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Response 
to 
talks 
at 
Big 
Data 
and 
Privacy 
in 
Human 
Subject 
Research 
(1st 
day) 
Hendrik 
Drachsler, 
@hdrachsler 
Welten 
Ins4tute 
Research 
Centre, 
Open 
University 
of 
the 
Netherlands 
Presenta4on 
given 
at: 
NSF 
expert 
mee4ng 
on 
‘Big 
Data 
and 
Privacy 
in 
Human 
Subjects 
Research’ 
(#BDEDU) 
11 
November 
2014
3 
WhoAmI 
• Hendrik 
Drachsler, 
Open 
University 
of 
the 
Netherlands 
• Research 
topics: 
Personaliza4on, 
Recommender 
Systems, 
Learning 
Analy4cs, 
Mobile 
devices 
• Applica4on 
domains: 
Science 
2.0 
Health 
2.0
Research 
communi4es 
3
Who 
are 
the 
good 
guys? 
4
They 
brought 
together 
some 
Super 
Hero’s 
5 
Who 
of 
you 
considers 
him-­‐ 
herself 
to 
be 
a 
Super 
Hero? 
• You 
are 
passionate 
about 
what 
you 
are 
doing. 
• You 
shape 
the 
future 
of 
society. 
• You 
touch 
ethical 
ques4ons 
with 
your 
super 
power. 
• You 
want 
to 
follow 
societal 
norms 
and 
advance 
those. 
With 
Big 
Power 
comes 
great 
responsibili5es.
Big 
Power 
-­‐> 
Big 
Data 
= 
Repurposing 
data 
6 
Jawbone 
data 
repurposed 
to 
measure 
earthquake 
strength
7 
Big 
Data 
is 
the 
new 
truth 
(the 
ulHmate 
truth?)
8 
Big 
Data 
is 
the 
new 
truth 
(the 
ulHmate 
truth?) 
Inaccurate 
Google 
Flue 
trend 
measures 
compared 
to 
CDC
Big 
Data 
has 
the 
potenHal 
to 
change 
EducaHon 
9 
• First 
4me 
monitoring 
learning 
while 
it 
happens 
• Personalize 
Educa4on 
• Iden4fy 
students 
at 
Risk 
• Learning 
Measures 
on 
demand 
• More 
…
Some 
QuesHons, 
Super 
Hero’s 
10 
Some 
Demographics: 
Who 
of 
you 
are 
data 
scien4sts, 
legal 
or 
educa4onal 
experts? 
Who 
of 
you 
read 
TOC 
of 
your 
online 
services? 
Who 
of 
you 
cares 
about 
his/her 
privacy? 
Who 
sees 
Privacy 
and 
Legal 
regula4ons 
as 
a 
burden 
we 
need 
to 
overcome?
Learning 
AnalyHcs 
Research 
Issues 
11 
Learning 
Analy4cs 
research 
always 
raises 
the 
P-­‐Word 
in 
EU 
(University 
of 
Amsterdam, 
2014) 
This 
stops 
innova4on 
and 
advancing 
research 
(dataTEL 
2010)
12 
• Privacy 
changes 
overHme 
• Privacy 
is 
bind 
to 
context 
• Privacy 
is 
bind 
to 
culture 
Slide 
supported 
byTore 
Hoel, 
@Tore
What 
if 
I 
would 
know 
… 
• How 
many 
days 
you 
have 
NOT 
been 
at 
school 
without 
any 
excuse. 
• All 
read 
and 
wrihen 
pages, 
and 
what 
your 
annota4ons 
have 
been. 
• The 
people 
you 
hangout 
with 
in 
your 
youth. 
• If 
you 
cheated 
in 
a 
test 
and 
how 
many 
ahempts 
you 
needed 
for 
your 
math 
class. 
• What 
if 
I 
use 
all 
those 
informaHon 
and 
predict 
your 
chances 
to 
be 
good 
or 
bad 
in 
a 
certain 
job 
aSer 
school? 
• How 
representaHve 
and 
reliable 
is 
this 
data 
I’m 
capturing 
to 
predict 
those 
chances? 
• And 
what 
if 
all 
this 
informaHon 
will 
be 
last 
forever! 
13
Approaches 
to 
prevent 
another 
inBloom 
… 
• Transparency 
(Purpose 
of 
analysis, 
Raw 
data 
access, 
opt-­‐out) 
• Data 
Security 
• Contextual 
Integrity 
(Smart 
Informed 
Consents) 
• Anonymisa4on 
& 
Data 
degrada4on 
14
Ethics 
& 
Privacy 
Issue 
in 
the 
ApplicaHon 
of 
Learning 
AnalyHcs 
(#EP4LA) 
Hendrik 
Drachsler, 
@hdrachsler 
Welten 
InsHtute 
Research 
Centre, 
Open 
University 
of 
the 
Netherlands 
Presenta4on 
given 
at: 
NSF 
expert 
mee4ng 
on 
‘Big 
Data 
and 
Privacy 
in 
Human 
Subjects 
Research’ 
(#BDEDU) 
11 
November 
2014
Building 
bridges 
between 
research, 
policy 
and 
prac4ce 
to 
realise 
the 
poten4al 
of 
learning 
analy4cs 
in 
EU 
FP7 
LACE 
– 
Hendrik 
Drachsler, 
@Hdrachsler, 
28 
October 
2014 
16
Who 
we 
are 
FP7 
LACE 
– 
Hendrik 
Drachsler, 
@Hdrachsler, 
28 
October 
2014 
17 
LACE 
Network 
LACE 
ConsorHum
Data 
Geology 
18 
PAST, 
single, 
centered 
IT 
solu4ons 
with 
single 
purpose 
(loosely 
couple 
data) 
PRESENT, 
mul4ple 
ubiquitous 
IT 
systems 
mul4ple 
func4onali4es 
(highly 
connected 
but 
unstructured 
data) 
FP7 
LACE 
– 
Hendrik 
Drachsler, 
@Hdrachsler, 
28 
October 
2014 
FUTURE, 
learner 
ac4vity 
tracking 
of 
ubiquitous 
systems 
(structured 
learner 
data)
Data 
Geology 
FP7 
LACE 
– 
Hendrik 
Drachsler, 
@Hdrachsler, 
28 
October 
2014 
19 
• Are 
our 
instruments 
measuring 
what 
we 
expect 
them 
to 
measure? 
• Can 
we 
isolate 
the 
noise 
in 
the 
data? 
• Are 
the 
measures 
accurate? 
Picture 
from: 
hhp://wsnblog.com/2012/05/28/how-­‐sensors-­‐can-­‐lead-­‐us-­‐to-­‐beher-­‐self-­‐ 
knowledge/human-­‐body-­‐sensors/
Evidence.laceproject.eu 
20
Privacy 
as 
Showstopper 
– 
The 
inBloom 
case 
• $100 
million 
investment 
• Aim: 
Personalized 
learning 
in 
public 
schools, 
through 
data 
& 
technology 
standards 
• 9 
US 
states 
par4cipated 
• In 
2013 
the 
database 
held 
informa4on 
on 
millions 
of 
children 
21
inBloom 
example 
in 
the 
Netherlands 
22
Privacy 
• What is privacy? 
– Right to be let alone (Warren and Brandeis) 
– Informational self-determination (Westin) 
– Degree of access (Gavison) 
– … Right to be forgotten … 
• Three dimensions (Roessler) 
– Informational privacy 
– Decisional privacy 
– Local privacy 
• What it is not 
– Anonymity, secrecy, data protection
What 
are 
the 
dangers 
of 
learning 
analyHcs? 
– Missing 
legal 
obligaHons: 
• Data 
protec4on 
• IRB 
• Educa4on 
laws 
– InflicHng 
harm: 
• Unfair 
discrimina4on 
• Unjus4fied 
discrimina4on 
(through 
errors) 
• Subjec4ve 
privacy 
harm 
(panop4c 
effect) 
• Unintended 
pressure 
to 
perform 
/ 
wrong 
incen4ves? 
• De-­‐iden4fica4on 
– ViolaHng 
human 
dignity 
– Unintended 
changes 
of 
educaHon 
norms? 
24
ModernizaHon 
of 
EU 
UniversiHes 
report 
RecommendaHon 
14 
Member 
States 
should 
ensure 
that 
legal 
frameworks 
allow 
higher 
educa4on 
ins4tu4ons 
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 
educa4onal 
purposes. 
RecommendaHon 
15 
Online 
plaoorms 
should 
inform 
users 
about 
their 
privacy 
and 
data 
protec4on 
policy 
in 
a 
clear 
and 
understandable 
way. 
Individuals 
should 
always 
have 
the 
choice 
to 
anonymise 
their 
data. 
hgp://ec.europa.eu/educaHon/ 
library/reports/modernisaHon-­‐ 
universiHes_en.pdf
#EP4LA 
on 
the 
European 
Agenda 
• Round 
table 
meeHng 
‘Ethiek 
en 
Learning 
AnalyHcs’ 
(Jan 
2014) 
hhps://www.surfspace.nl/media/bijlagen/ar4kel-­‐1499-­‐ 
b315e61001041bf52a6b1c5d80053cea.pdf 
• Learning 
AnalyHcs 
Summer 
InsHtute 
(July 
2014) 
hhp://lasiutrecht.wordpress.com/ 
• Call 
for 
a 
‘Code 
of 
Ethics 
for 
LA’ 
in 
NL 
(August 
2014) 
hhps://www.surfspace.nl/ar4kel/1311-­‐towards-­‐a-­‐uniform-­‐code-­‐of-­‐ethics-­‐and-­‐ 
prac4ces-­‐for-­‐learning-­‐analy4cs/ 
• Call 
for 
a 
‘Code 
of 
Ethics 
for 
LA’ 
in 
the 
UK 
(September 
2014) 
hhp://analy4cs.jiscinvolve.org/wp/2014/09/18/code-­‐of-­‐prac4ce-­‐essen4al-­‐ 
for-­‐learning-­‐analy4cs/ 
26
27 
1. Privacy 
2. Ethics 
3. Data 
4. Transparency 
hgp://bit.ly/ep4la
The 
rise 
of 
the 
#EP4LA 
project 
28 
• 1st 
EP4LA 
@ 
Utrecht, 
NL, 
28 
October 
2014 
• 2nd 
EP4LA 
@ 
Educa4on 
Days, 
NL, 
11 
November 
2014 
• 3rd 
EP4LA 
@ 
BDEDU, 
Washington, 
US, 
11 
November 
2014 
• 4th 
EP4LA 
@ 
Apereo 
Founda4on, 
FR, 
February 
2015 
• 5th 
EP4LA 
@ 
JISC, 
February, 
UK, 
2015 
• 6th 
EP4LA 
@ 
LAK15, 
NY, 
USA, 
March 
2015
What 
#EP4LA 
is 
aiming 
for 
29
Example 
Issues 
from 
Stakeholders 
• Who 
is 
in 
charge 
(who 
is 
the 
owners) 
of 
the 
data 
created 
by 
persons? 
• What 
is 
the 
impact 
of 
privacy 
concerns 
for 
the 
management? 
How 
to 
deal 
with 
these 
concerns? 
• Should 
students 
be 
allowed 
to 
opt-­‐out 
of 
having 
their 
personal 
digital 
footprints 
harvested 
and 
analysed? 
• How 
to 
prevent 
reuse 
of 
collected 
data 
for 
non-­‐educa4onal 
needs. 
(e.g. 
finance, 
insurance, 
research), 
or 
is 
it 
no 
problem? 
Full 
list: 
hgp://bit.ly/raw_ep4la
We 
are 
pracHcal 
people 
– 
our 
approach 
31 
• Invite 
5 
legal 
experts, 
15 
members 
of 
the 
SURF 
SIG 
LA 
• Task 
groups 
to 
answer 
issues 
of 
the 
stakeholders 
• Open 
Working 
doc 
for 
all 
#EP4LA 
events
32 
Four 
examples 
how 
we 
addressed 
the 
issues 
submiged 
by 
the 
stakeholders
1. 
Boundaries 
of 
Learning 
AnalyHcs 
data 
Where 
is 
the 
boundary 
on 
data 
use 
for 
learning 
analy3cs 
(courses, 
grades, 
LMS, 
GoogleDrive, 
library 
system, 
residence 
halls, 
dining 
halls, 
…)? 
– Contextual 
Integrity: 
context 
and 
norms 
of 
learning 
environment 
– It 
depends 
on 
• Awareness 
of 
students 
about 
processes 
• Possible 
consequences 
for 
students 
• Safeguards 
that 
are 
in 
place 
33
2. 
Outsourcing 
What 
are 
the 
concerns 
when 
outsourcing 
the 
collec3on 
and 
analysis 
of 
data? 
Who 
owns 
the 
data? 
– Concerns: 
• Undue 
third 
country 
data 
transfers 
• Less 
control 
about 
processing 
• Less 
transparency 
for 
the 
data 
subject 
– Ownership: 
• No 
complete 
ownership 
for 
any 
party 
• Relevant: 
data 
protec4on 
and 
intellectual 
property 
rights 
• See 
discussion 
concerning 
`data 
portability’ 
in 
DP 
regula4on 
(NDA 
agreement 
required) 
34
3. 
Undesirable 
data 
collecHon 
Are 
there 
any 
circumstances 
when 
collecHng 
data 
about 
students 
is 
unacceptable/undesirable? 
– Yes, 
there 
are: 
• Data 
which 
is 
not 
of 
any 
purpose 
• Data 
outside 
of 
the 
learning 
context 
• Data 
of 
which 
the 
student 
is 
not 
aware 
• Data 
which 
poses 
a 
risk 
to 
the 
student 
• Data 
which 
is 
not 
well 
protected 
35
4. 
Data 
access 
by 
students 
What 
data 
should 
students 
be 
able 
to 
view, 
i.e. 
what 
and 
how 
much 
informaHon 
should 
be 
provided 
to 
the 
student? 
– Data 
Protec4on 
Direc4ve 
(ar4cle 
12): 
• Everything 
concerning 
them 
(at 
least 
upon 
request) 
– Human 
subjects 
research: 
• Everything 
concerning 
study 
(at 
least 
arer 
experiment) 
• Avoidance 
of 
decep4on 
– But 
• Possible 
conflict 
of 
full 
data 
access 
with 
goals 
of 
LA? 
• How 
to 
provide 
meaningful 
access 
while 
excluding 
other 
students 
data? 
36
We 
idenHfied 
9 
main 
themes 
that 
are 
relevant 
for 
LA 
in 
Europe 
37
9 
Themes 
around 
privacy 
(1/3) 
1. 
LegiHmate 
grounds 
-­‐ 
Why 
are 
you 
allowed 
to 
have 
the 
data? 
2. 
Purpose 
of 
the 
data 
-­‐ 
Repurposing 
is 
an 
issue 
vs. 
MIT 
Social 
Machine 
lab 
3. 
Inventory 
of 
data 
-­‐ 
What 
data 
do 
you 
have? 
-­‐ 
What 
can 
you 
do 
with 
that 
data 
already?
9 
Themes 
around 
privacy 
(2/3) 
4. 
Data 
quality 
-­‐ 
How 
good 
is 
the 
data? 
(eg. 
Bb 
log 
file 
is 
weak 
predictor) 
-­‐ 
When 
do 
you 
I 
delete 
data 
and 
what 
data? 
5. 
Transparency 
-­‐ 
Informing 
students 
(Purpose, 
Approach) 
-­‐ 
Checklist 
what 
to 
communicate 
for 
researchers 
6. 
The 
rights 
of 
the 
data 
subject 
to 
access 
their 
data 
from 
the 
data 
client 
-­‐ 
For 
teachers 
who 
are 
employees 
other 
rights 
apply
9 
Themes 
around 
privacy 
(3/3) 
7. 
Outsource 
processing 
to 
external 
parHes 
-­‐ 
Prevent 
external 
par4es 
to 
not 
do 
addi4onal 
analysis 
(NDA 
agreement) 
8. 
Transport 
data, 
legal 
locaHon 
-­‐ 
e.g. 
Safe 
Harbour 
agreement 
9. 
Data 
Security 
-­‐> 
Shuangbao 
Wang
Value Sensitive Design 
(Batya Friedman) 
Goal: 
address 
human 
values 
in 
a 
technical 
design 
Source: presentation by Jeroen van den Hoven
www.laceproject.eu 
@laceproject 
“Ethics 
& 
Privacy 
Issues 
in 
the 
Applica4on 
of 
Learning 
Analy4cs” 
by 
Hendrik 
Drachsler, 
Open 
University 
of 
the 
Netherlands 
was 
presented 
at 
NSF 
Mee4ng 
– 
Big 
Data 
in 
Educa4on, 
Washington, 
USA, 
on 
09-­‐11.10.2014. 
Hendrik.drachsler@ou.nl, 
@hdrachsler 
This 
work 
was 
undertaken 
as 
part 
of 
the 
LACE 
Project, 
supported 
by 
the 
European 
Commission 
Seventh 
Framework 
Programme, 
grant 
619424. 
These 
slides 
are 
provided 
under 
the 
Crea4ve 
Commons 
Ahribu4on 
Licence: 
hhp:// 
crea4vecommons.org/licenses/by/4.0/. 
Some 
images 
used 
may 
have 
different 
licence 
terms. 
42
43 
43 
You 
are 
free 
to: 
copy, 
share, 
adapt, 
or 
re-­‐mix; 
photograph, 
film, 
or 
broadcast; 
blog, 
live-­‐blog, 
or 
post 
video 
of 
this 
presenta4on 
provided 
that: 
You 
ahribute 
the 
work 
to 
its 
author 
and 
respect 
the 
rights 
and 
licences 
associated 
with 
its 
components.

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Ethics and Privacy in the Application of Learning Analytics (#EP4LA)

  • 1. Response to talks at Big Data and Privacy in Human Subject Research (1st day) Hendrik Drachsler, @hdrachsler Welten Ins4tute Research Centre, Open University of the Netherlands Presenta4on given at: NSF expert mee4ng on ‘Big Data and Privacy in Human Subjects Research’ (#BDEDU) 11 November 2014
  • 2. 3 WhoAmI • Hendrik Drachsler, Open University of the Netherlands • Research topics: Personaliza4on, Recommender Systems, Learning Analy4cs, Mobile devices • Applica4on domains: Science 2.0 Health 2.0
  • 4. Who are the good guys? 4
  • 5. They brought together some Super Hero’s 5 Who of you considers him-­‐ herself to be a Super Hero? • You are passionate about what you are doing. • You shape the future of society. • You touch ethical ques4ons with your super power. • You want to follow societal norms and advance those. With Big Power comes great responsibili5es.
  • 6. Big Power -­‐> Big Data = Repurposing data 6 Jawbone data repurposed to measure earthquake strength
  • 7. 7 Big Data is the new truth (the ulHmate truth?)
  • 8. 8 Big Data is the new truth (the ulHmate truth?) Inaccurate Google Flue trend measures compared to CDC
  • 9. Big Data has the potenHal to change EducaHon 9 • First 4me monitoring learning while it happens • Personalize Educa4on • Iden4fy students at Risk • Learning Measures on demand • More …
  • 10. Some QuesHons, Super Hero’s 10 Some Demographics: Who of you are data scien4sts, legal or educa4onal experts? Who of you read TOC of your online services? Who of you cares about his/her privacy? Who sees Privacy and Legal regula4ons as a burden we need to overcome?
  • 11. Learning AnalyHcs Research Issues 11 Learning Analy4cs research always raises the P-­‐Word in EU (University of Amsterdam, 2014) This stops innova4on and advancing research (dataTEL 2010)
  • 12. 12 • Privacy changes overHme • Privacy is bind to context • Privacy is bind to culture Slide supported byTore Hoel, @Tore
  • 13. What if I would know … • How many days you have NOT been at school without any excuse. • All read and wrihen pages, and what your annota4ons have been. • The people you hangout with in your youth. • If you cheated in a test and how many ahempts you needed for your math class. • What if I use all those informaHon and predict your chances to be good or bad in a certain job aSer school? • How representaHve and reliable is this data I’m capturing to predict those chances? • And what if all this informaHon will be last forever! 13
  • 14. Approaches to prevent another inBloom … • Transparency (Purpose of analysis, Raw data access, opt-­‐out) • Data Security • Contextual Integrity (Smart Informed Consents) • Anonymisa4on & Data degrada4on 14
  • 15. Ethics & Privacy Issue in the ApplicaHon of Learning AnalyHcs (#EP4LA) Hendrik Drachsler, @hdrachsler Welten InsHtute Research Centre, Open University of the Netherlands Presenta4on given at: NSF expert mee4ng on ‘Big Data and Privacy in Human Subjects Research’ (#BDEDU) 11 November 2014
  • 16. Building bridges between research, policy and prac4ce to realise the poten4al of learning analy4cs in EU FP7 LACE – Hendrik Drachsler, @Hdrachsler, 28 October 2014 16
  • 17. Who we are FP7 LACE – Hendrik Drachsler, @Hdrachsler, 28 October 2014 17 LACE Network LACE ConsorHum
  • 18. Data Geology 18 PAST, single, centered IT solu4ons with single purpose (loosely couple data) PRESENT, mul4ple ubiquitous IT systems mul4ple func4onali4es (highly connected but unstructured data) FP7 LACE – Hendrik Drachsler, @Hdrachsler, 28 October 2014 FUTURE, learner ac4vity tracking of ubiquitous systems (structured learner data)
  • 19. Data Geology FP7 LACE – Hendrik Drachsler, @Hdrachsler, 28 October 2014 19 • Are our instruments measuring what we expect them to measure? • Can we isolate the noise in the data? • Are the measures accurate? Picture from: hhp://wsnblog.com/2012/05/28/how-­‐sensors-­‐can-­‐lead-­‐us-­‐to-­‐beher-­‐self-­‐ knowledge/human-­‐body-­‐sensors/
  • 21. Privacy as Showstopper – The inBloom case • $100 million investment • Aim: Personalized learning in public schools, through data & technology standards • 9 US states par4cipated • In 2013 the database held informa4on on millions of children 21
  • 22. inBloom example in the Netherlands 22
  • 23. Privacy • What is privacy? – Right to be let alone (Warren and Brandeis) – Informational self-determination (Westin) – Degree of access (Gavison) – … Right to be forgotten … • Three dimensions (Roessler) – Informational privacy – Decisional privacy – Local privacy • What it is not – Anonymity, secrecy, data protection
  • 24. What are the dangers of learning analyHcs? – Missing legal obligaHons: • Data protec4on • IRB • Educa4on laws – InflicHng harm: • Unfair discrimina4on • Unjus4fied discrimina4on (through errors) • Subjec4ve privacy harm (panop4c effect) • Unintended pressure to perform / wrong incen4ves? • De-­‐iden4fica4on – ViolaHng human dignity – Unintended changes of educaHon norms? 24
  • 25. ModernizaHon of EU UniversiHes report RecommendaHon 14 Member States should ensure that legal frameworks allow higher educa4on ins4tu4ons 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 educa4onal purposes. RecommendaHon 15 Online plaoorms should inform users about their privacy and data protec4on policy in a clear and understandable way. Individuals should always have the choice to anonymise their data. hgp://ec.europa.eu/educaHon/ library/reports/modernisaHon-­‐ universiHes_en.pdf
  • 26. #EP4LA on the European Agenda • Round table meeHng ‘Ethiek en Learning AnalyHcs’ (Jan 2014) hhps://www.surfspace.nl/media/bijlagen/ar4kel-­‐1499-­‐ b315e61001041bf52a6b1c5d80053cea.pdf • Learning AnalyHcs Summer InsHtute (July 2014) hhp://lasiutrecht.wordpress.com/ • Call for a ‘Code of Ethics for LA’ in NL (August 2014) hhps://www.surfspace.nl/ar4kel/1311-­‐towards-­‐a-­‐uniform-­‐code-­‐of-­‐ethics-­‐and-­‐ prac4ces-­‐for-­‐learning-­‐analy4cs/ • Call for a ‘Code of Ethics for LA’ in the UK (September 2014) hhp://analy4cs.jiscinvolve.org/wp/2014/09/18/code-­‐of-­‐prac4ce-­‐essen4al-­‐ for-­‐learning-­‐analy4cs/ 26
  • 27. 27 1. Privacy 2. Ethics 3. Data 4. Transparency hgp://bit.ly/ep4la
  • 28. The rise of the #EP4LA project 28 • 1st EP4LA @ Utrecht, NL, 28 October 2014 • 2nd EP4LA @ Educa4on Days, NL, 11 November 2014 • 3rd EP4LA @ BDEDU, Washington, US, 11 November 2014 • 4th EP4LA @ Apereo Founda4on, FR, February 2015 • 5th EP4LA @ JISC, February, UK, 2015 • 6th EP4LA @ LAK15, NY, USA, March 2015
  • 29. What #EP4LA is aiming for 29
  • 30. Example Issues from Stakeholders • Who is in charge (who is the owners) of the data created by persons? • What is the impact of privacy concerns for the management? How to deal with these concerns? • Should students be allowed to opt-­‐out of having their personal digital footprints harvested and analysed? • How to prevent reuse of collected data for non-­‐educa4onal needs. (e.g. finance, insurance, research), or is it no problem? Full list: hgp://bit.ly/raw_ep4la
  • 31. We are pracHcal people – our approach 31 • Invite 5 legal experts, 15 members of the SURF SIG LA • Task groups to answer issues of the stakeholders • Open Working doc for all #EP4LA events
  • 32. 32 Four examples how we addressed the issues submiged by the stakeholders
  • 33. 1. Boundaries of Learning AnalyHcs data Where is the boundary on data use for learning analy3cs (courses, grades, LMS, GoogleDrive, library system, residence halls, dining halls, …)? – Contextual Integrity: context and norms of learning environment – It depends on • Awareness of students about processes • Possible consequences for students • Safeguards that are in place 33
  • 34. 2. Outsourcing What are the concerns when outsourcing the collec3on and analysis of data? Who owns the data? – Concerns: • Undue third country data transfers • Less control about processing • Less transparency for the data subject – Ownership: • No complete ownership for any party • Relevant: data protec4on and intellectual property rights • See discussion concerning `data portability’ in DP regula4on (NDA agreement required) 34
  • 35. 3. Undesirable data collecHon Are there any circumstances when collecHng data about students is unacceptable/undesirable? – Yes, there are: • Data which is not of any purpose • Data outside of the learning context • Data of which the student is not aware • Data which poses a risk to the student • Data which is not well protected 35
  • 36. 4. Data access by students What data should students be able to view, i.e. what and how much informaHon should be provided to the student? – Data Protec4on Direc4ve (ar4cle 12): • Everything concerning them (at least upon request) – Human subjects research: • Everything concerning study (at least arer experiment) • Avoidance of decep4on – But • Possible conflict of full data access with goals of LA? • How to provide meaningful access while excluding other students data? 36
  • 37. We idenHfied 9 main themes that are relevant for LA in Europe 37
  • 38. 9 Themes around privacy (1/3) 1. LegiHmate grounds -­‐ Why are you allowed to have the data? 2. Purpose of the data -­‐ Repurposing is an issue vs. MIT Social Machine lab 3. Inventory of data -­‐ What data do you have? -­‐ What can you do with that data already?
  • 39. 9 Themes around privacy (2/3) 4. Data quality -­‐ How good is the data? (eg. Bb log file is weak predictor) -­‐ When do you I delete data and what data? 5. Transparency -­‐ Informing students (Purpose, Approach) -­‐ Checklist what to communicate for researchers 6. The rights of the data subject to access their data from the data client -­‐ For teachers who are employees other rights apply
  • 40. 9 Themes around privacy (3/3) 7. Outsource processing to external parHes -­‐ Prevent external par4es to not do addi4onal analysis (NDA agreement) 8. Transport data, legal locaHon -­‐ e.g. Safe Harbour agreement 9. Data Security -­‐> Shuangbao Wang
  • 41. Value Sensitive Design (Batya Friedman) Goal: address human values in a technical design Source: presentation by Jeroen van den Hoven
  • 42. www.laceproject.eu @laceproject “Ethics & Privacy Issues in the Applica4on of Learning Analy4cs” by Hendrik Drachsler, Open University of the Netherlands was presented at NSF Mee4ng – Big Data in Educa4on, Washington, USA, on 09-­‐11.10.2014. Hendrik.drachsler@ou.nl, @hdrachsler This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424. These slides are provided under the Crea4ve Commons Ahribu4on Licence: hhp:// crea4vecommons.org/licenses/by/4.0/. Some images used may have different licence terms. 42
  • 43. 43 43 You are free to: copy, share, adapt, or re-­‐mix; photograph, film, or broadcast; blog, live-­‐blog, or post video of this presenta4on provided that: You ahribute the work to its author and respect the rights and licences associated with its components.