Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments
Vasiliki Matzavela, Efthimios Alepis
Abstract
In the area of machine learning and data science, decision tree learning is considered as one of the most popular classification techniques. Therefore, a decision tree algorithm generates a classification and predictive model, which is simple to understand and interpret, easy to display graphically, and capable to handle both numerical and categorical data. The intelligent m-learning systems, enjoy recently an explosive growth of interest, for more effective education and adaptive learning tailored to each student's learning abilities. The goal of this paper is to further improve personalization in student academic performance, that includes dynamic tests with a predictive model. One major objective of this research is to create adaptive dynamic tests for assessing student academic performance, while constantly comparing the results of the assessment which exhibits the individual student profile, with the results of the decision tree's algorithm which formulates a predictive model for students' knowledge level, according to the weights of the decision tree.