First Social Media analysis of "Teaching by Twitter" with @OUCisco & @CiscoNetAcad ...

I have been collating analytic data from twitter and Facebook, regarding the impact of the +Open University Cisco social media 'teaching by twitter' endeavour. The results from the last fourteen months are interesting.

The raw data can be seen as ...

MonthTweetsimpressionsvisitsmentions
1 (Sept 14)36725117128
21121370015434
3109240009429
4851140020523
51561420037645
6147155009740
71181540039432
81251340043147
9861610049026
10721390056348
1110834700768101
12449611845
1317894735413
14 (Oct 15)701220069139

... Oct 15 is still incomplete as we have another ten twitter days to go. However it does show a trend that reflects assumed norms of an academic year.

Firstly lets look at the impressions data:
Impressions: having sight of the outputs from the feed
If you follow the fact that teaching commences early October with a lead in around the model of September. We see activity at the start - a healthy participation during the year and spike towards the end months of May to July when our students revise for, take exams and discuss Cisco certification.

Why impressions interest me is that participants can see the output without clicking on the output - where the micro-teaching that has been created has been viewed 347,200 times during this fourteen month period. I am sure there are many better social media streams, but from an academic perspective you cannot buy this kind of impact.

Now, lets review the tweets, visits and mentions data:
Analysis of Tweets, Visits (clicks) and mentions
What is interesting is that visits != impressions, tweets also do not always equate to impressions - getting your message out there is all that is needed. In my view, it simply requires presence. Getting the balance right - too many tweets may disengage your audiences. Too few, never engages anyone - once you work on a regular output which peaks some days at around six messages. You have a happy medium.

I push the same data out to multiple platforms at exactly the same time - the Facebook analytic data is very helpful. Providing a potential correlation, the next dataset provides impressions over a typical twenty four hour period during the last seven days:

24 Analysis of Facebook visits
Social media is a 24x7 experience, I am aware that Open University students exhibit unusual study patterns. I am also aware that we attract others from external interests as the topic covered is internationally significant. I often push an output AM, occasionally lunchtime and always PM. The data tells me that 0700 to 22:00 is optimal - with 18:00 being the best time. As most of our students are in the UK - this is an academic "no s**t Sherlock" experience, yet it is nice to see the data prove this.

I will continue to dig, explore, understand and do more with the data - now that I have an annual view there is more to understand and of course evaluate.







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