Fascinated by how neutral we appear to be. #mscedc pic.twitter.com/WAn6hQmjcC
— Helen Walker (@helenwalker7) March 26, 2017
Favourite tweets!
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Tag Archives: Twitter
Tweet! Community questions
@helenwalker7 James said difficult to dictate count for artefact. Using my judgement đŸ˜± #mscedc
— Cathy Hills (@fleurhills) March 26, 2017
Favourite tweets!
@Digeded Keyhole analytics of #mscedc showing sentiment, gender split etc. https://t.co/qMGag1zvcS pic.twitter.com/2M2gL4Eqj5
— Nigel Painting (@nigelchpainting) March 24, 2017
Studying different analytical tools allows us a better view of ‘the processes inherent to analysis itself‘ (Knox, 2015), allowing us to see what might be considered success under their terms.
Knox, J. (2014). Abstracting Learning Analytics. Code Acts in Education ESRC seminar series blog. http://codeactsineducation.wordpress.com/2014/09/26/abstracting-learning-analytics/
Favourite tweets!
Great #mscedc tutorial there, thanks everyone for your contribution. And thanks to @c4miller for bringing his green screen to class. pic.twitter.com/adHCTgOjFp
— James Lamb (@james858499) March 24, 2017
After some analysis, I consider this great picture to be one suitable representation of an interesting and useful tutorial!
Tweet! Tweetorial inkling on Thinglink
Haven't done my blog post yet, but quick thinglink on #mscedc tweetorial https://t.co/3x5nZzIAK9
— Cathy Hills (@fleurhills) March 23, 2017
Tweet! Tools for analysis
@ClareThomsonQUB @mhawksey #mscedc That's fantastic Clare and a great tool for analysis. Wld be interesting to compare to Twitter archive.
— Cathy Hills (@fleurhills) March 23, 2017
It is easy to see how different tools used to analyse data affect what is reported to us.
Tweet! Political geographies
'class and race' visualised as geog of academic achievement #mscedc https://t.co/BjzxP9ndYK
— Cathy Hills (@fleurhills) March 23, 2017
This map makes visible a geography of academic achievement with inherent class and race implications and shows educational achievement as both marking and constructing divisions in society.
Mapping the geography of academic attainment
Big data can be harnessed for good or wielded as unimpeachable fact to direct our action in pursuit of anothers’ gain. As Enyon states,
as a community we need to shape the agenda rather than simply respond to the one offered by others
(Enyon, 2013, p.238)
Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology. pp.237-240.
Tweet! Never a single view of anything
culture, comms and algos 'we r past the days whre thr can be one single picture that encaps national taste' https://t.co/n8eZFua6NN #mscedc
— Cathy Hills (@fleurhills) March 23, 2017
Learning analytics can add to our understanding of the conditions for learning when used in conjunction with human judgement and triangulated with information from other sources. More importantly, it is a measure of wider political and societal concerns such as the marketization of education led by Silicon Valley giant corporations.
Tweet! Wearing algorithms
@j_k_knox How could we use 'non-representational' data? We could wear it! https://t.co/tSWWXkg0BK #mscedc
— Cathy Hills (@fleurhills) March 23, 2017
Tweet! Absence and presence
@HerrSchwindenh_ #mscedc Thanks for posting this Dirk. I did notice Angela was missing!
— Cathy Hills (@fleurhills) March 23, 2017
What Learning Analytics prioritises may have little to do with what we consider important for learning.