1. Intelligent Blended Learning Objects: Developing Student Models Trevor Barker Blended Learning Unit Introduction In Higher Education today, and in general, increasing reliance is being placed upon the use of online learning (Dearing, 1997; DfES, 2005),. Often such systems are used to manage learning, present information and test learners in an entirely undifferentiated way, all users having exactly the same view of the system. With the development of increasingly large and complex computer applications and greater diversity in learner groups, consideration of individual differences has become an important issue in designing usable and useful applications. This project relates to the use of student modelling with blended learning objects in order to individualise the presentation of learning Blended Learning Objects These are the objects that are taught or learned that are smaller than a course or module (Scope). They may be thought of as the things that we blend in blended learning. Often they are self-contained blocks of learning. There are many different types and classifications. An important aspect of learning objects is that they may be designed to be re-usable in different contexts. They may be delivered online or off-line and they may be Instructive or Constructive or both. They may be highly structured or exploratory in their nature Student modelling Student modelling is a form of user modelling, applied to learning. Student models hold information about the characteristics or performance of a learner which can be used to individualise learning. There are several techniques or approaches which all have the following four problems to solve: What aspects or characteristics of a user to model; How a student model can be represented within an application; How to inform and update a student model based on performance within a domain; What aspects of the interface to change for individual users. Our model is a composite model that holds information about the psychological characteristics of a learner, such as language level, visual and verbal skills as well as information on his or her performance in the subject. The model uses computer adaptive testing to establish performance levels. We have developed a range of tests and strategies to establish the psychological characteristics of learners. The model is presented below in figure 1. Agents are used as a way of communicating between the student and the application. The model is used essentially to inform the selection of the most appropriate learning object for a learner and the best presentation strategy, based on their performance and psychological characteristics. We are currently engaged in designing and testing a whole range of learning object, from simple, fine grained objects intended to deliver one or two ideas, to complex objects that include face to face meetings and group activities and individualised feedback. We are developing applications, based on similar ideas to Intelligent Tutoring Systems that implement our composite student model to inform selection and presentation of these objects. Our experience has shown that it is possible to understand a great deal about the needs of individuals and to help configure learning opportunities according to these needs. It will be important to use a range of quantitative and qualitative evaluation strategies and techniques to assess the effectiveness of this approach. Figure 1. A composite student model