Serviços Personalizados
Journal
Artigo
Indicadores
- Citado por SciELO
- Acessos
Links relacionados
- Citado por Google
- Similares em SciELO
- Similares em Google
Compartilhar
Tecnura
versão impressa ISSN 0123-921X
Resumo
SALAZAR-OSPINA, Oscar Mauricio; RODRIGUEZ-MARIN, Paula Andrea; OVALLE-CARRANZA, Demetrio Arturo e DUQUE-MENDEZ, Néstor Darío. Personalized adaptive interfaces for supporting recommendation from learning object repositories. Tecnura [online]. 2017, vol.21, n.53, pp.107-118. ISSN 0123-921X. https://doi.org/10.14483/udistrital.jour.tecnura.2017.3.a07.
Context:
There are many repositories that allow searching and retrieving learning objects, so a lot of learning resources can be accessed. However, it is required to improve the presentation and visualization of those learning resources considering the student’s preferences, needs, and cognitive features.
Method:
The aim of this paper is to incorporate a customized interface with an adaptive multi-agent system for learning objects recommendation from local and remote repositories based on the student’s cognitive profile.
Results:
The prototype validation was made through a case study in which the interface has adapted not only the presentation but the visualization of learning objects taking into account the student’s preferences, needs and cognitive features.
Conclusions:
We can conclude that personalized adaptive interfaces demonstrate their efficacy and represent a great contribution to e-learning environments since they modify in real time the visualization and presentation of educational resources using the student’s cognitive profile.
Palavras-chave : Learning styles; multi-agent systems; personalized adaptive interfaces; recommendation systems; repositories of learning objects; student profiles.