Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.
Using student data: (Not) solving the
student departure puzzle?
Student data: the missing
link in solving the student
depa...
Acknowledgements
This presentation (excluding the images) is
licensed under a Creative Commons Attribution-
NonCommercial ...
The role of student data in solving student
departure
We do have a lot of student data but how do the pieces
fit together?
Or is the issue that we are just looking for that ‘one’
missing piece of data that will solve everything? If we
could only...
And where are
students in all of this?
What happens when
students say…
#NoMore
#DoNotTrack
Image credit: https://en.wikipe...
Image credit: https://www.flickr.com/photos/haydnseek/2534088367
Exploring the issues at the intersections of
• Our fiduci...
Overview of the presentation
• Map, question, interrupt and attempt to slow down
some of the current discourses about (stu...
Imagine what we could learn if we put a tracker on
everyone and everything (Jurdak, 2016)
Image credit: https://www.flickr...
Page credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits
Page credit: http://www.teenvogue.com/story/oral-roberts-university-fitbit-freshman
Page credit: http://www.huffingtonpost.com/2012/10/08/texas-school-district-rep_n_1949415.html
Page credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother
http://www.abc.net.au/news/2016-08-12/university-of-melbourne-tracking-students-through-wifi/7723468
Page credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-in...
Page credits: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0
Source: http://chronicle.com/article/Are-Struggling-College/235311
(Student) data as Medusa
Higher education is
mesmerized and
seduced by the
potential of the
collection, analysis
and use o...
‘how much is enough data
to solve my problem?’
(Adryan, 2015)
Image credit: https://www.flickr.com/photos/uncle-
leo/13419...
• What responsibility comes with knowing our
students? [Can we un-know knowing…?]
• To know more about our students does n...
While many analysts accept data
at face value, and treat them as if
they are neutral, objective, and
pre-analytic in natur...
We should continuously and relentlessly
contest the assumptions that data are neutral
and raw, that quantitative data are ...
We live in a “scored society” (Citron
& Pasquale, 2013) and consumers are
increasingly reduced to single
numbers (Pasquale...
Boyd and Crawford (2011) point to the fact that just because
we have access to increasing amounts and granularity of
perso...
Collecting, analyzing student data in complex
and chaotic environments (Cynefin framework)
SIMPLE/KNOWN
Cause & effect rel...
Image credit: http://www.tylervigen.com/spurious-correlations
Mistaking correlation with causation…
Image credit: http://www.tylervigen.com/spurious-correlations
Mistaking correlation with causation…
Silver (2012) warns that in noisy systems with
underdeveloped theory there is a real danger in
mistaking noise for signals...
(1)
Humans perform
the task
(2)
Task is shared with
algorithms
(3)
Algorithms
perform task:
human supervision
(4)
Algorith...
• What does research tell us?
• What is our understanding
of how the different variables
intersect, at which
stages of the...
We know that the following impact on student
success…
• Socioeconomic circumstances
• Primary and secondary school
backgro...
We know that the following impact on student
success… (2)
• Institutional efficiencies or inefficiencies
• Complexity of c...
We know that the following impact on student
success… (3)
What we don’t know
(yet), and possibly never
may know…
What is the impact when these different sets
of impact combine?
What happens when we see student success as the
result of ...
Processes
Inter & intra-
personal
domains
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Processes
Modalitie...
If…
student success is the result of mostly non-linear,
multidimensional, interdependent interactions at
different phases ...
So what data do we already have?
• Demographic details – provided on application/registration
• Registration data – qualif...
Who knows these things of our students?
• The ‘system’ – disparate databases that do not
(necessarily) talk to one another...
Who acts (if we do) on what we (think we)
know?
• Faculty/adjunct faculty – often, due to workloads
and student: staff rat...
How do we (they) verify & update what
we (they) know
• Do students have access to what we know
and/or think we know about ...
And… who has access to what we know, & under
what conditions?
We protect students from harm when we approve
research but h...
We therefore need to critically consider the
ethical implications of …
• Knowing
• Not knowing
• Knowing what we don’t kno...
Collecting, analysing and using student data:
towards an ethics of care
1. Do no harm. Repeat after me. Do no harm
2. Stud...
Collecting, analysing and using student data:
towards an ethics of care (2)
6. Context matters. Downstream use of data for...
THANK YOU
Paul Prinsloo (Prof)
Research Professor in Open Distance Learning (ODL)
College of Economic and Management Scien...
Bibliography and additional reading
Adryan, B. (2015, October 20). Is it all machine learning? [Web log post]. Retrieved f...
Brock, A. (2015). Deeper data: a response to boyd and Crawford. Media, Culture & Society, 37(7), 1084-1088.
Chamayou, G. (...
Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Harford, T. (2014, April 26). Big data: are ...
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July-
December), ...
Bibliography and additional reading (cont.)
Pasquale, F. [FrankPasquale]. (2016, February 19). "We know where you are. We ...
Bibliography and additional reading (cont.)
Rosenbaum, R. (1995, January 15). The great Ivy League nude posture photo scan...
Bibliography and additional reading (cont.)
Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretive ke...
Próxima SlideShare
Cargando en…5
×

2

Compartir

Descargar para leer sin conexión

Student data: the missing link in solving the student departure puzzle?

Descargar para leer sin conexión

Invited presentation at the University of Wisconsin Milwaukee on Tuesday 16 August 2016

Libros relacionados

Gratis con una prueba de 30 días de Scribd

Ver todo

Audiolibros relacionados

Gratis con una prueba de 30 días de Scribd

Ver todo

Student data: the missing link in solving the student departure puzzle?

  1. 1. Using student data: (Not) solving the student departure puzzle? Student data: the missing link in solving the student departure puzzle? Paul Prinsloo (University of South Africa, Unisa) @14prinsp Tuesday 16 August 2016 University of Wisconsin Milwaukee
  2. 2. Acknowledgements This presentation (excluding the images) is licensed under a Creative Commons Attribution- NonCommercial 4.0 International License I do not own the copyright of any of the images in this presentation. I therefore acknowledge the original copyright and licensing regime of every image used. Image credit on first slide: https://www.flickr.com/photos/ofernandezberrios/2720569216
  3. 3. The role of student data in solving student departure We do have a lot of student data but how do the pieces fit together?
  4. 4. Or is the issue that we are just looking for that ‘one’ missing piece of data that will solve everything? If we could only have more data… Image credit: https://commons.wikimedia.org/wiki/File:Puzzle2_found_bw.jpg
  5. 5. And where are students in all of this? What happens when students say… #NoMore #DoNotTrack Image credit: https://en.wikipedia.org/wiki/Privacy#/media/File:Surveillance_cameras.jpg
  6. 6. Image credit: https://www.flickr.com/photos/haydnseek/2534088367 Exploring the issues at the intersections of • Our fiduciary duty to care • The collection of student data as dancing with the devil in the search for salvation • Respecting student privacy and agency
  7. 7. Overview of the presentation • Map, question, interrupt and attempt to slow down some of the current discourses about (student) data • What does research tell us about student retention and dropout? • What do we already know about our students, where are the data located and who has access to this data under what conditions? • Tentatively map the way forward towards collecting, analyzing and using student data as an ethics of care
  8. 8. Imagine what we could learn if we put a tracker on everyone and everything (Jurdak, 2016) Image credit: https://www.flickr.com/photos/jeepersmedia/13966485507
  9. 9. Page credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits
  10. 10. Page credit: http://www.teenvogue.com/story/oral-roberts-university-fitbit-freshman
  11. 11. Page credit: http://www.huffingtonpost.com/2012/10/08/texas-school-district-rep_n_1949415.html
  12. 12. Page credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother
  13. 13. http://www.abc.net.au/news/2016-08-12/university-of-melbourne-tracking-students-through-wifi/7723468
  14. 14. Page credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data- drones
  15. 15. Page credits: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0
  16. 16. Source: http://chronicle.com/article/Are-Struggling-College/235311
  17. 17. (Student) data as Medusa Higher education is mesmerized and seduced by the potential of the collection, analysis and use of student data. If only we know more… Image credit: http://en.wikipedia.org/wiki/Medusa
  18. 18. ‘how much is enough data to solve my problem?’ (Adryan, 2015) Image credit: https://www.flickr.com/photos/uncle- leo/1341913549 How much (more) student data do we need?
  19. 19. • What responsibility comes with knowing our students? [Can we un-know knowing…?] • To know more about our students does not necessarily imply understanding … • Even if we knew and understood our students, do we have the will and the resources to do something about what we (think we) know? • And what happens if our students don’t want to be known when they feel that revealing their identities will make then more vulnerable? (De)constructing knowing more…
  20. 20. While many analysts accept data at face value, and treat them as if they are neutral, objective, and pre-analytic in nature, data are in fact framed technically, economically, ethically, temporally, spatially and philosophically. Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them” (Kitchen, 2014, p. 2). Image credit: http://www.iatropedia.gr/tag/opioucha-pafsipona/ Data are never raw but always framed and cooked
  21. 21. We should continuously and relentlessly contest the assumptions that data are neutral and raw, that quantitative data are better than qualitative data, that large data sets are not prone to data errors and gaps and that big data have less bias than smaller, qualitative data sets (boyd & Crawford, 2011; Gitelman, 2013). Image credit: https://en.wikipedia.org/wiki/Egg_%28food%29
  22. 22. We live in a “scored society” (Citron & Pasquale, 2013) and consumers are increasingly reduced to single numbers (Pasquale, 2015). We do not only need large data sets, but also deep data (Scharmer, 2014) or thick qualitative data (Shacklett, 2015; Wang, 2013). We should not underestimate the contribution and value of small data (boyd & Crawford, 2011).Image credit: https://commons.wikimedia.org/wiki/File:1_August_2008_partial_eclipse_from_UK.jpg Our students’ lives are so much more than the data we have of them
  23. 23. Boyd and Crawford (2011) point to the fact that just because we have access to increasing amounts and granularity of personal data, does not mean that we have to collect the data, analyse the data and use the data. While research participant involvement in research is governed by institutional review boards and policies, the (automatic) collection, analysis and use of individuals’ digital data often falls and take place outside of these policies and review boards (Willis, Slade & Prinsloo, 2016) Just because we can, does not mean we have to…
  24. 24. Collecting, analyzing student data in complex and chaotic environments (Cynefin framework) SIMPLE/KNOWN Cause & effect relationships known & predictable Best practice Standard operating procedures Sense-Categorize-Respond COMPLICATED Cause & effect relationships separated over time & space Analytical/Reductionist Scenario planning Sense-Analyze-Respond COMPLEX Cause & effect relationships are only coherent in retrospect and do not repeat Pattern management Complex adaptive systems Probe-Sense-Respond CHAOS No cause & effect relationships perceivable Stability-focused intervention Crisis management Act-Sense-Respond
  25. 25. Image credit: http://www.tylervigen.com/spurious-correlations Mistaking correlation with causation…
  26. 26. Image credit: http://www.tylervigen.com/spurious-correlations Mistaking correlation with causation…
  27. 27. Silver (2012) warns that in noisy systems with underdeveloped theory there is a real danger in mistaking noise for signals, not realising that noise pollutes our data with false alarms “setting back our ability to understand how the system really works” (p. 162) Mistaking the noise for the signal
  28. 28. (1) Humans perform the task (2) Task is shared with algorithms (3) Algorithms perform task: human supervision (4) Algorithms perform task: no human input Seeing Yes or No? Yes or No? Yes or No? Yes or No? Processing Yes or No? Yes or No? Yes or No? Yes or No? Acting Yes or No? Yes or No? Yes or No? Yes or No? Learning Yes or No? Yes or No? Yes or No? Yes or No? Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html Human-algorithm interaction in the collection, analysis and use of student data
  29. 29. • What does research tell us? • What is our understanding of how the different variables intersect, at which stages of the learning journey? • What data do we already have, where are the data located, who has access to this data under what conditions? • How do students fit into all of this and can they opt out? Image credit: https://pixabay.com/p-316638/ Making sense of student data…
  30. 30. We know that the following impact on student success… • Socioeconomic circumstances • Primary and secondary school background • Educational background of parents and immediate family • Geographical distance between family home and institution • Subjects and subject marks on school level • Proficiency in the language of tuition • Support networks or lack of • Peer pressure • Family and community pressure • Access to resources • Mathematics on school level • Role models or lack of • Locus of control • Attribution • Self awareness • Self-discipline • Habits and behaviours • Parental status • Health status • Employment status • Probability of employment or career progress
  31. 31. We know that the following impact on student success… (2) • Institutional efficiencies or inefficiencies • Complexity of curricula • Curriculum coherence • Epistemologies and ways of seeing the world • Assessment strategies • Tuition periods • Examination schedules • Server reliability • Faculty understanding of ODL • Faculty expertise • Institutional culture • Whether the institution is the choice of last resort for students • Integration of student support, curriculum, pedagogy and technology
  32. 32. We know that the following impact on student success… (3)
  33. 33. What we don’t know (yet), and possibly never may know…
  34. 34. What is the impact when these different sets of impact combine? What happens when we see student success as the result of mostly non-linear, multidimensional, interdependent interactions at different phases in the nexus between student, institution and broader societal factors?
  35. 35. Processes Inter & intra- personal domains Modalities: • Attribution • Locus of control • Self-efficacy Processes Modalities: • Attribution • Locus of control • Self-efficacy Domains Academic Operational Social TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES THE STUDENT AS AGENT IDENTITY, ATTRIBUTES, HABITUS Success THE INSTITUTION AS AGENT IDENTITY, ATTRIBUTES, HABITUS SHAPING CONDITIONS: (predictable as well as uncertain) SHAPING CONDITIONS: (predictable as well as uncertain) Choice, Admission Learning activities Course success Gradua- tion THE STUDENT WALK Multiple, mutually constitutive interactions between student, institution & networks F I T FIT F I T FIT Employ- ment/ citizenship TRANSFORMED STUDENT IDENTITY & ATTRIBUTES F I T F I T F I T F I T F I T F I T F I T F I T Retention/Progression/Positive experience (Subotzky & Prinsloo, 2011)
  36. 36. If… student success is the result of mostly non-linear, multidimensional, interdependent interactions at different phases in the nexus between student, institution and broader societal factors (Prinsloo, 2009) … what data do we already have, where are the data located, who has access to this data under what conditions, and what prevents us from using it?
  37. 37. So what data do we already have? • Demographic details – provided on application/registration • Registration data – qualification, number of courses • Historical registration data of students • Learning data – assignments (not) submitted, learning histories – asynchronous, synchronous and (increasingly) digital • Contact/correspondence with various actors in the institution • Personal information collected from a range of sources – defaulting on payments, students submitting bank statements, health records, etc. • Published and unpublished research and department/institutional reports
  38. 38. Who knows these things of our students? • The ‘system’ – disparate databases that do not (necessarily) talk to one another • Various stakeholders – student advisors, ICT, counsellors, researchers, academics, tutors, etc. • Other external stakeholders – employers, law enforcement agencies, data brokers, labor brokers, commercial stakeholders • Social media platforms and networks
  39. 39. Who acts (if we do) on what we (think we) know? • Faculty/adjunct faculty – often, due to workloads and student: staff ratios in a generalised, one-size- fits-all way • Course success coaches • Administrators – for everyone (new) contact, a different administrator, starting over, explaining everything again • Counselors, support staff, regional staff
  40. 40. How do we (they) verify & update what we (they) know • Do students have access to what we know and/or think we know about them? • How do we verify our assumptions about our students, their learning needs and trajectories? • How do they verify and provide context to their (digital) profiles? (See Slade & Prinsloo, 2013, Prinsloo & Slade, 2014, 2015)
  41. 41. And… who has access to what we know, & under what conditions? We protect students from harm when we approve research but how do we protect students from harm when we act – change pedagogy, assessment, staff allocation based on learning analytics? (Willis, Slade & Prinsloo, 2016) How do we govern student databases, for how long do we keep student data, on what conditions do we share student data, with whom?
  42. 42. We therefore need to critically consider the ethical implications of … • Knowing • Not knowing • Knowing what we don’t know • Knowing what we may never know • Knowing more The solution is not only (or necessarily?) in knowing more, but ensuring that once we know, we respond in ethical, caring, discipline and context-appropriate ways
  43. 43. Collecting, analysing and using student data: towards an ethics of care 1. Do no harm. Repeat after me. Do no harm 2. Students have a right to know. If they do not know, our research constitutes surveillance and spying, and not research 3. Make it clear what data are collected, when, for what purpose, for how long it will be kept and who will have access and under what circumstances 4. Provide students access to information and data held about them, to verify and/or question the conclusions drawn, and where necessary, provide context 5. Provide access to a neutral ombudsperson (See Prinsloo & Slade, 2015)
  44. 44. Collecting, analysing and using student data: towards an ethics of care (2) 6. Context matters. Downstream use of data for purposes other than the original purpose for the collection of data compromises the contextual integrity of data 7. Involve students in the meaning-making. They are not data points on a PowerPoint at a conference. They have contexts, histories. They are infinitely more than their data. 8. Who will we hold accountable for algorithms? 9. What are the benefits for students? For you? For the institution? Be transparent. (See Prinsloo & Slade, 2015)
  45. 45. THANK YOU Paul Prinsloo (Prof) 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 Skype: paul.prinsloo59 Personal blog: http://opendistanceteachingandlearning.wordpress.com Twitter profile: @14prinsp
  46. 46. Bibliography and additional reading Adryan, B. (2015, October 20). Is it all machine learning? [Web log post]. Retrieved from http://iot.ghost.io/is- it-all-machine-learning/ Apple, M.W. (Ed.). (2010). Global crises, social justice, and education. New York, NY: Routledge. Ariely, D. [Dan Ariely]. (2013, January 6). Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... “[Facebook status update]. Retrieved from https://www.facebook.com/dan.ariely/posts/904383595868 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/ Bauman, Z. (2012). On education. conversations with Riccardo Mazzeo. Cambridge, UK: Polity. 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 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
  47. 47. Brock, A. (2015). Deeper data: a response to boyd and Crawford. Media, Culture & Society, 37(7), 1084-1088. Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://www.mpiwg- berlin.mpg.de/en/news/features/feature14 Citron, D.K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. http://ssrn.com/abstract=2376209 Crawford, K. (2013, April 1). The hidden biases in Big Data. Harvard Business Review. Retrieved from https://hbr.org/2013/04/the-hidden-biases-in-big-data/ 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 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 Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7. Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism. DOI: 10.1080/21670811.2014.976411 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. . Bibliography and additional reading (cont.)
  48. 48. Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press. Harford, T. (2014, April 26). Big data: are we making a big mistake? [Web log post]. Retrieved from http://timharford.com/2014/04/big-data-are-we-making-a-big-mistake/ 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/ 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 ouse of learning: a typology of surveillance in online learning environments. Paper presented at Edge, Memorial University of Newfoundland, Canada, 12-15 October. Kranzberg, M. (1986) Technology and history: Kranzberg's laws’. Technology and Culture, 27(3), 544—560 Bibliography and additional reading (cont.)
  49. 49. Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July- December), 1-11. Leonhard, G. (2014, February 25). How tech is creating data "cravability," to make us digitally obese. Retrieved from http://www.fastcoexist.com/3026862/how-tech-is-creating-data-cravability-to-make-us-digitally- obese?utm_content=buffer643a8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer Lupton, D. (2015). The thirteen Ps of Big Data. This Sociological Life. Retrieved from https://www.researchgate.net/profile/Deborah_Lupton/publication/276207564_The_Thirteen_Ps_of_Big_ Data/links/5552c2d808ae6fd2d81d5f20.pdf 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. Morozov, E. (2013). 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 Pasquale, F. (2015, October 14). Scores of scores: how companies are reducing consumers to single numbers. The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2015/10/credit- scores/410350/ Bibliography and additional reading (cont.)
  50. 50. Bibliography and additional reading (cont.) 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 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. (2015, September 3). The ethics of (not) knowing our students. Presentation at the University of South Africa, Pretoria. Retrieved from http://www.slideshare.net/prinsp/the-ethics-of-not-knowing-our- students-52373670 Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research in Open and Distributed Learning, 15(4), 306-331. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1881/3060 Prinsloo, P., & Slade, S. (2015, March). Student privacy self-management: implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 83-92). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2723585 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). Rambam, S. (2008). Privacy is dead. Get over it. Retrieved from https://www.youtube.com/watch?v=Vsxxsrn2Tfs&index=1&list=PL8C71542205AA51E5 Rosen, J. (2010, July 21). The web means the end of forgetting. New York Times [Online].
  51. 51. Bibliography and additional reading (cont.) Rosenbaum, R. (1995, January 15). The great Ivy League nude posture photo scandal. The New York Times. Retrieved from http://www.nytimes.com/1995/01/15/magazine/the-great-ivy-league-nude-posture- photo-scandal.html Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY: Routledge Scharmer, O. (2014, July 18). From Big Data to deep data. Huffington Post. Retrieved from http://www.huffingtonpost.com/otto-scharmer/from-big-data-to-deep-dat_b_5599267.html Shacklett, M. (2015, January 6). Thick data closes the gaps in big data analytics. TechRepublic. Retrieved from http://www.techrepublic.com/article/thick-data-closes-the-gaps-in-big-data-analytics/ Silver, N. 2012. The signal and the noise: Why most predictions fail – but some don’t. New York, NY: Routledge. Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist 57(1) ,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), 177-193. 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.
  52. 52. Bibliography and additional reading (cont.) 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 Therborn, G. (ed.).(2006). Inequalities of the world. New theoretical frameworks, multiple empirical approaches. London, UK: Verso Books Uprichard, E. (2013). Big data, little questions. Discover Society, 1 October. Retrieved from http://discoversociety.org/2013/10/01/focus-big-data-little-questions/ Wagner, D., & Ice, P. (2012, July 18). Data changes everything: delivering on the promise of learning analytics in higher education. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/data- changes-everything-delivering-promise-learning-analytics-higher-education 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 Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,(June), 46-53. Willis, J. E., Slade, S., & Prinsloo, P. (in review). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Submitted to special issue of Educational Technology Research and Development (Exploring the Relationship of Ethics in Design and Learning Analytics: Implications for the Field of Instructional Design and Technology), guest edited by M. Tracey and D. Ifenthaler.
  • AndrasSzucs

    Aug. 17, 2016
  • goodwaa

    Aug. 17, 2016

Invited presentation at the University of Wisconsin Milwaukee on Tuesday 16 August 2016

Vistas

Total de vistas

1.263

En Slideshare

0

De embebidos

0

Número de embebidos

27

Acciones

Descargas

10

Compartidos

0

Comentarios

0

Me gusta

2

×