Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

• A collaborative methodology for searching, selecting, rating and recommending learning objects.
• Voting aggregation strategies and meta-learning techniques are used in order to automatically obtain or predict the final ratings.
• A functional prototype system has been implemented and tested with 50 different groups of users.

Nowadays, there is a wide variety of e-learning repositories that provide digital resources for education in the form of learning objects. Some of these systems provide recommender systems in order to help users in the search for and selection of the learning objects most appropriate to their individual needs. The search for and recommendation of learning objects are usually viewed as a solitary and individual task. However, a collaborative search can be more effective than an individual search in some situations – for example, when developing a digital course between a group of instructors. The problem of recommending learning objects to a group of users or instructors is much more difficult than the traditional problem of recommending to only one individual. To resolve this problem, this paper proposes a collaborative methodology for searching, selecting, rating and recommending learning objects. Additionally, voting aggregation strategies and meta-learning techniques are used in order to automatically obtain the final ratings without having to reach a consensus between all the instructors. A functional model has been implemented within the DELPHOS hybrid recommender system. Finally, various experiments have been carried out using 50 different groups in order to validate the proposed learning object group recommendation approach.