2011•IGI Global eBooksOpen access

Improving User Profiling for a Richer Personalization

Isabela Gasparini, Victoria Eyharabide, Silvia Schiaffino, Marcelo Soares Pimenta, Analı́a Amandi, José Palazzo Moreira de Oliveira

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Abstract

This chapter presents the context-aware aspects of ADAPTSUR, a personalization approach designed for e-learning environments. The main features of ADAPTSUR are described and illustrated, showing how to use it to model context and culture for personalization in e-learning environments. The authors describe two materializations of the proposed approach, an adaptive e-learning system and an intelligent tutor, which provide personalized assistance to students taking into account their profiles. Finally, the authors discuss the benefits of their proposal.

About this research paper

What this paper is about

This chapter presents the context-aware aspects of ADAPTSUR, a personalization approach designed for e-learning environments. The main features of ADAPTSUR are described and illustrated, showing how to use it to model context and culture for personalization in e-learning environments. The authors describe two materializations of the proposed approach, an adaptive e-learning system and an intelligent tutor, which provide personalized assistance to students taking into account their profiles. Finally, the authors discuss the benefits of their proposal.

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OpenAlex reports 24 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This chapter presents the context-aware aspects of ADAPTSUR, a personalization approach designed for e-learning environments. The main features of ADAPTSUR are described and illustrated, showing how to use it to model context and culture for personalization in e-learning environments. The authors describe two materializations of the proposed approach, an adaptive e-learning system and an intelligent tutor, which provide personalized assistance to students taking into account their profiles. Finally, the authors discuss the benefits of their proposal.

Key concepts: Personalization, Profiling (computer programming), Computer science, TUTOR, Context (archaeology), Personalized learning, Human–computer interaction, World Wide Web

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