Affinity spaces refer to digital and geographical spaces in which learning can happen, and some of the largest affinity spaces dedicated to education may be those that currently exist on Twitter.
These spaces are public, largely unmoderated, and thriving, yet very little is known about participants and their patterns of activity.
This paper seeks to shed some light on state-based educational affinity spaces on Twitter by presenting descriptive data about the participants and their patterns of activity.
We identified 47 SETHs using collected lists of education hashtags based in the United States (from Blumengarten, Hamilton, McMurray, Evans, and Rochelle, Junkins, and Mazza.
In cases where a state was associated with multiple SETHs, we chose to focus on the single that appeared to be the most active in terms of the number of recent tweets.
We used the Twitter archiver TAGS to collect these data and the programming language and statistical software R to analyze the data.
We examined how different measures about participants and their activities to begin to describe participants and their patterns of engagement:
Unique Participants - The number of participants per SETH throughout the six months for which data were collected.
Participants per Teacher - The number of participants per SETH throughout the six months for which data were collected divided by the number of teachers in the associated state as determined by the 2014 State Nonfiscal Public Elementary/Secondary Education Survey.
Number of Tweets - The number of tweets per SETH throughout the six months for which data were collected.
Active Weeks - The mean number of weeks for which each user sent at least one tweet throughout the six months for which data were collected.
We used a participant category measure, which describes the role (e.g., teacher, administrator, unknown) that a SETH participant plays in the educational community to better understand the characteristics of participants the given steps:
Two raters collected 100 randomly sampled participant profiles and used the resulting data to develop a coding frame for categorizing participants along mutually exclusive roles, such as “Teacher” or “Administrator.”
This resulted in 10 different categories (or possible codes for this measure)
To apply the participant category measure, the raters first coded a small sample of participant profiles to establish the reliability of the frame, and then one rater coded a random sample of 450 participant profiles.
SETHs are sufficiently large and distinct to be considered an established and important educational phenomenon and have some characteristics of affinity spaces as digital space organized around content through which people interact.
What are the characteristics of participants?
How are these SETHs and other educational hashtags organized?
Do interactions differ across synchronous chats and asynchronous interactions with SETHs?
This project is the work of Joshua M. Rosenberg, Matthew J. Koehler, Mete Akcaoglu, Spencer P. Greenhalgh, and Erica Hamilton for the Society for Information Technology and Teacher Education Society for Information Technology and Teacher Education (SITE) 2016 Conference in Savannah, Georgia. The SITE conference proceedings paper associated with this project can be found here at Academic Experts.
The citation for the journal article associated with this project is:
Rosenberg, J. M., Greenhalgh, S. P., Koehler, M. J., Hamilton, E., & Akcaoglu, M. (2016). An investigation of State Educational Twitter Hashtags (SETHs) as affinity spaces. E-Learning and Digital Media. http://dx.doi.org/10.1177/2042753016672351