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

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

  1. 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. 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
  3. 3. Research communi4es 3
  4. 4. Who are the good guys? 4
  5. 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. 6. Big Power -­‐> Big Data = Repurposing data 6 Jawbone data repurposed to measure earthquake strength
  7. 7. 7 Big Data is the new truth (the ulHmate truth?)
  8. 8. 8 Big Data is the new truth (the ulHmate truth?) Inaccurate Google Flue trend measures compared to CDC
  9. 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. 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. 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. 12 • Privacy changes overHme • Privacy is bind to context • Privacy is bind to culture Slide supported byTore Hoel, @Tore
  13. 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. 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. 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. 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. 17. Who we are FP7 LACE – Hendrik Drachsler, @Hdrachsler, 28 October 2014 17 LACE Network LACE ConsorHum
  18. 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. 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/
  20. 20. Evidence.laceproject.eu 20
  21. 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. 22. inBloom example in the Netherlands 22
  23. 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. 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. 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. 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. 27 1. Privacy 2. Ethics 3. Data 4. Transparency hgp://bit.ly/ep4la
  28. 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. 29. What #EP4LA is aiming for 29
  30. 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. 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. 32 Four examples how we addressed the issues submiged by the stakeholders
  33. 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. 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. 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. 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. 37. We idenHfied 9 main themes that are relevant for LA in Europe 37
  38. 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. 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. 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. 41. Value Sensitive Design (Batya Friedman) Goal: address human values in a technical design Source: presentation by Jeroen van den Hoven
  42. 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 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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