Approach for using Learner Satisfaction to Evaluate the Learning Adaptation Policy

The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze the data the authors will use the space Density-based clustering algorithm of clustering applications with noise. This proposed approach aims to help managers of human learning environments to improve policy used in the learning adaptation phase for learners from the analysis results of collected data.