Week 8 visual artefact "An understanding of Amazon's recommendation algorithms" on Prezi #mscedc https://t.co/CS5gvptLDm
— Nigel Painting (@nigelchpainting) March 12, 2017
Category Archives: Block 3 – Algorithm culture
Week 8 – visual artefact
I guess this isn’t strictly a visual artefact as there’a a lot of text in it, but I’ve tried to present it in a very visual way and, in the interests of variety and familiarisation with different digital tools, I’ve tried something different.
I’ve presented this artefact in Prezi:
As far as possible I attempted to introduce some scientific rigour to my experimentation and avoid the temptation to force the results I expected. The element of playfulness made this more difficult than I expected!
With the benefits of hindsight, in my case Amazon recommendations was probably not the best choice of algorithm to experiment with, as my account is used by other members of my family. If anything my recommendations are more skewed to my son’s preferences, as he is a more prolific user of Amazon’s services than I am.
TWEET: Use data to build better schools
https://t.co/tEYYcCW7a7 #mscedc
— Nigel Painting (@nigelchpainting) March 9, 2017
“Learning is not a place but an activity”
Andreas Schleicher talks about the PISA test. This is a global measurement that ranks countries against one another and uses the data to help schools improve.
“Measuring how much time people spend in school or what degree they’ve got is not always a good way of seeing what they can actually do”
PISA tests whether students can extrapolate what they’ve learned and apply their knowledge in novel situations. Apparently we’re so, so in the rankings of the readiness of our young people for today’s economy.
Most relevant to this algorithmic cultures block “Data can be more powerful than administrative control or financial subsidy through which we usually run education”
Big data TED talks
I found these videos provided some useful context, particularly the last two, which I have included in my visual artefact for this week.
TWEET: algorithm experimentation
Finding unexpected results with Amazon's algorithms, expected high ticket price searches to be privileged, but appears they're not #mscedc
— Nigel Painting (@nigelchpainting) March 9, 2017
TWEET: too easily distracted
hmmm must focus on watching for signs of algorithms doing their thing TO SIDE OF Facebook posts NOT the Facebook posts themselves 👉 #mscedc
— Nigel Painting (@nigelchpainting) March 8, 2017
A shocking indictment of how ineffective those side bar adverts can be that I’m having to force myself to look at them. In contrast the ads that appear in the timeline are virtually impossible to miss, which is probably why people find those more annoying.
Possible learning here for the placement of information in e-learning design.
SEO Lesson 8 Keyword Optimizing Header Tags H1 – H6 on Each Webpage by Tidyrank
SEO Lesson 8 Keyword Optimizing Header Tags H1 – H6 on Each Webpage by Tidyrank
via YouTube
One of the concerns about algorithms is the way that certain information can be privileged over other information due to its position in search rankings, and the bias that can be introduced by only showing search engine users links to content that is similar to content they’ve previously looked at. This video shows some of the mechanisms behind how that happens.
SEO Lesson 7 Using keywords in the title of the webpage to improve your rankings and click through r
SEO Lesson 7 Using keywords in the title of the webpage to improve your rankings and click through r
via YouTube
One of the concerns about algorithms is the way that certain information can be privileged over other information due to its position in search rankings, and the bias that can be introduced by only showing search engine users links to content that is similar to content they’ve previously looked at. This video shows some of the mechanisms behind how that happens.
TWEET: How ad-targeting ruined Christmas
Algorithmic cultures and how ad-retargetting ruined Christmas https://t.co/vIGza43JyL #mscedc
— Nigel Painting (@nigelchpainting) March 6, 2017
A great example of when ‘making visible the invisible’ isn’t necessarily a good thing.
TWEET: What we’re learning from online education
Apologies if this has already been linked. https://t.co/95Og37X375 #mscedc
— Nigel Painting (@nigelchpainting) February 28, 2017
“Big breakthroughs happen when what is suddenly possible meets what is desperately necessary”
This quote from NY Times columnist and Pulitzer Prize winner Thomas Friedman sets the scene for this TED talk from Daphne Koller, co-founder of Coursera.
The talk is relevant to both the community culture and algorithm cultures of this course. From a community perspective Koller describes cultural norms in MOOCs that we have also seen develop during this course, including students asking and answering each others questions and forming into smaller study groups of their own volition.
From an analytics perspective Koller talks about the way massive open online courses have enabled turning “the study of human learning from a hypothesis driven mode to the data driven mode”. Koller states that the data Coursera collects enables fundamental questions such as “what are good learning strategies versus ones that are not” to be examined. She also talks about the personalisation that is possible by virtue of having large volumes of data available, making it easier to spot anomalies and address them with targeted guidance for students.
Interestingly she doesn’t see MOOCs making traditional universities obsolete, but calls upon them to move away from the lecture based format and embrace active learning.
She finishes with a vision of the possibilities that online education brings for fundamental change in the world.