library(tidyverse)

data_to_viz <- read_csv("data/data-to-explore.csv")

data_to_viz  %>% 
  select(subject, 
         section, 
         time_spent_hours, 
         proportion_earned, gender) %>%
  mutate(subject = recode(subject, 
                          "AnPhA" = "Anat",
                          "BioA" = "Bio", 
                          "FrScA" = "Forens", 
                          "OcnA" =  "Ocean", 
                          "PhysA" = "Phys")) %>%
  mutate(grade = proportion_earned * 100) %>%
  ggplot() +
  geom_boxplot(mapping = aes(x = subject, y = grade)) +
  xlab("Subject") +
  ylab("Grade") +
  labs(title = "Grade Distribution by Subject",
       subtitle = "Variance between and among course subject and gender expression",
       caption = "Which subject has the highest median grade?") +
  facet_wrap(~ gender) +
  theme_linedraw()

Considering a recent, widening achievement gap between men and women in education (Edsall, 2021), it is important to acknowledge that there might also be increasing precarity of student performance in online education. When examining grade distributions across various online STEM courses, men’s average test scores are generally lower than that of females. Most notably, men score less well on average in biology. More research is needed to understand covariance and/or other confounding variables. Also noted: Students who listed “NA” for their gender expression.

References
Edsall, T. (2021, September). ‘It’s become increasingly hard for them to feel good about themselves’. The New York Times. https://www.nytimes.com/2021/09/22/opinion/economy-education-women-men.html