An Automatic and Dynamic Knowledge Assessment Module for Adaptive Educational Systems

Adaptive Educational Systems (AES) make use of Artificial Intelligence techniques aiming at adapting themselves to the real needs of the student, and through such provide a personalized and individualized teaching. In order for this adaptation to be successful, it is important that the system knows the level of knowledge concerning the real cognitive state of the students. In this manner, this article presents an approach for predicting academic performance based on ontologies and Bayesian networks. A knowledge assessment module was developed and integrated to a Ubiquitous Learning Environment (ULE) in real and actual use. During three semesters, five graduate classes were analyzed with the intent of verifying the correlation between the grades generated by the module and the actual grades obtained by the students. It was noted that 80% of the analyzed samples obtained very high or substantial correlations. This indicates that the proposed module is adequate for identifying and predicting academic performance.