In learning analytics contexts one of the things we’re interested in is how stakeholders – managers, educators, students, parents, etc. – interact with ‘their’ data at the various levels of granularity. Of course part of that is about how that data is represented and visualised, and the kinds of collaborative sensemaking processes that stakeholders engage in. One of the things I’m also interested in is inviting students into the processes of learning analytics and the space of data representations (per ‘dear learner’).

An area I’ve just come across is ‘Human Data Interaction’ – riffing on human computer interaction to explore specific interactions with data to “support end-users in the day-to-day management of their personal digital data…” with an understanding of data as of an “inherently social and relational character” [zotpressInText item=”{EWD26TCW,1}”]. Thus, “HDI is a distinctively socio-technical problematic, driven as much by a range of social concerns with the emerging personal data ‘ecosystem’ as it is by technological concerns, to develop digital technologies that support future practices of personal data interaction within it” [zotpressInText item=”{EWD26TCW,3}”]. In that piece, they highlight the tensions between ‘our’ and ‘my’ data, and issues of data ownership and control. HDI, then, is concerned not only with how people use and create data, but with how they both visualise and understand the data, and how that data is made use of within social relational systems (by data creators and processors).

In that paper they then outline some challenges:

• Personal data discovery, including meta-data publication, consumer analytics, discoverability policies, identity mechanisms, and app store models supporting discovery of data processers

• Personal data ownership and control, including group management of data sources, negotiation, delegation and transparency/awareness mechanisms, and rights management.

• Personal data legibility, including visualisation of what processors would take from data sources and visualisations that help users make sense of data usage, and recipient design to support data editing and data presentation.

• Personal data tracking, including real time articulation of data sharing processes (e.g., current status reports and aggregated outputs), and data tracking (e.g., subsequent consumer processing or data transfer).

[zotpressInText item=”{EWD26TCW,18}”] (emphasis added). For learning analytics the particularly interesting point here I think is around tracking and legibility (ideas which I think the Code Acts seminar series also touched on) – data interaction should be ‘legible’ in such a way as to make it clear to learners not only what behaviour or change is expected/observed in them, but how their data has been collated and used, how their data-feedback is both an end-product and fundamental component of the analytic process, and how changes to the data (for whatever reason) might relate to them and the fuller analytic set. In a draft post on a slightly different topic, I was reflecting on the issues of texture and clarity in learning analytics – that, per discussion at the philosophy of education society GB, conceptual clarity targeted at making ideas ‘accessible’ should not come at the cost of reduction. Some ideas are hard, and working with their coarseness is exactly what makes them productive. Of course part of HDI must be how we facilitate data subjects to understand their data-relations; some of this will be difficult, understanding the balance of clarity and accessibility alongside conceptual (and methodological) complexity is an important challenge.

[zotpressInTextBib style=”apa” sort=”ASC”]