The Roles of the Twitter Hashtag #NGSSchat in the Context of Science Education Reform Efforts

# The Roles of the Twitter Hashtag #NGSSchat in the Context of Science Education Reform Efforts
### Joshua M Rosenberg, Joshua W Reid, Matthew J Koehler, Christian Fischer, and Thomas J. McKenna
### 2019-01-04

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background-image: url("/chat.png")
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# Background

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# The Next Generation Science Standards

- NGSS (or NGSS-inspired) standards in 39 states (National Science Teachers Association, 2018)
- Catalyzed by a series of reports (National Research Council, 2007, 2012)
- While reform efforts in science education are *not* new (DeBoer, 2014), the new reforms have some cause to be thought of as potentially different

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# Is this time different?

- More discussion between practitioners and researchers than before (i.e., RPPs; Coburn & Penuel, 2016)
  - Including in science education (Loucks-Horsely, Stiles, Mundry, Love, & Hewson, 2010; Luft & Hewson, 2014)
- Increased prominence of digital technologies in teacher professional development (e.g., Reiser et al., 2017)
- Some benefits of forming professional learning networks (PLNs; Trust, 2012)

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# Teacher learning

- Teacher learning can occur anywhere (Desimone & Garet, 2015), but involvement is often limited by barriers especially geographic ones
- One way to break out of these boundaries is to conceptualize professional learning more broadly and as *participation in online and communities and networks*
- Can lead to more heterogeneous learning communities (Luft & Hewson, 2014)

???

Should align with current science standards, practices, and content that teachers will encounter (i.e., coherency and content focus); afford teachers opportunities to actively and collaboratively construct appropriate requisite knowledge (i.e., active engagement and collaboration); and foster lifelong learning mindsets within teachers
Effective teacher professional development and learning is facilitated through interactions with other stakeholders that provide opportunities to foster discussions about research and practice (Borko, Jacobs, & Koellner, 2010; Darling-Hammond, Hyler, & Gardner, 2017; Luft & Hewson, 2014)

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# Teacher Learning Through Twitter (Part 1)

- Some PLNs include Twitter communities (Lord & Lomicka, 2014; Rehm & Notten, 2016; Wesely, 2013)
- Twitter's design infrastructure has several affordances that relate to effective professional teacher learning
- For educators, Twitter's informal, *just for me* approach to learning is in contrast to common *one-size fits all* perspective of traditional professional development

???
- such as for sharing work done in class with a wider audience in the context of graduate degree programs, (Greenhalgh, Rosenberg, & Wolf, 2016)
- discussing how to respond in the classroom to a National crisis after terrorism in Paris (Greenhalgh & Koehler, 2017)
- to engage in activism related to the condition of the educational system in Oklahoma (Krutka, Asino, & Haselwood, 2018)
- In considering the importance of PLNs for teachers’ continuing education, it is important to understand how a nationally active and prominent network such as the #NGSS hashtag may provide access and support to teachers

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![tweet-picture](/tweet-picture.png)

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# Teacher Learning Through Twitter (Part 2)

- Chats can be considered as distinct in terms of individuals are interacting (relative to other times of the day or week; Rosenberg et al., 2017)
- Past research has found that Twitter can be used for a myriad of purposes
- Some past research has focused on a convention designed to make the *firehose* of information more manageable, a focus on hashtags
- A hashtag is a convention on Twitter that is used to organize conversations, and, allows for regularly occurring (or synchronous) chats at pre-specified times (e.g., Rosenberg, Akcaoglu, Staudt Willet, Greenhalgh, & Koehler, 2017).

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# What is the role of #NGSSchat Regarding Teacher Learning Through Twitter?

- Twitter and #NGSSchat are *potentially* valuable to science educators 
  - May be one of the largest, and perhaps important, networks for science educators
  - Also may play a role in how the NGSS are implemented
- Can be a place for teachers to interact with other teachers and with other science education stakeholders
  - Important because doing so can shape how teachers engage in sensemaking (Coburn, 2001)

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# Need for study

- Unlike many formal opportunities and PLNs, this informal - yet growing and inclusive of a myriad of educators and researchers - context has not yet been the focus of any studies
  - One *NSTA Reports* article by Shelton and Ende (2015)
- Repeated calls for more empirical research on the effectiveness of online teacher education opportunities (Borko, Jacobs, & Koellner, 2010; Dede, Ketelhut, Whitehouse, Breit, & McCloskey, 2008)

???
Articulates learning as a process enacted through networks and communities, and these communities are enhanced through technological mediums (Siemens, 2005) such as #NGSSchat

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# Framing and Research Questions

- Within web-based communities like #NGSSchat, participants may be a part of a dynamic, cultural community (Gutiérrez and Rogoff)
- Use of connectivism (Siemens, 2013) and social network theory (Carolan, 2014)

1. Which groups of individuals have used #NGSSchat?
2. How frequent have interactions through the hashtag been?
3. Which groups of individuals have received and sent interactions?
4. Which groups of individuals are the most central?

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# Method

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# Research Approach

- Social Network Analysis (SNA)
   - Conventionally offline *and also* online and using digital traces (Spillane, Kim, & Frank, 2012)
   - Recently, scholars have begun to look at augmenting and conducting analyses with alternative sources of information, including archival data or data from digital traces of interactions (e.g., McFarland, Lewis, & Goldberg, 2015)
- In this latter tradition, we employed SNA to describe and examine #NGSSchat

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# Measures

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<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Measure </th>
   <th style="text-align:left;"> Description </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Actors </td>
   <td style="text-align:left;"> Represents the social unit of analysis in a social network (i.e., person); serves as a measure as a count for how many individuals are involved in the #NGSSchat network </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Edges </td>
   <td style="text-align:left;"> Represents the relationship between two actors; measures the number of interactions within the network </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Density </td>
   <td style="text-align:left;"> The proportion of the number of edges in a network to the possible number of edges and indicates a more </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Endorsement </td>
   <td style="text-align:left;"> A type of edge in the #NGSSchat network: A Twitter interaction that is either a favorite, retweet, or quote tweet </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Conversation </td>
   <td style="text-align:left;"> A type of an edge in the #NGSSchat network: A Twitter interaction which is either a mention or a reply </td>
  </tr>
  <tr>
   <td style="text-align:left;"> In-degree centrality </td>
   <td style="text-align:left;"> The number of edges received by an individual (being favorited, retweeted, quoted, replied to, or mentioned) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Out-degree centrality </td>
   <td style="text-align:left;"> The number of edges sent by an individual (favoriting, retweeting, quoting, replying to, or mentioning someone else) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Group membership </td>
   <td style="text-align:left;"> The category assigned to each individual, based upon qualitative coding of individual users’ Twitter profile information </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Number of tweets </td>
   <td style="text-align:left;"> The number of tweets an individual sent </td>
  </tr>
</tbody>
</table>

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# Data Sources

- Novel data source: Tweets that were archived in the #NGSSchat community between 2012 and 2017 on *Storify*
- Tweets come from "chats," the synchronous periods of time during which #NGSSchat users arranged to meet to discuss pre-arranged topics
- In total, we collected data from 103 chats

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# Sample

## Interactions

- 10,658 original tweets (1,926 quote tweets; 13,123 reply tweets) in data from Storify
- Data processed into a key social network analysis data type, an edge list
  - 34,668 favorites
  - 13,498 replies
  - 10,382 mentions
  - 8,912 retweets
  - 1,899 quotes
  - *a total of 69,359 interactions*
- Filtered the edge list only to contain participants who sent more than one original tweet
  - Final edge list consisted of 55,807 interactions

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# Sample

## Participants

- Edge list included information about who sent and received the interaction
- 787 individuals
- Focused in on the n = 517 unique Twitter user profiles who posted *more than one original tweet*
- On average, they sent 14.88 (SD = 50.47) original tweets

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# Data Analysis

- Analysis for RQ1
  - Qualtiatively code Twitter user profiles for their professional affiliation/group
- Analysis for RQ2
  - Visualize relations using social network analysis (using *igraph* and *ggraph* in **R**)
- Analysis for RQ3
  - Predict interactions received (in-degree) and sent (out-degree) for both conversing and endorsing
- Analysis for RQ4
  - Use of centrality measures by their professional affiliation/group

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# Results

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### 1. Which groups of individuals have used #NGSSchat?

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<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Code </th>
   <th style="text-align:left;"> Group </th>
   <th style="text-align:right;"> % </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Teacher </td>
   <td style="text-align:left;"> Teacher </td>
   <td style="text-align:right;"> 44.5 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Administrator </td>
   <td style="text-align:left;"> Support </td>
   <td style="text-align:right;"> 6.2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Instructional Support </td>
   <td style="text-align:left;"> Support </td>
   <td style="text-align:right;"> 12.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Educational Researcher or University Faculty </td>
   <td style="text-align:left;"> Research </td>
   <td style="text-align:right;"> 6.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Educational Institution </td>
   <td style="text-align:left;"> Research </td>
   <td style="text-align:right;"> 0.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Educational Organization </td>
   <td style="text-align:left;"> Research </td>
   <td style="text-align:right;"> 6.4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Education-Connected </td>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 10.1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Media </td>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 1.0 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Hashtag / Chat Accounts </td>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 2.3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Not Clear </td>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 9.3 </td>
  </tr>
</tbody>
</table>

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### 2. How frequent have interactions through the hashtag been?

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background-image: url("/endorsing-ngsschat-soc.png")
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background-image: url("/conversing-ngsschat-soc.png")
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class: center, middle

### 3. Which groups of individuals have received and sent interactions?

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#### Regression models

<table>
 <thead>
  <tr>
   <th style="text-align:left;">  </th>
   <th style="text-align:left;"> Conversing In-degree </th>
   <th style="text-align:left;"> Conversing Out-Degree </th>
   <th style="text-align:left;"> Endorsing In-degree </th>
   <th style="text-align:left;"> Endorsing Out-degree </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Intercept </td>
   <td style="text-align:left;"> 2.18 (0.14)*** </td>
   <td style="text-align:left;"> 2.09 (0.13)*** </td>
   <td style="text-align:left;"> 2.78 (0.12)*** </td>
   <td style="text-align:left;"> 2.88 (0.13)*** </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Administrator </td>
   <td style="text-align:left;"> 0.17 (0.19) </td>
   <td style="text-align:left;"> 0.29 (0.17) </td>
   <td style="text-align:left;"> 0.25 (0.17) </td>
   <td style="text-align:left;"> 0.24 (0.17) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Researcher </td>
   <td style="text-align:left;"> 0.43 (0.19)* </td>
   <td style="text-align:left;"> 0.34 (0.18) </td>
   <td style="text-align:left;"> 0.37 (0.17)* </td>
   <td style="text-align:left;"> 0.17 (0.19) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Teacher </td>
   <td style="text-align:left;"> 0.28 (0.16) </td>
   <td style="text-align:left;"> 0.29 (0.15)* </td>
   <td style="text-align:left;"> 0.28 (0.14) </td>
   <td style="text-align:left;"> 0.24 (0.15) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Original Tweets </td>
   <td style="text-align:left;"> 0.29 (0.01)*** </td>
   <td style="text-align:left;"> 0.26 (0.01)*** </td>
   <td style="text-align:left;"> 0.24 (0.01)*** </td>
   <td style="text-align:left;"> 0.22 (0.02)*** </td>
  </tr>
</tbody>
</table>

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### 4. Which groups of individuals are the most central?

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#### Endorsing (favoriting, retweeting, or quoting)

<table>
 <thead>
  <tr>
   <th style="text-align:left;">  </th>
   <th style="text-align:left;"> Betweenness </th>
   <th style="text-align:left;"> Centrality </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Administrator </td>
   <td style="text-align:left;"> 1217.82 (6134.22) </td>
   <td style="text-align:left;"> 0.06 (0.14) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Researcher </td>
   <td style="text-align:left;"> 625.81 (2277.89) </td>
   <td style="text-align:left;"> 0.05 (0.12) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Teacher </td>
   <td style="text-align:left;"> 510.48 (3723.25) </td>
   <td style="text-align:left;"> 0.03 (0.09) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:left;"> 124.63 (395.26) </td>
   <td style="text-align:left;"> 0.02 (0.04) </td>
  </tr>
</tbody>
</table>

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#### Conversing (mentioning or replying)

<table>
 <thead>
  <tr>
   <th style="text-align:left;">  </th>
   <th style="text-align:left;"> Betweenness </th>
   <th style="text-align:left;"> Centrality </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Administrator </td>
   <td style="text-align:left;"> 1391.2 (6560.08) </td>
   <td style="text-align:left;"> 0.07 (0.15) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Researcher </td>
   <td style="text-align:left;"> 732.88 (2453.52) </td>
   <td style="text-align:left;"> 0.05 (0.13) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Teacher </td>
   <td style="text-align:left;"> 531.62 (3805.09) </td>
   <td style="text-align:left;"> 0.04 (0.09) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:left;"> 142.33 (431.96) </td>
   <td style="text-align:left;"> 0.02 (0.04) </td>
  </tr>
</tbody>
</table>

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# Discussion

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# Key Findings

- NGSSchat is used by a wide array of individuals
- Many interactions between many individuals (from different groups)
  - Based upon descriptions of *other* PLNs (e.g., Trust, 2012), this seems like a potential candidate
- Has characterstics in-line with other communities, such as state-based networks (Rosenberg et al., 2016)

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# Key Findings

- Researchers are popular in terms of being the recipient of conversing and endorsing ties
- Teachers, on the other hand, were more likely to send conversing ties
- In all, individuals—and teachers in particular—who participate in the hashtag can expect to be popular

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# Limitations

- Use of a large data set from a social media we source 
- Data from over five years, with more than 500 individuals and 60,000 observed interactions
- But, this is also, in other ways, a very limited data set
- Partial example of the new research methods that can be termed *computational social science* (Salganik, 2017; Lazer et al., 2009)
- There are also other ways we could use the data we have (DeLaat & Schreurs, 2013)

???

- While we have rich information about the interactions we observed, these were four specific interactions made possible through Twitter - and what we can say about relations is necessarily limited. 
- Such methods have many strengths, such as being able to study groups of individuals who may not otherwise be studied or to do so in ways that would not be feasible, such as examining interactions from such a diverse group of individuals—from beginning teachers to superintendents—as in the present study
- Nevertheless, questions such as how representative our sample was, and how much we can know about participants beyond their profession/role, limit the present study. They also suggest future directions that involve collecting survey data from participants (separately or perhaps in conjunction with data from social media). 
	Another limitation of this study comes out of the assumptions that are made when using social network analysis (Wasserman & Faust, 1994)
  - Described three ways of studying learning in networks: surveying about relations (i.e., social network analysis), content analysis of conversations that are occurring, and understanding the reasons behind the specific behaviors and conversations observed in the network (i.e., contextual analysis)

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# Future Directions: Selection

- Interested in who chooses to interact with who
  - Are teachers more likely to interact with researchers (and vice vera)
- Selection models may be helpful:
  - A *p2* model (Van Duijn, Snijders, & Zijlstra, 2004)
  - Exponential random graph model (Hunter, Handcock, Butts, Goodreau, & Morris, 2008)

# Future Directions: Topics Discussed

- Future work can explore topics discussed in the networks
  - Preliminary analyses suggest that topic discussed ranged from those common to conversations about science teaching and learning as well as those related to the NGSS
- Can analyze what is discussed when different groups interact (e.g., Miranda, Kim, and Summers, 2015).

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# Implications for practice

- Usefulness of Twitter for fostering relationships that center on current science education ideas
- Social media can be important to science educators concerning their professional development and learning
- Can be important to teacher educators, too
- This type of communication aligns with recommendations for teacher professional development, such as, aligning to national and state standards (NGSS), focusing on content (science), and supporting a collaborative learning environment (Desimone & Garet, 2001)
- One promise of social media is that it helps to connect individuals who may not be otherwise (Gunawardena, Hermans, Sanchez, Richmond, Bohley, & Tuttle, 2009)

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# Thank you

Website: [https://joshuamrosenberg.com](https://joshuamrosenberg.com)
Email: [jmrosenberg@utk.edu](mailto:jmrosenberg@utk.edu)
Twitter: [@jrosenberg6432](https://twitter.com/jrosenberg6432)
Paper and data: [https://osf.io/9ex7k/](https://osf.io/9ex7k/)

*I welcome your questions and feedback on this work!*