Usage of learning management system web analytics in blended learning self-study evaluation

Abstract

Learning Management System (LMS) analytics data is proposed to be used in developing algorithms for evaluating students’ self-studies. Development of such algorithms is relevant considering annual growth of disciplines that apply blended learning. In blended learning model self-study can be done online in LMS which makes it possible to analyze patterns how students interact with learning materials and perform exercises of various complexity. Different criteria and indicators are aggregated into numeric metrics that following designed methodology evaluates self-study performance of each student. Designed methodology uses algorithms that evaluate self-study results by using empirical LMS analytics data. Developed algorithms allow us on one hand to interpret empirical data for self-studies evaluation, and on the other hand to correct and improve students’ learning path. This paper presents results of using developed methodology deployed in LMS BlackBoard on the example of Information Technology blended learning course in Far Eastern Federal University.