Student behavior in a web-based educational system

The behavior of users over the web is one of the most relevant and research topic nowadays. Not only mining the user׳s behavior in order to provide better content is popular, but the prediction of the user׳s behavior is interesting and can increase user experience. Moreover, the business clearly desires such information to improve their services. In this paper we focus to the education domain as it belongs to the most dynamically transforming areas. Web based e-learning systems are nowadays reaching still greater popularity, because of possibilities they offer to students. We analyze various sources of “e-students” feedback and discuss today׳s challenges from the logging and feedback collecting point of view. Next, we focus on the prediction of student׳s next action within an e-learning application (in the mean of “stay or leave?” question). Such information can improve students׳ attrition rate by introducing various personalized approaches. We proposed the classifier based on polynomial regression and stochastic gradient descent to learn the attributes importance. In this way we are able to process a stream of data in one single iteration and thus we are able to reflect dynamic users׳ behavior changes. Our experiments are based on the log data collected from our web-based education system ALEF during three-year period. We found that there is an extensive heterogeneity in the users׳ (student) behavior which we were able to handle by using individual weights calculated for every user.