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A high-level R 📦 for clean, filter, and combine 🎓 Moodle Quiz Report

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moodleQuiz

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R package {moodleQuiz} contains high-level functions for cleaning, encoding, filtering, and combining student’s score and responses from Moodle Quiz report.

Installation

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("Lightbridge-KS/moodleQuiz")

Overview

The 4 main functions of the {moodleQuiz} are as follows:

  • check_sub(): check student’s submission of the Moodle quiz report by looking at the state column.

  • combine_resp(): combine student’s responses (Response x) from the Responses report.

  • count_resp(): count how many responses each student answered from the Responses report.

  • combine_grades(): filter, adjust, and combine student’s grade from Grade/xx column.

All of theses 4 main functions are generic function that operate on data frame and list of data frames of Moodle Quiz report. The latter will be particularly useful when performing a data aggregation across multiple Moodle quiz report of the same type.

History

A long time ago, I’ve build a Shiny app for my colleagues to manipulate Moodle Quiz report. As the app grows bigger, I realized that I have to separate the business logic from the application logic, so that the app can be maintainable. Instead, I’ve focus on writing functions, and shift my aim to build system of functions to manipulate Moodle Quiz report as easy as possible for R user.

Afterwards, My collection of functions become this {moodleQuiz} package which drives the logic of this shiny app to facilitate the process of retrieve and combine student’s score from SELECx (a Moodle platform from Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand).

Example

library(moodleQuiz)
library(dplyr)

Let’s say I have a Moodle Grades Report data frame of “Quiz_1”

glimpse(grades_ls$Quiz_1)
#> Rows: 27
#> Columns: 18
#> $ Surname         <chr> "Roquemore", "Ali", "Hoffpauir", "Babbitt", "Huynh", "…
#> $ `First name`    <chr> "Jada", "Ronin", "Jerry", "Nathan", "Rohith", "Joy", "…
#> $ Institution     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ Department      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ `Email address` <chr> "u017@example.com", "u001@example.com", "u002@example.…
#> $ State           <chr> "Finished", "Finished", "Finished", "Finished", "Finis…
#> $ `Started on`    <chr> "2 June 2021 8:28 AM", "2 June 2021 2:21 PM", "2 June …
#> $ `Time taken`    <chr> "16 mins 49 secs", "17 mins 11 secs", "11 mins 30 secs…
#> $ Completed       <chr> "2 June 2021 8:45 AM", "2 June 2021 2:38 PM", "2 June …
#> $ `Grade/10.00`   <dbl> 9.47, 9.74, 9.47, 10.00, 10.00, 10.00, 9.21, 9.47, 10.…
#> $ `Q. 1 /0.79`    <dbl> 0.26, 0.79, 0.26, 0.79, 0.79, 0.79, 0.79, 0.26, 0.79, …
#> $ `Q. 2 /0.26`    <dbl> 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, …
#> $ `Q. 3 /2.37`    <dbl> 2.37, 2.37, 2.37, 2.37, 2.37, 2.37, 2.11, 2.37, 2.37, …
#> $ `Q. 4 /2.11`    <dbl> 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 1.58, 2.11, 2.11, …
#> $ `Q. 5 /2.11`    <dbl> 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, …
#> $ `Q. 6 /0.26`    <dbl> 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, …
#> $ `Q. 7 /1.05`    <chr> "1.05", "0.79", "1.05", "1.05", "1.05", "1.05", "1.05"…
#> $ `Q. 8 /1.05`    <dbl> 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, …

Combine & Adjust Grades of Quiz 1

How to choose the maximum score of each student and readjust maximum grades to 100?

Calling combine_grades() will do that in one step, plus it also cleans column names and extracts numeric student ID from Email address.

grades_ls$Quiz_1 %>%
  combine_grades(
    extract_id_from = "Email address", # Extract Student ID from Email
    id_regex = "[:digit:]+", # Regular expression to extract student ID
    choose_grade = "max", # Choose only maximum grade of each student
    new_max_grade = 100 # Adjust maximum grade to 100
  ) %>%
  select(Name, ID, starts_with("G"), starts_with("Q"))
#> # A tibble: 26 × 11
#>    Name          ID    Grade_100    Q1    Q2    Q3    Q4    Q5    Q6    Q7    Q8
#>    <chr>         <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 Jada Roquemo… 017        94.7   2.6   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  2 Ronin Ali     001        97.4   7.9   2.6  23.7  21.1  21.1   2.6   7.9  10.5
#>  3 Jerry Hoffpa… 002        94.7   2.6   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  4 Nathan Babbi… 026       100     7.9   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  5 Rohith Huynh  024       100     7.9   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  6 Joy Ellis     013       100     7.9   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  7 Jeremy Nelson 023        92.1   7.9   2.6  21.1  15.8  21.1   2.6  10.5  10.5
#>  8 Christopher … 003        94.7   2.6   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#>  9 Israel Munoz  018       100     7.9   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#> 10 Zubaida al-A… 022       100     7.9   2.6  23.7  21.1  21.1   2.6  10.5  10.5
#> # … with 16 more rows

Combine Grades for All Quizzes

Supposed in a semester student have to do a graded assignment in a multiple quizzes, you can put each individual Grades Report in a list.

Now it’s a list of data frames (here as grades_ls).

grades_ls %>% 
  purrr::map_lgl(is.data.frame)
#> Quiz_1 Quiz_2 Quiz_3 
#>   TRUE   TRUE   TRUE

Supply a list of data frames into combine_grades(), you can combine and weight grades from multiple quizzes into one data frame with a sum of student’s score in the Total_x column.

grades_ls %>% 
  combine_grades(
    extract_id_from = "Email address", # Extract Student ID from Email
    id_regex = "[:digit:]+", # Regular expression to extract student ID
    choose_grade = "max", # Choose only maximum grade of each student
    new_max_grade = c(25, 25, 50) # Adjust maximum grade to 100
  ) %>% 
  select(Name, ID, contains("Grade"), starts_with("Total"))
#> # A tibble: 26 × 6
#>    Name          ID    Quiz_1_Grade_25 Quiz_2_Grade_25 Quiz_3_Grade_50 Total_100
#>    <chr>         <chr>           <dbl>           <dbl>           <dbl>     <dbl>
#>  1 Jada Roquemo… 017              23.7            19.4            50.0      93.1
#>  2 Ronin Ali     001              24.4            20.4            46.4      91.1
#>  3 Jerry Hoffpa… 002              23.7            16.6            50.0      90.3
#>  4 Nathan Babbi… 026              25              18.5            50.0      93.4
#>  5 Rohith Huynh  024              25              25              50.0     100. 
#>  6 Joy Ellis     013              25              13.9            50.0      88.8
#>  7 Jeremy Nelson 023              23.0            25              46.4      94.4
#>  8 Christopher … 003              23.7            22.2            50.0      95.8
#>  9 Israel Munoz  018              25              21.3            50.0      96.2
#> 10 Zubaida al-A… 022              25              22.2            50.0      97.2
#> # … with 16 more rows

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Last updated: 2022-06-02

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A high-level R 📦 for clean, filter, and combine 🎓 Moodle Quiz Report

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