{"id":43,"date":"2022-10-20T22:09:25","date_gmt":"2022-10-20T21:09:25","guid":{"rendered":"http:\/\/cde22.education.ed.ac.uk\/jhendry\/?p=43"},"modified":"2022-10-20T22:09:25","modified_gmt":"2022-10-20T21:09:25","slug":"screen-time-visualisation","status":"publish","type":"post","link":"http:\/\/cde22.education.ed.ac.uk\/jhendry\/2022\/10\/20\/screen-time-visualisation\/","title":{"rendered":"Screen time – visualisation"},"content":{"rendered":"\n

Having reflected on personalisation in a previous blog I wanted to consider the earlier stage of that process, data collection. To be able to have personalised information, such as targeted ads or tailored choices within a learning platform, there first must be data collected from the consumer to use as a comparator against other similar consumers so \u2018personalised\u2019 choices can be presented. I didn\u2019t have to look far for a piece of technology that I use frequent that could be the subject of this type of data collection exercise as my phone is never far from my side! My aim was to find out just how much I did in fact use it, so I enabled \u2018screen time\u2019 and carried on with life for a few days before plucking up the courage to see what it had to reveal to me.<\/p>\n\n\n\n

The usage stats were interesting in that they were reasonably consistent across the 3 days, so I took a note of the information, and disabled screen time!<\/p>\n\n\n\n

My previous visualisations have been illustrated in days, with the findings for each day being recorded by volume\/instances etc. For this task I wanted to think about the information a little differently, though some of this was done for me as the iPhone automatically categorises apps used into categories. I opted for a circle to represent each category with the content of the circle reflecting the app usage. I found the categories logical however there were other ways to divide up the data so I grouped them a little differently, according to my preferences.<\/p>\n\n\n\n

\"\"
Screen time visualisation <\/figcaption><\/figure>\n\n\n\n

Reflections on the screen time data<\/strong><\/p>\n\n\n\n

The first thing that struck me regarding the data is just how easy it was to find detailed information on what I had been viewing and interacting with, considering the other tech I interact with at home and work the volume of information felt considerably more than I had previously considered. Kitchin (2014) resonated here as he describes big data in terms of \u2018volume, velocity, variety, exhaustive in scope, whilst also relational and flexible\u2019. In a small way it made me appreciate how \u2018modelling\u2019 of an individual, essentially the datafication of the individual, could be done through analysing their data trends and behaviours.<\/p>\n\n\n\n

I was also reminded of the phrase Knox et al (2020) highlight, as attributed to Rushkoff (2010), as the social media \u2018user\u2019 I was also the \u2018product\u2019, and if I was in the role of a learner I could be a \u2018prized product\u2019 when considering learner analytics, as the data derived from the learner could be used to help improve algorithmic systems.<\/p>\n\n\n\n

This to me feels a step further from Biesta\u2019s \u2018learnification\u2019 (2013) as education has not just moved on to be learning from a tutor\/facilitator role, but the learner is not even the centre of the learning process anymore but is instead a facilitator themselves as they facilitate the learning analytics process which in turn feeds into the developing world of machine learning. The learner just happens to \u2018learn\u2019 along that journey whilst improving the journey for the learners coming behind them.<\/p>\n\n\n\n

Reflecting on the content of the visualisation, or the data that derived it, I reflected on my habits in a more visual form and considered areas I may wish to change. Habits however are not always easily changed which is perhaps there is room for \u2018nudging\u2019. However, as Ga\u0161evic (2020) recalls, nudging has not been significantly used in learning analytics as it is not well aligned to the set-up of the learning platform\/dashboard. So perhaps we may not see nudging being used to alter learner behaviours but perhaps is more successful in nudging people to better habits due to its incremental nature. Time will tell regarding that one for me and my social media habits \ud83d\ude09<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

Biesta, Gert. 2013. \u201cGiving Teaching Back to Education: Responding to the Disappearance of the Teacher.\u201d Phenomenology & Practice 6 (2): 35\u201349.<\/p>\n\n\n\n

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https:\/\/doi.org\/10.1177\/2053951714528481<\/a> Big Data, new epistemologies and paradigm shifts – Rob Kitchin, 2014 (sagepub.com)<\/a><\/p>\n\n\n\n

Jeremy Knox, Ben Williamson & Sian Bayne (2020) Machine behaviourism: future visions of \u2018learnification\u2019 and \u2018datafication\u2019 across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080\/17439884.2019.1623251<\/p>\n\n\n\n

Neil Selwyn & Dragan Ga\u0161evic (2020) The datafication of higher education: discussing the promises and problems, Teaching in Higher Education, 25:4, 527-540, DOI: 10.1080\/13562517.2019.1689388<\/p>\n","protected":false},"excerpt":{"rendered":"

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