Modeling the flow experience for personalized context aware e-learning

Flow state, as a psychological experience is one of important aspects in a learning environment. Involving a psychological experience as a context in a context aware personalized e-learning becomes very important. It is due to that engagement and interest in a learning process are determined by its psychological conditions. However, development of a model personalized context aware e-learning by considering a psychological experience as a context is limited. Previous studies have implemented the flow state as psychological experience in face-to-face classroom. Whereas in an e-learning environment, the flow state is measured after learners interact with e-learning. This paper proposes a modeling flow experience for the personalized context aware e-learning. In this study, the flow state is measured and inferred when the learners interact with e-learning. The flow state can be determined automatically as soon as the learners finish doing a certain topic. Furthermore, the identified flow can be used as a context of a model of the personalized context aware e-learning. Based on the theory of the flow, the psychological experience consists of anxiety, boredom and optimum. In the proposed model, the states are measured by parameters that represent the elements of the flow, even though it still needs to be tested. For further research, through conducting experiment and implementing a machine learning approach, the flow state can be identified. By considering the psychological experience, the proposed model is expected to improve the current models of the personalized context aware e-learning.