Adaptive course generation through learning styles representation

Abstract

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student's knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student's preferences and learning performances. Learning material is chosen by the system matching the student's learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed

A Framework to Support Development of Learning Applications for Disabled Children

“…Other approaches focus on adaptive course generation and adaptive e-learning platforms (Diogene and Intraserv [11], CEPIAH [12], eMEMORA [13]...). These genér-ate contents automatically taking into account some metadata related to student profiles.…”

Aquisição Automática de Competências num Ambiente Educacional Ubíquo

“…Várias abordagens e técnicas de sequenciamento de objetos de aprendizagem têm sido propostos na literatura, entre os quais pode-se citar: sequenciamento baseado em regras [De Bra and Calvi 1998]; sequenciamento baseado em planejamento [Limongelli et al 2009]; sequenciamento baseado em grafos, como encontrado no sistema Intelligent Web Teacher [Sangineto et al 2008]; sequencimento baseado em redes bayesianas, tal como no sistema BITS [Butz et al 2004]; sequenciamento baseado em PSO (Particle Swarm Optmization) [de Marcos et al 2007]; e computação evolutiva [S. and Menai 2011]. No entanto, cada solução tem seus pontos fortes e limitações.…”

Implementation of Affective States and Learning Styles Tactics in Web-Based Learning Management Systems

“…The possibility to provide personalize web-based courses based on student's learning requirements and methodological preference is an interesting open research area with large application perspective [1]. Presently, the structure and the pedagogical material of web-based courses are usually statically defined by web learning designers (or by teachers).…”

“…Most of the currently used learning systems cannot take into account the students' preferred ways of learning as well as their learning behavior. On the other hand, modern pedagogical theories and research/models emphasize to personalize the course material, in order to enhance the students' learning [1,2].…”