Journey for crafting excellence: West Texas A&M University teacher educator standards development and implementation of standards for rural educators

Published

October 1, 2024

The website contains outputs and code used to analyze numeric, ordinal, and text data in Garcia et al. (in press). Reproducible materials are also posted at the Center for Open Science.

Garcia, B., Shin, M., Clifton, A., Bingham, T., Coneway, B., & Hughes, C. (in press). Journey for crafting excellence: West Texas A&M University teacher educator standards development and implementation of standards for rural educators. Texas Forum of Teacher Education.

Code

demo_tbl_ft

Characteristic	N = 291
teaching_years
0-5 years	8 (28%)
10-20 years	4 (14%)
20-30 years	7 (24%)
5-10 years	6 (21%)
Over 30 years	4 (14%)
gender
Female	20 (69%)
Male	9 (31%)
age
21-40	3 (10%)
41-60	13 (45%)
51-70	13 (45%)
program
Alternative Certification (PACE)	6 (21%)
Both equally	4 (14%)
Traditional	19 (66%)
1n (%)

Calculate Cronbach’s alpha

calculate_alpha <- function(data, prefix) {
  category_cols <- grep(paste0("^", prefix), colnames(data), value = TRUE)
  alpha_result <- alpha(data[, category_cols])
  cat(paste("Cronbach's alpha for", prefix, ":", format(alpha_result$total$raw_alpha, digits = 2, nsmall = 2)), "\n")
  return(alpha_result)
}

alpha_standard1 <- calculate_alpha(data, "standard1")
alpha_standard2 <- calculate_alpha(data, "standard2")
alpha_standard3 <- calculate_alpha(data, "standard3")
alpha_standard4 <- calculate_alpha(data, "standard4")
alpha_standard5 <- calculate_alpha(data, "standard5")
alpha_standard6 <- calculate_alpha(data, "standard6")

standard 1


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.93      0.93    0.93      0.73  13 0.021  3.4 0.92     0.73

standard 2


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.88      0.88    0.89       0.6 7.6 0.036  3.7 0.82     0.63

standard 3


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
      0.84      0.84    0.85      0.51 5.2 0.05  3.5 0.94     0.51

standard 4


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.89       0.9    0.85      0.74 8.7 0.035  3.9 0.87     0.75

standard 5


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.81      0.82    0.85      0.44 4.7 0.056  3.5 0.76     0.45

standard 6


Reliability analysis   
 raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.79      0.79    0.81      0.43 3.7 0.058  3.4 0.89     0.49

Heatmap of Semantic Similarity Between Teacher Educator Standards

Python code

Teacher Education Standard 1

Code

create_graph <- function(data) {
  
  color_font <- c("Beginning" = "black", "Developing" = "black", "Implementing" = "black", "Maintaining" = "black", "Transforming" = "white")
  
  summary  <- data %>%
    group_by(standard, response) %>%
    summarise(Count = n(), .groups = 'drop') %>%
    group_by(standard) %>% 
    mutate(Percentage = Count / sum(Count) * 100) %>% 
    ungroup() 
  
  summary <- summary %>%
    mutate(color_font = color_font[response])
  
  ggplot(summary, aes(x = standard, y = Percentage, fill = response)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent) +
    scale_fill_brewer(palette = "YlGnBu", breaks = c("Beginning", "Developing", "Implementing", "Maintaining", "Transforming")) +
    labs(title = "",
         x = "Standard",
         y = "Percentage",
         fill = "Response") +
    theme_minimal(base_size = 11) +
    theme(
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.line = element_line(color = "#3B3B3B", linewidth = 0.3),
      axis.ticks = element_line(color = "#3B3B3B", linewidth = 0.3),
      strip.text.x = element_text(size = 11, color = "#3B3B3B"), 
      axis.text.x = element_text(size = 11, color = "#3B3B3B"),
      axis.text.y = element_text(size = 11, color = "#3B3B3B"),
      axis.title = element_text(size = 11, color = "#3B3B3B"),
      legend.title = element_text(size = 11),
      legend.text = element_text(size = 11)) +
    geom_text(aes(label = sprintf("%.1f%%", Percentage), color = color_font),
              position = position_fill(vjust = 0.5),
              size = 4) +
    scale_color_identity()
}

create_graph(S1)

Code

ggsave("results/standard1.png")

Saving 8 x 5 in image

Teacher Education Standard 2

Code

create_graph(S2)

Code

ggsave("results/standard2.png")

Saving 8 x 5 in image

Teacher Education Standard 3

Code

create_graph(S3)

Code

ggsave("results/standard3.png")

Saving 8 x 5 in image

Teacher Education Standard 4

Code

create_graph(S4)

Code

ggsave("results/standard4.png")

Saving 8 x 5 in image

Teacher Education Standard 5

Code

create_graph(S5)

Code

ggsave("results/standard5.png")

Saving 8 x 5 in image

Teacher Education Standard 6

Code

create_graph(S6)

Code

ggsave("results/standard6.png")

Saving 8 x 5 in image

--- title: 'Journey for crafting excellence: West Texas A&M University teacher educator standards development and implementation of standards for rural educators' date: "`r Sys.Date()`" format: html: code-fold: true code-tools: true --- ```{=html} <style> .responsive-figure { width: 100% !important; height: auto; } </style> ``` ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, warning = FALSE) ``` The website contains outputs and code used to analyze numeric, ordinal, and text data in Garcia et al. (in press). Reproducible materials are also posted at the [Center for Open Science](https://doi.org/10.17605/OSF.IO/23PDM). - Garcia, B., Shin, M., Clifton, A., Bingham, T., Coneway, B., & Hughes, C. (in press). Journey for crafting excellence: West Texas A&M University teacher educator standards development and implementation of standards for rural educators. *Texas Forum of Teacher Education*. ::: panel-tabset ## Participants ```{r, echo=FALSE} suppressPackageStartupMessages({ library(readxl) library(officer) library(likert) library(gtsummary) library(flextable) library(ggplot2) library(tidyr) library(dplyr) library(reshape2) library(psych) library(corrplot) }) load("data/teacher_education_standards.RData") ``` ```{r, echo=FALSE, eval=FALSE} demo_tbl <- data %>% dplyr::select(teaching_years, gender, age, program) %>% tbl_summary( percent = "column") demo_tbl_ft <- as_flex_table(demo_tbl) ``` ```{r} demo_tbl_ft ``` ## Internal Consistency #### Calculate Cronbach's alpha ```{r, eval=FALSE} #| code-fold: false calculate_alpha <- function(data, prefix) { category_cols <- grep(paste0("^", prefix), colnames(data), value = TRUE) alpha_result <- alpha(data[, category_cols]) cat(paste("Cronbach's alpha for", prefix, ":", format(alpha_result$total$raw_alpha, digits = 2, nsmall = 2)), "\n") return(alpha_result) } alpha_standard1 <- calculate_alpha(data, "standard1") alpha_standard2 <- calculate_alpha(data, "standard2") alpha_standard3 <- calculate_alpha(data, "standard3") alpha_standard4 <- calculate_alpha(data, "standard4") alpha_standard5 <- calculate_alpha(data, "standard5") alpha_standard6 <- calculate_alpha(data, "standard6") ``` #### standard 1 ```{r, echo=FALSE} summary(alpha_standard1) ``` #### standard 2 ```{r, echo=FALSE} summary(alpha_standard2) ``` #### standard 3 ```{r, echo=FALSE} summary(alpha_standard3) ``` #### standard 4 ```{r, echo=FALSE} summary(alpha_standard4) ``` #### standard 5 ```{r, echo=FALSE} summary(alpha_standard5) ``` #### standard 6 ```{r, echo=FALSE} summary(alpha_standard6) ``` ## Semantic Similarity #### Heatmap of Semantic Similarity Between Teacher Educator Standards [Python code](python.html) ![](heatmap.png) ## Standards ```{r, echo=FALSE, eval=FALSE} selected_data <- data %>% select(id:program, standard1_1:standard6_5) init_long_data <- melt(selected_data, id.vars = c('id', 'teaching_years', 'gender', 'age', 'program'), variable.name = 'standard', value.name = 'response') openxlsx::write.xlsx(init_long_data, "data/init_long_data.xlsx", asTable = TRUE) init_long_data <- read_excel("data/long_data.xlsx") long_data <- init_long_data %>% mutate(response = case_when( response == "1" ~ "Beginning", response == "2" ~ "Developing", response == "3" ~ "Implementing", response == "4" ~ "Maintaining", response == "5" ~ "Transforming" )) long_data$response <- factor(long_data$response, levels = c("Beginning", "Developing", "Implementing", "Maintaining", "Transforming"), ordered = TRUE) long_data$standard <- dplyr::case_when( long_data$standard == "standard1_1" ~ "Standard 1-1", long_data$standard == "standard1_2" ~ "Standard 1-2", long_data$standard == "standard1_3" ~ "Standard 1-3", long_data$standard == "standard1_4" ~ "Standard 1-4", long_data$standard == "standard1_5" ~ "Standard 1-5", long_data$standard == "standard2_1" ~ "Standard 2-1", long_data$standard == "standard2_2" ~ "Standard 2-2", long_data$standard == "standard2_3" ~ "Standard 2-3", long_data$standard == "standard2_4" ~ "Standard 2-4", long_data$standard == "standard2_5" ~ "Standard 2-5", long_data$standard == "standard3_1" ~ "Standard 3-1", long_data$standard == "standard3_2" ~ "Standard 3-2", long_data$standard == "standard3_3" ~ "Standard 3-3", long_data$standard == "standard3_4" ~ "Standard 3-4", long_data$standard == "standard3_5" ~ "Standard 3-5", long_data$standard == "standard4_1" ~ "Standard 4-1", long_data$standard == "standard4_2" ~ "Standard 4-2", long_data$standard == "standard4_3" ~ "Standard 4-3", long_data$standard == "standard5_1" ~ "Standard 5-1", long_data$standard == "standard5_2" ~ "Standard 5-2", long_data$standard == "standard5_3" ~ "Standard 5-3", long_data$standard == "standard5_4" ~ "Standard 5-4", long_data$standard == "standard5_5" ~ "Standard 5-5", long_data$standard == "standard5_6" ~ "Standard 5-6", long_data$standard == "standard6_1" ~ "Standard 6-1", long_data$standard == "standard6_2" ~ "Standard 6-2", long_data$standard == "standard6_3" ~ "Standard 6-3", long_data$standard == "standard6_4" ~ "Standard 6-4", long_data$standard == "standard6_5" ~ "Standard 6-5", TRUE ~ as.character(long_data$standard) ) S1 <- long_data %>% filter(grepl("^Standard 1-[1-5]$", standard)) S2 <- long_data %>% filter(grepl("^Standard 2-[1-5]$", standard)) S3 <- long_data %>% filter(grepl("^Standard 3-[1-5]$", standard)) S4 <- long_data %>% filter(grepl("^Standard 4-[1-3]$", standard)) S5 <- long_data %>% filter(grepl("^Standard 5-[1-6]$", standard)) S6 <- long_data %>% filter(grepl("^Standard 6-[1-5]$", standard)) ``` #### Teacher Education Standard 1 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph <- function(data) { color_font <- c("Beginning" = "black", "Developing" = "black", "Implementing" = "black", "Maintaining" = "black", "Transforming" = "white") summary <- data %>% group_by(standard, response) %>% summarise(Count = n(), .groups = 'drop') %>% group_by(standard) %>% mutate(Percentage = Count / sum(Count) * 100) %>% ungroup() summary <- summary %>% mutate(color_font = color_font[response]) ggplot(summary, aes(x = standard, y = Percentage, fill = response)) + geom_bar(stat = "identity", position = "fill") + scale_y_continuous(labels = scales::percent) + scale_fill_brewer(palette = "YlGnBu", breaks = c("Beginning", "Developing", "Implementing", "Maintaining", "Transforming")) + labs(title = "", x = "Standard", y = "Percentage", fill = "Response") + theme_minimal(base_size = 11) + theme( panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(color = "#3B3B3B", linewidth = 0.3), axis.ticks = element_line(color = "#3B3B3B", linewidth = 0.3), strip.text.x = element_text(size = 11, color = "#3B3B3B"), axis.text.x = element_text(size = 11, color = "#3B3B3B"), axis.text.y = element_text(size = 11, color = "#3B3B3B"), axis.title = element_text(size = 11, color = "#3B3B3B"), legend.title = element_text(size = 11), legend.text = element_text(size = 11)) + geom_text(aes(label = sprintf("%.1f%%", Percentage), color = color_font), position = position_fill(vjust = 0.5), size = 4) + scale_color_identity() } create_graph(S1) ggsave("results/standard1.png") ``` #### Teacher Education Standard 2 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph(S2) ggsave("results/standard2.png") ``` #### Teacher Education Standard 3 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph(S3) ggsave("results/standard3.png") ``` #### Teacher Education Standard 4 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph(S4) ggsave("results/standard4.png") ``` #### Teacher Education Standard 5 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph(S5) ggsave("results/standard5.png") ``` #### Teacher Education Standard 6 ```{r, fig.width=8, fig.height=5, out.extra='class="responsive-figure"'} create_graph(S6) ggsave("results/standard6.png") ``` :::