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Case Study: Moderate Syllabus

Source: vignettes/moderate-syllabus.Rmd
moderate-syllabus.Rmd

Now, we will walk through a moderately complex syllabus and its nemogb implementation with 5 students in the class. Let’s start with what this syllabus would look like on a class website.

Syllabus

The final student’s grade is computed from 1) homework assignments, 2) midterm exam, and 3) the final exam. Their grade will be computed as follows:

  • Homework 20%
    • Homework 1, Homework 2, Homework 3, Homework 4, Homework 5
    • All assignments are aggregated equally
    • The lowest 2 assignments are dropped
    • All components of the lab individually experience a lateness penalty:
      • 10% reduction of the score, if assignment is submitted late but within 24 hours of the deadline
      • 30% reduction of the score, if assignment is submitted after 24 hours of the deadline
  • Midterm 40%
  • Final Exam 40%

Policy File

Here is what the policy file looks like:

coursewide:
  course: Stat 101 Moderate Syllabus
  description: This is the description for a course with moderate syllabus complexity
  
categories:
  - category: Overall Grade
    aggregation: weighted_mean
    assignments:
    - category: Homework
      lateness:
      - between:
         from: '00:00:01'
         to: '24:00:00'
      - scale_by: 0.9
      - after: '24:00:01'
      - scale_by: 0.7
      drop_n_lowest: 2
      weight: 0.40
      aggregation: equally_weighted
      assignments:
      - "Homework 1"
      - "Homework 2"
      - "Homework 3"
      - "Homework 4"
      - "Homework 5"
    - category: Midterm
      weight: 0.20
      aggregation: none
      assignments:
      - "Midterm Exam"
    - category: Final
      weight: 0.40
      aggregation: none
      assignments:
      - "Final Exam"

Gradescope Assigments File

Here is what the Gradescope assignments file looks like:

moderate_syllabus_data <- read_gs(system.file("extdata", "moderate_syllabus_data.csv", package = "nemogb"))
Names Email SID Sections Homework 1 Homework 1 - Max Points Homework 1 - Submission Time Homework 1 - Lateness (H:M:S) Homework 2 Homework 2 - Max Points Homework 2 - Submission Time Homework 2 - Lateness (H:M:S) Homework 3 Homework 3 - Max Points Homework 3 - Submission Time Homework 3 - Lateness (H:M:S) Homework 4 Homework 4 - Max Points Homework 4 - Submission Time Homework 4 - Lateness (H:M:S) Homework 5 Homework 5 - Max Points Homework 5 - Submission Time Homework 5 - Lateness (H:M:S) Midterm Exam Midterm Exam - Max Points Midterm Exam - Submission Time Midterm Exam - Lateness (H:M:S) Final Exam Final Exam - Max Points Final Exam - Submission Time Final Exam - Lateness (H:M:S)
Alice Smith 3032412514 section-1 9 10 1/19/23 22:20 00:00:00 5 20 9/21/22 23:54 47:55:53 7 15 1/24/23 20:38 00:00:00 9 10 1/26/23 19:51 00:00:00 0 5 1/31/23 18:59 00:00:00 44 50 2/14/23 21:06 00:00:00 10 100 1/20/23 15:13 00:00:00
Bob Johnson 3032412515 section-1 5 10 1/19/23 12:16 00:00:00 20 20 9/19/22 23:32 00:00:00 1 15 1/24/23 16:18 00:00:00 5 10 1/26/23 8:54 00:00:00 1 5 1/31/23 13:04 00:00:00 0 50 2/14/23 18:25 00:00:00 78 100 1/20/23 10:04 00:00:00
Charlie Brown 3032412516 section-1 10 10 1/19/23 10:26 00:00:00 12 20 9/19/22 23:44 00:00:00 15 15 1/24/23 16:10 00:00:00 8 10 1/26/23 10:12 00:00:00 3 5 1/31/23 16:01 00:00:00 17 50 2/14/23 18:00 00:00:00 24 100 1/23/23 8:22 00:00:00
David Wilson 3032412517 section-1 10 10 1/20/23 8:12 00:00:00 1 20 9/13/22 20:29 00:00:00 8 15 1/25/23 8:56 08:57:32 6 10 1/26/23 17:27 00:00:00 1 5 1/31/23 14:57 00:00:00 15 50 2/14/23 20:00 00:00:00 89 100 1/24/23 0:50 00:00:00
Eva White 3032412518 section-1 8 10 1/19/23 12:06 00:00:00 10 20 9/19/22 0:15 00:00:00 9 15 1/25/23 14:58 14:59:55 1 10 1/27/23 14:09 14:10:40 4 5 1/31/23 15:44 00:00:00 8 50 2/15/23 13:51 00:52:01 97 100 1/20/23 15:53 00:00:00

Workflow

Overall grade is calculated using the three categories, with the weights as shown above; This is represented by Overall Grade with an aggregation of weighted_mean.

The Midterm and Final Exam categories contain only a single assignment, as shown in the moderate_syllabus_data file, which is usually downloaded from gradescope.

The Homework category is compiled of five assignments: “Homework 1”, “Homework 2”, “Homework 3”, “Homework 4”, “Homework 5” in the moderate_syllabus_data.

Grade Calculations 

moderate_syllabus_data <- read_gs(system.file("extdata", "moderate_syllabus_data.csv", package = "nemogb"))

policy <- read_policy(system.file("extdata", "moderate_syllabus.yaml", package = "nemogb"))

grades <- get_grades(policy = policy, gs = moderate_syllabus_data)

First, read in your YAML policy file. process_policy() checks the formatting of the policy file. Then,reconcile_policy_with_gs() checks for compatibility between the Gradescope file (moderate_syllabus_data) and the policy file (policy). Finally, get_grades() grades your assignments based on your policy file.

The output of the grades looks like:

Names Email SID Sections Homework 1 Homework 1 - Max Points Homework 1 - Submission Time Homework 1 - Lateness (H:M:S) Homework 2 Homework 2 - Max Points Homework 2 - Submission Time Homework 2 - Lateness (H:M:S) Homework 3 Homework 3 - Max Points Homework 3 - Submission Time Homework 3 - Lateness (H:M:S) Homework 4 Homework 4 - Max Points Homework 4 - Submission Time Homework 4 - Lateness (H:M:S) Homework 5 Homework 5 - Max Points Homework 5 - Submission Time Homework 5 - Lateness (H:M:S) Midterm Exam Midterm Exam - Max Points Midterm Exam - Submission Time Midterm Exam - Lateness (H:M:S) Final Exam Final Exam - Max Points Final Exam - Submission Time Final Exam - Lateness (H:M:S) Homework Homework - Max Points Homework - Lateness (H:M:S) Midterm Midterm - Max Points Midterm - Lateness (H:M:S) Final Final - Max Points Final - Lateness (H:M:S) Overall Grade Overall Grade - Max Points Overall Grade - Lateness (H:M:S)
Alice Smith 3032412514 section-1 0.9 10 4 00:00:00 NA NA 5 47:55:53 0.4666667 15 3 00:00:00 0.9 10 3 00:00:00 NA NA 5 00:00:00 0.88 50 4 00:00:00 0.10 100 2 00:00:00 0.7555556 35 47:55:53 0.88 50 00:00:00 0.10 100 00:00:00 0.5182222 185 47:55:53
Bob Johnson 3032412515 section-1 0.5 10 3 00:00:00 1 20 3 00:00:00 NA NA 2 00:00:00 0.5 10 4 00:00:00 NA NA 1 00:00:00 0.00 50 2 00:00:00 0.78 100 1 00:00:00 0.6666667 40 00:00:00 0.00 50 00:00:00 0.78 100 00:00:00 0.5786667 190 00:00:00
Charlie Brown 3032412516 section-1 1.0 10 1 00:00:00 NA NA 4 00:00:00 1.0000000 15 1 00:00:00 0.8 10 1 00:00:00 NA NA 4 00:00:00 0.34 50 1 00:00:00 0.24 100 4 00:00:00 0.9333333 35 00:00:00 0.34 50 00:00:00 0.24 100 00:00:00 0.5373333 185 00:00:00
David Wilson 3032412517 section-1 1.0 10 5 00:00:00 NA NA 1 00:00:00 0.4800000 15 5 08:57:32 0.6 10 2 00:00:00 NA NA 2 00:00:00 0.30 50 3 00:00:00 0.89 100 5 00:00:00 0.6933333 35 08:57:32 0.30 50 00:00:00 0.89 100 00:00:00 0.6933333 185 08:57:32
Eva White 3032412518 section-1 0.8 10 2 00:00:00 NA NA 2 00:00:00 0.5400000 15 4 14:59:55 NA NA 5 14:10:40 0.8 5 3 00:00:00 0.16 50 5 00:52:01 0.97 100 3 00:00:00 0.7133333 30 14:59:55 0.16 50 00:52:01 0.97 100 00:00:00 0.7053333 180 14:59:55

Syllabus as a Grading Script

Here is the coded version of the same syllabus.

# function to convert HH:MM:SS into minutes
convert_to_min <- function(hms) {
  purrr::map_vec(hms, function(time) {
    units <- stringr::str_split(time, pattern = ":") |>
      unlist() |>
      as.numeric()
    sum(units * c(60, 1, 1 / 60))
  })
}

# function to implement lateness policy
lateness_penalty <- function(score_vec, lateness_vec) {
  lateness <- convert_to_min(lateness_vec)
  dplyr::case_when(
    lateness == 0 ~ score_vec,
    lateness <= 1440 ~ 0.9 * score_vec,
    .default = 0.7 * score_vec
  )
}

# function to drop lowest two grades
two_drops <- function(data) {
  apply(dplyr::select(data, `Homework 1`, `Homework 2`, `Homework 3`, `Homework 4`, `Homework 5`), 1, function(x) sort(x, decreasing = TRUE)[1:3]) |> t()
}

data_with_lateness <- moderate_syllabus_data |>
  # calculate percentages
  dplyr::mutate(
    `Homework 1` = `Homework 1` / `Homework 1 - Max Points`,
    `Homework 2` = `Homework 2` / `Homework 2 - Max Points`,
    `Homework 3` = `Homework 3` / `Homework 3 - Max Points`,
    `Homework 4` = `Homework 4` / `Homework 4 - Max Points`,
    `Homework 5` = `Homework 5` / `Homework 5 - Max Points`,
    `Midterm Exam` = `Midterm Exam` / `Midterm Exam - Max Points`,
    `Final Exam` = `Final Exam` / `Final Exam - Max Points`
  ) |>
  # lateness
  dplyr::mutate(
    `Homework 1` = lateness_penalty(`Homework 1`, `Homework 1 - Lateness (H:M:S)`),
    `Homework 2` = lateness_penalty(`Homework 2`, `Homework 2 - Lateness (H:M:S)`),
    `Homework 3` = lateness_penalty(`Homework 3`, `Homework 3 - Lateness (H:M:S)`),
    `Homework 4` = lateness_penalty(`Homework 4`, `Homework 4 - Lateness (H:M:S)`),
    `Homework 5` = lateness_penalty(`Homework 5`, `Homework 5 - Lateness (H:M:S)`)
  )
script_grades <- data_with_lateness |>
  # calculate category grade for "Homework"
  dplyr::mutate(
    `Homework` = rowMeans(two_drops(data_with_lateness))
  ) |>
  # calculate final Overall Grade
  dplyr::mutate(`Overall Grade` = 0.4 * `Homework` + 0.2 * `Midterm Exam` + 0.4 * `Final Exam`)

And, as you can see, the computations align.

Disclaimers:

  • The weight of a category with aggregation weighted_mean should be corresponding order with the assignments.

  • Categories cannot have the same name as any other assignment name.