Segmentação Colaborativa de Objetos de Aprendizagem Utilizando Bookmarks em Ambientes Educacionais Ubíquos

Brant-Ribeiro

Anais Do XXIX Simpósio Brasileiro De Informática Na Educação (SBIE 2018)

et al. 2018

Self Cite

Ubiquitous Learning Environments (ULEs) enhance the automatic generation of Learning Objects (LOs), which increases the need of more adequate presentation strategies. To meet different needs and preferences of students, retrieval and personalization resources have been explored, adding cognitive theories to the student model. This paper presents a probabilistic proposal of the Felder and Silverman (FSLSM) model that includes a semiautomatic approach to its measurement in ULEs. Preliminary results indicate the existence of correlations between different types of interactions carried out by students and their respective learning styles.Resumo. Ambientes Educacionais Ubíquos (AEUs) potencializam a geração automática de Objetos de Aprendizagem (OAs) que, por sua vez, necessitam de estratégias mais adequadas de apresentação. Para atender as diferentes necessidades e preferências dos estudantes, recursos de recuperação e personalização têm sido explorados, agregando teorias cognitivas ao modelo do estudante. Este artigo apresenta uma proposta probabilística do modelo de Felder e Silverman (FSLSM) que inclui uma abordagem semiautomática para sua aferição em AEUs. Resultados preliminares indicam a existência de correlações entre diferentes tipos de interações realizadas pelos estudantes e seus respectivos estilos de aprendizagem.

Section: Methodsmentioning

confidence: 99%

“…Simultaneously, ubiquitous learning environments have boosted the automatic generation of Learning Objects (LOs) [Araújo et al 2016]. In this context, it becomes essential to explore different resources to guide each student in a personalized way, given the large amount of generated content.…”

Section: Introductionmentioning

confidence: 99%

A Semiautomatic and Probabilistic Approach for Student Modeling in Ubiquitous Learning Environments

Brant-Ribeiro

Anais Do XXIX Simpósio Brasileiro De Informática Na Educação (SBIE 2018)

et al. 2018

Self Cite

“…Knowing that, a single lecture generates many other LOs, which have been structured in three hierarchical levels: lecture, subject, and slide. A lecture consists of several subjects, which are composed of several slides (Araújo et al, 2016;Araújo, Brant-Ribeiro, Freitas, Dorça, & Cattelan, 2014).…”

Section: Content Adaptationmentioning

confidence: 99%

Using Learning Styles for Creating and Personalizing Educational Content in Ubiquitous Learning Environments

Brant-Ribeiro

et al. 2020

RBIE

Self Cite

The fact that people behave and learn in a different pace requires individual differences to be properly considered in the teaching/learning process. Among several cognitive theories that could be used for this purpose, a promising one is to explore the use of students' learning styles (LSs), with several research studies indicating that their use has positive impacts on learning outcomes. At the same time, Ubiquitous Learning Environments (ULEs) have the potential to make the multimedia authoring of Learning Objects (LOs) an automated process, resulting on even larger educational content repositories and increasing the need for more adequate presentation strategies to students. This article presents an approach for creating and personalizing LOs through a probabilistic proposal of the Felder and Silverman Learning Styles Model. A prototype of the proposed model was integrated into a ubiquitous educational platform and experimented in real settings. Results indicate the existence of correlations between different types of interactions carried out by students and their respective LSs.

A Hybrid Architecture for Adaptive, Intelligent, and Ubiquitous Educational Systems

Digital Technologies and Instructional Design for Personalized Learning

Cattelan

et al. 2018

Ubiquitous learning environments (ULEs) allow real and virtual study materials to be combined to enrich learning experiences. Classrooms equipped with electronic devices produce artifacts that can reconstruct the captured experiences for later use and review. Those environments have the potential to turn themselves into a factory of learning objects (LOs), which may become useless if appropriate means for reaching them are not provided to students. On the other hand, adaptive educational hypermedia (AEH) have appeared as a way of personalizing educational content in web environments and modern intelligent tutoring systems (ITS) provide personalized resources for automating pedagogical tasks. In this way, this chapter explores the concept of ULEs together with AEH and ITS for generating and providing personalized LOs to students. The proposed approach is grounded in artificial intelligence, pedagogical concepts, and computational systems technologies such as ontologies, Bayesian networks, learning styles, and ULEs for creating better individual learning experiences.