Model-based optimisation for enhanced training of individuals based on abilities, learning styles and preferences
Computer based training of individuals is becoming more common. Computer based systems increasingly are filled with devices and appliances that enhance the user’s interaction with the computer. These new devices and appliances present new modalities of interaction with the user. This opens new possibilities for computer based training. However, not much is known about mapping these modalities to the user for enhanced learning. This thesis presents an artificial learning model for on-line training of individuals. The model supplied is a multi-modal system in that it links multiple input and output modalities to a user profile. The model contains a non-linear mapping between the user profile and the modalities. The non-linear mapping has been achieved through the use of an Artificial Neural Network. The learning model has been extended to include time dependencies of the suggested modalities via a feedback mechanism within the Artificial Neural Network. The presented results indicate the complexity in choosing the most appropriate mapping for an individual. Results are presented showing the robustness of the learning model. By taking cognisance of the user profile and context (e.g. the user is bored or tired) appropriate modalities are suggested which facilitate learning.