Big Data Provocations in Education

Presentation at the Apereo Africa Conference, 9-10 March 2016,
Burgerspark Hotel, Pretoria
Image credit: Image compiled from two images -
https://pixabay.com/en/photos/data/
https://upload.wikimedia.org/wikipedia/commons/1/15/Fingerprint_detail_on_male_finger.jpg
Learning analytics and Big Data: A
tentative exploration
By Paul Prinsloo (Unisa)

Adapted & refined from
Prinsloo, P., Archer, L., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big(ger) Data as Better Data in Open Distance Learning: Some Provocations and Theses. International Review of
Research in Open and Distributed Learning (IRRODL), 16(1), 284-306. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1948/3259
Prinsloo, P. (2014). A brave new world. Presentation at SAAIR, 16-18 October http://www.slideshare.net/prinsp/a-brave-new-world-student-surveillance-in-higher-education

What is the (current and future) relation
between learning analytics and Big Data?
What are the potential and perils?
Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm
https://commons.wikimedia.org/wiki/File:You_are_here.svg

How will having access to more data,
collected from disparate sources, in real-
time and responding in real-time, increase
the effectiveness of teaching and
learning?

• What responsibility comes with knowing our
students? [Can we un-know knowing…?]
• To know more about our students does not
necessarily imply understanding …
• Even if we knew and understood our
students, do we have the will and the
resources to do something about what we
(think we) know?

Acknowledgements
I do not own the copyright of any of the images in this
presentation. I therefore acknowledge the original
copyright and licensing regime of every image used.
This presentation (excluding the images) is licensed under
a Creative Commons Attribution-NonCommercial 4.0
International License

Acknowledgement and disclaimer
This presentation presents a tentative exploration of the
relationship between Big Data and learning analytics flowing
from and expanding personal and collaborative research over
the last decade (see bibliography).
Disclaimer: I am not a data scientist, ‘geek’, data analyst, or
computer scientist. I am an educator and educational researcher
trying to make sense of a phenomenon and the underlying
assumptions and use of technical terms I often don’t
comprehend.

OVERVIEW OF THE PRESENTATION
• Big Data – glimpses of what ‘it’ is, claims to be, where ‘it’ is
going and dystopian dreams
• How do we think about Big Data and learning analytics in a
higher education context that…
• When is a data set Big Data? Dreams of large data that are small
and Big Data that are large
• 15 provocations for Big Data (and learning analytics)
• Penetrating the fog – learning analytics - limitations and
questions
• (In)conclusions

Reference: Ariely, D. [Dan Ariely]. (2013, January 6). Big data is like teenage sex: everyone talks about it, nobody really knows how
to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... “[Facebook status update]. Retrieved from
https://www.facebook.com/dan.ariely/posts/904383595868

Rather than scarce and
limited in access, the
production of data is
increasingly becoming a
deluge; a wide, deep
torrent of timely, varied,
resolute and relational
data that are relatively
low in cost and, outside of
business, increasingly
open and accessible
(Kitchen, 2014, p. xv)
Image credit:
https://commons.wikimedia.org/wiki/File:Shots_of_a_raging_creek_near_the_
Crow_Creek_Pass_Trailhead_parking_lot_%285368679731%29.jpg

We know where you
are. We know where
you’ve been. We can
more or less know
what you're thinking
about
(@FrankPasquale, 2016)
Image credit: https://en.wikipedia.org/wiki/Surveillance

Imagine what we could learn if we put a tracker on
everyone and everything (Jurdak, 2016)
Image credit: https://www.flickr.com/photos/jeepersmedia/13966485507

“…while our mediated world becomes
increasingly transparent, those who seek to profit
from our data are incredibly opaque” (Gandy,
2000, cited in McStay, 2013)
Image credit: http://fleishmanhillard.com/2014/11/true/big-datas-inaccuracy-hurts-people/

What counts as ‘big’
changes from one year
to another, and the
relevant question really
is ‘how much is enough
data to solve my
problem?’ (Adryan,
2015)
When is ‘big’, big enough?
Image credit: https://www.flickr.com/photos/uncle-
leo/1341913549

… has become saturated with data – ranging from automatically
collected, analysed and used, purposefully collected, analysed
and used and volunteered on social media and in exchange of
(perceived) benefits despite concerns about privacy, the
uncertainty of how the data will be used (and combined with
other sources of data) downstream and in the context where our
trust in the collectors of data is often misplaced, irrational or
wishful thinking
How do we think of (Big) data
in higher education in a world
that…
Image credit: https://commons.wikimedia.org/wiki/File:Big_Hand_-_geograph.org.uk_-_644552.jpg

How do we think of (Big) data in higher education in a
world that…
• The current obsession with ‘evidence’ to secure due to funding that follows
performance rather than preceding it (Hartley, 1995)
• Claims that Big Data in higher education will change everything & that
student data are “the new black” (Booth, 2012) & “the new oil” (Watters, 2013)
• Our “quantification fetish”, the “algorithmic turn” & “techno-solutionism”
(Morozov, 2013a, 2013b)
• The current meta-narratives of “techno-romanticism” in education (Selwyn, 2014)
• The belief that data are “raw”, “speak for itself” (Boyd & Crawford, 2013; Gitelman, 2013) &
that collecting even more data equals necessarily results in better
understanding & interventions

While many analysts accept data
at face value, and treat them as if
they are neutral, objective, and
pre-analytic in nature, data are in
fact framed technically,
economically, ethically,
temporally, spatially and
philosophically. Data do not exist
independently of the ideas,
instruments, practices, contexts
and knowledges used to
generate, process and analyse
them” (Kitchen, 2014, p. 2).
Image credit: http://www.iatropedia.gr/tag/opioucha-pafsipona/

HOW DO WE DESCRIBE BIG DATA?
The “Vs”
(Uprichard, 2013)
Velocity
Veracity
Volume
Variety
Versatility
Vitality
Visionary
Vigour
Viability
Vibrancy
Virility
The alternative list
of “Vs” (Uprichard,
2013)
Valueless
Vampire-like
Venomous
Vulgar
Violating
Violent
The 13 Ps of Big Data
(Lupton, 2015)
Portentous
Perverse
Personal
Productive
Partial
Practices
Predictive
Political
Provocative
Privacy
Polyvalent
Polymorphous
Playful

HOW DO WE DESCRIBE BIG DATA?
• Volume – number of records, storage required by the
record, total storage required terabytes (240 bytes) to
petabytes (250bytes)
• Velocity – fast & continuous – (1) frequency of generation
and (2) frequency of handling, recording & publishing
• Variety – various, seemingly unrelated sources generated in
(un)related contexts for (un)related purposes and under
(un)disclosed conditions for reuse
• And…

• Exhaustivity – entire populations – n = all
• Resolution and granularity = fine-grained in resolution and
uniquely indexical in identification
• Relationality – strong – containing common fields that
enable the conjoining of different data sets
• Flexibility/scalability – can change/add new fields easily
and expand the size dramatically
• Veracity – messy, noise and contain uncertainty and error
• Value – many insights can be extracted and the data can
be re-purposed
HOW DO WE DESCRIBE BIG DATA? (cont.)

Large but small
Image credit: https://en.wikipedia.org/wiki/Demographics_of_South_Africa
A national census, taking place once
every 10 years, asking 30 structured
questions, once collected, impossible
to add/remove fields (Kitchen &
McArdle, 2016)
In 2014 Facebook processed “10 billion
messages, 4.5 billion ‘Like’ buttons, and
350- million uploads per day and
constantly refining and tweaking their
algorithms and terms and conditions,
changing what and how data were
generated” (Kitchen & McArdle, 2016, p.
2; emphasis added)
Large and Big

After analysing 26 data sets…
The “key boundary characteristics” of Big Data are
• “velocity (both the frequency of generation, and frequency of handling,
recording, and publishing) and
• …exhaustivity” (Kitchen & McArdle, 2016, p. 8)
• “Small data are slow and sampled. Big data are quick and n = all. Small
data can hold all the other characteristics (volume, resolution,
indexicality, relationality, extensionality and flexibility) and still be
considered small in nature. It is the qualities of velocity and exhaustivity
which set Big Data apart…” (Kitchen & McArdle, 2016, p. 8).
• The much-hyped aspect of volume “is a by-product of velocity and
exhaustivity: the real-time flow of data across a whole system can
produce a deluge of data, especially if each record is large in size”
(Kitchen & McArdle, 2016, p. 19)

Image credit: https://www.flickr.com/photos/photosofsrilanka/3406040079
Some provocations for Big Data (and possibly
for learning analytics)

Some provocations for Big Data (and possibly
for learning analytics)
1. Big data cannot (yet) deal with big questions

Some provocations (cont.)…
2. Big data cannot- at least at the
moment – tell you what you will
or should do. While the algorithms
of Amazon and Facebook still look
for the links between our past
online behaviours (what we
bought and what we shared) and
what we will buy and share in
future, Uprichard (2013) points to
the impossibility of Big Data to
“design local, regional and global
policies” (par. 9).
Image credit:
https://commons.wikimedia.org/wiki/File:Fork_in_the_ro
ad_-_geograph.org.uk_-_1355424.jpg

3. Big data (only) provides snapshots of
the now and do not tell us the why.
This also refers to the claim by Mayer-
Schönberger and Cukier (2013) that Big
Data “is about what, not why. We don’t
always need to know the cause of the
phenomenon; rather, we can let data
speak for itself” (p. 14).
Image credit:
https://en.wikipedia.org/wiki/Monkey_selfie

4. Big data as methodological genocide. Though Uprichard
(2013) acknowledges that the claim of methodological
genocide is “melodramatic” (par. 12), she states “We are all,
whether we like it or not, slowly but surely, becoming
complicit to a deeply positivist, reductionist kind of social
science, where variables are the be all and end all, where
causality is devoid of meaning, and where non social
scientists are the ones ruling the roost in terms of access,
collection and analysis – of big data, which is social data”
(par. 11).

5. Big Data change our understanding of knowledge. At the
core of the question is how Big Data changes our
understanding of knowledge and the generation and
verification of knowledge (Floridi, 2013). Big Data as
phenomenon constitutes a “profound change at the levels of
epistemology and ethics. It reframes key questions about the
constitution of knowledge, the processes of research, how we
should engage with information, and the nature and the
categorisation of reality” (boyd & Crawford, 2011, p. 3). At the
core of our reflections on Big Data are therefore
epistemological, ontological and possibly even metaphysical
concerns and implications.

6. Numbers do not speak for themselves and do not equal
objectivity and/or accuracy. Boyd and Crawford (2011) warn
that there “remains a mistaken belief that qualitative
researchers are in the business of interpreting stories and
quantitative researchers are in the business of producing
facts” (pp. 4-5). We should continuously and relentlessly
contest the assumptions that data are neutral and raw, that
quantitative data are better than qualitative data, that large
data sets are not prone to data errors and gaps and that Big
Data have less bias than smaller, qualitative data sets (boyd &
Crawford, 2011; Gitelman, 2013).

We should continuously and relentlessly contest the
assumptions that data are neutral and raw, that quantitative
data are better than qualitative data, that large data sets are not
prone to data errors and gaps and that Big Data have less bias
than smaller, qualitative data sets (boyd & Crawford, 2011;
Gitelman, 2013).
Image credit:
https://en.wikipedia.org/wiki/Egg_%28food%29
6. Numbers do not speak for themselves and do not equal
objectivity and/or accuracy (cont.)…

7. Big Data may be big but does not
provide the total picture. In the context of
the “scored society” (Citron & Pasquale,
2013), and the fact that consumers are
increasingly reduced to single numbers
(Pasquale, 2015) there are many authors
who support the need to move from Big
Data to deep data (Scharmer, 2014) or at
least supplement Big Data with thick data
or qualitative data (Shacklett, 2015; Wang,
2013). We should not underestimate the
contribution and value of small data (boyd
& Crawford, 2011).
Image credit:
https://commons.wikimedia.org/wiki/File:1
_August_2008_partial_eclipse_from_UK.jpg

8. More data are not always better data. In the context of
the increasing data hunger and even obsession for data, also
in the context of higher education, boyd and Crawford (2011;
Prinsloo, Archer, Barnes, Chetty & Van Zyl, 2014) state that
bigger and more data do not, per se, imply a better
understanding of the research focus. Just because Big Data
claim not to work with samples but with whole populations
(Mayer-Schönberger & Cukier, 2014) does not mean that less
bias, or that the big data set allows you to ask any question
(Crawford, 2013). As boyd and Crawford (2011) warn,
combining data from multiple large datasets may be prone to
amplify the errors in the individual datasets.

9. Not all data are equivalent. It is often presumed that data
are interchangeable but boyd and Crawford (2011) warn that
when data are taken out of context, data loose meaning and
value, and contextual integrity (Nissenbaum, 2015). What
individuals share and do in specific online contexts does not
necessarily apply to all contexts. Brock (2015) also pointed to
the reality that some individuals carefully curate and manage
their digital footprints and profiles and that an analysis of
their digital footprints does not, necessarily, reveal their
authentic selves.

10. Just because we can, does not mean we have to. Boyd
and Crawford (2011) points to the fact that just because we
have access to increasing amounts and granularity of personal
data, does not mean that we have to and need to collect these
data, analyse the data and use the data. Willis, Slade and
Prinsloo (in review) point out that while research participant
involvement in research is governed by institutional review
boards and policies, the (automatic) collection, analysis and
use of individuals’ digital data often falls and take place
outside of these policies and review boards.

11. The ideological nature of data. “The processes of
encoding and decoding data are never neutral” (Johnson,
2015). Also see Henman (2004). What are the implications for
our methodologies and understanding of the information
produced by Big Data when we accept the proposition by
Nakaruma (2013) –“What is algorithm but ideology in
executable form?”

Some provocations (finally)…
12. In search of authentic, holistic data profiles…Brock (2015)
quotes Manovich (2011) who state that digital data produced
in social media are not necessarily ‘authentic’ but “often
carefully curated and systematically managed” (p. 1087). The
general assumption is that Big Data provides ‘real’ and
‘authentic’, ‘holistic’ descriptions of individuals while there is
ample evidence that these instrumental analyses and
methodologies fail to deliver of these claims (Brock, 2015;
Reigeluth, 2014).

13. Increasing digital divides. boyd and Crawford (2011) ask –
“…who gets access? For what purposes? In what contexts?
And with what constraints?” (p. 12). Not only are Big Data sets
not accessible for most people, but the technical skills
required in utilising and analysing these large and contingent
data sets excludes many. This results a new kind of digital
divide – “the Big Data rich and the Big Data poor” (boyd &
Crawford, 2011, p. 13) where non-access to these data sets
and the information made possible by their analysis
perpetuates existing and creates new inequalities and
injustices.

14. Caught between correlation and causation
Image credit: http://www.tylervigen.com/spurious-correlations

14. Caught between correlation and causation (cont.)
Image credit: http://www.tylervigen.com/spurious-correlations

15. Mistaking the noise for the signal
Silver (2012) warns that in noisy systems with
underdeveloped theory there is a real danger in
mistaking noise for signals, not realising that noise
pollutes our data with false alarms “setting back
our ability to understand how the system really
works” (p. 162)

Page credit: http://www.teenvogue.com/story/oral-roberts-university-fitbit-freshman

Page credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits

Page credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother

Page credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data-
drones

Page credits: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0

Page credit: http://www.huffingtonpost.com/2012/10/08/texas-school-district-rep_n_1949415.html

Image credit: Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning
and education. EDUCAUSE Review, p. 34. Retrieved from
http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-
education

“… learning analytics is the measurement,
collection, analysis and reporting of data about
learners and their contexts, for purposes of
understanding and optimising learning and the
environments in which it occurs”
Retrieved from https://tekri.athabascau.ca/analytics/; emphasis added

Image credit: Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE
Review, p. 34. Retrieved from http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-
and-education

The (current)limitations to learning analytics
“… most LMS analytics models do not capture activity by online
learners outside of an LMS (i.e., in Facebook, Twitter, or blogs).
Similarly, most analytics models do not capture or utilize
physical-world data, such as library use, access to learning
support, or academic advising. Mobile devices such as
smartphones and tablets/iPads offer the prospect of bridging
the divide between the physical and digital worlds by capturing
location and activity. Similarly, clickers in classrooms can be
integrated with data from learners’ activity in online
environments, providing additional insight into factors that
contribute to learners’ success”
(Siemens & Long, 2011, p. 36; emphasis added)

So what do we (currently) know about our
students?
• Demographic details – provided on application/registration
• Registration data – qualification, number of courses
• Historical data of previously registered students
• Learning data – assignments (not) submitted, learning histories –
asynchronous, synchronous and (increasingly) digital
• Contact/correspondence with various actors in the institution
• And increasingly personal information pick-up/collected from a
range of sources – defaulting on payments and students
submitting bank statements, health records, etc.

Who knows these things of our students?
• The ‘system’ – disparate databases that do not
(necessarily) talk to one another
• Various stakeholders – student advisors, ICT,
counsellors, academics, tutors, e-tutors, &
researchers, external markers
• Other external stakeholders – employers, law
enforcement agencies, data brokers, labor
brokers, commercial stakeholders
• Social media platforms and networks

We also know what we (currently) don’t know…
• Is s/he a “first generation” student or not?
• Socio-economic circumstances?
• Access, sustainability of access and cost of access to the
Internet?
• Do they have access to prescribed learning resources?
• Motivation for registering for the qualification?
• Reading/comprehension skills?
• Support networks?
• Health and parental status, etc.?

What we (currently) don’t know and may never
know…
What happens in the nexus between students’ (and
their life-worlds) and institutional (operational,
academic and social) identities and processes?
What are the implications for learning analytics if we
accept that student success and retention are a
complex, dynamic, non-linear, unfolding processes
consisting of mutually constitutive and often
incommensurable factors?

Processes
Inter & intra-
personal
domains
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Processes
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Domains
Academic
Operational
Social
TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES
THE STUDENT AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
Success
THE INSTITUTION AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
SHAPING CONDITIONS: (predictable as well as uncertain)
SHAPING CONDITIONS: (predictable as well as uncertain)
Choice,
Admission
Learning
activities
Course
success
Gradua-
tion
THE STUDENT WALK
Multiple, mutually constitutive
interactions between student,
institution & networks
F
I
T
FIT
F
I
T
FIT
Employ-
ment/
citizenship
TRANSFORMED STUDENT IDENTITY & ATTRIBUTES
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
Retention/Progression/Positive experience
(From Subotzky & Prinsloo, 2011)

Who acts (if we do) on what we (think we)
know?
• Faculty – often, due to workloads and
student: staff ratios in a generalised, one-
size-fits-all way
• E-tutors/adjunct faculty
• Administrators – for everyone (new) contact,
a different administrator, starting over,
explaining everything again
• Tutors, counsellors, regional staff

How do we (they) verify & update what we (they)
know
• Do students have access to what we know
and/or think we know about them?
• How do we verify our assumptions about our
students, their learning needs and trajectories?
• How do they verify and provide context to their
(digital) profiles?
(See Slade & Prinsloo, 2013, Prinsloo & Slade, 2014, 2015)

And… who has access to what we know, & under
what conditions?
We protect students from harm when we approve
research but how do we protect students from harm
when we act – change pedagogy, assessment, staff
allocation based on learning analytics?
(Willis, Slade & Prinsloo, 2016, in press)
How do we govern student databases, for how long do
we keep student data, on what conditions do we share
student data, with whom?

Are we (currently) stumbling through a dark room,
not knowing the meaning of the noises we hear,
reacting in uncoordinated kneejerk fashion, our
actions based on assumptions, hearsay, well-intended
but non-empirical, context-disjointed, fragmented
and possibly discipline-inappropriate ways…?
Image credit: http://www.elmundodehector.com/wp-content/uploads/2015/04/door-dark.jpg

In considering the potential of the nexus
between Big Data and learning analytics we
need to critically consider the ethical
implications of …
• Knowing
• Not knowing
• Knowing what we don’t know
• Knowing what we may never know
• Knowing more
The solution is not only (or necessarily?) in knowing more, but
ensuring that once we know, we respond in ethical, caring,
discipline and context-appropriate ways

THANK YOU
Paul Prinsloo (Prof)
Research Professor in Open Distance Learning (ODL)
College of Economic and Management Sciences, Office number 3-15, Club 1,
Hazelwood, P O Box 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
T: +27 (0) 82 3954 113 (mobile)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog: http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp

Bibliography and additional reading
Adryan, B. (2015, October 20). Is it all machine learning? [Web log post]. Retrieved from http://iot.ghost.io/is-
it-all-machine-learning/
Apple, M.W. (Ed.). (2010). Global crises, social justice, and education. New York, NY: Routledge.
Ariely, D. [Dan Ariely]. (2013, January 6). Big data is like teenage sex: everyone talks about it, nobody really
knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...
“[Facebook status update]. Retrieved from https://www.facebook.com/dan.ariely/posts/904383595868
Ball, N. (2013, November 11). Big Data follows and buries us in equal measure. [Web log post]. Retrieved from
http://www.popmatters.com/feature/175640-this-so-called-metadata/
Bauman, Z. (2012). On education. conversations with Riccardo Mazzeo. Cambridge, UK: Polity.
Bergstein, B. (2013, February 20). The problem with our data obsession. MIT Technology Review. Retrieved
from https://www.technologyreview.com/s/511176/the-problem-with-our-data-obsession/
Bertolucci, J. (2014, July 28). Deep data trumps Big Data. Information Week. Retrieved from
http://www.informationweek.com/big-data/big-data-analytics/deep-data-trumps-big-data/d/d-
id/1297588
Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from
http://www.educause.edu/ero/article/learning-analytics-new-black
Boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431

Brock, A. (2015). Deeper data: a response to boyd and Crawford. Media, Culture & Society, 37(7), 1084-1088.
Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://www.mpiwg-
berlin.mpg.de/en/news/features/feature14
Citron, D.K., & Pasquale, F. (2014). The scored society: Due process for automated predictions.
http://ssrn.com/abstract=2376209
Crawford, K. (2013, April 1). The hidden biases in Big Data. Harvard Business Review. Retrieved from
https://hbr.org/2013/04/the-hidden-biases-in-big-data/
Crawford, K. (2014, May 30). The anxieties of Big Data. The New Inquiry. Retrieved from
http://thenewinquiry.com/essays/the-anxieties-of-big-data
Danaher, J. (2014, January 6). Rule by algorithm? Big Data and the threat of algocracy.[Web log post].
Retrieved from http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data-and-
threat.html
Danaher, J. (2015, June 15). How might algorithms rule our lives? Mapping the logical space of algocracy.
[Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.co.za/2015/06/how-might-
algorithms-rule-our-lives.html
Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7.
Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism. DOI: 10.1080/21670811.2014.976411
Eubanks, V. (2014, January 15). Want to predict the future of surveillance? Ask poor communities. The
American Prospect. Retrieved from http://prospect.org/article/want-predict-future-surveillance-ask-
poor-communities
Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 1-3.
.
Bibliography and additional reading (cont.)

Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Harford, T. (2014, April 26). Big data: are we making a big mistake? [Web log post]. Retrieved from
http://timharford.com/2014/04/big-data-are-we-making-a-big-mistake/
Hartfield, T. (2015, May 12 ). Next generation learning analytics: Or, how learning analytics is passé. [Web log
post]. Retrieved from http://timothyharfield.com/blog/2015/05/12/next-generation-learning-analytics-
or-how-learning-analytics-is-passe/
Hartley, D. 1995. The ‘McDonaldisation’of higher education: food for thought? Oxford Review of Education,
21(4), 409-423.
Henman, P. (2004). Targeted!: Population segmentation, electronic surveillance and governing the unemployed
in Australia. International Sociology, 19, 173-191
Johnson, J.A. (2015, October 7). How data does political things: The processes of encoding and decoding data
are never neutral. [Web log post]. Retrieved from
http://blogs.lse.ac.uk/impactofsocialsciences/2015/10/07/how-data-does-political-things/
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in Human
Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Kitchen, R. (2014). The data revolution. London, UK: SAGE.
Kitchen, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of
26 datasets. Big Data & Society, January-June, 1-10. DOI: 10.1177/2053951716631130
Knox, D. (2010). Spies in the ouse of learning: a typology of surveillance in online learning environments. Paper
presented at Edge, Memorial University of Newfoundland, Canada, 12-15 October.
Kranzberg, M. (1986) Technology and history: Kranzberg's laws’. Technology and Culture, 27(3), 544—560
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July-
December), 1-11.

Leonhard, G. (2014, February 25). How tech is creating data "cravability," to make us digitally obese. Retrieved
from http://www.fastcoexist.com/3026862/how-tech-is-creating-data-cravability-to-make-us-digitally-
obese?utm_content=buffer643a8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
Lupton, D. (2015). The thirteen Ps of Big Data. This Sociological Life. Retrieved from
https://www.researchgate.net/profile/Deborah_Lupton/publication/276207564_The_Thirteen_Ps_of_Big_
Data/links/5552c2d808ae6fd2d81d5f20.pdf
Mayer-Schönberger, V. (2009). Delete. The virtue of forgetting in the digital age. Princeton, NJ: Princeton
University Press.
Mayer-Schönberger, V., Cukier, K. (2013). Big data. London, UK: Hachette.
Morozov, E. (2013a, October 23). The real privacy problem. MIT Technology Review. Retrieved from
http://www.technologyreview.com/featuredstory/520426/the-real-privacy-problem/
Morozov, E. (2013b). To save everything, click here. London, UK: Penguin Books.
Morozov, E. (2013). To save everything, click here. London, UK: Penguin Books.
Napoli, P. (2013). The algorithm as institution: Toward a theoretical framework for automated media production
and consumption. In Media in Transition Conference (pp. 1–36). DOI: 10.2139/ssrn.2260923
Nissenbaum, H. 2015. Respecting context to protect privacy: Why meaning matters. Science and engineering
ethics. Retrieved from http://link.springer.com/article/10.1007/s11948-015-9674-9
Pasquale, F. (2015, October 14). Scores of scores: how companies are reducing consumers to single numbers.
The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2015/10/credit-
scores/410350/

Pasquale, F. [FrankPasquale]. (2016, February 19). "We know where you are. We know where you’ve been. We
can more or less know what you're thinking about.”
http://www.theatlantic.com/technology/archive/2016/02/google-cute-evil/463464/ … #Jigsaw [Tweet].
Retrieved from https://twitter.com/FrankPasquale/status/700473628605947904
Pasquale, F. (2015). The black box society. Harvard Publishing, US.
Prinsloo, P. (2009). Modelling throughput at Unisa: The key to the successful implementation of ODL. Retrieved
from http://uir.unisa.ac.za/handle/10500/6035
Prinsloo, P. (2015, September 3). The ethics of (not) knowing our students. Presentation at the University of
South Africa, Pretoria. Retrieved from http://www.slideshare.net/prinsp/the-ethics-of-not-knowing-our-
students-52373670
Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The
International Review of Research in Open and Distributed Learning, 15(4), 306-331. Retrieved from
http://www.irrodl.org/index.php/irrodl/article/view/1881/3060
Prinsloo, P., & Slade, S. (2015, March). Student privacy self-management: implications for learning analytics. In
Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 83-92). ACM.
Retrieved from http://dl.acm.org/citation.cfm?id=2723585
Prinsloo, P., Archer, E., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big (ger) data as better data in open distance
learning. The International Review of Research in Open and Distributed Learning, 16(1).
Rambam, S. (2008). Privacy is dead. Get over it. Retrieved from
https://www.youtube.com/watch?v=Vsxxsrn2Tfs&index=1&list=PL8C71542205AA51E5
Rosen, J. (2010, July 21). The web means the end of forgetting. New York Times [Online].

Rosenbaum, R. (1995, January 15). The great Ivy League nude posture photo scandal. The New York Times.
Retrieved from http://www.nytimes.com/1995/01/15/magazine/the-great-ivy-league-nude-posture-
photo-scandal.html
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY:
Routledge
Scharmer, O. (2014, July 18). From Big Data to deep data. Huffington Post. Retrieved from
http://www.huffingtonpost.com/otto-scharmer/from-big-data-to-deep-dat_b_5599267.html
Shacklett, M. (2015, January 6). Thick data closes the gaps in big data analytics. TechRepublic. Retrieved from
http://www.techrepublic.com/article/thick-data-closes-the-gaps-in-big-data-analytics/
Silver, N. 2012. The signal and the noise: Why most predictions fail – but some don’t. New York, NY:
Routledge.
Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist
57(1) ,1509–1528.
Slade, S., & Prinsloo, P. (2015). Student perspectives on the use of their data:
between intrusion, surveillance and care. European Journal of Open, Distance and Elearning. (pp.16-
28).Special Issue. http://www.eurodl.org/materials/special/2015/Slade_Prinsloo.pdf
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for improving
student success in open distance learning at the University of South Africa. Distance Education, 32(2),
177-193.
Tene, O. & Polonetsky, J. (2013). Judged by the Tin Man: Individual rights in the age of Big Data. J. on
Telecomm. & High Tech. L., 11, 351.

Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretive key of modern rationality. Theory
Culture Society 31, pp. 29—49. DOI: 10.1177/0263276413510051
Therborn, G. (ed.).(2006). Inequalities of the world. New theoretical frameworks, multiple empirical
approaches. London, UK: Verso Books
Uprichard, E. (2013). Big data, little questions. Discover Society, 1 October. Retrieved from
http://discoversociety.org/2013/10/01/focus-big-data-little-questions/
Wagner, D., & Ice, P. (2012, July 18). Data changes everything: delivering on the promise of learning analytics in
higher education. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/data-
changes-everything-delivering-promise-learning-analytics-higher-education
Wang, T. (2013, January 20). Why Big Data needs thick data. Medium. Retrieved from
https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7#.4jbatgurh
Watters, A. (2013, October 13). Student data is the new oil: MOOCs, metaphor, and money. [Web log post].
Retrieved from http://www.hackeducation.com/2013/10/17/student-data-is-the-new-oil/
Watters, A. (2014). Social justice. [Web log post]. Retrieved from http://hackeducation.com/2014/12/18/top-
ed-tech-trends-2014-justice
Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,(June), 46-53.
Willis, J. E., Slade, S., & Prinsloo, P. (in review). Ethical oversight of student data in learning analytics: A typology
derived from a cross-continental, cross-institutional perspective. Submitted to special issue of Educational
Technology Research and Development (Exploring the Relationship of Ethics in Design and Learning
Analytics: Implications for the Field of Instructional Design and Technology), guest edited by M. Tracey and
D. Ifenthaler.

Big Data Provocations in Education

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Big Data Provocations in Education

Similar to Big Data Provocations in Education (20)

More from University of South Africa (Unisa)

More from University of South Africa (Unisa) (20)

Recently uploaded

Recently uploaded (20)

Big Data Provocations in Education

Editor's Notes