Lifestream Summary Week 10
What kind of algorithms have been involved and how do the identified algorithmic operations relate to particular readings from this block?
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Algorithmic Operations (General) |
Readings |
Algorithmic Operations (Lifestream) |
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Filtering Prioritising/Sorting Shaping decision-making and behaviour Creating, maintaining, cementing notions of normality/abnormality Predicting Presenting/Displaying Exercising social power |
Beer, D. (2017) |
Filtering (Beer, D. 2017) Prioritising (Beer, D. 2017) Predicting (Beer, D. 2017) Presenting/Displaying (Beer, D. 2017) Creating capital (Kitchin, R. 2017) Producing worlds (Knox, J., 2015) Shaping decision-making (Beer, D. 2017) Structuring preferences (Kitchin, R., 2017) Nudging of behaviour (Knox et al., 2020) |
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Value and capital creation Nudging of behaviour Structuring preferences Shaping of weltanschauung Construction and implementation of regimes of power and knowledge Shaping of life chances |
Kitchin, R. (2017) |
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Arranging, cataloguing, ranking of people, places and knowledge Prioritising Privileging Producing worlds Detecting plagiarism Censoring educational content Constructing learning subjects, academic practices and institutional strategies |
Knox, J. (2015) |
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Reinforcement learning Nudging of human decisions Intervention in learner conduct Shaping of learner behaviour |
Knox, J., Williamson, B. and Bayne, S. (2020) |
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Re-coding of education |
Ross, J., Bayne, S. and Lamb, J. (2019) |
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Data mining Data visualisation Computer-adaptive assessment |
Williamson, B. (2017). |
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How do you perceive the algorithms to have operated and how were your activities influenced?
Beer (2017) makes a salient observation on algorithmic activity by contending that while many blithely fail to adequately reflect on its sheer scale and diversity of purposewe should, in fact, be increasingly alert to its ‘social power’and influence (p. 3). I felt that the ways in which algorithms operated in my lifestream were often dictated (inter alia) by the keywords I inputted, the types of website I visited, by thenature of the information, service or product I searched for,and by the search engines I chose to use.
On registering with Pinterest, I was immediately presented with a range of ‘personalised’ images.
Also, although I did not follow anyone on Twitter, I received suggestions for followers which were clearly based on my live feeds.
I was also cognisant of the ‘rabbit hole’ effect on my browsing.
By focussing on identifying ways in which algorithmic activity was influencing my lifestream activity, I was prompted to consider the extent to which algorithms also impacted my work and social environments. In addition to the lifestream operations identified above, I could see the influence of very different algorithmic operations at playincluding the re-coding of education, reinforcement learning, censoring educational content and data mining.
Our behaviour as course participants in EDC were most definitely influenced by the course creators as has how we structured our blog assignments.
Which ideas from the readings help you to explain what might have been happening?
1. Algorithms are not perfect, but continue to evolve (Kitchin, 2017).
2. Algorithms perpetuate a restricted ‘personalised’ world(Knox, 2015).
3. Many agencies are at work in the ‘production’ ofknowledge and culture (Knox, 2015).
What does algorithmic activity potentially mean for notions of (i) learning and teaching, (ii) authorship and (iii) agency?
Learning and Teaching
Given that algorithmic activity is in constant flux, teaching strategies must continuously be reconsidered and adapted as information literacy itself is necessarily redefined. As Seaver(2013, cited in Kitchin, 2017) observes, algorithmic systemsare ‘massive networked [boxes] with hundreds of hands reaching into them, tweaking and tuning, swapping out parts and experimenting with new arrangements’ (p.18).
Head (2020) contends that in an age of algorithms both university students and society are being short-changed because of a failure to innovate in learning and teaching ‘[…]by confining research assignments and information literacy instruction efforts to the walled garden of peer-reviewed scholarship, where truth is plucked from well-pruned sources and carefully packaged for instructors following explicit instructions’ (p. 28). Adherence to and advocacy of outmoded HE curricula, teaching methodologies, and legacy assessment practices should not be tolerated in 2020.
To innovate in learning and teaching we must not, as Ross et al. (2016) caution, eschew an uncritical acceptance of technological instrumentalism or adopt outright resistance to it. Algorithmic activity is an integral part of contemporary learning and teaching and, as such, must be reflected in what and how we teach and learn.
Authorship
‘Remixing digital content redefines authorship’ (Ross et al., 2019, p.26).
A move from purely text-based scholarship to one in which the nature of authorship continues to evolve and which embraces multimodality and the blending of text with images, video and sound will bring about a paradigm shift in education. This has positive implications for the ongoing appraisal of assessment and evaluation practices (Bayne et al., 2019).
Agency
The notion of agency in relation to algorithmic activity is complex (Beer, 2017; Kitchin, 2017, Knox et al. 2020, Williamson, 2017) and ‘results cannot easily be reduced to the intentional agency of any one identifiable human person […] or non-human algorithm’ ( Knox, 2015, p.1.)
To foster a learning and teaching culture in which personal agency is positively promoted (and in which students and tutors are encouraged and supported to enhance theiralgorithmic and digital literacy), reverse-mentoring (student to tutor), co-learning (student and tutor) and peer-to peer learning (student to student, and tutor to tutor) deserve consideration. In reconceiving traditional student and teacher roles ’both parties must be willing to set aside the reassuring familiarity of hierarchies of power and information and, instead, encourage curiosity and tolerate ambiguity, even if the result of such learning is difficult to predict’ (Head et al.,2020, p.30).
References
Bayne, S., Ewins, R., Knox, J., Lamb, J. Macleod, H., O’Shea, C., Ross, J., Sheail, P., Sinclair, C. (2019, DRAFT). The Manifesto for Teaching Online.
Beer, D. (2017) The Social Power of Algorithms. Information, Communication and Society. Vol. 20 (1), pp 1-13. DOI10.1080/1369118X.2016.126147
Head, A. J., Fister, B. and MacMillan, M. (2020) Information literacy in the age of algorithms: Student experiences with news and information, and the need for change, Project Information Research Institute ALGO Report. (January 15).Available at: https://www.projectinfolit.org/uploads/2/7/5/4/27541717/algoreport.pdf. Accessed: March 18, 2020.
Kitchin, R. (2017) Thinking Critically about Researching Algorithms. Information. Communication and Society. Vol. 20 (1), pp. 14-29. DOI 10.1080/1369118X.2016.1154087
Knox, J. (2015) Algorithmic Cultures. Excerpt from ‘Critical Education and Digital Cultures’ in Encyclopedia of Educational Philosophy and Theory, Peters, M. A. (Ed.). DOI10.1007/978-981-287-532-7_124-1
Knox, J., Williamson, B. and Bayne, S. (2020) Machine behaviourism: future visions of learnification and datafication across humans and digital technologies. Learning, Media and Technology, Vol. 45 (1), pp. 35-45. DOI 10.1080/17439884.2019.1623251
Ross, J., Bayne, S. and Lamb, J. (2019) Critical approaches to valuing digital education: learning with and from the Manifesto for Teaching Online. Digital Culture & Education. Vol. 11 (1), pp. 22-35.
Williamson, B. (2017) Introduction: Learning machines, digital data and the future of education. Ch. 1 in Big Data and Education the digital future of learning, policy and practice. London, Sage.