This document discusses learning analytics at the intersection of student support, privacy, agency, and institutional survival in higher education. It notes increasing competition and constraints that universities face, and the need for data and evidence to demonstrate student retention, success, and throughput. However, it also discusses concerns about educational triage, focusing only on certain students, and the lack of transparency around algorithmic decision making. The document calls for consideration of student privacy, agency, and the moral implications of admission practices and levels of support provided.
Dr Linda Corrin, University of Melbourne, discusses all things learning analytics. One of the important take-aways from this presentation is to define the question(s) before you start collecting data.
Learning analytics as an academic research space has been growing in influence for nearly a decade. Campuses globally are deploying learning analytics to address a range of challenges including student dropout, poor engagement and targeted marketing as well as predict teaching and resource needs. As a field, learning analytics has advanced rapidly both as a research domain and as a practical on-campus activity to increase organizational use of data. In this presentation, Dr. George Siemens will explore both the research and the practice of analytics in education, focusing on the development of the Society for Learning Analytics, models for research and organizational data use and growing sophistication of data collection through psychophysiological approaches.
State and Directions of Learning Analytics Adoption (Second edition)Dragan Gasevic
The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on learning, teaching, and decision making. The talk will conclude by discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that
- systemic approaches to the development and adoption of learning analytics are critical,
- multidisciplinary teams are necessary to unlock a full potential of learning analytics, and
- capacity development at institutional levels through the inclusion of diverse stakeholders is essential for full learning analytics adoption.
This is the second edition of the talk that previously gave under the same title on several occasions. The second edition reflects many developments happened in the field of learning analytics, especially those in the following two projects - http://he-analytics.com and http://sheilaproject.eu.
Using a digital knowledge repository to personalise learning in medical educa...Poh-Sun Goh
TLHE 2014 final draft submission for peer review and consideration for paper presentation. Conference website -http://www.cdtl.nus.edu.sg/tlhe/
(accepted as paper for 30 minute oral presentation after peer review, on Aug 5, 2014)
Meaningful Conversations about EdTech: Transforming Student LearningBradford Wheeler
Co-Presenter: Meaningful Conversations about EdTech: Transforming Student Learning. Professional and Organizational Development (POD) Network 2016, November 10; Louisville, KY.
Dr Linda Corrin, University of Melbourne, discusses all things learning analytics. One of the important take-aways from this presentation is to define the question(s) before you start collecting data.
Learning analytics as an academic research space has been growing in influence for nearly a decade. Campuses globally are deploying learning analytics to address a range of challenges including student dropout, poor engagement and targeted marketing as well as predict teaching and resource needs. As a field, learning analytics has advanced rapidly both as a research domain and as a practical on-campus activity to increase organizational use of data. In this presentation, Dr. George Siemens will explore both the research and the practice of analytics in education, focusing on the development of the Society for Learning Analytics, models for research and organizational data use and growing sophistication of data collection through psychophysiological approaches.
State and Directions of Learning Analytics Adoption (Second edition)Dragan Gasevic
The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on learning, teaching, and decision making. The talk will conclude by discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that
- systemic approaches to the development and adoption of learning analytics are critical,
- multidisciplinary teams are necessary to unlock a full potential of learning analytics, and
- capacity development at institutional levels through the inclusion of diverse stakeholders is essential for full learning analytics adoption.
This is the second edition of the talk that previously gave under the same title on several occasions. The second edition reflects many developments happened in the field of learning analytics, especially those in the following two projects - http://he-analytics.com and http://sheilaproject.eu.
Using a digital knowledge repository to personalise learning in medical educa...Poh-Sun Goh
TLHE 2014 final draft submission for peer review and consideration for paper presentation. Conference website -http://www.cdtl.nus.edu.sg/tlhe/
(accepted as paper for 30 minute oral presentation after peer review, on Aug 5, 2014)
Meaningful Conversations about EdTech: Transforming Student LearningBradford Wheeler
Co-Presenter: Meaningful Conversations about EdTech: Transforming Student Learning. Professional and Organizational Development (POD) Network 2016, November 10; Louisville, KY.
Can medical education take advantage of Learning Analytics techniques? How? Where? In this presentation a study is analyzed pinpointing three areas in which Medical Education needs to invest and all three are related to Learning Analytics.
Faculty Adoption of Technologies in Team-Based Learning ClassroomsBradford Wheeler
Wheeler, B., Shih, M. , & Weaver, GC. Faculty Adoption of Technologies in Team-Based Learning Classrooms. Poster session presented at: New England Faculty Development Consortium (NEFDC) 2015, May 29; Fairfield, CT.
Adopting Classroom Technology: A Faculty Development ProgramBradford Wheeler
Wheeler, B., Adopting Classroom Technology: A Faculty Development Program. Poster presented at: New England Faculty Development Consortium (NEFDC) 2016, May 24; Somerville, MA.
AI in Education Amsterdam Data Science (ADS) What have we learned after a dec...Bart Rienties
The Open University UK (OU) has been implementing learning analytics and learning design on a large scale since 2012. With its 170+ students and 4000+ teaching staff, the OU has been at the forefront of testing, implementing, and evaluating the impact of learning analytics and learning design on students outcome and retention. A range of reviews and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics and learning design in the world. However, despite the large uptake of learning analytics at the OU there are a range of complex issues in terms of buy-in from staff, data infrastructures, ethics and privacy, student engagement, and perhaps most importantly how to make sense of big and small data in a complex organisation like the OU. During his talk Bart will be presenting on the implementation and learnings.
Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR)Adam Beauchamp
Presentation given at ACRL 2015, with Christine Murray, on teaching undergraduate students to discover and evaluate datasets for secondary data analysis.
The adoption and impact of OEP and OER in the Global South: Theoretical, conc...ROER4D
The adoption and impact of OEP and OER in the Global South: Theoretical, conceptual & methodological framework for the ROER4D project meta-synthesis
Presentation at Open Education Global 2017
Cheryl Hodgkinson-Williams
Learning analytics: An opportunity for higher education?Dragan Gasevic
Slides used in my keynote at the Annual Conference of the European Association of Distance Teaching Universities - The open, online, flexible higher education conference - #OOFHEC2015
PhD Recruition, Retention and Completion remain a problem to be dealt with and there are supports needed at the university, supervisor and student level. Here we discuss what they are, based on research into the issue.
Presentation on 27 October 2016 at an Ethics Symposium as part of the Siyaphumelela Project, Kopanong Hotel & Conference Centre, Johannesburg, South Africa
Can medical education take advantage of Learning Analytics techniques? How? Where? In this presentation a study is analyzed pinpointing three areas in which Medical Education needs to invest and all three are related to Learning Analytics.
Faculty Adoption of Technologies in Team-Based Learning ClassroomsBradford Wheeler
Wheeler, B., Shih, M. , & Weaver, GC. Faculty Adoption of Technologies in Team-Based Learning Classrooms. Poster session presented at: New England Faculty Development Consortium (NEFDC) 2015, May 29; Fairfield, CT.
Adopting Classroom Technology: A Faculty Development ProgramBradford Wheeler
Wheeler, B., Adopting Classroom Technology: A Faculty Development Program. Poster presented at: New England Faculty Development Consortium (NEFDC) 2016, May 24; Somerville, MA.
AI in Education Amsterdam Data Science (ADS) What have we learned after a dec...Bart Rienties
The Open University UK (OU) has been implementing learning analytics and learning design on a large scale since 2012. With its 170+ students and 4000+ teaching staff, the OU has been at the forefront of testing, implementing, and evaluating the impact of learning analytics and learning design on students outcome and retention. A range of reviews and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics and learning design in the world. However, despite the large uptake of learning analytics at the OU there are a range of complex issues in terms of buy-in from staff, data infrastructures, ethics and privacy, student engagement, and perhaps most importantly how to make sense of big and small data in a complex organisation like the OU. During his talk Bart will be presenting on the implementation and learnings.
Promoting Data Literacy at the Grassroots (ACRL 2015, Portland, OR)Adam Beauchamp
Presentation given at ACRL 2015, with Christine Murray, on teaching undergraduate students to discover and evaluate datasets for secondary data analysis.
The adoption and impact of OEP and OER in the Global South: Theoretical, conc...ROER4D
The adoption and impact of OEP and OER in the Global South: Theoretical, conceptual & methodological framework for the ROER4D project meta-synthesis
Presentation at Open Education Global 2017
Cheryl Hodgkinson-Williams
Learning analytics: An opportunity for higher education?Dragan Gasevic
Slides used in my keynote at the Annual Conference of the European Association of Distance Teaching Universities - The open, online, flexible higher education conference - #OOFHEC2015
PhD Recruition, Retention and Completion remain a problem to be dealt with and there are supports needed at the university, supervisor and student level. Here we discuss what they are, based on research into the issue.
Presentation on 27 October 2016 at an Ethics Symposium as part of the Siyaphumelela Project, Kopanong Hotel & Conference Centre, Johannesburg, South Africa
Learning analytics: Threats and opportunitiesMartin Hawksey
Slides used at ALT's White Rose Learning Technologist's SIG to introduce threats and opportunities for using Learning Analytics. Links related to this presentation are at http://bit.ly/LAWhiteRose
What data from 3 million learners can tell us about effective course designJohn Whitmer, Ed.D.
Presentation of research findings and implications from a large-scale analysis of LMS activity and grade data from across 927 institutions, 70,000 courses, and 3.3 million students. This webinar will speak to the promise (and potential pitfalls) of large-scale learning analytics research to promote student success.
Visions of the Future of Learning AnalyticsDoug Clow
Eight visions of the future of learning analytics, created as a way of exploring possible futures by the LACE (Learning Analytics Community Exchange) Project, and presented at Bett 2016, London, 20 January 2016
Learning design meets learning analytics: Dr Bart Rienties, Open UniversityBart Rienties
8th UK Learning Analytics Network Meeting, The Open University, 2nd November 2016
1) The power of 151 Learning Designs on 113K+ students at the OU?
2) How can we use learning design to empower teachers?
3) How can Early Alert Systems improve Student Engagement and Academic Success? (Amara Atif, Macquarie University)
4) What evidence is there that learning design makes a difference over time and how students engage?
Invited presentation at "Transforming the Curriculum: South African Imperatives and 21st Century Possibilities", University of Pretoria 28 January 2016. A voice-over of the presentation is available on YouTube at https://www.youtube.com/watch?v=YFwQ6oa8_y0
A full draft version of the presentation can be found at https://www.researchgate.net/publication/292502252_Curricula_as_contested_and_contesting_spaces_Geographies_of_identity_resistance_and_desire
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
Presentation at the European Distance Education and E-Learning Network (EDEN) Conference, Genoa, Italy, 17-20 June 2018. Authors: Paul Prinsloo, Sharon Slade and Mohammad Khalil
What have we learned from 6 years of implementing learning analytics amongst ...Bart Rienties
By Professor Bart Rienties, Head of Academic Professional Development, Institute of Educational Technology, The Open University, UK
Abstract
The Open University UK (OU) has been implementing learning analytics since 2014, starting with one or two modules to its current practice of large-scale implementation across all its 400+ modules and 170.000+ students and 4000+ teaching staff. While a range of reviews (e.g., Adenij, 2019) and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics in the world, behind the flashy publications and practitioner outputs there are a range of complex issues in terms of ethics and privacy, data infrastructures, buy-in from staff, student engagement, and how to make sense of big data in a complex organisation like the OU.
Based upon large-scale big data research we found some interesting tensions in both design and educational theory, such as:
– 69% of engagement by students on a week by week basis is determined by how teachers are designing courses (i.e., learning design and instructional design indeed directly influence behaviour and cognition), but many teachers seem reluctant to change their learning design based upon data of what works and what does not work (e.g., making sense of data, agency);
– How teachers engage with predictive learning analytics (PLA) significantly improves student outcomes, but only a minority of teachers actually use PLA;
– Some disadvantaged groups engage more actively in OU courses, but nonetheless perform lower than non-disadvantaged students.
During this CELDA keynote I would like to share some of my own reflections of how the OU has implemented learning analytics, and how these insights are helping towards a stronger evidence-base for data-informed change. Furthermore, by sharing some of the lessons learned from implementing learning analytics on a large scale I hope to provide some dos and don’ts in terms of how you might consider to use data in your own practice and context.
Bigger data as better data an exploration in the context of distance educatio...Elizabeth Archer
Archer, E., Barnes, G., Chetty, Y., Prinsloo, P. & Van Zyl, D. (2013). Bigger data as better data: an exploration in the context of distance education. Paper presented at the HELTASA Conference, 27 November 2013, Pretoria: South Africa.
Guest presentation: SASUF Symposium: Digital Technologies, Big Data, and Cybersecurity, Vaal University of Technology, Vanderbijlpark, South Africa, 15 May 2018
This presentation was made at the BCcampus Festival of Learning 2018 conference in Vancouver. It explored the health of individuals who participate in open education and how it might affect the well-being of the open education community as a whole. We asked: If members are not well, does this mean the group is also not thriving? This topic was also presented at the Open Education 2018 conference in Niagara Falls.
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Bart Rienties
This keynote will help you:
-Understand where to start with learning analytics
-Understand how to effectively support your staff to use data
-Critically review whether learning analytics is something for your organisation
https://www.terrapinn.com/exhibition/edutech-europe/speaker-bart-RIENTIES.stm
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Education research is growing, but has not enough impact to tackle the systemic risks of education systems (quality, productivity, equity, innovation). Why? Do we work with outdated theories? And can the science of learning help to do better? Keynote at ECER2019.
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Keynote Address to The Impact of Higher Education: Addressing the Challenges of the 21st CenturyEuropean Association for Institutional Research (EAIR) 35th Annual Forum 2013, Erasmus University, Rotterdam, the Netherlands, 28-31 August 2013. http://www.eair.nl/forum/rotterdam
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Learning analytics: At the intersections between student support, privacy, agency and institutional survival
1. Learning analytics: At the intersections between
student support, privacy, agency and institutional
survival
Paul Prinsloo (University of South Africa, Unisa) @14prinsp
Sharon Slade (Open University, OU) @sharonslade
Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367
4. HIGHER EDUCATION
• Increasing competition, changing contexts, internationalisation
• Rankings and quality regimes/criteria
• Increasing funding constraints and austerity measures
• Funding follows performance rather than preceding it – the need for
evidence
• Persisting concerns about student retention, failure and dropout
• History of well-intentioned but often bang-bang approaches to increasing
student retention and success
• The mandate and fiduciary duty of higher education
• Optimising the student experience, ensuring student success/throughput
Survivor – the Higher
Education series (new
rules, new contestants,
better than ever)
5. • Determine criteria/characteristics
• Calculate cost of care/intervention/return on
investment
• Implementation – educational triage
• Evaluation
Moving the murky middle/drowning the
bunnies
6. Engaging with (some) assumptions &
practices re the need for (more) data
• Our (mis)understanding of student
retention, success and failure
• Can we assume that knowing more, per
se, results in understanding and care; that
more data will necessarily contribute to
better teaching and learning?
• The danger of context collapse and the need to ensure context integrity
when data collected from disparate sources and for a variety of purposes
are combined
• The inherent biases, dangers and potential of algorithmic decision-
making
• The scope of students’ right to privacy
7. Educational triage in practice
• School league tables can lead to a
focus on key boundaries
– Evidence that the ‘murky middle’
overlooked in favour of those most
able to support achievement of
key results
• Traditional classroom-based
universities
– potential focus on ‘seen’ or
perceived need
– often driven by individual subject
tutors
• Distance learning institutions
– largely reliant on student data to
direct support
– often driven by available data and
assumed patterns
8. Analytics in practice at the Open University
Framework for consistent support
– Drives minimum set of proactive
interventions through curriculum focused
Student Support Teams to all students
– Additional core interventions target students
based on characteristics (potentially ‘at risk’)
and/or study behaviours
– Large number of possible proactive
interventions (e.g., missed milestones, etc)
– Prioritising interventions is complex: which
characteristics/milestones/behaviours/
modules take precedence? Who decides?
– Results in non-standard support largely not
transparent to students and driven by
available staff resource
9. Some considerations…
• We cannot ignore the reality of ‘Survivor: Higher Education’
• The impact of funding, resources and contexts on the ‘murky middle’
• The moral implications of our admission requirements: admission without a
reasonable chance of success? The cost of support to make ‘success’ happen?
• Educational triage’s potential to exclude students from access/support based
on criteria that disregard context, structural inequalities and inter-
generational debt
• The need for transparency re rationales for inclusion/exclusion & decisions
made
• The scope of students’ agency: can students refuse advice/support provided
they understand the consequences of their opting out?
11. Thank you
Prof Paul Prinsloo
Research Professor in Open Distance
Learning (ODL)
College of Economic and Management
Sciences, Office number 3-15, Club 1,
Hazelwood, P O Box 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
T: +27 (0) 82 3954 113 (mobile)
prinsp@unisa.ac.za
Personal blog:
http://opendistanceteachingandlearning
.wordpress.com
Twitter profile: @14prinsp
Dr Sharon Slade
Senior Lecturer
Faculty of Business and Law
The Open University, Walton Hall,
Milton Keynes, MK7 6AA, United
Kingdom
T: +44 (0) 1865 486250
sharon.slade@open.ac.uk
www.linkedin.com/profile/view?id=53
123496&trk=tab_pro
Twitter profile: @sharonslade
12. References and additional reading
Ball, N. (2013, November 11). Big Data follows and buries us in equal measure. [Web log
post]. Retrieved from http://www.popmatters.com/feature/175640-this-so-called-
metadata/
Beauchamp T. L., & Childress J.F. (2001). Principles of Biomedical Ethics. (5th ed). Oxford:
Oxford University Press.
Bergstein, B. (2013, February 20). The problem with our data obsession. MIT Technology
Review. Retrieved from https://www.technologyreview.com/s/511176/the-problem-
with-our-data-obsession/
Bertolucci, J. (2014, July 28). Deep data trumps Big Data. Information Week. Retrieved
from http://www.informationweek.com/big-data/big-data-analytics/deep-data-
trumps-big-data/d/d-id/1297588
Biesta, G. (2007). Why “what works” won’t work: evidence-based practice and the
democratic deficit in educational research, Educational Theory, 57(1),1–22. DOI:
10.1111/j.1741-5446.2006.00241.x .
13. References and additional reading
(cont.)
Biesta, G. (2010). Why ‘what works’ still won’t work: from evidence-based education to
value-based education, Studies in Philosophy of Education, 29, 491–503. DOI
10.1007/s11217-010-9191-x.
Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online].
Retrieved from http://www.educause.edu/ero/article/learning-analytics-new-black
boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
Crawford, K. (2014, May 30). The anxieties of Big Data. The New Inquiry. Retrieved from
http://thenewinquiry.com/essays/the-anxieties-of-big-data
Danaher, J. (2014, January 6). Rule by algorithm? Big Data and the threat of
algocracy.[Web log post]. Retrieved from
http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data-
and-threat.html
14. Danaher, J. (2015, June 15). How might algorithms rule our lives? Mapping the logical
space of algocracy. [Web log post]. Retrieved from
http://philosophicaldisquisitions.blogspot.co.za/2015/06/how-might-algorithms-
rule-our-lives.html
Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism. DOI:
10.1080/21670811.2014.976411
Diefenbach, T. (2007). The managerialistic ideology of organisational change
management, Journal of Organisational Change Management, 20(1), 126 — 144.
Eubanks, V. (2014, January 15). Want to predict the future of surveillance? Ask poor
communities. The American Prospect. Retrieved from
http://prospect.org/article/want-predict-future-surveillance-ask-poor-communities
Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology,
1-3.
References and additional reading
(cont.)
15. References and additional reading
(cont.)
Gitelman, L. (ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Hartfield, T. (2015, May 12 ). Next generation learning analytics: Or, how learning
analytics is passé. [Web log post]. Retrieved from
http://timothyharfield.com/blog/2015/05/12/next-generation-learning-analytics-
or-how-learning-analytics-is-passe/
Hartley, D. (1995). The ‘McDonaldisation’ of higher education: food for thought?
Oxford Review of Education, 21(4), 409—423.
Henman, P. (2004). Targeted!: Population segmentation, electronic surveillance and
governing the unemployed in Australia. International Sociology, 19, 173-191
Johnson, J.A. (2015, October 7). How data does political things: The processes of
encoding and decoding data are never neutral. [Web log post]. Retrieved from
http://blogs.lse.ac.uk/impactofsocialsciences/2015/10/07/how-data-does-political-
things/
16. References and additional reading
(cont.)
Joynt, G.M., & Gomersall, C.D. (2005). Making moral decisions when resources are limited
– an approach to triage in ICY patients with respiratory failure. South African Journal of
Critical Care (SAJCC), 21(1), 34—44. Retrieved from
http://www.ajol.info/index.php/sajcc/article/view/35543
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks.
Dialogues in Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Kitchen, R. (2014). The data revolution. London, UK: SAGE.
Kitchen, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the
ontological characteristics of 26 datasets. Big Data & Society, January-June, 1-10. DOI:
10.1177/2053951716631130
Knox, D. (2010). Spies in the house of learning: a typology of surveillance in online
learning environments. Paper presented at Edge, Memorial University of
Newfoundland, Canada, 12-15 October.
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data
& Society (July-December), 1-11.
17. References and additional reading
(cont.)
Mayer-Schönberger, V. (2009). Delete. The virtue of forgetting in the digital age.
Princeton, NJ: Princeton University Press.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data. London, UK: Hachette.
Morozov, E. (2013a, October 23). The real privacy problem. MIT Technology Review.
Retrieved from http://www.technologyreview.com/featuredstory/520426/the-real-
privacy-problem/
Morozov, E. (2013b). To save everything, click here. London, UK: Penguin Books.
Napoli, P. (2013). The algorithm as institution: Toward a theoretical framework for
automated media production and consumption. In Media in Transition Conference
(pp. 1–36). DOI: 10.2139/ssrn.2260923
Nissenbaum, H. (2015). Respecting context to protect privacy: Why meaning matters.
Science and engineering ethics. Retrieved from
http://link.springer.com/article/10.1007/s11948-015-9674-9
18. References and additional reading
(cont.)
Open University. (2014). Policy on ethical use of student data for learning analytics.
Retrieved from http://www.open.ac.uk/students/charter/essential-documents/ethical-
use-student-data-learning-analytics-policy
Manning, C. (2012, March 14). Educational triage. [Web log post]. Retrieved from
http://colinmcit.blogspot.co.uk/2012/03/educational-triage.html.
Pasquale, F. (2015, October 14). Scores of scores: how companies are reducing consumers
to single numbers The Atlantic. Retrieved
fromhttp://www.theatlantic.com/business/archive/2015/10/credit-
scores/410350/
Pasquale, F. [FrankPasquale]. (2016, February 19). "We know where you are. We know
where you’ve been. We can more or less know what you're thinking about.
http://www.theatlantic.com/technology/archive/2016/02/google-cute-evil/463464/ …
#Jigsaw [Tweet]. Retrieved from
https://twitter.com/FrankPasquale/status/700473628605947904
19. References and additional reading
(cont.)
Pasquale, F. (2015). The black box society. Harvard Publishing, US.
Prinsloo, P. (2009). Modelling throughput at Unisa: The key to the successful
implementation of ODL. Retrieved from http://uir.unisa.ac.za/handle/10500/6035
Prinsloo, P., Archer, E., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big (ger) data as better
data in open distance learning. The International Review of Research in Open and
Distributed Learning, 16(1).
Prinsloo, P., & Slade, S. (2014). Educational triage in higher online education: walking a
moral tightrope. International Review of Research in Open Distributed Learning
(IRRODL), 14(4), pp. 306-331.
http://www.irrodl.org/index.php/irrodl/article/view/1881.
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: an
exploration. Journal of Learning Analytics, 3(1), 159-182.
20. References and additional reading
(cont.)
Prinsloo, P., & Slade, S. (2016). Here be dragons: Mapping student responsibility in
learning analytics, in Mark Anderson and Collette Gavan (eds.), Developing Effective
Educational Experiences through Learning Analytics (pp. 174-192). Hershey,
Pennsylvania: ICI-Global.
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing
times. New York, NY: Routledge
Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American
Behavioural Scientist, 57(1) pp. 1509–1528.
Slade, S., & Prinsloo, P. (2015). Student perspectives on the use of their data: between
intrusion, surveillance and care. European Journal of Open, Distance and Elearning.
(pp.16-28). Special Issue.
http://www.eurodl.org/materials/special/2015/Slade_Prinsloo.pdf
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: a socio-critical model and framework
for improving student success in open distance learning at the University of South
Africa. Distance Education, 32(2): 177-19.
21. References and additional reading
(cont.)
Tene, O. & Polonetsky, J. (2013). Judged by the Tin Man: Individual rights in the age of Big
Data. J. on Telecomm. & High Tech. L., 11, 351.
Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretive key of modern
rationality. Theory Culture Society 31, pp. 29—49. DOI: 10.1177/0263276413510051
Uprichard, E. (2013, October 1). Big data, little questions. Discover Society. Retrieved
from http://discoversociety.org/2013/10/01/focus-big-data-little-questions/
Wang, T. (2013, January 20). Why Big Data needs thick data. Medium. Retrieved from
https://medium.com/ethnography-matters/why-big-data-needs-thick-data-
b4b3e75e3d7#.4jbatgurh
Watters, A. (2013, October 13). Student data is the new oil: MOOCs, metaphor, and
money. [Web log post]. Retrieved from
http://www.hackeducation.com/2013/10/17/student-data-is-the-new-oil/
Watters, A. (2014). Social justice. [Web log post]. Retrieved from
http://hackeducation.com/2014/12/18/top-ed-tech-trends-2014-justice
22. References and additional reading
(cont.)
Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences.
Computer,(June), 46-53.
Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning
analytics: A typology derived from a cross-continental, cross-institutional perspective.
Educational Technology Research and Development. DOI: 10.1007/s11423-016-9463-
4 Retrieved fromhttp://link.springer.com/article/10.1007/s11423-016-9463-4