Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis

Over the last decade, artificial intelligence has offered a wide range of tools that have proved to be of vital importance for educational research. Indeed, logic, classifiers and machine learning methods, probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various approaches that artificial intelligence has offered in dealing with real life problems. This paper attempts to explore the research that has been conducted on the application of the most typical and popular soft computing techniques [fuzzy logic, neural networks, Bayesian networks, genetic programming and hybrid approaches such as neuro-fuzzy systems and genetic programming neural networks (GPNNs)] in student modelling over the decade 1999-2009. This latest research trend is a part of every intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used so as to point out their qualities and then we describe the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.