Ctrl+K

Grading curve

Grading curve#

This course has a curve due to SIPA policy:

Grades submitted for SIPA core courses must have an average GPA between 3.2 and 3.4, with the goal being 3.3. Courses with enrollments over 35 are also recommended to follow this rule.

As a compromise with Academic Affairs, the upper limit will be set to 3.8.

Getting your estimated course grade#

  1. Open CourseWorks.

  2. Go to Grades.

  3. View the current grade.

How course grades work#

Within the Grades section of CourseWorks: The Total course grade for each student is computed as a percentage, based on the weighted scores. It also shows an estimated letter grade.

A few times during the semester:

  1. The full gradebook is exported and run through this notebook.

  2. The percentage required for all the letter grades is adjusted up or down as necessary to hit the target GPA range.

  3. Those cutoffs are updated in CourseWorks.

  4. The estimated letter grades potentially change.

The min_score column of the new cutoffs shows the current minimum Total percentage required for each letter grade.

Methodology#

The rest of this notebook shows how the grade cutoffs are computed. This methodology, the distribution of student percentages, and thus your estimated course grade are subject to change up until final grades are submitted.

MIN_AVG_GPA = 3.2
MAX_AVG_GPA = 3.8

Load current scores#

The scores below are the total scores based on everything that has grades released thus far across both sections. See the timestamp in the filename below to know when it was updated. The grade data is anonymous for privacy reasons.

import pandas as pd

path = "/Users/afeld/Downloads/2024-03-20T2340_Grades-INAFU6504_ALL_2024_1_-_Python_for_Public_Policy.csv"
grades = pd.read_csv(path, skiprows=[1, 2])

# exclude the test student built into CourseWorks
grades = grades[grades["Student"] != "Student, Test"]

# obfuscate whose score is whose
grades = grades[["Current Score"]]
grades = grades.sort_values("Current Score").reset_index(drop=True)

grades
Current Score
0 0.14
1 64.91
2 77.05
3 86.21
4 86.48
... ...
76 100.00
77 100.00
78 100.00
79 100.00
80 100.00

81 rows × 1 columns

Distribution#

import plotly.express as px

fig = px.histogram(
    grades,
    x="Current Score",
    title="Distribution of the overall grades as a percentage, computed by CourseWorks",
    labels={"Current Score": "Current Score (percent)"},
)
fig.update_layout(yaxis_title_text="Number of students")
fig.show()

Match to letter grades / GPAs#

Creating the grading notation table in Pandas:

letter_grade_equivalents = pd.DataFrame(
    index=["A", "A-", "B+", "B", "B-", "C+", "C", "C-", "D", "F"],
    data={"gpa": [4.00, 3.67, 3.33, 3.00, 2.67, 2.33, 2.00, 1.67, 1.00, 0.00]},
)

Assign starting minimum scores, roughly based on the Default Canvas Grading Scheme:

letter_grade_equivalents["min_score"] = [94.0, 90.0, 87.0, 84.0, 80.0, 77.0, 74.0, 70.0, 60.0, 0.0]
letter_grade_equivalents
gpa min_score
A 4.00 94.0
A- 3.67 90.0
B+ 3.33 87.0
B 3.00 84.0
B- 2.67 80.0
C+ 2.33 77.0
C 2.00 74.0
C- 1.67 70.0
D 1.00 60.0
F 0.00 0.0

Adjust cutoffs#

Raise or lower the minimum scores for each grade (not including F) until the average GPA is in the acceptable range.

# merge_asof() needs columns sorted ascending
orig_grade_cutoffs = letter_grade_equivalents.sort_values(by="min_score")
grade_cutoffs = orig_grade_cutoffs.copy()

grades_to_adjust = grade_cutoffs.index != "F"

adjustment = 0
STEP_SIZE = 0.1

while True:
    grade_cutoffs.loc[grades_to_adjust, "min_score"] = (
        orig_grade_cutoffs[grades_to_adjust]["min_score"] + adjustment
    )

    # make the letter grades a column so they show up in the merged DataFrame
    grade_cutoffs_with_letters = grade_cutoffs.reset_index().rename(
        columns={"index": "letter_grade"}
    )

    # find the letter grade / GPA for each student
    adjusted_grades = pd.merge_asof(
        grades,
        grade_cutoffs_with_letters,
        left_on="Current Score",
        right_on="min_score",
        direction="backward",
    )

    new_mean = adjusted_grades["gpa"].mean()
    print(f"Adjustment: {adjustment:+.1f}, Average: {new_mean:.3f}")

    # check if we've hit the target range
    if MIN_AVG_GPA <= new_mean < MAX_AVG_GPA:
        # success
        break
    elif new_mean >= MAX_AVG_GPA:
        # raise
        adjustment += STEP_SIZE
    else: # new_mean < MIN_AVG_GPA:
        # lower
        adjustment -= STEP_SIZE

Adjustment: +0.0, Average: 3.811
Adjustment: +0.1, Average: 3.807
Adjustment: +0.2, Average: 3.807
Adjustment: +0.3, Average: 3.807
Adjustment: +0.4, Average: 3.803
Adjustment: +0.5, Average: 3.803
Adjustment: +0.6, Average: 3.790

Confirm the A cutoff is still achievable:

assert grade_cutoffs.at["A", "min_score"] <= 100

New cutoffs#

grade_cutoffs.sort_values("min_score", ascending=False)
gpa min_score
A 4.00 94.6
A- 3.67 90.6
B+ 3.33 87.6
B 3.00 84.6
B- 2.67 80.6
C+ 2.33 77.6
C 2.00 74.6
C- 1.67 70.6
D 1.00 60.6
F 0.00 0.0

These will be reflected in the CourseWorks grading scheme.

Check results#

Double-check the new average is in line with policy:

assert MIN_AVG_GPA <= new_mean < MAX_AVG_GPA, f"{new_mean} not in acceptable range"

new_mean

3.790246913580247

fig = px.histogram(adjusted_grades, x="letter_grade", title="Distribution of grades")
fig.update_layout(yaxis_title_text="Number of students")
fig.show()
Contents