Modeling Student Teachers’ Information

Information seeking and acquisition is fundamental to learning from hypermedia, but student teachers often fail to regulate their own learning. A total of 68 students learned with either a static or a dynamic version of nBrowser, a network-based tutor that adapted recommendations of online resources to support information seeking and acquisition. Results indicated that the student teachers’ ratings for the usefulness of online resources can be predicted with 69% accuracy and 77% accuracy on the entire data set using the fully specified model. The learner model is discussed in designing a recommender system that supports information-seeking behaviors in the context of network-based tutoring systems