Bootstrapping Learning Analytics: Case: Aalto Online Learning

The digital transformation of learning brings forth data having unprecedented granularity and coverage of learning activity. The research area of Learning Analytics (LA) uses this data to understand and improve learning. The practice of LA is a cyclic process where learning data is collected from different sources and analytics is developed according to stakeholder objectives. Finally, current results are delivered that lead into action which improves learning and produces new data.

The goal of this thesis is to bootstrap LA in multiple courses that implement different weekly online learning activities. The term bootstrap underlines the aim to support continuity, further development, and expansion of LA. The research questions were: what learning data the courses currently instrument, and what LA objectives the course staff find most important.

This thesis conducts software engineering to construct an LA solution for the research case. Requirements are defined via examination of the case and interviews of the course staff. The developed solution enables real time access to learning data and possibility to integrate data from both Moodle and A-plus learning environments for joined analysis. Novel interactive visualizations are developed according to the user requirements.

The work in bootstrapping LA at course level lead to two general findings. First, the integration of learning data from multitude of sources is a common challenge that requires design. Second, teachers’ initial LA objectives include aims to monitor expected progress, improve allocation of learning material, identify problematic areas in learning material, and improve interaction with learners.