MODEL OF A PERSONALISED E-LEARNING PROCESS BASED ON A DECISION TREE ALGORITHM

The supply and demand for training, having undergone a revolution, involves information and communication technologies including artificial intelligence. However, following training adapted to the evolution of the learners skills remains a challenge. Our study aims to provide a solution aimed at promoting a personalized online learning process. Our approach consisted of choosing a decision tree algorithm following a comparative study and developing an architecture based upstream on the evaluation of the learners knowledge. This architecture directs the learner, according to their performance, towards educational resources (documents, courses, sections, videos, etc.) or learning devices, in an iterative and incremental manner until the end of the learning process. The results obtained reside in the proposed model based on the gradient boosting algorithm adapted to the personalization of human learning. This model takes into account three essential components driven by artificial intelligence and covers an entire personalized learning process from checking prerequisites to the end of successful learning.