Note: This work is at an early stage; conference presentations for NARST and ASTE are currently under review. We welcome your feedback on any and all parts of this work.

Background of the study

Social media is changing our understanding of whether, how and why changes in science teaching and learning happen.
Social media can assist with supporting changes (i.e., recent science education reform efforts) by allowing various stakeholders to interact outside of traditional educational learning environments
Although research has shown the importance of social media for supporting reform efforts in science education (Shelton & Ende, 2015), there are questions that remain to be answered: how individuals use social media to discuss science education reform related topics, who is using social media to discuss these topics, how are they are interacting, and what is being discussed.

Context of the study

In this study, we explored answers to these questions in the context of twitter conversations using the hashtag #NGSSchat. A hashtag is a convention on Twitter that is used to organize conversations, and, allowing for regularly occurring (“synchronous”) chats at pre-specified times
While research on the educational uses of Twitter, and specifically Twitter hashtags, is new, there is a growing literature base on their uses (see Carpenter & Krutka, 2014, for a review).
However, very little research has been carried out in science education contexts, particularly on topics related to the NGSS.
RQ (for this presentation): What patterns of interactions between #NGSSchat participants are present?

Method

The study included all tweets that were archived in the #NGSSchat community between 2012 and 2017.
This resulted in 25,720 individual tweets, each of which were categorized into one of 103 chats via Storify1.
We used Storify because the #NGSSchat community chose to self-archive all of the tweets associated with the chats.
Tweet IDs and additional tweet data was collected via Storify and the Twitter Application Programming Interface (API).
Next, relational data was analyzed via sociograms or social network graphs using the R (R Core Team, 2018) software and the igraph (Csardi & Nepusz, 2006) and ggraph (Pederson, 2018) packages.
To then model the interactions, we used the lme4 package (Bates, Machler, & Bolker, 2015) and/or the ergm package (Hunter, Hancock, & Butts, 2008) to estimate an exponential random graph model (ERGM) suitable for exploring who interacts with whom.
To streamline the modeling and to aid in the interpretation, we chose to combine two of the types of interactions into two more general categories: In particular, we combined replying and mentioning into a category for “Conversing,” and retweeting or favoriting into one for “Endorsing.”

Loading, setting up

Conversing (mentioning and replying) data

#> # A tibble: 238,144 x 10
#>    sender receiver weight sender_profile receiver_profile sender_n_tweets
#>    <chr>  <chr>     <dbl> <fct>          <fct>                      <int>
#>  1 21sts… bravese…      1 Administration Teacher                        6
#>  2 21sts… brunsell      3 Administration Other                          6
#>  3 21sts… cduke62       0 Administration Unclear                        6
#>  4 21sts… fredende      6 Administration Administration                 6
#>  5 21sts… jasperf…      1 Administration Other                          6
#>  6 21sts… jenarns…      1 Administration Administration                 6
#>  7 21sts… krscien…      0 Administration Administration                 6
#>  8 21sts… lpugh3        0 Administration Administration                 6
#>  9 21sts… reiserb…      1 Administration Policy/Research                6
#> 10 21sts… tdishel…      0 Administration Teacher                        6
#> # ... with 238,134 more rows, and 4 more variables:
#> #   receiver_n_tweets <int>, sum_var <chr>, tie <dbl>, weight_l <dbl>

Endorsing (retweeting and favoriting/liking)

#> # A tibble: 247,009 x 9
#>    sender receiver weight sender_profile receiver_profile sender_n_tweets
#>    <chr>  <chr>     <dbl> <fct>          <fct>                      <int>
#>  1 21sts… amykfmu…      0 Administration Teacher                        6
#>  2 21sts… dtcampbe      0 Administration Policy/Research                6
#>  3 21sts… fredende      0 Administration Administration                 6
#>  4 21sts… karalu79      0 Administration Teacher                        6
#>  5 21sts… kastidh…      0 Administration Teacher                        6
#>  6 21sts… reiserb…      0 Administration Policy/Research                6
#>  7 21sts… starrsc…      0 Administration Administration                 6
#>  8 21sts… tdishel…      0 Administration Teacher                        6
#>  9 21sts… 8blah8b…      0 Administration Teacher                        6
#> 10 21sts… alliebb…      0 Administration Administration                 6
#> # ... with 246,999 more rows, and 3 more variables:
#> #   receiver_n_tweets <int>, sum_var <chr>, tie <dbl>

0. Null P2 models predicting tie

0A. Conversing

There appear to be moderate-large sender and receiver effects (in terms of random effects):

#> 
#> Generalized linear mixed model
#>  Family: binomial (logit)
#> Formula: tie ~ 1 + (1 | sender) + (1 | receiver)
#> 
#>     ICC (sender): 0.255228
#>   ICC (receiver): 0.386557

0B. Endorsing

There appear to be small sender and receiver effects for endorsing, too (in terms of random effects):

#> 
#> Generalized linear mixed model
#>  Family: binomial (logit)
#> Formula: tie ~ 1 + (1 | sender) + (1 | receiver)
#> 
#>     ICC (sender): 0.333777
#>   ICC (receiver): 0.290476

1. Initially built P2 models predicting tie

Sender and receive random effects; sender and receiver n-tweets; sender and receive profile

1A. Conversing

m1c <-  glmer(tie ~ 1 +
                I(sender_n_tweets/10) + I(receiver_n_tweets/10) +
                sender_profile*receiver_profile +
                (1|sender) + (1|receiver), 
              family = "binomial",
              control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)), 
              data = tmc)
saveRDS(m1c, "model-output/m1c.rds")

1B. Endorsing

m1e <-  glmer(tie ~ 1 +
                I(sender_n_tweets/10) + I(receiver_n_tweets/10) +
                sender_profile*receiver_profile +
                (1|sender) + (1|receiver), 
              family = "binomial",
              control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)), 
              data = tme)

saveRDS(m1e, "model-output/m1e.rds")

2. ERGMs with full dataset

2A. Conversing

#> 
#> ==========================
#> Summary of model fit
#> ==========================
#> 
#> Formula:   gg ~ edges + mutual + nodecov("n_tweets") + nodemix("profile_code", 
#>     base = 25)
#> 
#> Iterations:  7 out of 30 
#> 
#> Monte Carlo MLE Results:
#>                                                   Estimate Std. Error
#> edges                                            -5.180056   0.233595
#> mutual                                            3.985294   0.056652
#> nodecov.n_tweets                                  0.316842   0.004578
#> mix.profile_code.Administration.Administration    0.881836   0.239400
#> mix.profile_code.Other.Administration             0.927815   0.256076
#> mix.profile_code.Policy/Research.Administration   1.083574   0.249292
#> mix.profile_code.Teacher.Administration           0.746865   0.241910
#> mix.profile_code.Unclear.Administration           0.735041   0.264143
#> mix.profile_code.Administration.Other             0.489695   0.263146
#> mix.profile_code.Other.Other                      0.439478   0.278216
#> mix.profile_code.Policy/Research.Other            1.028006   0.259063
#> mix.profile_code.Teacher.Other                    0.412104   0.247085
#> mix.profile_code.Unclear.Other                    0.138546   0.319491
#> mix.profile_code.Administration.Policy/Research   0.415201   0.263286
#> mix.profile_code.Other.Policy/Research            0.362689   0.263793
#> mix.profile_code.Policy/Research.Policy/Research  0.749510   0.251597
#> mix.profile_code.Teacher.Policy/Research          0.216281   0.247161
#> mix.profile_code.Unclear.Policy/Research          0.379460   0.286361
#> mix.profile_code.Administration.Teacher           0.635578   0.240539
#> mix.profile_code.Other.Teacher                    0.686185   0.242986
#> mix.profile_code.Policy/Research.Teacher          0.731733   0.244379
#> mix.profile_code.Teacher.Teacher                  0.623727   0.232525
#> mix.profile_code.Unclear.Teacher                  0.591886   0.247053
#> mix.profile_code.Administration.Unclear          -0.504453   0.313841
#> mix.profile_code.Other.Unclear                    0.117043   0.315637
#> mix.profile_code.Policy/Research.Unclear          0.063245   0.297451
#> mix.profile_code.Teacher.Unclear                  0.080134   0.258251
#>                                                  MCMC % z value Pr(>|z|)
#> edges                                                 0 -22.175  < 1e-04
#> mutual                                                0  70.347  < 1e-04
#> nodecov.n_tweets                                      1  69.204  < 1e-04
#> mix.profile_code.Administration.Administration        0   3.684 0.000230
#> mix.profile_code.Other.Administration                 0   3.623 0.000291
#> mix.profile_code.Policy/Research.Administration       0   4.347  < 1e-04
#> mix.profile_code.Teacher.Administration               0   3.087 0.002019
#> mix.profile_code.Unclear.Administration               0   2.783 0.005390
#> mix.profile_code.Administration.Other                 0   1.861 0.062755
#> mix.profile_code.Other.Other                          0   1.580 0.114193
#> mix.profile_code.Policy/Research.Other                0   3.968  < 1e-04
#> mix.profile_code.Teacher.Other                        0   1.668 0.095343
#> mix.profile_code.Unclear.Other                        0   0.434 0.664545
#> mix.profile_code.Administration.Policy/Research       0   1.577 0.114797
#> mix.profile_code.Other.Policy/Research                0   1.375 0.169163
#> mix.profile_code.Policy/Research.Policy/Research      0   2.979 0.002892
#> mix.profile_code.Teacher.Policy/Research              0   0.875 0.381541
#> mix.profile_code.Unclear.Policy/Research              0   1.325 0.185134
#> mix.profile_code.Administration.Teacher               0   2.642 0.008234
#> mix.profile_code.Other.Teacher                        0   2.824 0.004743
#> mix.profile_code.Policy/Research.Teacher              0   2.994 0.002751
#> mix.profile_code.Teacher.Teacher                      0   2.682 0.007309
#> mix.profile_code.Unclear.Teacher                      0   2.396 0.016585
#> mix.profile_code.Administration.Unclear               0  -1.607 0.107977
#> mix.profile_code.Other.Unclear                        0   0.371 0.710776
#> mix.profile_code.Policy/Research.Unclear              0   0.213 0.831621
#> mix.profile_code.Teacher.Unclear                      0   0.310 0.756336
#>                                                     
#> edges                                            ***
#> mutual                                           ***
#> nodecov.n_tweets                                 ***
#> mix.profile_code.Administration.Administration   ***
#> mix.profile_code.Other.Administration            ***
#> mix.profile_code.Policy/Research.Administration  ***
#> mix.profile_code.Teacher.Administration          ** 
#> mix.profile_code.Unclear.Administration          ** 
#> mix.profile_code.Administration.Other            .  
#> mix.profile_code.Other.Other                        
#> mix.profile_code.Policy/Research.Other           ***
#> mix.profile_code.Teacher.Other                   .  
#> mix.profile_code.Unclear.Other                      
#> mix.profile_code.Administration.Policy/Research     
#> mix.profile_code.Other.Policy/Research              
#> mix.profile_code.Policy/Research.Policy/Research ** 
#> mix.profile_code.Teacher.Policy/Research            
#> mix.profile_code.Unclear.Policy/Research            
#> mix.profile_code.Administration.Teacher          ** 
#> mix.profile_code.Other.Teacher                   ** 
#> mix.profile_code.Policy/Research.Teacher         ** 
#> mix.profile_code.Teacher.Teacher                 ** 
#> mix.profile_code.Unclear.Teacher                 *  
#> mix.profile_code.Administration.Unclear             
#> mix.profile_code.Other.Unclear                      
#> mix.profile_code.Policy/Research.Unclear            
#> mix.profile_code.Teacher.Unclear                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#>      Null Deviance: 329461  on 237656  degrees of freedom
#>  Residual Deviance:  35741  on 237629  degrees of freedom
#>  
#> AIC: 35795    BIC: 36075    (Smaller is better.)

2B. Endorsing

#> 
#> ==========================
#> Summary of model fit
#> ==========================
#> 
#> Formula:   gg ~ edges + mutual + nodecov("n_tweets") + nodemix("profile_code", 
#>     base = 25)
#> 
#> Iterations:  5 out of 30 
#> 
#> Monte Carlo MLE Results:
#>                                                   Estimate Std. Error
#> edges                                            -4.304840   0.155870
#> mutual                                            2.755104   0.041665
#> nodecov.n_tweets                                  0.298806   0.005544
#> mix.profile_code.Administration.Administration    0.800308   0.167877
#> mix.profile_code.Other.Administration             0.658639   0.169519
#> mix.profile_code.Policy/Research.Administration   0.695980   0.171350
#> mix.profile_code.Teacher.Administration           0.758270   0.161688
#> mix.profile_code.Unclear.Administration           0.342926   0.186515
#> mix.profile_code.Administration.Other             0.755279   0.171655
#> mix.profile_code.Other.Other                      0.456908   0.181746
#> mix.profile_code.Policy/Research.Other            0.739460   0.179328
#> mix.profile_code.Teacher.Other                    0.668976   0.163771
#> mix.profile_code.Unclear.Other                    0.277946   0.203963
#> mix.profile_code.Administration.Policy/Research   0.797051   0.167642
#> mix.profile_code.Other.Policy/Research            0.659795   0.171534
#> mix.profile_code.Policy/Research.Policy/Research  0.735273   0.168435
#> mix.profile_code.Teacher.Policy/Research          0.762931   0.163539
#> mix.profile_code.Unclear.Policy/Research          0.466388   0.190655
#> mix.profile_code.Administration.Teacher           0.611808   0.162195
#> mix.profile_code.Other.Teacher                    0.571671   0.161260
#> mix.profile_code.Policy/Research.Teacher          0.337815   0.162239
#> mix.profile_code.Teacher.Teacher                  0.652635   0.157122
#> mix.profile_code.Unclear.Teacher                  0.413473   0.166239
#> mix.profile_code.Administration.Unclear           0.109275   0.194849
#> mix.profile_code.Other.Unclear                    0.312056   0.198019
#> mix.profile_code.Policy/Research.Unclear          0.102418   0.197712
#> mix.profile_code.Teacher.Unclear                  0.269391   0.172532
#>                                                  MCMC % z value Pr(>|z|)
#> edges                                                 0 -27.618  < 1e-04
#> mutual                                                0  66.125  < 1e-04
#> nodecov.n_tweets                                      0  53.894  < 1e-04
#> mix.profile_code.Administration.Administration        0   4.767  < 1e-04
#> mix.profile_code.Other.Administration                 0   3.885 0.000102
#> mix.profile_code.Policy/Research.Administration       0   4.062  < 1e-04
#> mix.profile_code.Teacher.Administration               0   4.690  < 1e-04
#> mix.profile_code.Unclear.Administration               0   1.839 0.065975
#> mix.profile_code.Administration.Other                 0   4.400  < 1e-04
#> mix.profile_code.Other.Other                          0   2.514 0.011937
#> mix.profile_code.Policy/Research.Other                0   4.124  < 1e-04
#> mix.profile_code.Teacher.Other                        0   4.085  < 1e-04
#> mix.profile_code.Unclear.Other                        0   1.363 0.172970
#> mix.profile_code.Administration.Policy/Research       0   4.754  < 1e-04
#> mix.profile_code.Other.Policy/Research                0   3.846 0.000120
#> mix.profile_code.Policy/Research.Policy/Research      0   4.365  < 1e-04
#> mix.profile_code.Teacher.Policy/Research              0   4.665  < 1e-04
#> mix.profile_code.Unclear.Policy/Research              0   2.446 0.014435
#> mix.profile_code.Administration.Teacher               0   3.772 0.000162
#> mix.profile_code.Other.Teacher                        0   3.545 0.000393
#> mix.profile_code.Policy/Research.Teacher              0   2.082 0.037324
#> mix.profile_code.Teacher.Teacher                      0   4.154  < 1e-04
#> mix.profile_code.Unclear.Teacher                      0   2.487 0.012874
#> mix.profile_code.Administration.Unclear               0   0.561 0.574921
#> mix.profile_code.Other.Unclear                        0   1.576 0.115051
#> mix.profile_code.Policy/Research.Unclear              0   0.518 0.604446
#> mix.profile_code.Teacher.Unclear                      0   1.561 0.118430
#>                                                     
#> edges                                            ***
#> mutual                                           ***
#> nodecov.n_tweets                                 ***
#> mix.profile_code.Administration.Administration   ***
#> mix.profile_code.Other.Administration            ***
#> mix.profile_code.Policy/Research.Administration  ***
#> mix.profile_code.Teacher.Administration          ***
#> mix.profile_code.Unclear.Administration          .  
#> mix.profile_code.Administration.Other            ***
#> mix.profile_code.Other.Other                     *  
#> mix.profile_code.Policy/Research.Other           ***
#> mix.profile_code.Teacher.Other                   ***
#> mix.profile_code.Unclear.Other                      
#> mix.profile_code.Administration.Policy/Research  ***
#> mix.profile_code.Other.Policy/Research           ***
#> mix.profile_code.Policy/Research.Policy/Research ***
#> mix.profile_code.Teacher.Policy/Research         ***
#> mix.profile_code.Unclear.Policy/Research         *  
#> mix.profile_code.Administration.Teacher          ***
#> mix.profile_code.Other.Teacher                   ***
#> mix.profile_code.Policy/Research.Teacher         *  
#> mix.profile_code.Teacher.Teacher                 ***
#> mix.profile_code.Unclear.Teacher                 *  
#> mix.profile_code.Administration.Unclear             
#> mix.profile_code.Other.Unclear                      
#> mix.profile_code.Policy/Research.Unclear            
#> mix.profile_code.Teacher.Unclear                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#>      Null Deviance: 341738  on 246512  degrees of freedom
#>  Residual Deviance:  67824  on 246485  degrees of freedom
#>  
#> AIC: 67878    BIC: 68159    (Smaller is better.)

#> Joining, by = "term"

An Exploration of #NGSSchat Through Selection Models

Joshua M. Rosenberg, Joshua W. Reid, Matthew J. Koehler, Christian Fischer, Thomas J. McKenna

9/9/2018

Table of contents: