Survey of Machine Learning Techniques for Student Profile Modelling

Abstract

Developments in information technology have led to the emergence of several online platforms for educational purposes, such as e-learning platforms, e-recommendation systems, e-recruitment system, etc. These systems exploit advances in Machine Learning to provide services tailored to the needs and profile of students. In this paper, we propose a state of art on student profile modelling using machine learning techniques during last four years. We aim to analyse the most used and most efficient machine learning techniques in both online and face-to-face education context, for different objectives such as failure, dropout, orientation, academic performance, etc. and also analyse the dominant features used for each objective in order to achieve a global view of the student profile model. Decision Tree is the most used and the most efficient by most of research studies. And academic, personal identity and online behaviour are the top characteristics used for the student profile. To strengthen the survey results, an experiment was carried out, based on the application of machine learning techniques extracted from the state of art analysis, on the same datasets. Decision tree gave the highest performance, which confirms the survey results.