During the phases of course construction, in Learning Management Systems, a teacher can be valuably helped by system's recommendations about learning objects to include in the course. A usual protocol is in that the teacher performs a query, looking for suitable learning material, and the system proposes a list of learning objects, with information shown for each one; then the teacher is supposed to make her choice, basing on the displayed information. Here we present a Recommender System for Learning Objects retrieved from Learning Objects Repositories, that is based on a "social teacher model", based on the similarities with the teacher in the system, and the potential model evolutions over time. The proposed system is available as a Moodle plug-in. In the paper we show the details of the information decorating the learning objects retrieved after a query, the definition of the teacher model, and the similarity measure underlying the recommendation strategy.
Course-Driven Teacher Modeling for Learning Objects Recommendation in the Moodle LMS
Sciarrone, Filippo;
2017-01-01
Abstract
During the phases of course construction, in Learning Management Systems, a teacher can be valuably helped by system's recommendations about learning objects to include in the course. A usual protocol is in that the teacher performs a query, looking for suitable learning material, and the system proposes a list of learning objects, with information shown for each one; then the teacher is supposed to make her choice, basing on the displayed information. Here we present a Recommender System for Learning Objects retrieved from Learning Objects Repositories, that is based on a "social teacher model", based on the similarities with the teacher in the system, and the potential model evolutions over time. The proposed system is available as a Moodle plug-in. In the paper we show the details of the information decorating the learning objects retrieved after a query, the definition of the teacher model, and the similarity measure underlying the recommendation strategy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.