Guest presentation: SASUF Symposium: Digital Technologies, Big Data, and Cybersecurity, Vaal University of Technology, Vanderbijlpark, South Africa, 15 May 2018

1. By Paul Prinsloo
(University of South Africa)
@14prinsp
A Blind Date With
(Big) Data: Student
Data in (Higher)
Education
Image credit: http://archives.wfpl.org/wp-
content/uploads/2011/12/missing-scales.jpg
SASUF Symposium: Digital
Technologies, Big Data, and
Cybersecurity, Vaal University of
Technology, Vanderbijlpark, South
Africa, 15 May 2018

2. Acknowledgements
• I don’t own the copyright of any of the images in this
presentation. I hereby acknowledge the original copyright and
licensing regime of every image and reference used.
• This work (excluding the images) is licensed under a Creative
Commons Attribution 4.0 International License.

3. Outline of the session
• Mapping the blind date – the excitement, the claims, the fears, the
disappointment, the reality and the hope
• Big Data and higher education
• Learning analytics: the collection, analysis and use of student data
• Towards understanding (Big) data (and higher education)
• Mapping some tentative pointers for the way forward
Panel discussion
• Myles Thies - Director of Digital Learning Services @Eiffelcorp
• Alanna Riley Senior Consultant: Teaching and Learning Centre @ the
University of Fort Hare
• Rodney Malope - Information Analyst @ the Vaal University of
Technology

4. Going on a blind
date encompasses
a combination of
curiosity, fear, and
excitement…
Imagecredit:https://pixabay.com/en/sculpture-boy-gold-fear-neizvesno-1628357/

5. Image credit: https://pixabay.com/en/more-flame-fire-brand-burn-better-687241/

6. Why does (higher) education go on a blind date
with (Big) Data?
1. Funding constraints – do more with less
2. Increasing accountability to a range of stakeholders
who look for evidence
3. The unquestioned status of evidence-based
management and managerialism
4. The lure and power of numbers/metrics
5. Access to increasing amounts of data
6. The move towards online/blended learning

7. Why does (higher) education go on a blind date
with (Big) Data? (2)
7. Software, analysis and visualisation tools/apps
8. The promise and hype surrounding machine learning
and Artificial (un)Intelligence
9. Legitimate concerns/despair about student pass rates
10. We still don’t understand and support student
success fully
11. Commercial applications providing one-stop solutions
Institutions do not have the internal capacity and
expertise) making this a blind date too tempting to
refuse

8. Image credit: https://westside-eldos.co.za/34671/whats-trending-look-yesterdays-date-family-episode/

9. 2016
Source credit: https://www.forbes.com/sites/schoolboard/2016/01/29/big-datas-coming-of-age-in-higher-
education/#5384ce8c1c41

10. “Despite the hype, the field remains nascent, the
implications uncertain.”
Source credit: https://www.forbes.com/sites/schoolboard/2016/01/29/big-datas-coming-of-age-in-higher-education/#5384ce8c1c41
• Big Data becomes useful
• Policy makers will take notice
• Data privacy and security concerns spike
• Collaboration is king

11. Page credit: http://www.npr.org/sections/ed/2017/01/11/506361845/the-higher-ed-learning-revolution-tracking-each-
students-every-move
2017

12. Page credit: http://er.educause.edu/articles/2012/7/learning-analytics-the-new-black
2012

13. Source credit: http://timoelliott.com/blog/2013/02/gartnerbi-emea-2013-part-1-analytics-moves-to-the-core.html
Using student data…

14. Imagecredit:https://www.flickr.com/photos/bionicteaching/2920562020

15. Image credit: All images sourced from Amazon

16. Some of our beliefs about data…
• Data are neutral
• Represents ‘the Truth’ – you can’t argue with data
• We talk about data as “raw”, “cooked”, “corrupted”, “cleaned”,
“scraped” “mined” and “processed” (Gitelman & Jackson,
2013)
• Data are self explanatory (Mayer-Schönberger & Cukier, 2013)
• We believe that Big Data works with the whole population
(n=all)
• Knowing ‘what’ is happening erases the need to know ‘why’
something is happening
• Big(ger) data are better data

17. Data are not neutral, raw, objective and pre-analytic
but 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)

18. When do data become
Big Data?
Image credit: https://commons.wikimedia.org/wiki/File:Big_%26_Small_Pumkins.JPG

19. Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big
Data? Exploring the ontological characteristics of 26
datasets. Big Data & Society, 3(1), 2053951716631130.

20. When data grow up… - towards a
definition
1. Volume (enormous quantities of data)
2. Velocity (created in real-time)
3. Variety (structured, semi-structured and
unstructured)

21. • Exhaustivity (an entire system is captured – n=all)
• Fine grained (in resolution) and uniquely indexical (in
identification)
• Relationality (containing common fields that enable the
conjoining of different datasets)
• Extensionality (can add/change new fields easily)
• Veracity (the data can be messy, noisy and contain uncertainty
and error)
• Value (many insights can be extracted and the data can be
repurposed)
• Variability (data whose meaning can be constantly shifting in
relation to the context in which they are generated)
When data grow up… - towards thinking
about Big Data (2)

22. “Our analysis reveals that the key
definitional boundary markers are the
traits of velocity and exhaustivity"
(emphasis added)

23. Three sources of data
Directed
A digital form of
surveillance
wherein the
“gaze of the
technology is
focused on a
person or place
by a human
operator”
Automated
Generated as “an
inherent,
automatic function
of the device or
system and
include traces …”
Volunteered
“gifted by users
and include
interactions
across social
media and the
crowdsourcing of
data wherein
users generate
data” (emphasis
added)
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in Human
Geography, 3, 262-267. SOI: 10.1177/2043820613513388

24. (1)
Humans
perform the
task
(2)
Task is shared
with
algorithms
(3)
Algorithms
perform task:
human supervision
(4)
Algorithms
perform task: no
human input
Seeing Yes or No? Yes or No? Yes or No? Yes or No?
Processing Yes or No? Yes or No? Yes or No? Yes or No?
Acting Yes or No? Yes or No? Yes or No? Yes or No?
Learning Yes or No? Yes or No? Yes or No? Yes or No?
Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from
http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html
Human-algorithmic interaction and oversight

25. Image credit: All images sourced from Amazon

26. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Source credit: http://crackedlabs.org/en/corporate-surveillance/

27. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Sourcecredit:http://crackedlabs.org/en/corporate-surveillance/

28. Image credit: https://upload.wikimedia.org/wikipedia/commons/3/39/BIG_Logo.svg
Do we* have access to Big Data in higher
education?
* It depends who the ‘we’ are referring to…

29. It depends…
• (Big) Data and a single institution?
• (Big) data and a combination of institutions –
provincial/national/international?
• (Big) data and Massive Open Online Courses
(MOOCs)?

30. Big Data
Big Data in Higher Education
Academic Analytics
Learning Analytics
Municipal accounts
Medical aid data
Criminal records
Union membership

31. Image credit: https://pixabay.com/en/maze-labyrinth-solution-lost-1804499/
The way
forward

32. Citation: Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an
emerging world. Big Data & Society, 5(1), 2053951717753228.
1. Our understanding of data

33. Source credit: http://www.justice.gov.za/legislation/acts/2013-004.pdf
2. South African legislation

34. Source credit: https://ico.org.uk/for-organisations/guide-to-the-general-data-protection-regulation-gdpr/
3. European Union
Regulation

35. 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).
4. Working with big(er) data
in South African (higher)
education

36. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
2013
5. The ethics in the
collection, analysis and
use of student data

37. Source credit: https://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-
use-of-student-data-policy.pdf
2014
6. Towards policy
frameworks

38. Available at: http://www.siyaphumelela.org.za/documents/5a61c7b737ff5.pdf

39. Available at - http://dl4d.org/portfolio-items/learning-analytics-for-the-global-south/
7. Student data in the
Global South

40. Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic
decision-making in higher education. E-Learning and Digital Media, 14(3), 138-163.
8. Human-algorithmic interaction
and student data

41. 1. Who/what drives initiatives to collect, analyse and use (more) student
data?
2. What are some of our beliefs pertaining to the data we have, the data
we don’t have, and the data we think we need?
3. What data do we currently have that we do not utilise effectively?
4. What data don’t we have access to that will allow us to teach more
effectively?
5. What data do students need to make (better) and (more) informed
choices?
6. What are some of the ethical issues in the collection, analysis and use
of student data?
7. Do we need specific policies and institutional oversight on the
collection, analysis and use of student data?
(In)conclusions: Questions for consideration

42. THANK YOU
Paul Prinsloo
Research Professor in Open Distance Learning (ODL)
College of Economic and Management Sciences, Samuel Pauw
Building, Office 5-21, P.O. Box 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp

43. Panel discussion
• Myles Thies - Director of Digital Learning Services
@Eiffelcorp
• Alanna Riley Senior Consultant: Teaching and Learning
Centre @ the University of Fort Hare
• Rodney Malope - Information Analyst @ the Vaal
University of Technology