The document discusses the complexities and challenges of implementing learning analytics (LA) in public education, emphasizing the balance between potential benefits and privacy concerns. It highlights the necessity for clear legal frameworks and informed consent for data collection and usage, while addressing fears related to data ownership and privacy. Furthermore, it suggests that transparency in data policies does not always equate to trust, highlighting the asymmetrical power dynamics between users and large organizations.

3. Learning Analy,cs
HE Managers see:
• Promise vs. Concerns
• Poten5al vs. Risks
• Benefits vs. Cost
• Purpose vs. Compe55ve Pressures
• Inten5ons vs. Hesita5ons
• Leading to Confusion
HEIs: How to implement LA?

4. 5
• $100 million investment
• Aim: Personalized learning in public schools, through data & technology
standards
• 9 US states par5cipated, in 2013 data about millions of children
have been stored
Privacy as Show-Stopper for LA

5. 6
Privacy as Show-Stopper for LA
Ignoring the fears and public
percep,on of the applica5on of
analy5cs can lead to a lack of
acceptance, protests, and even failure
of en5re LA implementa5ons.

6. 7
Related Research Work
Prinsloo & Slade (2013)
Slade & Prinsloo (2013)
Pardo & Siemens (2014)
Prinsloo & Slade (2015)
Hoel & Chen (2015)
Sclater, Bailey (2015)
Steiner, Kickmeier-Rust, Albert (2015)
hGp://bit.ly/lace-privacy.

7. 8
Related Policies
hYp://www.open.ac.uk/students/charter/
essen5al-documents/ethical-use-student-
data-learning-analy5cs-policy#
hYps://www.jisc.ac.uk/sites/default/files/
jd0040_code_of_prac5ce_for_learning_anal
y5cs_190515_v1.pdf

8. 9
Related Policies
hYp://www.ed.ac.uk/informa5on-services/learning-technology/learning-analy5cs

9. 10
Related Policies
• HEI law on data collec5on in NL like in all
EU countries since Nuremberg trials (data
collec5on allowed to improve educa5on,
clear purpose, consent, limited access)
• Engelfriet, A., Jeunink, E., Manderveld, J.
(2015). Learning analy,cs onder de Wet
bescherming persoonsgegevens.
hYps://www.surf.nl/kennis-en-innova5e/
kennisbank/2015/learning-analy5cs-
onder-de-wet-bescherming-
persoonsgegevens.html
• SURF follow-up report with use cases in
prepara5on

10. Research Ethics
Origins lie in post-WWII. – Milestones:
• Nuremberg Code (1949)
• Helsinki Declara5on (1964)
• Belmont Report (1978)
• 2000s: Biomedical Science
• RRI (Responsible Research and Innova5on)

11. Unethical Research
Milgram
Experiment
Stanford
Prison
Experiment

12. Facebook Study

13. Privacy
• The right to be leh alone (Wes5n 1968)
• Informa5onal self-determina5on (Flaherty
1989)
• Informa5onal, decisional, local privacy
(Roessler 2005)
• Privacy is not anonymity or data security!

14. Privacy
Contextual Integrity vs.
Big Data Research
Context bound information vs.
Repurposing of data

15. Who are the Bad Guys?
ME
&
MY DATA
Government? Commerce?
Educa5on? Hackers & Bad Guys?

16. Legal Frameworks
• EU Data Protec5on Direc5ve 95/46/EC
(automated processing of ‘personal data’)
2016: General Data Protec5on Regula5on
(GDPR)
• Restric5ng the (re-)use of data
à vs. and contradic5ng
• Big Data business models
• European Data Reten5on Direc5ve 2006/24/EC
(data storage for security purposes)

17. Moderniza,on of EU Universi,es report
Recommenda,on 14
Member States should ensure that legal frameworks allow higher
educa5on ins5tu5ons to collect and analyse learning data. The full
and informed consent of students must be a requirement and the
data should only be used for educa5onal purposes.
Recommenda,on 15
Online plaoorms should inform users about their privacy and data
protec5on policy in a clear and understandable way. Individuals
should always have the choice to anonymise their data.
hGp://ec.europa.eu/educa,on/
library/reports/modernisa,on-
universi,es_en.pdf

18. 19
Legal Frameworks
hYps://studentprivacypledge.org/

19. 20
Legal Frameworks

20. Fears
• Power-rela5onship, user exploita5on
• Data ownership
• Anonymity and data security
• Privacy and data iden5ty
• Transparency and trust

21. Power-rela,onship
• Tracking and id-ing users or ci5zens by state or
corpora5ons (e.g. insurance companies, banks,
car manufacturers, etc.) = benefit not to the user!
• Power-rela5onship is asymmetrical

22. Exploita,on
• Free labour as business model of for-profit
companies
• Crowd sourcing outside the „commons“

23. Data Ownership

24. Data Ownership

25. Anonymity and Data Security
• No absolute anonymity or de-iden5fica5on
• Integra5on of mul5ple data sources increase
compromised personal iden5ty
• Data stores are not 100% secure

26. Privacy and Data Iden,ty
• System iden5ty vs. Social iden5ty
• People approximated onto data models by
probability
• Power: data subjects have no say in the design
of data model

27. Transparency and Trust
• Assump5on: more transparency = more trust
• But: rela5onship is mostly asymmetrical
(individual vs. big corpora5on)
• Transparency as instrument of control

28. Introducing: DELICATE
• Is not replacing
deep thoughts!
• Checklist for quick
data conversa5ons
• To facilitate
trusted LA
• Guide for data
scien5sts, decision
makers
• Inspired by
medical checklists
for pa5ent
informa5on

29. Introducing: DELICATE

30. Introducing: DELICATE

31. Introducing: DELICATE

33. Call for Papers
Special Issue: Journal of HE
Development (ZfHE)
Learning Analy,cs:
Implica,ons for Higher
Educa,on
hYp://bit.ly/1qXTaNz
Deadline: 10 June 2016
Publica5on date: Spring
2017