Evaluation of intelligent information system based on user cognitive features(Case Study: E-learning environment)

One of the most important goals of information systems is to provide services based on users’ cognitive characteristic. This increases the system efficiency, user satisfaction and interest in the use of information systems. Since e-learning has been widely used around the world in human-computer interaction domain, in this research the e-learning environment has been chosen to evaluate the model. The goal of this research is performance evaluating of an intelligent e-learning system is compared with a simple e-learning system. The intelligent e-learning system which is based on user’s personality dimension and emotion. In this research, the intelligent e-learning system use the model which desirability as an important variable to calculate emotions and interact with users based on this model. The model can detect users’ emotion based on personality dimension, user’ goals, and environmental event. Evaluating of the model is in two steps: the first step is checking generality of the model in different learning domains and the second one is to determine whether presenting suitable actions to users after predicting their emotion status has an influence on satisfaction and learning rate or not. In the first step, the model has been evaluated in two real e-learning environments which are designed to teach: “Introduction to computing systems and programming” course and “English language vocabulary”. The results show that accuracy in predicting desirability in emotion module in e-learning environments with different materials learning are the same and they confirm the generality of the model. In the second step, two e-learning environment which is designed to teach English vocabulary. The e-learning environment has been implemented in two versions, i.e. a basic system with no intelligence and an intelligent system which can predict a user’s desirability. The intelligent e-learning environment acts according to the user’s status based on her/his desirability. The results in this step show that users believe the intelligent environment is believable and more attractive than the basic one. Also, they express this environment can understand their emotional status and react based on it, and it can improve their learning process.