During week 8, I began with readings on algorithmic cultures and education, several are discussed below. I also started ‘playing with algorithms’ task. I chose to look at Instagram and how the Instagram feed tells me what to like and who to follow. Lastly  I looked at musings from the web that might help me understand discussions around algorithms and how they are shape our everyday learning lives.

Review of the main readings;

Williamson 2017 and Knox 2015 articles gave a good overview of the operation of web algorithms, and the ways these automated, non-human agents influence contemporary educational practices.

Both papers underlined how complex a job it is to understand how algorithims are critically analysed to see how they are influencing us, because there are so many interweaving agendas – “Businesses with products to sell, venture capital firms with return on investment to secure, think tanks with new ideas to promote, policy makers with problems to solve and politicians with agendas to set have all become key advocates for data driven education” Williamson 2017.

Kitchin follows up on this theme, explaining that algorithms are usually “woven together with hundreds of other algorithms to create algorithmic systems, and the rules generated by them are compressed and hidden”. They are “works of collective authorship, made, maintained, and revised by many people with different goals at different times”, (Kitchin 2017) and they are embedded in complex socio-technical assemblages. Therefore we do not encounter algorithmic generative rules in a clear manner and in a way that makes it easy to understand them.

In the paper on machine behaviourism: future visions of ‘learnification’ and ‘datafication’ (Knox et al, 2020) they explain the growing influence of behavioural psychology in the educational sector and how it interacts with datafication and machine learning to nudge education towards new forms of behavioural governance. The Knox et al paper did a good job in defining terms such as digital choice architectures, behavioural psychology, behavioural economics, machine learning, & learnification and these are now added to my terminology page. Behavioural governance can work “against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims”. (Knox et al 2020)

The Knox et al 2020 paper covers a huge amount of ground as it looks into the future of datification. They explained that Learnification theory (Biesta 2015), where the learner is the (potential) consumer, whose needs are being met by ‘education’, will soon be less dominant in education.

With the rise of datification, the learner is becoming more ‘modelled’ and therefore so too is the ability of machine learning to  ‘predict’ the learner.  When one can predict a human’s next steps, it becomes easier to manipulate those next steps.  To compound the issue, learners come into education not really knowing what their preferences are, therefore they are easier to nudge towards the predetermined path as designed by the algorithm.  Knox et al state “Here, learners are assumed to respond directly to what the dashboard reveals, rather than evoking some kind of consumerist desire. “

Knox et al (2020) describe this as a ‘crucial shift’ away from Biesta’s learnification model. In the future we will become more influenced by behavioural psychology and algorithmic generative rules (Kitchin 2017) that nudge us towards ‘correct’ forms of performance and conduct that have already been decided (Knox et al 2020).

Activity online- Critical research on machine learning can be negative, so it was nice to find this article on from “Data Science for Social good” which demonstrates a positive use of machine learning for social good and education. It describes the development of an algorithm that assigns a fire risk score to each property on the fire department’s inspection list.

The ‘bias of algorithm’s article here reminds me that “We must not assume the transparency and necessity of automation”. (Knox 2015), and to maintain a “more general, critical account of algorithms, their nature and how they perform work” (Kitchin 2017).

On the article about ‘kids growing up with algorithms’; Kitchin describes this kind of algorithmic play in his paper, discussing how he would like to see us “explore the ways in which people resist, subvert and transgress against the work of algorithms, and re-purpose and re-deploy them for purposes they were not originally intended”. Kids would be the best kind of subversive players I think.

References for the readings

Kitchin, R. (2017) Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 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. M. A. Peters (ed.). DOI 10.1007/978-981-287-532-7_124-1

Knox J., Williamson B., & Bayne S., (2020) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251

Williamson, B. 2017. Introduction: Learning machines, digital data and the future of education (chapter 1). In Big Data and Education: the digital future of learning, policy, and practice

Week 6 has been one of those weeks where notes were lodged everywhere on my desk but I was short on order and integration. It is all part of the educational experience. I was reminded of a quote from a Knox paper (Data power in education)on the educational value of failure. And while I haven’t quite failed yet, I’m aware that this grinding pace is “ a part of a learning experience that may lead to deeper engagements, and richer and more profound experiences in the long run”. So I’m looking forward to the upward curve. 🙂

The ethnography:  I spent some time on the Mooc, making notes on structure, design, interaction/communication, and looking at ways  to get inside the culture and community.

The artefact: In terms of reporting on the experience, I am looking at breaking it down into headings and keeping it concise, hoping to push myself to try a new tool like Prezi or articulate Storyline, but may fall back on Thinglink or a new wordpress site to display it.

Peer Interactions: The Google hangouts on Wednesday with several classmates was interesting and we talked about the question of ethics and other themes of community that we are exploring in this course. I really appreciate the opportunity to have this synchronous time with peers and Jeremy provides a very safe environment for asking questions.

Readings: Kozinets was an excellent launch pad for looking at the science of community, how community develops, how all of that relates to an educational community.  There was a lot to this chapter and I wrote a longer review of Kozinets here. I think it will provide a useful way to frame the evolution of community in my MOOC, using the models that he has provided.

The “Manifesto for teaching online” helped me to pull back, to a strategic view of where digital education is predicted to go.  It is important to pull ourselves out of the minutiae of ethnographies to look at the multiple factors that are reshaping digital education. The pinterest here ( the report on the future of education in 2030) was complementary to this.

I found the section 16 of the Manifesto (Face-time is over-valued) relevant to our work on the MOOC. It’s the quality of the contact that counts, the nearness of the connection that people feel with each other that is of value. “It is in extended sociomaterial assemblages that students and teachers meet and make – or produce – contact.”.  “The greater the centrality of the consumption interest to the person, the higher the interest level and concomitant level of activity knowledge and skill.” Kozinets (2010)

Student apathy; This kind of quality connection remains hard to create on a MOOC, but it’s not always the fault of the MOOC.  When a student is getting the MOOC for free, it’s quite common to undervalue it and not commit the minimal amount of time to learn, engage and revise.

Perhaps we in the West have also become too apathetic. Populous countries with underdeveloped infrastructure could only dream of participating on a mooc with 5 minute videos followed by multiple choice SAQ’s and the ability to rewind and replay the teacher until the student understands the material. This system is 100 times more preferable to this scene from a university in Nigeria as outlined by Dr. Anant Agarwal  in his video linked here.

In my review of the ‘5 points to increased pass rates on a MOOC‘,  the presenter, Dr. Anant Agarwal  (an MIT professor with an electronics course MOOC), found that failure rates in his MOOC fell from 40/41% to 9% using the flipped classroom blended learning model. The test was run on a Californian university where students completed the MOOC at a distance but came together for the lab work.  He was able to replicate this same effect by licensing the MOOC to other locations around the world. Blended learning remains consistently more productive.

The Agarwal video  was valuable as this seasoned teacher listed 5 key points that he mentions as relevant to helping students embed the learning in a massive class. I think I will be assessing my ethnography MOOC across these 5 areas, see link above.

Lastly; the area that interests me is the power of today’s technology to fuse young people’s interests, friendships and academic achievement.  When academic studies and institutions draw from and connect to young people’s peer culture, communities and interest driven pursuits, learners flourish and realise their true potential. I made a comment on connected learning here.

Bayne, S., Evans, P., Ewins, R., Knox, J., Lamb, J., Macleod, H., O’Shea, C., Ross, J., Sheail, P., Sinclair,  C. (2019 DRAFT). The Manifesto for Teaching Online.

Knox, J. (2017). Data power in education: exploring critical awareness with the ‘Learning Analytics Report Card’ (LARC). Journal of Television and Media, 18(8), pp. 734-752.

Kozinets, R. V. (2010) Chapter 2 ‘Understanding Culture Online’, Netnography: doing ethnographic research online. London: Sage. pp. 21-40.

This week I invested a lot of time in reading the 75 page Lister 2009 chapter “New Media- A Critical introduction.” The chapter contained many threads, which remained loosely linked but not tied together. Published in 2009, the chapter referenced Web sites that are no longer operating (MScape, Delicious, Napster), and authors (OReilly Web2.0) that don’t seem to have a current online presence.

Nevertheless there is some material in the latter half of the chapter that was helpful in my study of Online Community.  It also contained definitions and examples for Long Tail theory, Web 2.0, counterculture, convergence culture & transmediality. I placed short definitions of each on my terminology page.

In Lister’s chapter, I found the concept of Web 2.0 interesting. This was also well explained in the paper by Lewis (2012) which I wrote about here.

Web 2.0 is a phrase coined by O’Reilly (2005) and pertains to internet applications focused on participatory information creation, tagging, sharing, and remixing—and, wherein tech companies rely almost entirely on user-generated content for monetization. Simultaneously voluntarily given and unwaged, enjoyed and exploited, free labour on the Net includes the activity of building Web sites, modifying software packages, reading and participating in mailing lists, “Web 2.0 shows how our creative expression becomes commodified and sold back to us”. ‘Lister 2009’

This resonated with me because The Mooc I chose to study was Sustainable Urban Development by University of Wageningen. Whilst thousands have enrolled, and the developers want the students to self- enroll in teams of 20-100 people each, to work on 100 challenges and to find solutions for 100 cities. This strikes me as a very valuable research tool for the university if they can succeed in convincing people to divide themselves up into such Web 2.0 teams.

My peers posted interesting articles that helped me with the study of my MOOC. Jiyoung Kwon posted the article on low completion rate of Moocs, which stands at 5%. The article concentrated on the other 95% who are still completing (and gaining) from some of the material. This might be the institution’s way of putting a positive spin on the non completion rates, but maybe it is an acceptance of how people learn in un- moderated environments

In the MOOC that I am looking at, the developers have created 5 themes or tracks, and students can follow their own track of interest, and still gain a cert of completion if they complete two thirds of the course. Is this an attempt to counteract the low completion rates  I wonder?

I commented on three other peer posts in this article, on ‘lurking in Moocs’, ‘lack of conversations in Moocs’, and ‘how the brain retains information’.