Block 1: Week 4 Personal data tracking
Introduction
For one week, I tracked a selection of personal data using an app. I used an app I have used before because I already knew how data is stored and shared. I chose just a few, less crucial and unrelated factors to record, to avoid sharing particularly personal data. I also restricted how much the app might deduce by not providing any contextual data and not linking to any monitoring devices.
Comparison with manual logging & visualisation
Key differences:
- this app allows the user to select from a menu of factors but not to create new ones, as manual logging does;
- having data recorded automatically reduces effort, and prevents the results be affected by forgetfulness or misreporting, but takes control away from the user;
- there are ‘ideals’ built-in but no justification for them, and rewards and nudges that trigger based on your data; manual logging requires you to pursue your own understanding of the data and set your own goals, possibly with expert help and possibly with information beyond this data.
Summary
Using the app reduces labour (Rivera-Pelayo et al, 2012) and possibly increases the accuracy of the data recording.
However, the app reduces the agency of the user, for instance, it has already decided which factors can and should be measured (Wise et al, 2013) with little input from the user. It then goes on to offer what appear to be personalised recommendations, but which may not be based on suitable goals for the user. It also focusses totally on the actions of the user, without references to the larger context (Eynon, 2015), and solely on those actions that directly affect the measures used (Wise et al, 2013).
References
Eynon, R. 2015. The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, pp.407-411, DOI: 10.1080/17439884.2015.1100797.
Rivera-Pelayo, V., V. Zacharias, L. Müller, and S. Braun. 2012. Applying Quantified Self Approaches to Support Reflective Learning. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 111–114. New York: ACM. http://dl.acm.org/citation.cfm?id=2330631.
Wise, A. F., Y. Zhao, and S. N. Hausknecht. 2013. April. Learning Analytics for Online Discussions: A Pedagogical Model for Intervention with Embedded and Extracted Analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge, 48–56. New York: ACM.
