Final Summary – Algorithms and liveblogs!

I decided the best way to summarize how algorithms have influenced my livestream would be to have a closer look into my fellow students’ conclusions on algorithms and comparing them with my own experiences.

Screenshots taken from algorithmic plays of EDC 2020 students

 

 

 

 

 

They played with You Tube, Netflix, Google, etc., algorithms and meddled with their recommendation feeds (video, audio, friends, ads, courses). The most interesting fact was that MSCEDC students had very similar experiences and many recurring conclusions on algorithms I similarly noticed.

  • Technology is not a passive “tool” as it used to be considered at the origins of Community Cultures (see Knox 2015, page 1). Algorithms are still making mistakes but autonomous reactions to personal behavior are identifiable.
  • Algorithms are shaping our reality. The “AI” is not the unknown force as it was considered during Cybercultures. Humans openly accept machines to take decisions for them.

  • Algorithms are in many cases not as objective as originally intended and show clear evidence of bias by economical or other interests. Due to their multilayered and highly complex design the authority behind the algorithm is non-transparent (see Williamson 2017 and Knox 2015, page 1

 

  • Although many algorithmic propositions seem misleading, wrong or deficient, there is a suspicion that they are still intended by hidden interests.
  • The entanglement of the agencies is leading to a reduction of cultural diversity and the production of knowledge (Knox 2015).
  • The assumption of the autonomous learner as guiding principle for the design of algorithms.
  • Algorithms provide a predetermined path, supporting a “goggle vision” leading to reduction of variability of search results and looping.

When developing and implementing my livestream, I witnessed most of these conclusions myself. I used You tube, twitter and google search frequently for research and their algorithms influenced my selection, my reality and therefore the outcome of my liveblog. “You Loops” or “Echoing” was frequently observable, inefficient results and predomination of certain information sources detectable. Troubleshooting of malfunctioning ITTT algorithms was difficult to solve due to multilayered connections. This reality shaping power algorithms have were well described by Kitchen (2007, page 15).

Although we tend to struggle with the accuracy or the missing information about hidden agendas, the use of algorithm has – and will have – a potential to influence learning and teaching.

The algorithm:

  • knows the learner and provides helpful alternatives (“You have skipped or stayed  longer than average on this page, maybe you want to have a look into …”)
  • shows additional information of whatever format to support learning (“ you seem to like this, maybe have a look into…”)
  • changes the interface, course thumbnails, etc. due to user preferences
  • using educative nudges to make learners make favorable decisions (see Knox et al. 2020, p. 39)
  • works in symbiosis with the teacher as provider of unfiltered or partially filtered information.

 

In order to work with algorithm-based technology educationalists must maintain a critical perspective. The algorithm is currently shaping the culture to a great extend and education is not excluded. Therefore, teachers should accept the new status quo and openly engage with the technology.

Developing a liveblog is a good start!

 

 

 

 

 

References:

Kitchin, R. (2017):  Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29

Knox, J. (2015): Algorithmic Cultures. Excerpt from Critical Education and Digital Cultures. in Encyclopedia of Educational Philosophy and Theory. M.A. Peters (ed.)

Knox, J. (2015): Community 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

Schmidt, E. and Rosenberg, J. (2018): How Google Works – Eric Schmidt and Jonathan Rosenberg, Retrieved from: https://www.alexjhughes.com/books/2018/3/11/how-google-works-eric-schmidt-and-jonathan-rosenberg, 29.03.2020

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.

Algorithmic play of all MSCEDC 2020 students. Thank you very much!

 

 

 

 

 

 

 

End Week Summary 9 – Who’s algorithm is it anyway?

While my algorithm play started to show results  as the Netflix algorithm, started to advertise pieces of its audio- visual fundus I have never seen before, while hiding others from my view, the question of the role of algorithm based AI in education (now and in the future) and the play started to connect.

Today digital learning is strongly linked with the idea of increased learning effectivity and efficiency. Automated algorithms are hereby considered as helpful tools, which collect huge quantities of data of the learners behaviour in order to focus educational content on these weaknesses. The center of an algorithm based education is the learner and its weaknesses to cope with a given curriculum. But is this all education and teaching is about?  The idea of the independent learner,  knowing exactly what she or he wants and needs to learn is a myth. Knox, WIlliamson and Bayne (2020) provide a consistent assessment on this neoliberal revisioning of the education sector by refering to Biestas identification  of “learnification” and is implications on the digital education future.

“Not only is the figure of the learner placed
at the centre of the educational arrangement, but the individual becomes the site of learning.”

and

“Learnification is portrayed as blind to broader questions about the role and purpose
of education in wider society,(…)”

I see comparision of this to the algorithms in the digital world. Netflix algorithms using my online behaviour only to shape my profile to be more effective (make me wach more movies?!?). There is no other interest beyond that.

So will the algorithm based AI embedded in digital education systems, apps or programs replace the teacher in future? No, the teacher – learner relationship is more then just effectiveness. It is about supporting, guiding, leading, allowing errors, building personality, shaping personality, etc and or as well as of course the success or achievement of learning targets.

The algorithm in many applications is in most cases focussing only on the achievement of the highest gains but the question who’s gains remains.

“Neoliberal business philosophies and practices promoted by corporations and their partner foundations, supported by international organizations, financiers, and bankers, and welcomed, or at least tolerated by compliant governments, are trying to transform education from a government responsibility and social right into investment opportunities.”(Huff Post (2017)

 

References:

Jeremy Knox, Ben Williamson & Sian Bayne (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

End Week Summary 8 – Algorithm play

Finally approaching the future of education. AI based robots or algorithm based AI systems have introduced and changed many of our private spaces. Knowingly or unknowingly algorithms react to our every virtual step, click, stay, buy, watch or else and create a persona of us and our potential preference, to advertise objects we may like or even better may want o buy.

Google, Facebook, Amazon, Netflix, You Tube  but also other plattforms like coursera or edex do collect data about our behaviour on their pages. Selling their idea of massive data collection with a algorithm which provide us with thinks we may like, something more personal/ individually. And yes for those of us who might have compared google results with a more discrete (non collecting) search engine like Startpage

Yes, it can be very comfortable to have google knowing where you are, where you usually go, what you usually search for and it can be much more frustrating to do the same with a (mostly) non learning search engine but still this comed with a price. Google collects and sells your information, netflix prevents you from other maybe good movies due to your personal preferences, Amazon offers you always similar items or even the MOOCs you’ve been offered are also coming from the same hosts.

In my algorithm play I demonstrate the power of the netflix algorithm, which is actively guiding, influenceing or even forcing me to watch certain movies.

So what does this do with algorithms, AI and education? Will there be robot teachers replacing human teachers in a sort of (for teachers) dystopian vision of the future? Most argue that this will not be the case but educationalists need to admit that they have to open themself for their new “robo” colleagues, who could (and will) deliver or take over certain activities while others will remain with the human teachers.

Freedom from routine, time-consuming tasks will allow teachers to devote more of their energies to the creative and very human acts that provide the ingenuity and empathy needed to take learning to the next level. (Luckin et al. 2016, p.31).

But the education sector needs to understand, criticize and work with these algorithm driven AI systems much more systematically as they do currently.

 

References:

Siân Bayne, Peter Evans, Rory Ewins, Jeremy Knox, James Lamb, Hamish Macleod, Clara O’Shea, Jen Ross, Phil Sheail, Christine Sinclair (2019): The Manifesto for teaching online (DRAFT)

End week summary 7 – Wrapping up community culture

Final week of Community cultures, final search for a web community in a MOOC. Final decision on how to transform literature, experience, MOOC enrollment and further discussion into a visual form.

Continuous discussion on the question to what extend MOOCs success depend on a learning community, the strategies to make MOOCs possibly more interactive and to what extend strategies – which are close to in classroom education – will work in a MOOC with hundreds to thousands of participants.

MOOCs Aren’t Interactive, So There’s No Real Learning Taking Place

 

Finally the question on lurkers kept the whole 4 weeks of community cultures.

https://files.eric.ed.gov/fulltext/EJ779934.pdf

 

 

 

 

 

End Week Summary 6 – How to establish effective MOOCs?

While finishing the search on possible MOOCs, it became obvious that in many MOOCs – intended or non intended is no proper interaction amongst participants and therefore a lack of community building/ a missing online learners community.

But is it important to build an online community amongst learners? Following discussion on twitter with @Eva07686348, @erin11k and @DavidYeats3 it is certainly ok to accept different learning styles and types.

But still we are in the century of intensive digital communication and interaction. As a trainers or course designer my general interest is to create as much interaction as possible by all means necessary.

https://www.yourtrainingedge.com/moocs-arent-interactive-so-theres-no-real-learning/

A livid learners community is helpful for learning and active interaction with course content, even though I have to accept that there are different types of learners.

 

 Instructors should strive to use strategies that provide the best match between curriculum content and outcomes as well as students’ past experiences, learning styles, and learning preferences. (Jason Alley and Karen Greenhaus (2007, page 21)

The web is full on strategies on how to improve your instructional design, improve interaction, turn lurkers into contributers.

Based on the assumption that for most participant even a generally low interaction with others is helpful, course designers should have the interest to know who is observing, learning and participating in “silence” and who is just enrolled without interest in achieving course objectives.

How could I find out? As course designer and can follow to some extend the behaviour of enrolled students but still how to distinguish? Yes, I could check the number of enrolled students, check who takes part in discussions and finally monitor, those who do the set assignments (if there are).

So even if I can’t distinguish them at least I should be able to motivate as many participants as possible and ‘lure’ them into participation in by design of the course as it was very well described by Jason Alley and Karen Greenhaus

The strategy on how to bring as much participants to discuss and engage is very much connected to the general question on how to build relationships and communities without having the face to face interaction.

I came across some articles on how to transfer human face to face interactions into digital communication and engagement.

I will further explore these scenarios in my micro – ethnography.

 

References:

Jason Alley and Karen Greenhaus (2007): Turning Lurkers into Learners, ISTE (International Society for Technology in Education),

https://vimeo.com/13192810TeYosh (2016): Turking learners into . 

 

 

 

End Week Summary 5 – The endless search for a functional MOOC

Is there a community in a MOOC and what is it like? Is there still a community without interaction. Without any doubt there is a potential in digital education through MOOCs. For university education as described by Rebecca Paddick  on edtechnology or as a possibility for the poor, the marginalized or those living in remote areas.

“Massive Open Online Courses, otherwise known as MOOCs, could have the potential to widen global access to higher education, particularly where higher education is currently in short supply” Diana Laurillard and Eileen Kennedy, 2017) 

Other sources highlight that even though there is a potential, there are not many making use of the potential or the way how the respective MOOCs are presented do not reach the intended target group, aren’t attractive and/or interactive or don’t develop a community as we see it in other social media platforms, blogs, etc. An interesting article  by Derek Newton in Forbes online highlights the current situation of reletively low course completition rates. 

In week 6 I enrolled myself in 4 tech or ed-tech courses.

In search for the community in these MOOCs, most results of online discussions show no content.

Even though only taking part in the course actions for research, there is already the feeling of isolation and loneliness.

The important questions for distance and digital learning opportunities is to what extend online community building is relevant for successful courses. Personally I found a functioning, interactive MOOC – but not in a tech related subject. Maybe this is another relevant issue!

 

References:

  • Derek Newton (2018). Not even teacher – bots will save massive open online courses. Forbes, published August 2018, https://www.forbes.com/sites/dereknewton/2018/08/22/not-even-teacher-bots-will-save-massive-open-online-courses/#7508ac2d2bb0
  • Diana Laurillard and Eileen Kennedy (2017). The potential of MOOCs  for learning at scale in the  Global South. Centre for Global Higher Education working paper series, published December 2017,  https://www.researchcghe.org/perch/resources/publications/wp31.pdf
  • Education Technology (2017). MOOCs’ massive potenial, published October 2017, https://edtechnology.co.uk/Article/moocs-massive-potential/