Process mining techniques and algorithms are popular with processes and workflows analysis and optimization in various domains. While these techniques have demonstrated their popularity, their applications often lack transparency and interpretability, limiting their effectiveness in addressing complex decision-making and process variations. Explainable process mining (XPM) bridges this gap by integrating principles of explainable artificial intelligence (XAI) into process mining techniques, providing clearer insights into the “why” and “how” of process behaviors. This paper proposes a novel framework for XPM, emphasizing AI transparency principles. Using learning analytics in Free/Libre Open-Source Software (FLOSS) repositories as a use case, the study highlights the potential of XPM to uncover decision-making pathways and enhance stakeholder understanding of learning processes. We take as case studies our previous works (2015–2024) on foundational framework for applying process mining for learning analytics. A key take-away of this consideration is to demonstrate how explainability can be achieved in the education domains, providing actionable insights for educators and community organizers to optimize learning environments. The contributions include an interpretable process mining approach, extended learning process models, and practical guidelines for fostering effective knowledge exchange in collaborative settings.