RT Journal Article
T1 A Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environments
A1 Leony Arreaga, Derick Antonio
A1 Parada Gélvez, Hugo A.
A1 Muñoz Merino, Pedro José
A1 Pardo Sánchez, Abelardo
A1 Delgado Kloos, Carlos
AB Cloud technology has provided a set of tools to learners and tutors to create a virtual personal learning environment. As these tools only support basic tasks, users of learning environments are looking for specialized tools to exploit the uncountable learning elements available on the internet. Thus, one of the most common functionalities in cloud-based learning environments is the recommendation of learning elements and several approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner; and services offering this functionality on cloud environments are scarce. Hence in this paper, we propose a generic cloud-based architecture for a system that recommends learning elements according to the affective state of the learner. Furthermore, we provide the description of some use cases along with the details of the implementation of one of them. We also provide a discussion on the advantages and disadvantages of the proposal.
PB J.UCS
SN 0948-6968 (online)
SN 0948-695X (print)
YR 2013
FD 2013-08-01
LK https://hdl.handle.net/10016/18702
UL https://hdl.handle.net/10016/18702
LA eng
NO Work partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project 2009/TIC-1650), and “Consejo Social - Universidad Carlos III de Madrid”.
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RD 30 may. 2024