This document discusses the potential of "bigger data" or large datasets in distance education. It uses a case study of the University of South Africa (Unisa) to explore the necessary conditions and frameworks for leveraging big data. Unisa has a large number of students taking modules over many years, resulting in a substantial amount of student data. However, this data currently resides in different systems and extracting and analyzing it poses challenges regarding skills, data quality, representation and ethics. Institutional stakeholders must address these challenges to better understand students and support learning through big data.

1. Bigger data as better data
an exploration in the context of distance education
Elizabeth Archer, Glen Barnes,
Yuraisha Chetty, Paul Prinsloo & Dion van Zyl
HELTASA 2013 - Pretoria
Creative Commons
Attribution 3.0 License
(some images fair dealing)

2. The promise of Bigger Data
Images are excerpts from Intel Big Data 101: How Big Data Makes Big Impacts www.youtube.com/watch?v=D4ZQxBPtyHg

3. The promise of Bigger Data
Images are excerpts from Intel Big Data 101: How Big Data Makes Big Impacts www.youtube.com/watch?v=D4ZQxBPtyHg

4. The promise of Bigger Data
Images are excerpts from Intel Big Data 101: How Big Data Makes Big Impacts www.youtube.com/watch?v=D4ZQxBPtyHg

5. Bigger Data
Images are excerpts from Intel Big Data 101: How Big Data Makes Big Impacts www.youtube.com/watch?v=D4ZQxBPtyHg

6. Bigger Data

7. The promise of Bigger Data in Higher
Education
http://www.opencolleges.edu.au/informed/learning-analytics-infographic/

8. Contextualisation
• Instrumental case study to insight into the institutional processes,
challenges and opportunities for realising the potential of bigger data to be
better data.
• Participants:
–
–
–
–
institutional researchers
statisticians
business information and systems analysts
academics and other institutional information

9. Will it help if we have
more data?
Do we use what we
already have?
What is noise and
what is signal?
Do we know what
data we already
have?
What are the ethical
implications of knowing so
much about our students?

10. Exploring the necessary conditions and enabling
framework that need to be in place to realise the
potential of bigger data as better data
Unisa as case study

11. Unisa, getting the (bigger data) picture
Data available
for 1 module

12. Unisa, getting the (bigger data) picture
Year 1
Each student
takes about 5
modules
annually

13. Unisa, getting the (bigger data) picture
1
2
3
4
5
6
7
It takes each student about 7 years to complete
their qualification

14. Unisa, getting the (bigger data) picture
Around 400 000 students x 5 modules/annum x 7
years = ….

15. Unisa, getting the (bigger data) picture

16. Student Success Framework
(Subotzky & Prinsloo, 2011)

17. SHAPING CONDITIONS: (predictable as well as uncertain)
TRANSFORMED STUDENT IDENTITY & ATTRIBUTES
THE STUDENT AS AGENT
IDENTITY, ATTRIBUTES, CAPITAL &
HABITUS
Processes
F
I
T
THE STUDENT WALK
Multiple, mutually constitutive
interactions between
student, institution & networks
Inter & intrapersonal
domains
F
I
T
F
I
T
F
I
T
Modalities:
• Attribution
• Locus of control
• Self-efficacy
F
I
T
FIT
Retention/Progression/Positive experience
Choice, Ad
mission
Learning
activities
Course
success
Graduation
Employment/
citizenship
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
Success
TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES
FIT
THE INSTITUTION AS AGENT
IDENTITY, ATTRIBUTES, CAPITAL &
HABITUS
Processes
Domains
Academic
Operational
Social
Modalities:
• Attribution
• Locus of control
• Self-efficacy
SHAPING CONDITIONS: (predictable as well as uncertain)

18. Issues and considerations when
dealing with bigger data
•
Where do data currently reside?
•
Who are the key stakeholders?
•
What systems are used to harvest and analyse data?
•
How are data extracted and for what purposes?
•
What are the issues surrounding the quality of the data?
•
Population versus sample statistics?
•
What skills are necessary and available for the harvesting and analyses of big data?
•
What is the value of historical and secondary data?
•
Ethical considerations

19. Study of Exam Result
http://www.flickr.com/photos/comedynose/4906846651/sizes/m/in/photolist-8tASyk-851XYA-dbNFPF-agm1Qq-7L9och-8zAJjW-f1ZeUm-8tvVRw-9WpdAh-9UQsR5-fzo8TL-dSgbGx-cT6ED9-ftqE1w-8Eu8zd-8EqYkH-8EqYsv-aB2Ghx-8MtD6y-7T2DLD-8

20. Where do data currently reside?
• Various databases
–
–
–
–
–
Student management system
Learning management system
Academic structure
Staff database
Exam database …
• The more nuanced interactions
–
–
–
–
–
E-mail, calls, letters
Downloads, use of OER
Logins, clicks
Library and e-tutor interactions
Discussion groups, social media

21. Who are the key stakeholders?
http://stevenlay.billyblueinteractive.net/wp-content/uploads/2010/12/Identify_Your_Audience.jpg

22. Who are the key stakeholders?
• Support units
– Registrations, examinations, student support,
academic quality units, curriculum development
units
• Teaching and learning committees,
management
• Lecturers
• Students

23. What systems are used to harvest and
analyse data?
• Fairly structured data
– Excel, SPSS, SQL,
SASS….
• Unstructured?
– Hardcopy RPL portfolio
– Discussion forums
– Social media
http://globalitconsulting.files.wordpress.com/2013/10/prdict.png?w=604

24. How are data extracted and for what
purposes?
• Students cancelled for
financial reasons
– Still allowed to sit exams
– Operational vs Financial
extraction
http://www.ius.edu/business/files/images/programs/why-study/Program-Why-Study-Finance.png

25. What are the issues surrounding the quality
of the data?
– Examinations
department responsible
exam data
– Dependent on
academics for capturing
– Cycle of capture,
moderation, review and
finalisation
http://www.rimell.com/images/data_scraping.jpg
• Timing of extraction

26. http://www.rogerscime.com/wp-content/uploads/2011/04/unrepresentative-sample.gif
Population versus sample statistics?
• Relating population data to
sample data
• Representativeness
– What criteria
• Non-response / Opting out
• Anonymous data / data not
related to a specific student
– Twitter, Facebook, blog

27. What skills are necessary and available for
the harvesting and analyses of big data?
• Technical / Analytical intelligence
• Issues intelligence
• Contextual intelligence
(Terrenzini, 1993)
Images is an excerpts from What is Predictive Analytics / Data Mining? http://www.youtube.com/watch?v=BjznLJcgSFI

28. Ethical considerations
• Can students opt out of / consent to being monitored
and tracked?
– Representativeness
– How would this influence our models
• If we know more are we obliged to respond?
• Cost – benefit analysis
http://www.realityuncovered.net/blog/wp-content/uploads/2011/07/ombb.jpg

29. This is the caveman era of big data. We are just
starting to understand how important it is and
how it is going to start influencing every aspect
of life on earth.
Smolan, 2013

30. Conclusion
In order to fully realise the potential of big data
in higher education, it is necessary to examine
the challenges already faced with institutional
intelligence