Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Personalised trails and learner profiling in an e-learning environment

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

A implementação da Teoria das Inteligências Múltiplas para a promoção de competências leitoras em alunos com NEE

O presente trabalho apresenta uma investigação que teve como objetivo analisar a eficácia da ativação e implementação das inteligências múltiplas no processo de ensino e aprendizagem, nomeadamente na aprendizagem da leitura e da escrita, em dois alunos do 1.º ano de escolaridade, com Necessidades Educativas Especiais. Os alunos apresentam diagnósticos diferentes: Incapacidade Intelectual e Perturbação de Hiperatividade e Défice de Atenção (PHDA). Recorreu-se a estudos de caso, de caráter exploratório, e à metodologia de investigação-ação, com a finalidade de refletir sobre a nossa prática pedagógica numa perspetiva de melhoria da ação que desenvolvemos com as crianças com NEE. Durante a intervenção, procedeu-se à observação numa perspetiva naturalista e reflexiva das aulas, por meio de diários de bordo. Posteriormente, procedeu-se à análise de conteúdo do texto dos diários, com base em duas grandes categorias: flexibilização e gestão flexível do currículo e a centralidade das Inteligências Múltiplas na diferenciação de metodologias e estratégias de ensino e de aprendizagem em crianças com NEE. Os resultados apurados apontam para um impacto bastante positivo da ativação das inteligências múltiplas no processo de ensino e aprendizagem dos alunos. Ao termos em conta as inteligências múltiplas, diferenciamos estratégias, recursos, contextos de aprendizagens, métodos de leitura e escrita, o que foi fulcral para o sucesso educativo, permitindo a aquisição de competências significativas em relação à leitura e à escrita e em outras áreas do currículo

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance