Hybrid course instructional design for better practice of learning analytics

The adoption of hybrid and distance learning modes, supported by pedagogical and digital innovations, has favored the availability of large volumes of learning traces, resulting from learners' interactions with digital learning environments and services. These traces represent the digital footprints in the form of actions such as logging on, navigating, passing, scoring, etc., throughout their learning process. With the practice of Learning Analytics (LA), these traces are exploited using several different approaches, to foster learners' success. However, educational organizations very often engage in the massive collection of both relevant and irrelevant traces, requiring significant resources (time, material, etc.) and making the preparation and analysis phases linked to these data complex. In this paper, we propose the adoption of a learning object-oriented instructional design methodology with a structured approach to learning outcomes and learning paths, enabling better LA practice for monitoring learners' progression, participation, and performance. This approach allows for the collection of relevant traces, optimal preparation, and effective analysis of learning traces, to take better advantage of the benefits of LA to improve and adapt learning processes. This also makes it possible to better measure the impact of instructional design in LA practice and vice versa.