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Abstract

Professional development in its most traditional form is a classroom setting with a lecturer and an overwhelming amount of information. It is no surprise, then, that informal professional development away from institutions and on the teacher's own terms is a growing phenomenon due to an increased presence of educators on social media. These communities of educators use hashtags to broadcast to each other, with general hashtags such as #edchat having the broadest audience. However, many math educators usethe hashtags #ITeachMath and #MTBoS, communities I was interested in learning more about. I built a python script that used Tweepy to connect to Twitter's API, using try/except blocks to catch HTTP status codes that Twitter occasionally passes through the API. When it was finally completed, a sample of such tweets was collected and then processed using python to determine polarity, objectivity, and word frequency, first as a group and then by choice of hashtag. Additional analysis included Latent Dirichlet Allocation and hierarchical clustering, and conversations between individuals were analyzed for topic and complexity to understand the extent of interactions. Furthermore, the user IDs of the individuals whose tweets were collected in the stream were run through another program to get all of their followers and create an edge list to be used for social network analysis. A social network graph (sociogram) of this edge list was madeusing Gephi. Between the social network analysis and the content analysis,the topics of the tweets gave an idea of what these teachers were talking about on Twitter. This information will be used to determine the extent of professional development (PD) that teachers do on Twitter simply by actively participating in such communities and ways to improve informal PD.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

DOI

https://doi.org/10.25172/jour.5.1.3

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