Highlights from OERS16

The 11th annual Ontario Education Research Symposium was held from February 9th to February 11th with the theme “Networking & Partnerships: The Core of Achieving Excellence in Education. Over 500 people from networks, organizations and stakeholders across the Education sector participated in the event which featured:

  • 27 speakers,
  • 18 workshops,
  • 6 Mobilizing sessions,
  • 4 Provocative Speaker sessions and
  • 1 Fireside chat
  • 1 Spectacular student jazz band
  • Students as symposium attendees

Throughout the conference, participants were active on Twitter using #OERS16. As with previous years, I have compiled the tweets over the course of the Symposium using ‘s  TAGS 6.0 utility (click here to access the tweet archive).

In an attempt to make the tweets more useful and accessible, this year I used R to extract links that were shared (click here for more information on the process) and then created a series of pdf resources that compile the shared links:

Tweeting Trivia

At the time of this summary there were 2,031 tweets from 298 different people. Using the interactive viewer with TAGS 6.0, we can see what this kind of networking looks like:


As you can see, the majority of tweets are isolated with only a few key people connecting and interacting through Twitter (largest names with the most connecting lines), though that isn’t to say that twitter hasn’t facilitated face-to-face interactions.

Over the course of the first day of the conference, I took a series of snapshots of a sentiment analysis (using an online utility “Sentiment Viz: Tweet Sentiment Visualization” developed at NC State University).  I created an animated gif to see how sentiment changed at five points in the day (morning, morning break, lunch, afternoon break, evening):

OERS animated sentiment

The left half of the oval (blue dots) represent tweets with “unpleasant” terms and the right half of the oval (green dots) represent tweets with “pleasant” terms.  Dots closer to the top of the oval represent tweets with “active” terms and dots closer to the bottom of the oval represent tweets with “subdued” terms.

Throughout the entire day, the tweets were very positive and those that are more negative on the sentiment analysis were tweets relaying the challenges that many of the speakers were addressing through their work.  (Note: in the interactive version you can highlight a dot and see the underlying tweet with the terms highlighted that have been coded as part of the sentiment analysis)

Top Tweeters

This year, the top 10 tweeters from #OERS16 were:

  • @DrKatinaPollock     (182)
  • @CarolCampbell4      (157)
  • @avanbarn                   (151)
  • @ResearchChat          (109)
  • @naturallycaren         (101)
  • @Jan__Murphy          (98)
  • @GregRousell               (75)
  • @KNAER_RECRAE   (66)
  • @OISENews                 (50)
  • @HeidiSiwak                (41)

However, of those 2,031 tweets, 47% were retweets (tweets that begin with RT) leaving 1,102 original tweets. Considered from the perspective of original tweets vs. retweets, the top tweeters begin to look very different:


This adjustment highlights two different but important approaches to the use of social media.  On the one hand, @avanbarn’s generation of so much “original content” is an example of using social media for note-taking (paraphrasing presenters, highlighting speaking points, sharing links to referenced material, sharing reflections and questions inspired by a presenter).  On the other hand, @DrKatinaPollock and @CarolCampbell4’s high level of retweets are examples of cross-network dissemination. As these two approaches work in tandem, the key messages of the symposium presenters reach far beyond the room of attendees and broadens opportunities for discussion and additional inquiry.

Adjusted for the percentage of original tweets, the top ten tweeters now becomes:

  • @avanbarn                   147 (97% )
  • @ResearchChat           106 (97%)
  • @GregRousell                63 (84%)
  • @KNAER_RECRAE     48 (73%)
  • @Jan__Murphy            65 (66%)
  • @HeidiSiwak                  25 (61%)
  • @naturallycaren            61 (60%)
  • @DrKatinaPollock        65 (36%)
  • @CarolCampbell4        54 (34%)
  • @OISENews                    11 (22%)

@GregRousell has also been archiving tweets from #OERS16 using the R package twitteR. In an upcoming post on the Data User Group, we will be sharing a detailed overview for each of our approaches and share the benefits and challenges of each approach.

This entry was posted in Data Visualization, Twitter and tagged , . Bookmark the permalink.

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