Abstract
User profile inference on online social networks is a promising way for building recommender and adaptive systems. In the context of adaptive learning systems, user models are still constructed by means of classical techniques such as questionnaires. Those are too time-consuming and present a risk of dissuading learners to use the system. This paper explores the feasibility of learner modeling based on a proposed set of features extracted and inferred from social networks, according to the IMS-LIP specification. A suitable general architecture of an AEHS is presented, whose adaptation combines three distinct aspects: Felder and Silverman learning style, knowledge level and personality traits. This latter is a novel adaptation criterion, it is an interesting user feature to be incorporated in user models, a feature that is not yet considered by existing AEHS. However, adapting such systems to personality traits contributes to achieving a better adaptation by varying learning approaches, integrating collaboration and adapting feedback. The aim of this paper is to show how this contribution is doable through the proposed framework.
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Andaloussi, K.S., Capus, L., Berrada, I. (2018). Including Personality Traits, Inferred from Social Networks, in Building Next Generation of AEHS. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_13
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DOI: https://doi.org/10.1007/978-3-319-74500-8_13
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