Abstract
The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.
Highlights
With the development of e-learning platforms and the availability of learner tracks, several classification and prediction systems have emerged
Several research studies have been carried out using different Machine learning techniques such as K Nearest Neighbor, Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayesian (NB), etc
LightGBM algorithm with Fisher Score gave the most accuracy with an average of 89.05%, using 15 features, followed by XGBoost when using 14 features (88.96%)
Summary
With the development of e-learning platforms and the availability of learner tracks, several classification and prediction systems have emerged. The learner profile describes several sides of the student, such as personal information, social situation, academic background, skills, personal characters, preferred learning styles, online behavior, etc. An example of a potential use in the academic field is the student failure and dropout prediction which are two serious problems in every educational system nowadays. To overcome these problems, several research studies have been carried out using different Machine learning techniques such as K Nearest Neighbor (kNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayesian (NB), etc. With the availability of huge volume of data, many studies have been conducted based on Deep Learning techniques to make efficient classification and/or prediction in the academic domain
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