build a course may turn out to be quite a challeng- ing task. In this paper we focus on such an aspect, related to the verification and respect of the relationships of pedagogical dependence existing between two LOs added to a course (meaning that if a given LO has another one as “pre-requisite”, then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). In our approach the sequencing of LOs in the course can still be managed by the instructor, basing on her/his taste and preferences, yet s/he can also be helped by a set of suggestions, related to the pre-requisite relationships existing among the LOs selected for the course. Such suggestions (such relationships, in effect) can be computed automatically and provide the instructor with significant help and guidance. We show a light-weight formalization of the LO, and how it can be “represented” by a set of WikiPedia Pages (“topics”); then we show how such set of topics, together with a set of relevant hypotheses we previously defined, can help establish the dependence relationship existing between two LOs. In this endeavor we exploit the classification in categories available for the WikiPedia topics, and obtain interesting results for our framework, in terms of precision and recall of the dependence relationships.