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Learning	Analy,cs	
HE	Managers	see:	
•  Promise	vs.	Concerns	
•  Poten5al	vs.	Risks	
•  Benefits	vs.	Cost	
•  Purpose	vs.	C...
5	
•  $100	million	investment		
•  Aim:	Personalized	learning	in	public	schools,	through	data	&	technology	
standards		
• ...
6	
Privacy	as	Show-Stopper	for	LA	
Ignoring	the	fears	and	public	
percep,on	of	the	applica5on	of	
analy5cs	can	lead	to	a	l...
7	
Related	Research	Work	
Prinsloo	&	Slade	(2013)	
Slade	&	Prinsloo	(2013)		
Pardo	&	Siemens	(2014)		
Prinsloo	&	Slade	(20...
8	
Related	Policies	
hYp://www.open.ac.uk/students/charter/
essen5al-documents/ethical-use-student-
data-learning-analy5cs...
9	
Related	Policies	
hYp://www.ed.ac.uk/informa5on-services/learning-technology/learning-analy5cs
10	
Related	Policies	
•  HEI	law	on	data	collec5on	in	NL	like	in	all	
EU	countries	since	Nuremberg	trials	(data	
collec5on...
Research	Ethics	
Origins	lie	in	post-WWII.	–	Milestones:	
	
•  Nuremberg	Code	(1949)	
•  Helsinki	Declara5on	(1964)	
•  Be...
Unethical	Research	
Milgram	
Experiment	
Stanford	
Prison	
Experiment
Facebook	Study
Privacy	
	
•  The	right	to	be	leh	alone	(Wes5n	1968)	
•  Informa5onal	self-determina5on	(Flaherty	
1989)	
•  Informa5onal,...
Privacy	
Contextual Integrity vs.
Big Data Research
Context bound information vs.
Repurposing of data
Who	are	the	Bad	Guys?	
ME	
&	
MY	DATA	
Government?	 Commerce?	
Educa5on?	 Hackers	&	Bad	Guys?
Legal	Frameworks	
•  EU	Data	Protec5on	Direc5ve	95/46/EC	
(automated	processing	of	‘personal	data’)	
2016:	General	Data	Pr...
Moderniza,on	of	EU	Universi,es	report	
Recommenda,on	14	
Member	States	should	ensure	that	legal	frameworks	allow	higher	
e...
19	
Legal	Frameworks	
hYps://studentprivacypledge.org/
20	
Legal	Frameworks
Fears	
•  Power-rela5onship,	user	exploita5on	
•  Data	ownership	
•  Anonymity	and	data	security	
•  Privacy	and	data	iden...
Power-rela,onship	
•  Tracking	and	id-ing	users	or	ci5zens	by	state	or	
corpora5ons	(e.g.	insurance	companies,	banks,	
car...
Exploita,on	
•  Free	labour	as	business	model	of	for-profit	
companies	
•  Crowd	sourcing	outside	the	„commons“
Data	Ownership
Data	Ownership
Anonymity	and	Data	Security	
•  No	absolute	anonymity	or	de-iden5fica5on	
•  Integra5on	of	mul5ple	data	sources	increase	
c...
Privacy	and	Data	Iden,ty	
•  System	iden5ty	vs.	Social	iden5ty	
•  People	approximated	onto	data	models	by	
probability	
•...
Transparency	and	Trust	
•  Assump5on:	more	transparency	=	more	trust	
•  But:	rela5onship	is	mostly	asymmetrical	
(individ...
Introducing:	DELICATE	
•  Is	not	replacing	
deep	thoughts!	
•  Checklist	for	quick	
data	conversa5ons		
•  To	facilitate	
...
Introducing:	DELICATE
Introducing:	DELICATE
Introducing:	DELICATE
Call	for	Papers	
Special	Issue:	Journal	of	HE	
Development	(ZfHE)	
	
Learning	Analy,cs:	
Implica,ons	for	Higher	
Educa,on	...
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics
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Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics

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The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistin-guishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this presentation, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.

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Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics

  1. 1. 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?
  2. 2. 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
  3. 3. 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.
  4. 4. 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.
  5. 5. 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
  6. 6. 9 Related Policies hYp://www.ed.ac.uk/informa5on-services/learning-technology/learning-analy5cs
  7. 7. 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
  8. 8. 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)
  9. 9. Unethical Research Milgram Experiment Stanford Prison Experiment
  10. 10. Facebook Study
  11. 11. 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!
  12. 12. Privacy Contextual Integrity vs. Big Data Research Context bound information vs. Repurposing of data
  13. 13. Who are the Bad Guys? ME & MY DATA Government? Commerce? Educa5on? Hackers & Bad Guys?
  14. 14. 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)
  15. 15. 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
  16. 16. 19 Legal Frameworks hYps://studentprivacypledge.org/
  17. 17. 20 Legal Frameworks
  18. 18. Fears •  Power-rela5onship, user exploita5on •  Data ownership •  Anonymity and data security •  Privacy and data iden5ty •  Transparency and trust
  19. 19. 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
  20. 20. Exploita,on •  Free labour as business model of for-profit companies •  Crowd sourcing outside the „commons“
  21. 21. Data Ownership
  22. 22. Data Ownership
  23. 23. 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
  24. 24. 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
  25. 25. Transparency and Trust •  Assump5on: more transparency = more trust •  But: rela5onship is mostly asymmetrical (individual vs. big corpora5on) •  Transparency as instrument of control
  26. 26. 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
  27. 27. Introducing: DELICATE
  28. 28. Introducing: DELICATE
  29. 29. Introducing: DELICATE
  30. 30. 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

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