Many educational institutions use a large number of information systems to automate their activities for different stakeholders' groups -- learning management systems, student diary, library system, digital repositories, management system, etc. This leads to a significant increase in the volume and variety of data that can be captured, stored, and harnessed to improve student learning and school effectiveness. Educational institutions are realising that, with the help of technology, they are collecting data that could be very valuable when properly analysed, aligned with learning outcomes, and integrated into a tighter feedback loop with stakeholders. In addition, the analysis of data can help their managers to take data-driven decision making at all levels of educational institutions. The paper presents a comprehensive approach to Learning Analytics in the field of Secondary Education from the perspective of all different stakeholders, which aims to improve its methods of approaching and analysing learning data. On the basis of a literature review in the field and an investigation of requirements for quality evaluation of learning in school education, the corresponding stakeholder groups are identified -- students, teachers, class teachers, managers, parents, inspectors 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. The proposed models are evaluated by the representatives of 4 stakeholder groups -- students, teachers, class teachers, parents.