A COMPREHENSIVE APPROACH TO LEARNING ANALYTICS

The wide range of data produced by participants in learning processes has led to increased interest in the collection and analysis of data to support data-driven decision making at all levels of educational institutions. The paper is devoted to the emerging field of Learning Analytics that has been researched to identify all stakeholders and their interests in the Learning Analytics results. It presents a comprehensive approach to Learning Analytics in the field of Higher Education from the perspective of all different stakeholders, which aims to improve its methods of approaching and analyzing learning data. On the basis of a literature review in the field and an investigation of requirements for quality evaluation of learning in higher education the corresponding stakeholder groups are identified (students, teachers, programme managers, deans, rectors, members of quality commissions and external quality experts) and 6 models (1 model per each stakeholder group) for data collection and personalized and meaningful analysis are proposed for the needs of Learning Analytics. Each model consists of measurable indicators allowing the relevant stakeholder to track data for students' learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. In the next stages of the study, the developed models will be verified with the help of representatives from the various stakeholders and then on that basis software tools for Learning Analytics automation will be developed.