Helping Teachers to Identify Students at Risk and Analyze their Learning Process

Abstract

Learning analytics is related to the analysis of user data in educational contexts in order to understand what is happening in the educational environment so that, remediation actions can be taken to improve the learning outcomes and the quality of teaching practice. However, getting the data and analyzing it is not an easy task for teachers due to the amount of data to analyze and the fact that many lack data literacy skills. Therefore, tools that facilitate the collection of data and its analysis is required. AdESMuS is a system that uses visual learning analytics techniques to facilitate those processes. This paper proposes the inclusion in AdESMuS of a module for the prediction of students at risk, what can help teachers to easily identify those students whilst there is the possibility of taking some remediation actions to improve the learning outcomes.