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Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios
aDeNu Research Group, Computer Science School, UNED, Calle Juan del Rosal, 16, Madrid 28040, Spain
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Received: 4 March 2011; in revised form: 23 May 2011 / Accepted: 1 July 2011 / Published: 20 July 2011
Abstract: This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations.
Keywords: semantic educational recommender systems; formal e-learning scenarios; learning management systems; design requirements; recommendation model; personalization; accessibility; service-oriented architectures; graphical user interfaces; educational standards and specifications; IMS; ISO; SCORM; W3C; TORMES methodology
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Santos, O.C.; Boticario, J.G. Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios. Algorithms 2011, 4, 131-154.
Santos OC, Boticario JG. Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios. Algorithms. 2011; 4(2):131-154.
Santos, Olga C.; Boticario, Jesus G. 2011. "Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios." Algorithms 4, no. 2: 131-154.