Social Intelligent Learning Management System for Demanding Users

With the ambition of providing teachers with a novel concrete tool called “Social Intelligent Learning Management System (SILMS)” for worldwide exploiting didactic contents to feature their courses, I faced the problem of creating a social platform with adequate functionalities to satisfy the teacher expectations. This goal involved many disciplines and practices ranging from DB management, content management, social networking, till the exploitation of new cognitive systems in the thread of WEB4.0 services. At the same time my approach was much oriented to realize a real tool of concrete usage, still with distinguishably advanced features. Thus, starting with a well designed architecture I endowed it with key functionalities that become the stakeholders of the emerging social networks: 1) a quality system ensuring the value of the materials the users put in the platform repository as their contribution to the social business, 2) a recommender system based on either ontology assisted navigator or computational intelligence techniques constituting the principal tool to guide teachers along the assembling of materials into courses. This work involved the cooperation of scientists in the mentioned disciplines, mainly at level of mentoring and discussing the best strategies. It also enjoyed contributions from foreign partners within the European Project NETT, which supported my PhD course and provided the lead case study as well. As a result we (me and the involved people) delineate an ecosystem where teachers exploit contents of a repository to which may contribute by themselves. They are encouraged in exploiting and contributing because the contents are of high quality; they are wisely assisted in the exploration of the repository by platform services yet under their full control; and they are variously reworded by this involvement. This thesis resumes all this work, highlighting the innovative aspects and the experimental evidences. It consists of 6 chapters plus introduction and conclusions. The first two (chapters 2 and 3) are devoted to needs’ analysis and related state of the art. While a second block (chapters 4 and 5) deals with the platform design and implementation, the last two chapters are devoted to user experience and the mentioned cognitive tools employed in the realization of a recommender system which learns from the users how to better satisfy their inquiries. The innovative contributions are reckoned in the Introduction, whereas a short appraisal of the work done and forewords are provided in the Conclusions chapter.