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 modeling using machine learning techniques during last four years. We aim to analyze 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 analyze 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 behavior 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.
Highlights
Student profile modelling relies on a profile representation that captures the main characteristics and gives the most coherent, complete and operational representation of the student
We focus on the student’s features categorization, machine learning techniques based-on and the context of the research study, to be able in the future, to develop a profile model and to generalize on the conclusions obtained in our previous work which has shown that decision trees are the most efficient based on academic data [5]
In figure (b), we notice that Decision Trees technique is always in first level compared to other techniques and it is most efficient in 40% of the research studies, followed by Naïve Bayes (NB) and Support Vector Machine (SVM) (13% for each) and neural network with 10%
Summary
Student profile modelling relies on a profile representation that captures the main characteristics and gives the most coherent, complete and operational representation of the student. Into three categories: Supervised learning, unsupervised learning, and semisupervised; but with the development in artificial intelligence others classes were introduced as deep, transfer and reinforcement learning This techniques were applied in several levels to achieve many academic objectives like predicting failure or dropout, orientation and academic decision making [2]–[4]. We focus on the student’s features categorization, machine learning techniques based-on and the context of the research study, to be able in the future, to develop a profile model and to generalize on the conclusions obtained in our previous work which has shown that decision trees are the most efficient based on academic data [5]. The last section presents a case of study where we applied different machine learning techniques on two online datasets and at last, we give a conclusion and some perspectives
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