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Case Study: Simple Syllabus

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

Now, we will walk through a simple 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 and 3) the final exam. Their grade will be computed as follows:

  • Homework 20%
    • Homework 1, Homework 2 (and they are aggregated equally)
  • Midterm 40%
  • Final Exam 40%

Policy File

Here is what the policy file looks like:

coursewide:
  course: Stat 101 Simple Syllabus

categories:
 - category: Overall Grade
   aggregation: weighted_mean
   assignments:
   - category: Homework
     weight: 0.20
     aggregation: equally_weighted
     assignments:
     - "Homework 1"
     - "Homework 2"
   - category: Midterm
     weight: 0.40
     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:

simple_syllabus_data <- read_gs(system.file("extdata", "simple_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) 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 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 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 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 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 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 simple_syllabus_data file, which is usually downloaded from Gradescope.

The Homework category is compiled of two assignments: “Homework 1” and “Homework 2” in the simple_syllabus_data.

Grade Calculations 

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

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

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

First, read in your YAML policy file. read_policy() and read_gs() read in their respective files and check for correct formatting. Finally, get_grades() computes your assignment grades 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) 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 0.25 20 5 47:55:53 0.88 50 4 00:00:00 0.10 100 2 00:00:00 0.575 30 47:55:53 0.88 50 00:00:00 0.10 100 00:00:00 0.507 180 47:55:53
Bob Johnson 3032412515 section-1 0.5 10 3 00:00:00 1.00 20 3 00:00:00 0.00 50 2 00:00:00 0.78 100 1 00:00:00 0.750 30 00:00:00 0.00 50 00:00:00 0.78 100 00:00:00 0.462 180 00:00:00
Charlie Brown 3032412516 section-1 1.0 10 1 00:00:00 0.60 20 4 00:00:00 0.34 50 1 00:00:00 0.24 100 4 00:00:00 0.800 30 00:00:00 0.34 50 00:00:00 0.24 100 00:00:00 0.392 180 00:00:00
David Wilson 3032412517 section-1 1.0 10 5 00:00:00 0.05 20 1 00:00:00 0.30 50 3 00:00:00 0.89 100 5 00:00:00 0.525 30 00:00:00 0.30 50 00:00:00 0.89 100 00:00:00 0.581 180 00:00:00
Eva White 3032412518 section-1 0.8 10 2 00:00:00 0.50 20 2 00:00:00 0.16 50 5 00:52:01 0.97 100 3 00:00:00 0.650 30 00:00:00 0.16 50 00:52:01 0.97 100 00:00:00 0.582 180 00:52:01

Syllabus as a Grading Script

Here is the coded version of the same syllabus.

script_grades <- simple_syllabus_data |>
  # calculate percentages
  dplyr::mutate(
    `Homework 1` = `Homework 1` / `Homework 1 - Max Points`,
    `Homework 2` = `Homework 2` / `Homework 2 - Max Points`,
    `Midterm Exam` = `Midterm Exam` / `Midterm Exam - Max Points`,
    `Final Exam` = `Final Exam` / `Final Exam - Max Points`
  ) |>
  # calculate category grade for "Homework"
  dplyr::mutate(`Homework` = (`Homework 1` + `Homework 2`) / 2) |>
  # calculate final Overall Grade
  dplyr::mutate(`Overall Grade` = 0.2 * `Homework` + 0.4 * `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.