autograde_example_learnr

autograde.R

library(dplyr)
library(learnrhash)

responses <- read.csv("prelabsubmission_lab1.csv", stringsAsFactors = F) ## alternatively read straight from Google Sheets

## my Google Form was:

## timestamp (automatically collected)
## name
## copy and paste hash
## lab number (so I could use one spreadsheet for all tutorials and easily filter)
## email address (automatically collected, submission limited to smith.edu)

names(responses)[3] <- "hash"

answer_key1 <- responses[1, ] ## this was when I did lesson 1 correctly to act as a reference

responses <- responses[13:nrow(responses), ] ## I had test responses in 1-12 so that I would know how many chunks for each tutorial

## you might need to do a filter here for lab number

question_answers <- extract_questions(responses, responses$hash)

exercise_answers <- extract_exercises(responses, responses$hash)

answer_key1_exercise_answers <- extract_exercises(answer_key1, responses$hash)
answer_key1_question_answers <- extract_questions(answer_key1, responses$hash)

answer_key1_exercise_answers %>%
  group_by(exercise_id) %>%
  summarise(isActive = checked[1]) %>%
  group_by(isActive) %>%
  summarise(count = n())

unique(answer_key1_exercise_answers$exercise_id) %>% length() ## number of exercises (code chunks)

nrow(answer_key1_question_answers) ## number of questions (multiple choice)

question_answers %>%
  group_by(Name) %>%
  summarise(count = n()) %>%
  arrange(count) %>%
  View() ## I would just go through this manually, you could do something fancier

exercise_answers %>%
  group_by(Name) %>%
  summarise(count = n(), total = sum(correct, na.rm = T)) %>%
  arrange(count) %>%
  View() ## I would just go through this manually, you could do something fancier