Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.

1. Digital data and professional
practices: New
accountabilities, stewardship,
and literacies
Terrie Lynn Thompson Jan 2018

2. datafication: “ways of seeing
and engaging with the world
by means of digital data”
(Gray, 2016)

3. wearable physical activity tracker

5. learning analytics: the
measurement, collection,
analysis and reporting of
data about learners and
their contexts, for purposes
of understanding and
optimizing learning and the
environments in which it
occurs
(Long & Siemens, 2011)
datafication of teaching
& research practices

6. changing how expertise of specialists is
made available in society
(Susskind & Susskind, 2016)
• algorithms to write earnings
reports and sports commentary
• eBay and online dispute
resolution software to replace
the lawyer
• tax preparation software rather
than accountants
• robotic pharmacists

8. why does this matter?
• the way the digital can “strip down complex phenomena into binary form
so that they can be manipulated more easily” (Savage, 2015). issues can
be made simpler
• datafication is “taking a process or activity that was previously invisible
and turning it into data”: data that can then be “tracked, monitored, and
optimized” (Sylvester, 2013). “dark data” and also “dark activities” are
now being made visible
• outsourcing to algorithms creates a significant shift in responsibility and
control (Marsden, 2015)
• powerful discourse that encourages professionals to view digital
technologies as a “natural and naturalised part of their work” (Edwards &
Fenwick, 2016)

9. platform capitalism: the intersection
of
commercial technological
infrastructures
and the new currency of data
Players like Google and Facebook enable
access while serving a set of global
commercial purposes as they harvest and
monetise data generated through everyday
work-learning-living activities. Operate in
gray areas of accountability and governance
around data practices.

10. How do we live with and trust the
algorithms and data analysis used
to shape key features of our
lives?

11. Digital data as the raw material
for much of what is unfolding in
people’s everyday work,
learning, and living. Most days,
most people will generate,
interact with, and interpret some
kind of digitally-rendered data:
some knowable and accessible
by the person and much that
churns quietly in the
background.

12. think about the data implicated in your own work-
learning-living activities
1. What data do you use to make decisions about what
you or others should do? about policies or processes?
2. What data is used to make decisions that impact
your work-learning-living activities?
3. What data would you like to have to help with things
you need to do: work – learning – living?
4. Have you ever been affected by a “data disaster”?

13. how the use of learning analytics,
coupled with social network analysis
and visualization software, worked in
and on the research and teaching
practices in an online post-graduate
course
the oPEN project (online
professional education network)

15. • posthumanism
• a posthuman sociomaterial analysis of
datafication practices (aka learning
analytics)
• implications vis a vis re-distribution of
labour: accountabilities, stewardship &
literacies/fluencies
today

16. theory and methodology

17. when computational
“solutions” come from an
“instrumentalising humanistic
perspective which sees the
technology as in service to
social ‘need’, resistance to such
methods also takes humanistic
forms positing essentialism”
(such as the human “touch”) as
the “main locus for resistance
to cold technocratic
imperative” (Bayne, 2015)

18. Badminton 2000
Braidotti 2013
Barad 2003
Graham 2002
Hayles 1999
Wolfe 2010

19. posthumanism pushes beyond human-centric notions of
being to a more hybrid and humble conception of human
actions in the world … re-envisioning the human as
intimately entangled and inseparable from technologies,
environments, and other species

20. sofas that
shout ‘sit!’

21. calling into question the
givenness of the differential
categories of “human” and
“nonhuman” (Barad, 2003)
beyond who or what to
who-what

22. One does not have to fix one’s gaze on a
material world from which all traces of
humanity have been expunged; or on its residue
– a social world from which the material world
has been magically whisked away by linguistic
conjuring tricks. … One can try seeing double:
seeing the human and the nonhuman at once,
without trying to strip either away. This shift in
the unit of analysis is the move to a
posthumanist perspective (Pickering, 2005)
Seeing Double

23. Attending to objects, attuning to things
• gathering anecdotes
• following the actors
• listening for the invitational quality of things
• studying breakdowns, accidents & anomalies
Loosening the meshwork, analyzing digital
materialities
• discerning the spectrum of human-technology
relations
• applying the Laws of Media
• unraveling translations
• tracing responses and passages
Researching a posthuman world: Interviews with
digital objects (Adams & Thompson, 2016 )
8 Heuristics
to Interview
Things

24. the oPEN project
• 16-week masters level online course
• 43 practicing professionals, 2 university tutors, and 5
Critical Colleagues (senior teachers recruited from the
partner local authorities)
• students required to actively engage with a range of online
artefacts, post in discussion forums and blogs, and use
wikis for group work in order to advance the learning of the
collective
• examined the digital traces of student activity in the online
space: relationship to learning?

25. Learning Analytics
measure learning efforts
predict learning outcomes
recommend learning
pathways & strategies

26. data detours

27. Adams, C., & Thompson, T. L. (2016). Researching a posthuman
world: Interviews with digital objects. London: Palgrave Macmillan.
Wilson, A., Watson, C., Thompson, T. L., Drew, V. and Doyle, S.
(2017). Learning analytics: Challenges and limitations. Teaching in
Higher Education.
Thompson, T. L. (June, 2017). Digital data and professional practices: A posthuman
exploration of new responsibilities and tensions. Paper to be presented at 3rd ProPEL
International Conference (Linköping, Sweden).
Wilson, A., Thompson, T. L., Watson, C., Drew, V., & Doyle, S. (2017). Big data and
learning analytics: Singular or plural? First Monday, 22(4).
Watson, C., Wilson, A., Drew, V., & Thompson, T. L. (2016a). Criticality and the exercise
of politeness in online spaces for professional learning. The Internet and Higher
Education, 31, 43-51.
Watson, C., Wilson, A., Drew, V., & Thompson, T. L. (2016b). Small data, online learning
and assessment practices in higher education: a case study of failure?. Assessment &
Evaluation in Higher Education, 1-16.

28. student—instructor—
device—network—
LMS—digital resource

32. Visualizations
• re-present patterns of interactions
• may inform professional decisions
• are implicated in changes in
professional practices

35. We tell ourselves that we live in an
era of aggregation and automation.
From this perspective, raw data
patiently await assembly….Click.
Shuttled from data storage to a
computing center, the analytical
engines of the twenty-first century
assemble statistics, graphs, and
ever more clever visualizations in
response to these and many other
questions we have not yet thought
to ask. (Ribes & Jackson, 2015)

36. imagining data as:
temperamental and
delicate creatures, whose
existence and fraternity
with one another depend
on a complex assemblage
of people, instruments,
and practices dedicated to
their production,
management, and care
(Ribes & Jackson, 2015)

38. the myriad of translations of the data
prompts a re-thinking of what data is
and what it does

39. data visualizations are not
neutral: they come with
particular “ways of seeing”:
particular analytic, mediation,
and narrative regimes (Gray et al.,
2016)

40. datafication as messy process: re-distribution of
labour between human actors and digital
counterparts

41. So …
• Who-what is being datafied? Who-what is doing this work?
• Is it the work of the professional to “tame” masses of potentially
unruly data? Or perhaps it is to avoid getting run over by these
rabbles of data?
• How is the availability of expertise changing?
• How do we open the black-box of “automated, algocratic systems”
(Prinsloo, 2017)?
• How do we continue to value (professional) work “within an
algorithmic culture defined by the new potentials of computation
and digital data” (Bayne, 2015)?

42. what “we” knew and how we knew it?
NodeXL, along with its charts and graphics, classified the student
learning activities in ways that have an array of consequences.
The relationships between patterns of interaction are not simply and
rigidly correlated with final performance.
The many anomalies and contradictions: student-resource
interaction data generated from this course—and illustrated through
visualizations—are a powerful reminder of the diversity of
approaches to online study.
Professionals in these spaces should “look beyond the average of all
outcomes” and instead to the “best outcome for each student”.
Wilson et al. (2017)

44. data controversies

45. workers are dealing with
datafication and
increasing demands to
make decisions, be
accountable, work with,
and care for the digital
data moving in and out of
their day-to-day work

46. There is no easy way for the average digitally
literate instructor to see, question, or change
the algorithms that generate the data in the
LMS and its assemblages of charts, tables, and
warnings.
Student A

47. study “data controversies”: new theorizing to
offer “new vocabularies of ‘data speak’ and new
repertoires of ‘data work’ to ensure that
different publics have the required literacies and
capacities to align these processes with their
interests” (Gray 2016)

48. posthuman fluencies (Adams &
Thompson, 2016)
fluencies: confluence of digital
expertise, responsibility /
citizenship, criticality, innovation &
well-being in human-technology
interactions

49. Student A
Student B
Student C
Student D

50. does the future researcher need to be
“statistician, mathematician, computer
scientist, database administrator, coder,
hardware guru, systems administrator,
researcher and interrogator, all in one” or
move to a more team based approach?
(Prinsloo et al., 2015)

51. worker + digital co-workers
but asymmetries of resources,
capacities, and power to participate in,
and influence, processes of data
production and interpretation (Gray 2016)

52. 1 The moral relational duty of learning analytics
2 Defining student success in the nexus of student,
institution and macro-societal agencies and context
3 Understanding data as framed and framing
4 Student data sovereignty
5 Accountability
6 Transparency
7 Co-responsibility
Guidelines on the Ethical Use of Student Data: A
Draft Narrative Framework (Prinsloo, 2017)

53. How do worker-learners, work
organizations, and educational
institutions understand their
choices, decisions, responsibilities,
accountability, compromises,
influence, and leadership
capacities as they engage with
differently powerful digital actors,
and especially data?

54. Yates et al. (2017) raise several pressing
issues:
• How do we wrestle with the power and
accountability for data and algorithms?
• What are the new data literacies required
by citizens and organizations?
• How well do we understand how people
are interacting with data and algorithms?
• What new forms of digital inclusion and
exclusion are emerging?

55. more questions
What new spaces of public dialogue can be created? “Data activism”
and its push for “data from below”?
Data itself is at the centre of storm around ownership, sovereignty, and
stewardship. How do worker-learners understand the value of their
digital data and the role it plays in their life activities?
What new critical digital and data fluencies (and leadership) are
needed in the face of the growing datafication and digital automation
of work, learning, and living?
How are governance practices and regulatory environments dealing
with the melee of data issues? In light of increasing platform capitalism
who is accountable for what WRT data practices?

56. • Identify the digital tools, systems, and services
at play in a particular practice and then ask:
Who created these and why?
• What data do these digital tools, systems, and
services render?
• What hidden limitations might there be to the
data rendered via these digital tools, systems,
and services? (Lynch & Gerber, 2017)

57. Diamante Murru
terrielynn.thompson@stir.ac.uk
propelmatters.stir.ac.uk