I’m very interested in learning analytics in general, but probably more so in their application to how they can be used to benefit both the academic team (to help guide and improve the offered course) and the students so they can be used to help them guide and motivate them through the learning materials.
This paper ‘How should we measure online learning activity?‘, from the ALT journal Research in Learning Technology, is a good place to start looking at the metrics involved, and how we should measure/interpret them too. If you’ve time, read it too. Let me know what you think?
The proliferation of Web-based learning objects makes finding and evaluating resources a considerable hurdle for learners to overcome. While established learning analytics methods provide feedback that can aid learner evaluation of learning resources, the adequacy and reliability of these methods is questioned. Because engagement with online learning is different from other Web activity, it is important to establish pedagogically relevant measures that can aid the development of distinct, automated analysis systems. Content analysis is often used to examine online discussion in educational settings, but these instruments are rarely compared with each other which leads to uncertainty regarding their validity and reliability. In this study, participation in Massive Open Online Course (MOOC) comment forums was evaluated using four different analytical approaches: the Digital Artefacts for Learning Engagement (DiAL-e) framework, Bloom’s Taxonomy, Structure of Observed Learning Outcomes (SOLO) and Community of Inquiry (CoI). Results from this study indicate that different approaches to measuring cognitive activity are closely correlated and are distinct from typical interaction measures. This suggests that computational approaches to pedagogical analysis may provide useful insights into learning processes.
O’Riordan, T., Millard, D., & Schulz, J. (2016). How should we measure online learning activity?. Research In Learning Technology, 24. doi:http://dx.doi.org/10.3402/rlt.v24.30088