Now, we will walk through a complex syllabus and its
nemogb implementation with 5 students in the class. Start
with what this syllabus would look like on a class website.
Syllabus
The final student’s grade is computed from 1) labs, 2) midterm exam and 3) final exam. Their grade will be computed as follows:
- Labs 20%
- Lab 1, Lab 2, and Lab 3
- They are aggregated equally
- Each “Lab #” is compiled of two parts, which are weighted by their
respective point value: “Lab #.1” & “Lab #.2”
- For example: if Lab 1.1 is worth 15 points and Lab 1.2 is worth 5 points, then Lab 1.1 accounts for 75% of Lab 1 score.
- The lowest 1 assignment is 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 deadline
- Midterm 40%
- Final Exam 40%
Policy File
Here is what the policy file looks like:
coursewide:
course: Stat 101 Complex Syllabus
description: This is the description for a course with a complex syllabus
categories:
- category: Overall Grade
aggregation: weighted_mean
assignments:
- category: Labs
weight: 0.40
drop_n_lowest: 1
aggregation: equally_weighted
assignments:
- category: Lab 1
lateness:
- between:
from: '00:00:01'
to: '24:00:00'
- scale_by: 0.9
- after: '24:00:01'
- scale_by: 0.7
aggregation: weighted_by_points
assignments:
- "Lab 1.1"
- "Lab 1.2"
- category: Lab 2
lateness:
- between:
from: '00:00:01'
to: '24:00:00'
- scale_by: 0.9
- after: '24:00:01'
- scale_by: 0.7
aggregation: weighted_by_points
assignments:
- "Lab 2.1"
- "Lab 2.2"
- category: Lab 3
lateness:
- between:
from: '00:00:01'
to: '24:00:00'
- scale_by: 0.9
- after: '24:00:01'
- scale_by: 0.7
aggregation: weighted_by_points
assignments:
- "Lab 3.1"
- "Lab 3.2"
- 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:
complex_syllabus_data <- read_gs(system.file("extdata", "complex_syllabus_data.csv", package = "nemogb"))| Names | SID | Sections | Lab 1.1 | Lab 1.1 - Max Points | Lab 1.1 - Submission Time | Lab 1.1 - Lateness (H:M:S) | Lab 1.2 | Lab 1.2 - Max Points | Lab 1.2 - Submission Time | Lab 1.2 - Lateness (H:M:S) | Lab 2.1 | Lab 2.1 - Max Points | Lab 2.1 - Submission Time | Lab 2.1 - Lateness (H:M:S) | Lab 2.2 | Lab 2.2 - Max Points | Lab 2.2 - Submission Time | Lab 2.2 - Lateness (H:M:S) | Lab 3.1 | Lab 3.1 - Max Points | Lab 3.1 - Submission Time | Lab 3.1 - Lateness (H:M:S) | Lab 3.2 | Lab 3.2 - Max Points | Lab 3.2 - Submission Time | Lab 3.2 - 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 | alice.smith@email.com | 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 | 4 | 10 | 2/2/23 12:16 | 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 | bob.johnson@email.com | 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 | 10 | 2/2/23 12:12 | 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 | charlie.brown@email.com | 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 | 6 | 10 | 2/2/23 10:13 | 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 | david.wilson@email.com | 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 | 10 | 10 | 2/2/23 16:43 | 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 | eva.white@email.com | 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 | 0 | 10 | 2/2/23 22:27 | 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
complex_syllabus_data file, which is usually downloaded
from gradescope.
The Labs category is compiled of 3 nested categories: “Lab 1”, “Lab 2”, “Lab 3”, and each has 2 corresponding assignments: “Lab 1.1”, “Lab 1.2”, “Lab 2.1”, “Lab 2.2”, “Lab 3.1”, “Lab 3.2”.
Grade Calculations
complex_syllabus_data <- read_gs(system.file("extdata", "complex_syllabus_data.csv", package = "nemogb"))
policy <- read_policy(system.file("extdata", "complex_syllabus.yaml", package = "nemogb"))
grades <- get_grades(policy = policy, gs = complex_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 (complex_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 | SID | Sections | Lab 1.1 | Lab 1.1 - Max Points | Lab 1.1 - Submission Time | Lab 1.1 - Lateness (H:M:S) | Lab 1.2 | Lab 1.2 - Max Points | Lab 1.2 - Submission Time | Lab 1.2 - Lateness (H:M:S) | Lab 2.1 | Lab 2.1 - Max Points | Lab 2.1 - Submission Time | Lab 2.1 - Lateness (H:M:S) | Lab 2.2 | Lab 2.2 - Max Points | Lab 2.2 - Submission Time | Lab 2.2 - Lateness (H:M:S) | Lab 3.1 | Lab 3.1 - Max Points | Lab 3.1 - Submission Time | Lab 3.1 - Lateness (H:M:S) | Lab 3.2 | Lab 3.2 - Max Points | Lab 3.2 - Submission Time | Lab 3.2 - 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) | Lab 1 | Lab 1 - Max Points | Lab 1 - Lateness (H:M:S) | Lab 2 | Lab 2 - Max Points | Lab 2 - Lateness (H:M:S) | Lab 3 | Lab 3 - Max Points | Lab 3 - Lateness (H:M:S) | Labs | Labs - Max Points | Labs - 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 | alice.smith@email.com | 3032412514 | section-1 | 0.9 | 10 | 4 | 00:00:00 | 0.175 | 20 | 5 | 47:55:53 | 0.4666667 | 15 | 3 | 00:00:00 | 0.90 | 10 | 3 | 00:00:00 | 0.0 | 5 | 5 | 00:00:00 | 0.4 | 10 | 3 | 00:00:00 | 0.88 | 50 | 4 | 00:00:00 | 0.10 | 100 | 2 | 00:00:00 | 0.4166667 | 30 | 47:55:53 | 0.640 | 25 | 00:00:00 | NA | NA | 00:00:00 | 0.5283333 | 55 | 47:55:53 | 0.88 | 50 | 00:00:00 | 0.10 | 100 | 00:00:00 | 0.4273333 | 205 | 47:55:53 |
| Bob Johnson | bob.johnson@email.com | 3032412515 | section-1 | 0.5 | 10 | 3 | 00:00:00 | 1.000 | 20 | 3 | 00:00:00 | 0.0666667 | 15 | 2 | 00:00:00 | 0.50 | 10 | 4 | 00:00:00 | 0.2 | 5 | 1 | 00:00:00 | 0.0 | 10 | 2 | 00:00:00 | 0.00 | 50 | 2 | 00:00:00 | 0.78 | 100 | 1 | 00:00:00 | 0.8333333 | 30 | 00:00:00 | 0.240 | 25 | 00:00:00 | NA | NA | 00:00:00 | 0.5366667 | 55 | 00:00:00 | 0.00 | 50 | 00:00:00 | 0.78 | 100 | 00:00:00 | 0.5266667 | 205 | 00:00:00 |
| Charlie Brown | charlie.brown@email.com | 3032412516 | section-1 | 1.0 | 10 | 1 | 00:00:00 | 0.600 | 20 | 4 | 00:00:00 | 1.0000000 | 15 | 1 | 00:00:00 | 0.80 | 10 | 1 | 00:00:00 | 0.6 | 5 | 4 | 00:00:00 | 0.6 | 10 | 1 | 00:00:00 | 0.34 | 50 | 1 | 00:00:00 | 0.24 | 100 | 4 | 00:00:00 | 0.7333333 | 30 | 00:00:00 | 0.920 | 25 | 00:00:00 | NA | NA | 00:00:00 | 0.8266667 | 55 | 00:00:00 | 0.34 | 50 | 00:00:00 | 0.24 | 100 | 00:00:00 | 0.4946667 | 205 | 00:00:00 |
| David Wilson | david.wilson@email.com | 3032412517 | section-1 | 1.0 | 10 | 5 | 00:00:00 | 0.050 | 20 | 1 | 00:00:00 | 0.4800000 | 15 | 5 | 08:57:32 | 0.60 | 10 | 2 | 00:00:00 | 0.2 | 5 | 2 | 00:00:00 | 1.0 | 10 | 4 | 00:00:00 | 0.30 | 50 | 3 | 00:00:00 | 0.89 | 100 | 5 | 00:00:00 | NA | NA | 00:00:00 | 0.528 | 25 | 08:57:32 | 0.7333333 | 15 | 00:00:00 | 0.6306667 | 40 | 08:57:32 | 0.30 | 50 | 00:00:00 | 0.89 | 100 | 00:00:00 | 0.6682667 | 190 | 08:57:32 |
| Eva White | eva.white@email.com | 3032412518 | section-1 | 0.8 | 10 | 2 | 00:00:00 | 0.500 | 20 | 2 | 00:00:00 | 0.5400000 | 15 | 4 | 14:59:55 | 0.09 | 10 | 5 | 14:10:40 | 0.8 | 5 | 3 | 00:00:00 | 0.0 | 10 | 5 | 00:00:00 | 0.16 | 50 | 5 | 00:52:01 | 0.97 | 100 | 3 | 00:00:00 | 0.6000000 | 30 | 00:00:00 | 0.360 | 25 | 14:59:55 | NA | NA | 00:00:00 | 0.4800000 | 55 | 14:59:55 | 0.16 | 50 | 00:52:01 | 0.97 | 100 | 00:00:00 | 0.6120000 | 205 | 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
one_drop <- function(data) {
apply(dplyr::select(data, `Lab 1`, `Lab 2`, `Lab 3`), 1, function(x) sort(x, decreasing = TRUE)[1:2]) |> t()
}
lab_grades_with_lateness <- complex_syllabus_data |>
# calculate percentages
dplyr::mutate(
`Lab 1.1` = `Lab 1.1` / `Lab 1.1 - Max Points`,
`Lab 1.2` = `Lab 1.2` / `Lab 1.2 - Max Points`,
`Lab 2.1` = `Lab 2.1` / `Lab 2.1 - Max Points`,
`Lab 2.2` = `Lab 2.2` / `Lab 2.2 - Max Points`,
`Lab 3.1` = `Lab 3.1` / `Lab 3.1 - Max Points`,
`Lab 3.2` = `Lab 3.2` / `Lab 3.2 - Max Points`,
`Midterm Exam` = `Midterm Exam` / `Midterm Exam - Max Points`,
`Final Exam` = `Final Exam` / `Final Exam - Max Points`
) |>
# lateness
dplyr::mutate(
`Lab 1.1` = lateness_penalty(`Lab 1.1`, `Lab 1.1 - Lateness (H:M:S)`),
`Lab 1.2` = lateness_penalty(`Lab 1.2`, `Lab 1.2 - Lateness (H:M:S)`),
`Lab 2.1` = lateness_penalty(`Lab 2.1`, `Lab 2.1 - Lateness (H:M:S)`),
`Lab 2.2` = lateness_penalty(`Lab 2.2`, `Lab 2.2 - Lateness (H:M:S)`),
`Lab 3.1` = lateness_penalty(`Lab 3.1`, `Lab 3.1 - Lateness (H:M:S)`),
`Lab 3.2` = lateness_penalty(`Lab 3.2`, `Lab 3.2 - Lateness (H:M:S)`)
) |>
# calculate Lab 1, Lab 2, Lab 3 category grades
dplyr::mutate(
`Lab 1` = (`Lab 1.1` * `Lab 1.1 - Max Points` + `Lab 1.2` * `Lab 1.2 - Max Points`) / (`Lab 1.1 - Max Points` + `Lab 1.2 - Max Points`),
`Lab 2` = (`Lab 2.1` * `Lab 2.1 - Max Points` + `Lab 2.2` * `Lab 2.2 - Max Points`) / (`Lab 2.1 - Max Points` + `Lab 2.2 - Max Points`),
`Lab 3` = (`Lab 3.1` * `Lab 3.1 - Max Points` + `Lab 3.2` * `Lab 3.2 - Max Points`) / (`Lab 3.1 - Max Points` + `Lab 3.2 - Max Points`)
)
script_grades <- lab_grades_with_lateness |>
# calculate category grade for "Homework"
dplyr::mutate(
`Labs` = rowMeans(one_drop(lab_grades_with_lateness))
) |>
# calculate final Overall Grade
dplyr::mutate(`Overall Grade` = 0.4 * `Labs` + 0.2 * `Midterm Exam` + 0.4 * `Final Exam`)And, as you can see, the computations align.
Disclaimers:
The
weightof a category with aggregationweighted_meanshould be corresponding order with theassignments.Categories cannot have the same name as any other assignment name.