Personalised learning has been utilised in education for many years to transform the traditional teaching model by adapting the learning process to the individual needs, interests, and learning styles of students. Personalised learning fosters greater student autonomy, boosts motivation and independence, and enhances learning outcomes. This study examines student activity log data from the educational platform Moodle learning resources to support personalised learning. Effective personalisation in Moodle relies on the use of both mutable (dynamic) and immutable (static) student attributes. A comparison of student survey data with information extracted from activity log entries illustrates the feasibility of using automatically recorded data, which facilitates the automation of its processing and application to personalise learning. The implementation of such a system necessitates a pedagogical approach, careful monitoring of data changes, and the capability of teachers to utilise the processed results to design learning materials, as well as the ability to create linear and branching learning paths. Automating routine tasks enables teachers to deliver adaptive and high-quality personalised learning.