Teacher Model to Personalize Support for the Implementer Teacher in Authoring an Educational Adaptive Hypermedia

This paper analyses how a Teacher Model supports the emulation of teaching styles for enhancing decision-making skills. Our general goal is to contribute to making the authoring of adaptive Web-based applications easier. This research also studies how an adaptive hypermedia system can use the Teacher Model to facilitate personalized support. We pay attention to the Teacher Model as the main source of information that a system uses for making decisions in adaptation and recommendation of examples that matches the teacher’s preferences. In our research, the system uses information from the Teacher Model that reduces the complexity of adapting to different teaching styles in order to propose appropriate examples for a particular teacher. A probabilistic method is used to infer the teaching style of a new teacher, whether the system does not have any information about he and he needs to start using the system. Instead of using a static Teacher Model, we use an evolving model that is applied to track the changes of the teacher’s teaching styles.