From interaction data to personalized learning: mining user-object interactions in intelligent environments

The aim of this work is to contribute to the personalization of intelligent learning environments by analyzing user-object interaction data to identify On-Task and Off-Task behaviors. This is accomplished by monitoring and analyzing users' interactions while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system to contribute to build personalized environments.