Efficient enabling of real time user modeling in on-line campus

Abstract

User modelling in on-line distance learning is an important research field focusing on two important aspects: describing and predicting students' actions and intentions as well as adapting the learning process to students' features, habits, interests, preferences, and so on. The aim is to greatly stimulate and improve the learning experience. In this context, user modeling implies a constant processing and analysis of user interaction data during long-term learning activities, which produces large and considerably complex information. As a consequence, processing this information is costly and requires computational capacity beyond that of a single computer. In order to overcome this obstacle, in this paper we show how a parallel processing approach can considerably decrease the time of processing log data that come from on-line distance educational web-based systems. The results of our study show the feasibility of using Grid middleware to speed and scale up the processing of log data and thus achieving an efficient and dynamic user modeling in on-line distance learning.