Plagiarism detectors are a crutch, and a problem

When it comes to plagiarism, many academics seem to believe in magic numbers. Last month, a company offering plagiarism-detection software announced that it would be acquired for US$1.7 billion later this year.
from Pocket https://www.nature.com/articles/d41586-019-00893-5
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Weekly summary: week 9

This week has been all about the algorithmic play. I spent a lot of time playing with the Youtube recommends algorithm and trying various ways to understand and manipulate it. It would have been naïve to think that I could figure out the algorithm within two weeks. I was baffled by a lot of the recommendations but I believe that there is a reason for suggesting these videos to me. What this exercise has taught me is how complex algorithms are and that no matter how aware we are of them, they can be very powerful in influencing our behaviour. This thought was reinforced by Kitchin’s (2017) article who stresses that algorithms aren’t purely technical and objective: ‘Other knowledge about algorithms – such as their applications, effects, and circulation – is strictly out of frame’ (Seaver, 2013, pp. 1–2). As are the complex set of decision-making processes and practices, and the wider assemblage of systems of thought, finance, politics, legal codes and regulations, materialities and infrastructures, institutions, inter-personal relations, which shape their production (Kitchin, 2014).
Another topic I explored on my lifestream this week was the rise of personalised learning. I can see why people get excited about it but, as with everything to do with Big Data, people often seem to forget the ethical side.
References
Kitchin, R. (2017). Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29, DOI: 10.1080/1369118X.2016.1154087.
Algorithmic play

Here is the link to my algorithmic play:
https://spark.adobe.com/page/0zoYZki7480xE/
Liked on YouTube: How China Is Using Artificial Intelligence in Classrooms | WSJ
Very worrying development. Children are facing so much pressure already these days.
How AI and Data Could Personalize Higher Education

Artificial intelligence (AI) is rapidly transforming and improving the ways that industries like healthcare, banking, energy, and retail operate. However, there is one industry in particular that offers incredible potential for the application of AI technologies: education.
from Pocket https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education
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Liked on YouTube: A vision for radically personalized learning | Katherine Prince | TEDxColumbus
Most videos on Youtube on personalised learning seem very positive. While it can offer huge benefits to learners, there’s not much being said about the disadvantages. Are students always at the centre of these technologies or are company interests playing a role too? What about disadvanted students who don’t have access to certain technologies?
YouTube’s recent algorithm change explains why your feed is full of children’s videos
Trying to better understand Youtube’s algorithm for my algorithm play.

YouTube quietly rolled out changes to its algorithm last month in an effort to surface more family-friendly content amid an investigation into the platform by the Federal Trade Commission, according to a new Bloomberg report.
from Pocket https://www.theverge.com/2019/8/1/20750054/youtube-algorithm-recommendation-kids-videos-cartoons-nursery-rhymes
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The Real Risk of Edtech-Based Personalized Learning

The excitement is almost palpable. Administrators purchase the latest and greatest edtech, hoping that this next program will be the panacea, the silver bullet that improves scores and helps the campus meet accountability goals.
from Pocket https://www.thetechedvocate.org/the-real-risk-of-edtech-based-personalized-learning/
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Big Data And The Problem Of Bias In Higher Education
‘Given the human element, can big data ever be bias-free in the context of diversity?’ Lots of interesting points in this article. It argues that data analytics in education is here to stay so we need to remember that data isn’t neutral.

The explosive use of big data, predictive analytics and other modeling techniques to help understand and drive outcomes in all types of organizations has significantly increased over the past decade.
from Pocket https://www.forbes.com/sites/audreymurrell/2019/05/30/big-data-and-the-problem-of-bias-in-higher-education/
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