Renata Zanella

Web-adaptive training system based on cognitive student style

This paper presents the results of the Project ‘Tapejara’ — intelligent training system on the intemet, a Brazilian Research Council funded project with a partnership of two universities and a Telecom company. This research project uses the students’ cognitive learning styles (CLS) to customise course presentation. The CLS is acquired from a sample of telecommunication organisation employees. Individual CLSs are correlated with the students’ learning trajectories, allowing automated style identification. Based on the CLS identification, an intelligent agent generates the best-adapted content presentation for each student.

O Ambiente de Autoria do AdaptHA

AdaptHA e um ambiente de ensino adaptativo na Web baseado no modelo Hyper-Automaton que objetiva proporcionar ao aluno uma experiencia de aprendizagem individualizada ao apresentar o material instrucional adaptado com base em seu nivel de conhecimento e preferencias. Este artigo objetiva apresentar o ambiente de autoria do AdaptHA, que reune um rico conjunto de ferramentas que visa facilitar as tarefas de autoria do professor .

EASy : recuperação de questões através de metadados e geração automática de instrumentos de avaliação via web

One of the most important characteristics of a Web learning environment on is the Evaluation task. Some of its functions are verify how much the student learned and supply feedback to the student and to the teacher. In this context, this paper presents EASy, a Web Evaluation System, designed to be integrated to XHA, a Web education environment based on Finite Automata with Output. EASy applies XHAs structural model, capitalizing the advantages presented in this model for Evaluations. The main focus of this system is in the questionss authoring , aiming automatic evaluations generation from configured metadata. This system also contemplates the functions of the evaluations, adaptive resources and feedback.