By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".