Invited presentation at the University of Wisconsin Milwaukee on Tuesday 16 August 2016

1. Using student data: (Not) solving the
student departure puzzle?
Student data: the missing
link in solving the student
departure puzzle?
Paul Prinsloo (University of South Africa, Unisa)
@14prinsp
Tuesday 16 August 2016
University of Wisconsin Milwaukee

2. Acknowledgements
This presentation (excluding the images) is
licensed under a Creative Commons Attribution-
NonCommercial 4.0 International License
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.
Image credit on first slide: https://www.flickr.com/photos/ofernandezberrios/2720569216

3. The role of student data in solving student
departure
We do have a lot of student data but how do the pieces
fit together?

4. Or is the issue that we are just looking for that ‘one’
missing piece of data that will solve everything? If we
could only have more data…
Image credit: https://commons.wikimedia.org/wiki/File:Puzzle2_found_bw.jpg

5. And where are
students in all of this?
What happens when
students say…
#NoMore
#DoNotTrack
Image credit: https://en.wikipedia.org/wiki/Privacy#/media/File:Surveillance_cameras.jpg

6. Image credit: https://www.flickr.com/photos/haydnseek/2534088367
Exploring the issues at the intersections of
• Our fiduciary duty to care
• The collection of student data as dancing with the devil in the
search for salvation
• Respecting student privacy and agency

7. Overview of the presentation
• Map, question, interrupt and attempt to slow down
some of the current discourses about (student) data
• What does research tell us about student retention
and dropout?
• What do we already know about our students, where
are the data located and who has access to this data
under what conditions?
• Tentatively map the way forward towards collecting,
analyzing and using student data as an ethics of care

8. 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

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

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

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

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

13. http://www.abc.net.au/news/2016-08-12/university-of-melbourne-tracking-students-through-wifi/7723468

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

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

16. Source: http://chronicle.com/article/Are-Struggling-College/235311

17. (Student) data as Medusa
Higher education is
mesmerized and
seduced by the
potential of the
collection, analysis
and use of student
data. If only we
know more…
Image credit: http://en.wikipedia.org/wiki/Medusa

18. ‘how much is enough data
to solve my problem?’
(Adryan, 2015)
Image credit: https://www.flickr.com/photos/uncle-
leo/1341913549
How much (more) student data do we need?

19. • 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?
• And what happens if our students don’t want to
be known when they feel that revealing their
identities will make then more vulnerable?
(De)constructing knowing more…

20. 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/
Data are never raw but always framed and
cooked

21. 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

22. We live in a “scored society” (Citron
& Pasquale, 2013) and consumers are
increasingly reduced to single
numbers (Pasquale, 2015). We do not
only need large data sets, but also
deep data (Scharmer, 2014) or thick
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
Our students’ lives are so much more than the
data we have of them

23. Boyd and Crawford (2011) point to the fact that just because
we have access to increasing amounts and granularity of
personal data, does not mean that we have to collect the data,
analyse the data and use the data.
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 (Willis, Slade & Prinsloo, 2016)
Just because we can, does not mean we have
to…

24. Collecting, analyzing student data in complex
and chaotic environments (Cynefin framework)
SIMPLE/KNOWN
Cause & effect relationships
known & predictable
Best practice
Standard operating procedures
Sense-Categorize-Respond
COMPLICATED
Cause & effect relationships
separated over time & space
Analytical/Reductionist
Scenario planning
Sense-Analyze-Respond
COMPLEX
Cause & effect relationships are
only coherent in retrospect and
do not repeat
Pattern management
Complex adaptive systems
Probe-Sense-Respond
CHAOS
No cause & effect relationships
perceivable
Stability-focused intervention
Crisis management
Act-Sense-Respond

25. Image credit: http://www.tylervigen.com/spurious-correlations
Mistaking correlation with causation…

26. Image credit: http://www.tylervigen.com/spurious-correlations
Mistaking correlation with causation…

27. 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)
Mistaking the noise for the signal

28. (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-algorithm interaction in the collection,
analysis and use of student data

29. • What does research tell us?
• What is our understanding
of how the different variables
intersect, at which
stages of the learning journey?
• What data do we already have, where are the data
located, who has access to this data under what
conditions?
• How do students fit into all of this and can they opt
out?
Image credit: https://pixabay.com/p-316638/
Making sense of student data…

30. We know that the following impact on student
success…
• Socioeconomic circumstances
• Primary and secondary school
background
• Educational background of parents and
immediate family
• Geographical distance between family
home and institution
• Subjects and subject marks on school
level
• Proficiency in the language of tuition
• Support networks or lack of
• Peer pressure
• Family and community pressure
• Access to resources
• Mathematics on school level
• Role models or lack of
• Locus of control
• Attribution
• Self awareness
• Self-discipline
• Habits and behaviours
• Parental status
• Health status
• Employment status
• Probability of employment or
career progress

31. We know that the following impact on student
success… (2)
• Institutional efficiencies or inefficiencies
• Complexity of curricula
• Curriculum coherence
• Epistemologies and ways of seeing the world
• Assessment strategies
• Tuition periods
• Examination schedules
• Server reliability
• Faculty understanding of ODL
• Faculty expertise
• Institutional culture
• Whether the institution is the choice of last resort for students
• Integration of student support, curriculum, pedagogy and technology

32. We know that the following impact on student
success… (3)

33. What we don’t know
(yet), and possibly never
may know…

34. What is the impact when these different sets
of impact combine?
What happens when we see student success as the
result of mostly non-linear, multidimensional,
interdependent interactions at different phases in the
nexus between student, institution and broader societal
factors?

35. 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
(Subotzky & Prinsloo, 2011)

36. If…
student success is the result of mostly non-linear,
multidimensional, interdependent interactions at
different phases in the nexus between student,
institution and broader societal factors
(Prinsloo, 2009)
… what data do we already have, where are
the data located, who has access to this data
under what conditions, and what prevents us
from using it?

37. So what data do we already have?
• Demographic details – provided on application/registration
• Registration data – qualification, number of courses
• Historical registration data of students
• Learning data – assignments (not) submitted, learning
histories – asynchronous, synchronous and (increasingly)
digital
• Contact/correspondence with various actors in the institution
• Personal information collected from a range of sources –
defaulting on payments, students submitting bank
statements, health records, etc.
• Published and unpublished research and
department/institutional reports

38. 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, researchers, academics, tutors, etc.
• Other external stakeholders – employers, law
enforcement agencies, data brokers, labor brokers,
commercial stakeholders
• Social media platforms and networks

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

40. 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)

41. 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)
How do we govern student databases, for how long do
we keep student data, on what conditions do we share
student data, with whom?

42. We therefore 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

43. Collecting, analysing and using student data:
towards an ethics of care
1. Do no harm. Repeat after me. Do no harm
2. Students have a right to know. If they do not know, our
research constitutes surveillance and spying, and not research
3. Make it clear what data are collected, when, for what
purpose, for how long it will be kept and who will have access
and under what circumstances
4. Provide students access to information and data held about
them, to verify and/or question the conclusions drawn, and
where necessary, provide context
5. Provide access to a neutral ombudsperson
(See Prinsloo & Slade, 2015)

44. Collecting, analysing and using student data:
towards an ethics of care (2)
6. Context matters. Downstream use of data for purposes
other than the original purpose for the collection of data
compromises the contextual integrity of data
7. Involve students in the meaning-making. They are not data
points on a PowerPoint at a conference. They have
contexts, histories. They are infinitely more than their data.
8. Who will we hold accountable for algorithms?
9. What are the benefits for students? For you? For the
institution? Be transparent.
(See Prinsloo & Slade, 2015)

45. 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

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