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Data for Good - From Privacy to Ethical Governance Frameworks

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Data for Good - From Privacy to Ethical Governance Frameworks

Learning Analytics

Charles Sturt University: Ethical framework
Botswana Open University: Learning Analytics Policy, Strategy

LASO model

Published in: Education
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Data for Good - From Privacy to Ethical Governance Frameworks

  1. 1. Division of Learning and Teaching, Charles Sturt University Assoc Prof Philip Uys Director, Learning Technologies Acting Director, Learning Resources Division of Learning and Teaching Charles Sturt University www.csu.edu.au Senior International Educational Consultant puys@csu.edu.au 2-3 May 2019, Data Science Summit, Sydney Slides will be available at https://www.slideshare.net/puys Data for GoodFrom Privacy to Ethical Governance Frameworks
  2. 2. Division of Learning and Teaching, Charles Sturt University 1. Introduction and background 2. Charles Sturt University: Ethical framework 3. Botswana Open University: Learning Analytics Policy, Strategy, Procedures Manual and Student Guide, Committees 4. Key lessons learned Summary
  3. 3. Division of Learning and Teaching, Charles Sturt University Acknowledgements At CSU - Dr Cassandra Colvin, Manager, Adaptive Learning and Teaching Services - Stewart Mckinney, Adaptive Learning and Teaching Project Officer - Ross McNair, Adaptive Learning and Teaching Reporting Officer - Ben Hicks, Adaptive Learning and Teaching Analyst - Ian Holder, former Adaptive Learning and Teaching Analyst - Simon Welsh, first Manager, Adaptive Learning and Teaching Services (formerly called Senior Learning Analytics Officer) At Botswana Open University (BOU) - Gabathuse Molelu, Manager, Centre for Instructional Technology
  4. 4. Division of Learning and Teaching, Charles Sturt University 1. Introduction and Background
  5. 5. Division of Learning and Teaching, Charles Sturt University Learning analytics related terms: big data; educational data mining Eilif Trondsen
  6. 6. Division of Learning and Teaching, Charles Sturt University Learning analytics represent the application of “big data” and analytics in education (John P. Campbell, Peter B. DeBlois, and Diana G. Oblinger (2007), EDUCAUSE Review, vol. 42, no. 4 (July/August 2007), pp. 53–54)
  7. 7. Division of Learning and Teaching, Charles Sturt University Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs Note that the learner context referred to above includes relevant computer systems, learning experience design, the role of teaching staff as well as learning and teaching support staff.
  8. 8. Division of Learning and Teaching, Charles Sturt University
  9. 9. Division of Learning and Teaching, Charles Sturt University A key learning analytics consideration  Multi-dimensionality
  10. 10. Division of Learning and Teaching, Charles Sturt University Siemens, George, What are Learning Analytics? eLearnspace, 25 August 2010. downloaded 15 May 2012 http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
  11. 11. Division of Learning and Teaching, Charles Sturt University
  12. 12. Division of Learning and Teaching, Charles Sturt University
  13. 13. Division of Learning and Teaching, Charles Sturt University Knowledge is power and …
  14. 14. Division of Learning and Teaching, Charles Sturt University "All is never said" African proverb
  15. 15. Division of Learning and Teaching, Charles Sturt University
  16. 16. Division of Learning and Teaching, Charles Sturt University
  17. 17. Division of Learning and Teaching, Charles Sturt University
  18. 18. Division of Learning and Teaching, Charles Sturt University The Perfect Storm in higher education Increasing public accountability and transparency Decreasing Funding Increasingly Distal Student Relationship Increasing expectations by students Successful use of analytics within Higher Ed & other sectors of society Increasing Competition Increasing expectations by students Student Success at scale Increase student retention and progress Evidence-based professional learning of teaching staff
  19. 19. Division of Learning and Teaching, Charles Sturt University Global guidelines: Ethics in Learning Analytics Authors: Sharon Slade and Alan Tait MARCH 2019 • Data ownership and control • Transparency • Accessibility of data • Validity and reliability of data • Institutional responsibility and obligation to act* • Communications • Cultural values • Inclusion • Consent • Student agency and responsibility https://www.icde.org/knowledge-hub/the-aim-of-the-guidelines-is-to-identify-which-core-principles- relating-to-ethics-are-core-to-all-and-where-there-is-legitimate-differentiation-due-to-separate-legal-or- more-broadly-cultural-environments
  20. 20. Division of Learning and Teaching, Charles Sturt University 2. Charles Sturt University
  21. 21. Division of Learning and Teaching, Charles Sturt University 1. Charles Sturt University
  22. 22. Division of Learning and Teaching, Charles Sturt University Learning analytics principles • Ethics • Use data for what it is collected for • Privacy • Guaranteeing confidentiality • The trust principle underlies LA • Uni can be seen as “digital Big Brother” • How much info to provide to the student • Will LA influence grading? • The accountability to act on what has been collected
  23. 23. Division of Learning and Teaching, Charles Sturt University Background at CSU oProgression of learning analytics https://www.csu.edu.au/division/learning-and- teaching/home/analytics-and-evaluations/learning-analytics oEvaluating Learning & Teaching oEducational Intelligence Services oEducational Intelligence Dashboards oAcademic Compass oThe Pulse oLearning Technologies Dashboard oFirst role at Australasian uni with “learning analytics” in the title oFirst uni in Australasia with an ethical framework
  24. 24. Division of Learning and Teaching, Charles Sturt University • 2013: • Established a cross-institutional Learning Analytics Working Party • Developed a Learning Analytics Strategy • Appointed a Senior Learning Analytics Officer to lead implementation of Strategy • 2014: • Implemented “At Risk” analytics capability • Developed Model of Learning Analytics to guide implementation • Mapping of drivers of student success
  25. 25. Division of Learning and Teaching, Charles Sturt University • 2015: • Implemented Blackboard Analytics • Established analytics Community of Practice • Developed Code of Practice and Policy Framework • Strategic development to data warehouse • New Distance Education Strategy and Online Learning Model
  26. 26. Division of Learning and Teaching, Charles Sturt University At CSU: • Focus on the change not the input: Adaptive Learning and Teaching, as a pathway to personalised online learning • Recognise Learning Analytics as a learning design challenge to create representations of knowledge and embed analytics in formative feedback processes • Adaptation (of learning activities and feedback) during learning through analysis of knowledge representations and a students’ digital footprint • Need to go beyond current adaptive learning approaches and support a wider range of pedagogies
  27. 27. Division of Learning and Teaching, Charles Sturt University Indicators of LA success at CSU •Increase in student success. •Increase in the quality and effectiveness of online learning as per assessment results. •Increase in the quality and effectiveness of online teaching as per Student Experience Survey due to adaptive online teaching practice and/or adaptive online systems. •Increase in student retention rates through more effective interventions either automated or human. •Increase in online engagement due to feedback on learning practices. •Increase in the appropriateness of subjects selected by students.
  28. 28. Division of Learning and Teaching, Charles Sturt University The Organisational Arrangements • Q: “Who does your Learning Analytics?” A: We all do • Developed the Adaptive Learning & Teaching Services team: • Focus on supporting personalised support, personalised learning and data- informed practice by building capability of people, as well as systems • Partner with academics on research and evaluation
  29. 29. Division of Learning and Teaching, Charles Sturt University
  30. 30. Division of Learning and Teaching, Charles Sturt University
  31. 31. Division of Learning and Teaching, Charles Sturt University https://www.csu.edu.au/division/learning-and- teaching/home/analytics-and-evaluations/learning-analytics oEvaluating Learning & Teaching oEducational Intelligence Services oEducational Intelligence Dashboards oAcademic Compass oThe Pulse oLearning Technologies Dashboard
  32. 32. Division of Learning and Teaching, Charles Sturt University
  33. 33. Division of Learning and Teaching, Charles Sturt University
  34. 34. Division of Learning and Teaching, Charles Sturt University
  35. 35. Division of Learning and Teaching, Charles Sturt University
  36. 36. Division of Learning and Teaching, Charles Sturt University
  37. 37. Division of Learning and Teaching, Charles Sturt University
  38. 38. Division of Learning and Teaching, Charles Sturt University
  39. 39. Division of Learning and Teaching, Charles Sturt University https://www.csu.edu.au/division/learning-and- teaching/home/analytics-and-evaluations/learning- technologies-dashboard
  40. 40. Division of Learning and Teaching, Charles Sturt University “At Risk” Model Insights–<School> DE Examples only
  41. 41. Division of Learning and Teaching, Charles Sturt University What went into the Model
  42. 42. Division of Learning and Teaching, Charles Sturt University
  43. 43. Division of Learning and Teaching, Charles Sturt University Student Responsiveness Rating ((Average SES Rating)2 x √MSI On-time Rate x √Assignment Turnaround On-Time Rate)/4.9
  44. 44. Division of Learning and Teaching, Charles Sturt University Using "Kumu" (open source) at CSU
  45. 45. Division of Learning and Teaching, Charles Sturt University CSU's Learning Analytics activities are governed by our Code of Practice http://www.csu.edu.au/__data/assets/pdf_file/0010/2507824/2016 -CSU-Learning-Analytics-Code-of-Practice_v3-3.pdf The Code of Practice is supported by two important documents: a Statement of Student Data Rights and Responsibilities - which summarises what students can expect of CSU with regard to the treatment of their data. a Learning Analytics Consent Statement - which describes what students and staff are consenting to with regard to analytics collection and use when using CSU learning and teaching systems
  46. 46. Division of Learning and Teaching, Charles Sturt University
  47. 47. Division of Learning and Teaching, Charles Sturt University
  48. 48. Division of Learning and Teaching, Charles Sturt University
  49. 49. Division of Learning and Teaching, Charles Sturt University Code of Practice • seven governing principles, arranged across three key areas of Ethical Intent, Student Success, and Transparency and Informed Participation.
  50. 50. Division of Learning and Teaching, Charles Sturt University Code of Practice • Ethical Intent oPrinciple 1: Learning Analytics contributes to equitable and inclusive participation in education by providing information in support of quality learning and teaching, and student-centred practice1 oPrinciple 2: Learning Analytics will be conducted in a way that: a) respects the rights and dignity of those who are the subject of data collection; b) accords with the obligations, commitments and values of the University; and c) after due consideration of risks/benefits, makes no unwarranted incursions into, or breaches of, an individual’s privacy. oPrinciple 3: Learning Analytics is a justified and ethical practice that is core to the University’s operations.
  51. 51. Division of Learning and Teaching, Charles Sturt University Code of Practice • Student Success oPrinciple 4: Data is collected from learning and teaching systems, retained and utilised for the purposes of enhancing learning and teaching by: Increasing the capacity for data-informed improvements in the learning, teaching and support practices of the University, incorporating its students, employees, systems and processes; Enabling personalised management of the relationship between the University and its students and employees; Managing the performance of online learning systems and resolving issues therein; and Contributing to research and scholarship in learning and teaching, including the field of Learning Analytics itself
  52. 52. Division of Learning and Teaching, Charles Sturt University Code of Practice • Transparency and Informed Participation oPrinciple 6: The University (and its employees) will be transparent with regard to the collection, retention and use of data from learning and teaching systems. oPrinciple 7: All users of the University’s learning and teaching systems will have access to clear explanations of their rights and obligations with respect to data from those systems.
  53. 53. Division of Learning and Teaching, Charles Sturt University
  54. 54. Division of Learning and Teaching, Charles Sturt University Code of Practice • Based on oPrivacy Management Plan o related to a range of University Policies: LA is an overarching and integrating agent • In operation: https://www.csu.edu.au/division/learning-and-teaching/home/analytics-and- evaluations/learning-analytics/inquiries-and-complaints o How to make a complaint o Role of ombudsman
  55. 55. Division of Learning and Teaching, Charles Sturt University 3.Botswana Open University
  56. 56. Division of Learning and Teaching, Charles Sturt University BOU digital flagship vision and learning analytics • High ed situation in Botswana, role of BOU through: o Technology Usage supported at Strategic Level o Development of Enabling Policies such as Learning Analytics Policy, Online Learning Assessment & Student Support Policy o Staff training is continuously carried out on systems and platforms used o Consultant used • LA from the “start” as part of this vision • Initiative from DVC (Academic Services) and Director, CIT
  57. 57. Division of Learning and Teaching, Charles Sturt University Institutional Commitment Anchored on Institutional Strategy 2016 - 2020: OBJECTIVE 6: Increase Technology Usage: CIT to train academic and support staff on use of Instructional technologies. OBJECTIVE 12: Leverage Technology Usage: Increase technology mediated programmes.
  58. 58. Division of Learning and Teaching, Charles Sturt University Governance: LA Policy, Strategy with Action Plan, Procedures Manual and Student Guide including committees • The four instruments decided on: LA Policy; Strategy and Action Plan; Procedures Manual; and Student Guide • Committees to approve: Council re Policy; Senate re rest • Committees to oversee the review of the instruments and the implementation of LA: APPQA Committee and Senate re all instruments
  59. 59. Division of Learning and Teaching, Charles Sturt University BOU dimensions of each instrument: 1. Transparency and consent 2. Confidentiality 3. Sensitive data 4. Validity 5. Student access to personal data/Lecturers and tutors access to personal data 6. Interventions 7. Minimising adverse impacts i.e. having a negative impact on the student experience
  60. 60. Division of Learning and Teaching, Charles Sturt University
  61. 61. Division of Learning and Teaching, Charles Sturt University
  62. 62. Division of Learning and Teaching, Charles Sturt University
  63. 63. Division of Learning and Teaching, Charles Sturt University Instruments development process • Presentations to Executive Management Team [EMT] and Senior Management group • Gathered international practices – particularly JISC institutional template; also others via ACODE list but actually already found most before • Developed draft Policy – sent to BOU in their template • Strategy with Action Plan, and Procedures manual followed similar layout i.e. the Strategy with Action Plan implements the Policy; the Procedures Manual is how the Strategy is implemented – iterative between consultant and BOU
  64. 64. Division of Learning and Teaching, Charles Sturt University • Then reviewed with Executive Management Team [EMT] on-site (Policy and Strategy) • Reviewed Procedures Manual with Senior Management group • In both visits earlier this year professional development of staff to enable lecturers to design their courses for effective use of Moodle learning analytics
  65. 65. Division of Learning and Teaching, Charles Sturt University 4. Key Lessons
  66. 66. Division of Learning and Teaching, Charles Sturt University Comparing the ethical frameworks of CSU, BOU and ICDE:
  67. 67. Division of Learning and Teaching, Charles Sturt University CSU Code of Practice • Ethical Intent oPrinciple 1: Learning Analytics contributes to equitable and inclusive participation in education by providing information in support of quality learning and teaching, and student-centred practice1 oPrinciple 2: Learning Analytics will be conducted in a way that: a) respects the rights and dignity of those who are the subject of data collection; b) accords with the obligations, commitments and values of the University; and c) after due consideration of risks/benefits, makes no unwarranted incursions into, or breaches of, an individual’s privacy. oPrinciple 3: Learning Analytics is a justified and ethical practice that is core to the University’s operations.
  68. 68. Division of Learning and Teaching, Charles Sturt University • Student Success oPrinciple 4: Data is collected from learning and teaching systems, retained and utilised for the purposes of enhancing learning and teaching by: Increasing the capacity for data-informed improvements in the learning, teaching and support practices of the University, incorporating its students, employees, systems and processes; Enabling personalised management of the relationship between the University and its students and employees; Managing the performance of online learning systems and resolving issues therein; and Contributing to research and scholarship in learning and teaching, including the field of Learning Analytics itself
  69. 69. Division of Learning and Teaching, Charles Sturt University • Transparency and Informed Participation oPrinciple 6: The University (and its employees) will be transparent with regard to the collection, retention and use of data from learning and teaching systems. oPrinciple 7: All users of the University’s learning and teaching systems will have access to clear explanations of their rights and obligations with respect to data from those systems.
  70. 70. Division of Learning and Teaching, Charles Sturt University BOU dimensions of each instrument: 1. Transparency and consent 2. Confidentiality 3. Sensitive data 4. Validity 5. Student access to personal data/Lecturers and tutors access to personal data 6. Interventions 7. Minimising adverse impacts i.e. having a negative impact on the student experience
  71. 71. Division of Learning and Teaching, Charles Sturt University Global guidelines: Ethics in Learning Analytics Authors: Sharon Slade and Alan Tait MARCH 2019 • Data ownership and control • Transparency • Accessibility of data • Validity and reliability of data • Institutional responsibility and obligation to act* • Communications • Cultural values • Inclusion • Consent • Student agency and responsibility https://www.icde.org/knowledge-hub/the-aim-of-the-guidelines-is-to-identify-which-core-principles- relating-to-ethics-are-core-to-all-and-where-there-is-legitimate-differentiation-due-to-separate-legal-or- more-broadly-cultural-environments
  72. 72. Division of Learning and Teaching, Charles Sturt University At CSU • LA is an overarching and integrating agent • The importance of the LASO model
  73. 73. Division of Learning and Teaching, Charles Sturt University Top down, bottom up and inside- out strategies need to be synchronised to ensure positive and effective change (Uys, 2007). LASO model
  74. 74. Division of Learning and Teaching, Charles Sturt University
  75. 75. Division of Learning and Teaching, Charles Sturt University At BOU • Learning analytics also include the teachers! Thus split the three instruments right at the top into two sections • LA governance same for developed (Australia, NZ, UK) and developing countries (Botswana, Tonga, Samoa, South Africa) • Excellent to get a head start with good practice internationally, and then contextualise.
  76. 76. Division of Learning and Teaching, Charles Sturt University "All is never said" African proverb
  77. 77. Division of Learning and Teaching, Charles Sturt University Assoc Prof Philip Uys Director, Learning Technologies Acting Director, Learning Resources Division of Learning and Teaching Charles Sturt University www.csu.edu.au Senior International Educational Consultant puys@csu.edu.au 2-3 May 2019, Data Science Summit, Sydney Slides will be available at https://www.slideshare.net/puys Thank You Data for Good From Privacy to Ethical Governance Frameworks
  78. 78. Division of Learning and Teaching, Charles Sturt University References Bain, A., & Parkes, R. J. (2006). Can Schools Realise the Learning Potential of Knowledge Management? Canadian Journal of Learning and Technology, 32(2). Diaz, V., & Fowler, S. (2012). Leadership and Learning Analytics. Educause Learning Initiative Brief, November 2012, pp. 1-4. Retrieved from: http://www.educause.edu/library/resources/leadership-and-learning-analytics Ferguson, R., (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317. ICDE (2014, March). Global guidelines: Ethics in Learning Analytics. Authors: Sharon Slade and Alan Tait. Available https://www.icde.org/knowledge-hub/the-aim-of-the-guidelines-is-to- identify-which-core-principles-relating-to-ethics-are-core-to-all-and-where-there-is- legitimate-differentiation-due-to-separate-legal-or-more-broadly-cultural-environments
  79. 79. Division of Learning and Teaching, Charles Sturt University Long, P & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, September/October 2011, pp. 31-40. Retrieved from: http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education Siemens, G. (2012). Learning analytics: new insight or new buzzword? ACODE webinar. October 2012 Siemens G., Dawson, S., Lynch, G (December 2013). Improving the Quality and Productivity of the Higher Education Sector Policy and Strategy for Systems-Level:Deployment of Learning Analytics Society for Learning Analytics Research (SoLAR). Available http://www.solaresearch.org Tynan, B., & Buckingham Shum, S. (2013). Designing Systemic Learning Analytics at the Open University. Retrieved from: http://www.slideshare.net/sbs/designing-systemic- learning-analytics-at-the-open-university

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