Semi-automatic generation of quizzes and learning artifacts from Linked Data

In this position paper, we illustrate how Linked Data can be effectively used in a Technology-enhanced Learning scenario. Specifically, we aim at using structured data to semi-automatically generate artifacts to support learning delivery and assessment: natural language facts, Q&A systems and quizzes, also used with a gaming favour, can be creatively generated to help teachers and learners to support and improve the learning path. Moreover, those artifacts can in turn be published on the Web as Linked Data, thus directly contributing to make the Web a global data space also for learning purposes

A Pattern Language for Intelligent lhtoring Systems

Abstrac

A Model Of Multitutor Ontology-Based Learning Environments

The paper proposes the M-OBLIGE model for building multitutor ontology-based learning environments. The model is based on local ontologies, describing the domain of each individual tutor in the environment, and external ontologies, describing more general concepts. The ontologies are used by ontology processors to decide which tutors might benefit a student who needs to learn new concepts. The model allows domain expertise to be shared, and can be used as a framework for integrating multiple tutors on the web. We show how the model can be applied to tutors in the database domain. We also illustrate the process of developing the ontologies for an existing system