Proposta de uma Abordagem Computacional para Detecção Automática de Estilos de Aprendizagem Utilizando Modelos Ocultos de Markov e FSLSM

Edson Sena, Alessandro Vivas, Luciana Assis, Cristiano Pitangui


One of the current challenges is to develop computational technologies that are able to respond properly to the teaching and learning methods. For this to occur, it is essential that virtual environments provide adequate content, and are dynamic and adaptable to the needs and interests of students. This papper proposes a probabilistic approach, combines the model proposed by Felder and Silverman (FSLSM) to learning styles, with probabilistic inference techniques of Hidden Markov Models (HMM), in order to perform the inference process of the preferences of the student for a particular learning style.

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