Adaptive learning supported by learning analytics for student teachers’ personalized training during in-school practices

Abstract

This paper presents the results of the second phase of the international project “Improving Educational Innovation, Competitiveness, and Quality of Higher Education through Collaboration between University and Companies (EKT)”. The use of adaptive learning supported by learning analytics is proposed as a pedagogical strategy to work on the collaborative and personalized learning process that takes place during the school placement period of initial teacher education. Learning analytics is expected to facilitate the analysis of the different sources of information and data generated in the learning process. The collected data will be centralized in a learning record store (LRS), which will serve as a repository for xAPI compatible traces from the tools that make up EKT intelligent system. The system is expected to provide a strong support to decision-making so that participant agents can collaborate, advise, and contribute to the future teacher’s personalized training according to his or her progress and the context in which the practice takes place. The need analysis of tutors in the five pilot countries is presented, which has made it possible to define the process variables that make up the learning analysis architecture of the EKT system.