Impact of different pre-processing tasks on effective identification of users’ behavioral patterns in web-based educational system

Abstract Analyzing the unique types of data that come from educational systems can help find the most effective structure of the elearning courses, optimize the learning content, recommend the most suitable learning path based on student's behavior, or provide more personalized environment. We focus only on the processes involved in the data preparation stage of web usage mining. Our objective is to specify the inevitable steps that are required for obtaining valid data from the stored logs of the webbased educational system. We compare three datasets of different quality obtained from logs of the web-based educational system and pre-processed in different ways: data with identified users' sessions and data with the reconstructed path among course activities. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules that represent the learners' behavioral patterns in a web-based educational system. The results confirm some initial assumptions, but they also show that the path reconstruction among visited activities in e-leaning course has not statistically significant effect on quality and quantity of the extracted rules