Authors:
Asma Hadyaoui
and
Lilia Cheniti-Belcadhi
Affiliation:
Sousse University, ISITC, PRINCE Research Laboratory, Hammam Sousse, Tunisia
Keyword(s):
Recommender System, Personalization, ePortfolio, Collaborative Learning, Elearning Standard, Assessment, Ontology.
Abstract:
Personalized recommendations can help learners to overcome the information overload problem, by recommending learning resources according to learners’ preferences and level of knowledge. In this context, we propose a Recommender System for a personalized formative assessment in an online collaborative learning environment based on an assessment ePortfolio. Our proposed Recommender System allows recommending the next assessment activity and the most suitable peer to receive feedback from, and give feedback to, by connecting that learner’s ePortfolio with the ePortfolios of other learners in the same assessment platform. The recommendation process has to meet the learners’ progressions, levels, and preferences stored and managed on the assessment ePortfolio models: the learner model, the pre-test model, the assessment activity model, and the peer-feedback model. For the construction of each one, we proposed a semantic web approach using ontologies and eLearning standards to allow reusa
bility and interoperability of data. Indeed, we used CMI5 specifications for the assessment activity model. IEEE PAPI Learner is used to describe learners and their relationships. To formalize the peer-feedback model and the pre-test model we referred to the IMS/QTI specifications. Our ontology for the assessment ePortfolio is the fundamental layer for our personalized Recommender System.
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