A model for Users Behavior Analysis and Forecasting in Moodle

Abstract

The learning process, among its different phases, involves monitoring of users behaviour in order to extract knowledge. Details about users have significant weight to understand the interests and intentions and produce forward-looking statements, as well as keep track of the learning management system (LMS). In this work, a model to investigate and predict the behavior of users, taken to explore the additional knowledge information and predict the learning outcomes, is described. In the first instance, the information are extracted through a suitable tool, and, subsequently, are submitted to an analysis phase. Time series analysis techniques are adopted to detect partial similarities between the navigation data and, subsequently, to extract a classification. Finally, performance are measured through statistical measures to evaluate the goodness of proposed approach and test its significance. The results, obtained on Moodle platform, show that the proposed model leads to accurate outcome prediction about users behavior and can be adopted to improve the learning paths, both in its implementation and design.