Modern cloud-based big data engineering approaches like machine learning and blockchain enable the collection of learner data from numerous sources of different modalities (like video feeds, sensor data etc.), allowing multimodal learning analytics (MMLA) and reflection on the learning process. In particular, complex psycho-motor skills like dancing or operating a complex machine are profiting from MMLA. However, instructors, learners, and other institutional stakeholders may have issues with

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... e traceability and the transparency of machine learning processes applied on learning data on the one side, and with privacy, data protection and security on the other side. We propose an approach for the acquisition, storage, processing and presentation of multimodal learning analytics data using machine learning and blockchain as services to reach explainable artificial intelligence (AI) and certified traceability of learning data processing. Moreover, we facilitate end-user involvement into to whole development cycle by extending established open-source software DevOps processes by participative design and community-oriented monitoring of MMLA processes. The MILKI-PSY cloud (MPC) architecture is extending existing MMLA approaches and Kubernetes based automation of learning analytics infrastructure deployment from a number of research projects. The MPC will facilitate further research and development in this field.