Evidence-based learning analytics: Reusing and reapplying successful methods and techniques in real learning settings

Learning Analytics has significantly grown as a research field over the last decade. Since the term was coined, numerous subfields, methodologies, instruments and scientific results have emerged, highlighting the importance of ongoing investigations to enhance learning processes and their contexts. Despite its expansion, concerns about advancing the field toward maturity and achieving impactful results persist (Papamitsiou et al., 2020). While important initiatives aim to broaden the field globally (eg, Pontual Falcão et al., 2020), other critical aspects, such as ethical issues, data compliance, data openness, explainability and the trustworthiness of decisions, have been discussed extensively. Two primary concerns remain central: Learning Analytics must focus on learning (Gašević et al., 2015) and provide feedback to improve learning contexts (Wise et al., 2021).While the experimentation with data from various learning settings in recent years is crucial, the field must advance by transferring this knowledge into concrete tools and prod-ucts for daily use by educational stakeholders. This is a significant challenge, as it requires not only the development and delivery of these tools but also demonstrating their effec-tiveness in real learning environments. Building solid evidence that Learning Analytics can improve real- world learning and student outcomes is essential.