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Learning analytics and Big Data: A tentative exploration

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Presentation at the Apereo Africa cConference, 10 March 2016, Burgerspark Hotel, Pretoria, South Africa

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Learning analytics and Big Data: A tentative exploration

  1. 1. Presentation at the Apereo Africa Conference, 9-10 March 2016, Burgerspark Hotel, Pretoria Image credit: Image compiled from two images - https://pixabay.com/en/photos/data/ https://upload.wikimedia.org/wikipedia/commons/1/15/Fingerprint_detail_on_male_finger.jpg Learning analytics and Big Data: A tentative exploration By Paul Prinsloo (Unisa)
  2. 2. Adapted & refined from Prinsloo, P., Archer, L., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big(ger) Data as Better Data in Open Distance Learning: Some Provocations and Theses. International Review of Research in Open and Distributed Learning (IRRODL), 16(1), 284-306. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1948/3259 Prinsloo, P. (2014). A brave new world. Presentation at SAAIR, 16-18 October http://www.slideshare.net/prinsp/a-brave-new-world-student-surveillance-in-higher-education
  3. 3. What is the (current and future) relation between learning analytics and Big Data? What are the potential and perils? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  4. 4. How will having access to more data, collected from disparate sources, in real- time and responding in real-time, increase the effectiveness of teaching and learning? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  5. 5. • 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? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  6. 6. Acknowledgements 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. This presentation (excluding the images) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
  7. 7. Acknowledgement and disclaimer This presentation presents a tentative exploration of the relationship between Big Data and learning analytics flowing from and expanding personal and collaborative research over the last decade (see bibliography). Disclaimer: I am not a data scientist, ‘geek’, data analyst, or computer scientist. I am an educator and educational researcher trying to make sense of a phenomenon and the underlying assumptions and use of technical terms I often don’t comprehend.
  8. 8. OVERVIEW OF THE PRESENTATION • Big Data – glimpses of what ‘it’ is, claims to be, where ‘it’ is going and dystopian dreams • How do we think about Big Data and learning analytics in a higher education context that… • When is a data set Big Data? Dreams of large data that are small and Big Data that are large • 15 provocations for Big Data (and learning analytics) • Penetrating the fog – learning analytics - limitations and questions • (In)conclusions
  9. 9. Reference: 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
  10. 10. Rather than scarce and limited in access, the production of data is increasingly becoming a deluge; a wide, deep torrent of timely, varied, resolute and relational data that are relatively low in cost and, outside of business, increasingly open and accessible (Kitchen, 2014, p. xv) Image credit: https://commons.wikimedia.org/wiki/File:Shots_of_a_raging_creek_near_the_ Crow_Creek_Pass_Trailhead_parking_lot_%285368679731%29.jpg
  11. 11. We know where you are. We know where you’ve been. We can more or less know what you're thinking about (@FrankPasquale, 2016) Image credit: https://en.wikipedia.org/wiki/Surveillance
  12. 12. 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
  13. 13. “…while our mediated world becomes increasingly transparent, those who seek to profit from our data are incredibly opaque” (Gandy, 2000, cited in McStay, 2013) Image credit: http://fleishmanhillard.com/2014/11/true/big-datas-inaccuracy-hurts-people/
  14. 14. What counts as ‘big’ changes from one year to another, and the relevant question really is ‘how much is enough data to solve my problem?’ (Adryan, 2015) When is ‘big’, big enough? Image credit: https://www.flickr.com/photos/uncle- leo/1341913549
  15. 15. … has become saturated with data – ranging from automatically collected, analysed and used, purposefully collected, analysed and used and volunteered on social media and in exchange of (perceived) benefits despite concerns about privacy, the uncertainty of how the data will be used (and combined with other sources of data) downstream and in the context where our trust in the collectors of data is often misplaced, irrational or wishful thinking How do we think of (Big) data in higher education in a world that… Image credit: https://commons.wikimedia.org/wiki/File:Big_Hand_-_geograph.org.uk_-_644552.jpg
  16. 16. How do we think of (Big) data in higher education in a world that… • The current obsession with ‘evidence’ to secure due to funding that follows performance rather than preceding it (Hartley, 1995) • Claims that Big Data in higher education will change everything & that student data are “the new black” (Booth, 2012) & “the new oil” (Watters, 2013) • Our “quantification fetish”, the “algorithmic turn” & “techno-solutionism” (Morozov, 2013a, 2013b) • The current meta-narratives of “techno-romanticism” in education (Selwyn, 2014) • The belief that data are “raw”, “speak for itself” (Boyd & Crawford, 2013; Gitelman, 2013) & that collecting even more data equals necessarily results in better understanding & interventions
  17. 17. 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/
  18. 18. HOW DO WE DESCRIBE BIG DATA? The “Vs” (Uprichard, 2013) Velocity Veracity Volume Variety Versatility Vitality Visionary Vigour Viability Vibrancy Virility The alternative list of “Vs” (Uprichard, 2013) Valueless Vampire-like Venomous Vulgar Violating Violent The 13 Ps of Big Data (Lupton, 2015) Portentous Perverse Personal Productive Partial Practices Predictive Political Provocative Privacy Polyvalent Polymorphous Playful
  19. 19. HOW DO WE DESCRIBE BIG DATA? • Volume – number of records, storage required by the record, total storage required terabytes (240 bytes) to petabytes (250bytes) • Velocity – fast & continuous – (1) frequency of generation and (2) frequency of handling, recording & publishing • Variety – various, seemingly unrelated sources generated in (un)related contexts for (un)related purposes and under (un)disclosed conditions for reuse • And…
  20. 20. • Exhaustivity – entire populations – n = all • Resolution and granularity = fine-grained in resolution and uniquely indexical in identification • Relationality – strong – containing common fields that enable the conjoining of different data sets • Flexibility/scalability – can change/add new fields easily and expand the size dramatically • Veracity – messy, noise and contain uncertainty and error • Value – many insights can be extracted and the data can be re-purposed HOW DO WE DESCRIBE BIG DATA? (cont.)
  21. 21. Large but small Image credit: https://en.wikipedia.org/wiki/Demographics_of_South_Africa A national census, taking place once every 10 years, asking 30 structured questions, once collected, impossible to add/remove fields (Kitchen & McArdle, 2016) In 2014 Facebook processed “10 billion messages, 4.5 billion ‘Like’ buttons, and 350- million uploads per day and constantly refining and tweaking their algorithms and terms and conditions, changing what and how data were generated” (Kitchen & McArdle, 2016, p. 2; emphasis added) Large and Big
  22. 22. After analysing 26 data sets… The “key boundary characteristics” of Big Data are • “velocity (both the frequency of generation, and frequency of handling, recording, and publishing) and • …exhaustivity” (Kitchen & McArdle, 2016, p. 8) • “Small data are slow and sampled. Big data are quick and n = all. Small data can hold all the other characteristics (volume, resolution, indexicality, relationality, extensionality and flexibility) and still be considered small in nature. It is the qualities of velocity and exhaustivity which set Big Data apart…” (Kitchen & McArdle, 2016, p. 8). • The much-hyped aspect of volume “is a by-product of velocity and exhaustivity: the real-time flow of data across a whole system can produce a deluge of data, especially if each record is large in size” (Kitchen & McArdle, 2016, p. 19)
  23. 23. Image credit: https://www.flickr.com/photos/photosofsrilanka/3406040079 Some provocations for Big Data (and possibly for learning analytics)
  24. 24. Some provocations for Big Data (and possibly for learning analytics) 1. Big data cannot (yet) deal with big questions
  25. 25. Some provocations (cont.)… 2. Big data cannot- at least at the moment – tell you what you will or should do. While the algorithms of Amazon and Facebook still look for the links between our past online behaviours (what we bought and what we shared) and what we will buy and share in future, Uprichard (2013) points to the impossibility of Big Data to “design local, regional and global policies” (par. 9). Image credit: https://commons.wikimedia.org/wiki/File:Fork_in_the_ro ad_-_geograph.org.uk_-_1355424.jpg
  26. 26. Some provocations (cont.)… 3. Big data (only) provides snapshots of the now and do not tell us the why. This also refers to the claim by Mayer- Schönberger and Cukier (2013) that Big Data “is about what, not why. We don’t always need to know the cause of the phenomenon; rather, we can let data speak for itself” (p. 14). Image credit: https://en.wikipedia.org/wiki/Monkey_selfie
  27. 27. Some provocations (cont.)… 4. Big data as methodological genocide. Though Uprichard (2013) acknowledges that the claim of methodological genocide is “melodramatic” (par. 12), she states “We are all, whether we like it or not, slowly but surely, becoming complicit to a deeply positivist, reductionist kind of social science, where variables are the be all and end all, where causality is devoid of meaning, and where non social scientists are the ones ruling the roost in terms of access, collection and analysis – of big data, which is social data” (par. 11).
  28. 28. Some provocations (cont.)… 5. Big Data change our understanding of knowledge. At the core of the question is how Big Data changes our understanding of knowledge and the generation and verification of knowledge (Floridi, 2013). Big Data as phenomenon constitutes a “profound change at the levels of epistemology and ethics. It reframes key questions about the constitution of knowledge, the processes of research, how we should engage with information, and the nature and the categorisation of reality” (boyd & Crawford, 2011, p. 3). At the core of our reflections on Big Data are therefore epistemological, ontological and possibly even metaphysical concerns and implications.
  29. 29. Some provocations (cont.)… 6. Numbers do not speak for themselves and do not equal objectivity and/or accuracy. Boyd and Crawford (2011) warn that there “remains a mistaken belief that qualitative researchers are in the business of interpreting stories and quantitative researchers are in the business of producing facts” (pp. 4-5). 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).
  30. 30. Some provocations (cont.)… 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 6. Numbers do not speak for themselves and do not equal objectivity and/or accuracy (cont.)…
  31. 31. Some provocations (cont.)… 7. Big Data may be big but does not provide the total picture. In the context of the “scored society” (Citron & Pasquale, 2013), and the fact that consumers are increasingly reduced to single numbers (Pasquale, 2015) there are many authors who support the need to move from Big Data to deep data (Scharmer, 2014) or at least supplement Big Data with thick data or 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
  32. 32. Some provocations (cont.)… 8. More data are not always better data. In the context of the increasing data hunger and even obsession for data, also in the context of higher education, boyd and Crawford (2011; Prinsloo, Archer, Barnes, Chetty & Van Zyl, 2014) state that bigger and more data do not, per se, imply a better understanding of the research focus. Just because Big Data claim not to work with samples but with whole populations (Mayer-Schönberger & Cukier, 2014) does not mean that less bias, or that the big data set allows you to ask any question (Crawford, 2013). As boyd and Crawford (2011) warn, combining data from multiple large datasets may be prone to amplify the errors in the individual datasets.
  33. 33. Some provocations (cont.)… 9. Not all data are equivalent. It is often presumed that data are interchangeable but boyd and Crawford (2011) warn that when data are taken out of context, data loose meaning and value, and contextual integrity (Nissenbaum, 2015). What individuals share and do in specific online contexts does not necessarily apply to all contexts. Brock (2015) also pointed to the reality that some individuals carefully curate and manage their digital footprints and profiles and that an analysis of their digital footprints does not, necessarily, reveal their authentic selves.
  34. 34. Some provocations (cont.)… 10. Just because we can, does not mean we have to. Boyd and Crawford (2011) points to the fact that just because we have access to increasing amounts and granularity of personal data, does not mean that we have to and need to collect these data, analyse the data and use the data. Willis, Slade and Prinsloo (in review) point out that 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.
  35. 35. Some provocations (cont.)… 11. The ideological nature of data. “The processes of encoding and decoding data are never neutral” (Johnson, 2015). Also see Henman (2004). What are the implications for our methodologies and understanding of the information produced by Big Data when we accept the proposition by Nakaruma (2013) –“What is algorithm but ideology in executable form?”
  36. 36. Some provocations (finally)… 12. In search of authentic, holistic data profiles…Brock (2015) quotes Manovich (2011) who state that digital data produced in social media are not necessarily ‘authentic’ but “often carefully curated and systematically managed” (p. 1087). The general assumption is that Big Data provides ‘real’ and ‘authentic’, ‘holistic’ descriptions of individuals while there is ample evidence that these instrumental analyses and methodologies fail to deliver of these claims (Brock, 2015; Reigeluth, 2014).
  37. 37. Some provocations (cont.)… 13. Increasing digital divides. boyd and Crawford (2011) ask – “…who gets access? For what purposes? In what contexts? And with what constraints?” (p. 12). Not only are Big Data sets not accessible for most people, but the technical skills required in utilising and analysing these large and contingent data sets excludes many. This results a new kind of digital divide – “the Big Data rich and the Big Data poor” (boyd & Crawford, 2011, p. 13) where non-access to these data sets and the information made possible by their analysis perpetuates existing and creates new inequalities and injustices.
  38. 38. Some provocations (cont.)… 14. Caught between correlation and causation Image credit: http://www.tylervigen.com/spurious-correlations
  39. 39. Some provocations (cont.)… 14. Caught between correlation and causation (cont.) Image credit: http://www.tylervigen.com/spurious-correlations
  40. 40. Some provocations (cont.)… 15. Mistaking the noise for the signal 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)
  41. 41. Adapted & refined from Prinsloo, P., Archer, L., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big(ger) Data as Better Data in Open Distance Learning: Some Provocations and Theses. International Review of Research in Open and Distributed Learning (IRRODL), 16(1), 284-306. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1948/3259 Prinsloo, P. (2014). A brave new world. Presentation at SAAIR, 16-18 October http://www.slideshare.net/prinsp/a-brave-new-world-student-surveillance-in-higher-education
  42. 42. Page credit: http://www.teenvogue.com/story/oral-roberts-university-fitbit-freshman
  43. 43. Page credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits
  44. 44. Page credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother
  45. 45. Page credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data- drones
  46. 46. Page credits: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0
  47. 47. Page credit: http://www.huffingtonpost.com/2012/10/08/texas-school-district-rep_n_1949415.html
  48. 48. Image credit: Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, p. 34. Retrieved from http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and- education
  49. 49. “… learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” Retrieved from https://tekri.athabascau.ca/analytics/; emphasis added
  50. 50. Image credit: Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, p. 34. Retrieved from http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning- and-education
  51. 51. The (current)limitations to learning analytics “… most LMS analytics models do not capture activity by online learners outside of an LMS (i.e., in Facebook, Twitter, or blogs). Similarly, most analytics models do not capture or utilize physical-world data, such as library use, access to learning support, or academic advising. Mobile devices such as smartphones and tablets/iPads offer the prospect of bridging the divide between the physical and digital worlds by capturing location and activity. Similarly, clickers in classrooms can be integrated with data from learners’ activity in online environments, providing additional insight into factors that contribute to learners’ success” (Siemens & Long, 2011, p. 36; emphasis added)
  52. 52. So what do we (currently) know about our students? • Demographic details – provided on application/registration • Registration data – qualification, number of courses • Historical data of previously registered students • Learning data – assignments (not) submitted, learning histories – asynchronous, synchronous and (increasingly) digital • Contact/correspondence with various actors in the institution • And increasingly personal information pick-up/collected from a range of sources – defaulting on payments and students submitting bank statements, health records, etc.
  53. 53. 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, academics, tutors, e-tutors, & researchers, external markers • Other external stakeholders – employers, law enforcement agencies, data brokers, labor brokers, commercial stakeholders • Social media platforms and networks
  54. 54. We also know what we (currently) don’t know… • Is s/he a “first generation” student or not? • Socio-economic circumstances? • Access, sustainability of access and cost of access to the Internet? • Do they have access to prescribed learning resources? • Motivation for registering for the qualification? • Reading/comprehension skills? • Support networks? • Health and parental status, etc.?
  55. 55. What we (currently) don’t know and may never know… What happens in the nexus between students’ (and their life-worlds) and institutional (operational, academic and social) identities and processes? What are the implications for learning analytics if we accept that student success and retention are a complex, dynamic, non-linear, unfolding processes consisting of mutually constitutive and often incommensurable factors?
  56. 56. 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 (From Subotzky & Prinsloo, 2011)
  57. 57. Who acts (if we do) on what we (think we) know? • Faculty – often, due to workloads and student: staff ratios in a generalised, one- size-fits-all way • E-tutors/adjunct faculty • Administrators – for everyone (new) contact, a different administrator, starting over, explaining everything again • Tutors, counsellors, regional staff
  58. 58. 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)
  59. 59. 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, in press) How do we govern student databases, for how long do we keep student data, on what conditions do we share student data, with whom?
  60. 60. Are we (currently) stumbling through a dark room, not knowing the meaning of the noises we hear, reacting in uncoordinated kneejerk fashion, our actions based on assumptions, hearsay, well-intended but non-empirical, context-disjointed, fragmented and possibly discipline-inappropriate ways…? Image credit: http://www.elmundodehector.com/wp-content/uploads/2015/04/door-dark.jpg
  61. 61. In considering the potential of the nexus between Big Data and learning analytics we 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
  62. 62. Adapted & refined from Prinsloo, P., Archer, L., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big(ger) Data as Better Data in Open Distance Learning: Some Provocations and Theses. International Review of Research in Open and Distributed Learning (IRRODL), 16(1), 284-306. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1948/3259 Prinsloo, P. (2014). A brave new world. Presentation at SAAIR, 16-18 October http://www.slideshare.net/prinsp/a-brave-new-world-student-surveillance-in-higher-education
  63. 63. (In)conclusions
  64. 64. What is the (current and future) relation between learning analytics and Big Data? What are the potential and perils? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  65. 65. How will having access to more data, collected from disparate sources, in real- time and responding in real-time, increase the effectiveness of teaching and learning? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  66. 66. • 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? Image credit: https://en.wikipedia.org/wiki/Maze_solving_algorithm https://commons.wikimedia.org/wiki/File:You_are_here.svg
  67. 67. 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
  68. 68. 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
  69. 69. 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.)
  70. 70. 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 Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July- December), 1-11. Bibliography and additional reading (cont.)
  71. 71. 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.)
  72. 72. 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].
  73. 73. 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.
  74. 74. 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.

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