## 5 Conclusions  
In this paper we have explored the requirements for the inclusive and personalization support required in formal e-learning scenarios. To cope with the distinctive issues that characterize the educational domain, we propose the SERS, which includes new conceptual and developmental approaches that extend the features provided by the current application of recommender systems in education. This proposal has been coined to describe those ERS that are developed to extend the adaptive capabilities of LMS (with adaptive navigation support) in an interoperable manner to support learners in a personalized and inclusive way. SERS consider all the potential actions in an LMS, beyond recommending learning resources or courses. For instance, active actions can be considered, such as contributing to a forum to promote self-reflection, which may be relevant from an educational point of view.  
SERS provide the infrastructure for the delivery of recommendations, but it is not involved in understanding and identifying recommendation opportunities in formal e-learning scenarios, which are educational issues that can be identified by educators. For this, the TORMES methodology has been defined. TORMES supports the elicitation and design of suitable educational oriented recommendations, which are delivered by the SERS when appropriate learner's needs and context are met. Thus, SERS are characterized by guiding, based on educational criteria, learners in their interaction within LMS. The recommendations are semantically described and can be obtained thanks to the information exchange among the different components involved in the process of generating and delivering the recommendations. SERS development draws on the following three elements: (1) a recommendation model to semantically characterize the recommendations; (2) an open standards-based SOA to guide the integration of the SERS with existing LMS and additional components such as the UM and DM; and (3) a GUI to be integrated in the LMS presentation layer to deliver the recommendations to the learners.  
The main difference of SERS with respect to existing ERS is the semantic interoperability among the different data available, such as user data (demographic, interaction preferences, learning styles, ability profile, learning needs, previous knowledge, interests, background, course outcomes, _etc._), context data (device, environment) and course data (metadata of contents and instructional design) in terms of existing specifications and standards which allow for the interpretation of the semantics of the data. Interoperability is achieved during the information exchange among the different software components involved.  
In summary, SERS extend the adaptive capabilities of existing LMS with adaptive navigation support to offer a personalized and inclusive guidance to learners in formal e-learning scenarios and have the following features:  
* Guide learners through the information overload and inexperience of alternatives in the LMS;
* Reuse existing infrastructure at educational institutions since it offers a new component (_i.e._, the SERS) to be integrated with existing LMS in an interoperable way;
* Manage the rich contextual information available in formal e-learning scenarios (_i.e._, diversity of actions on LMS objects).  
SERS are to be exploited by the emerging generation of LMS, which are increasingly focused on service-oriented approaches and on incorporating semantic descriptions to achieve the required semantic support with information exchange among components. Moreover, they can also serve to cope with those personalized guiding needs that characterize the development of Personal Learning Environments (PLE) [43], where users apply Web 2.0-style services to create their own learning management tools [44].