Graph Entropy-Based Learning Analytics

The goal of this research is to strengthen the teaching strategy with quantitatively measured learning analytics. The entropy-based learning analytics aims to measure and understand students’ progress by quantitatively measuring the difference between the content to be learned, the tutors’ expectation of understanding, and the student’s knowledge. This quantification will take similar steps than taken by Shannon for his information theory using a mathematical formalism to quantitatively measure knowledge (equivalent to Shannon’s entropy) and knowledge transfer (equivalent to Shannon’s mutual information). Knowledge graphs will be used to represent the content to be learned, the tutors’ expectations, and the student’s knowledge. Early results reveal that advanced analytical algorithms and graph entropy specified for educational applications is necessary for this research project to succeed.