Detailed Grade Calculations

# import os
# from datetime import date,timedelta
# import calendar
# import pandas as pd
# import numpy as np
# import seaborn as sns

# from myst_nb import glue
# import plotly.express as px
# learning complexities

from cspt import grade_constants, grade_calculation

Grade cutoffs for total influence are:

grade_constants.letter_df

The total influence of each badge is as follows:

# display
grade_constants.influence_df

Bonuses¶

In addition to the weights for each badge, there also bonuses that will automatically applied to your grade at the end of the semester. These are for longer term patterns, not specific assignments. You earn these while workng on other assignments, not separately.

Auto bonuses will be calculated from your other list of badges. Event bonuses will be logged in your KWL repo, where you get instructions when you meet the criteria.

Bonus Implications¶

Attendance and participation is very important:

• 14 experience, 6 labs, and 9 practice is an F

• 22 experience, 13 labs, and 9 practice is a C-

• 14 experience, 6 labs, 9 practice and one build is a C-

• 22 experience, 13 labs, 9 practice and one build is a C+

Missing one thing can have a nonlinear effect on your grade. Example 1:

• 22 experience, 13 labs, and 18 review is a C

• 21 experience, 13 labs, and 18 review is a C-

• 21 experience, 13 labs, and 17 review is a D+

• 21 experience, 12 labs, and 17 review is a D

Example 2:

• 22 experience, 13 labs, and 17 practice is a C

• 22 experience, 13 labs, 17 practice, and 1 review is a B-

• 22 experience, 13 labs, and 18 practice is a B

The Early Bird and Descriptive Commits bonuses are straight forward and set you up for success. Combined, they are also the same amount as the participation and lab bonuses, so getting a strong start and being detail oriented all semester can give you flexibility on attendance or labs.

Early Bird, Descriptive commits, Community Star, and Git-ing Unstuck are all equal to the half differnce between steps at a C or above. So earning any two can add a + to a C or a B for example:

• 22 experience, 13 labs, 18 practice, Descriptive Commits, and Early Bird is a B+

• 22 experience, 13 labs, 18 review, Descriptive Commits, and Early Bird is a C+

in these two examples, doing the work at the start of the semester on time and being attentive throughout increases the grade without any extra work!

If you are missing learning badges required to get to a bonus, community badges will fill in for those first. If you earn the Participation, Lab, and Breadth bonuses, then remaining community badges will count toward the community bonus.

For example, at the end of the semester, you might be able to skip some the low complexity learning badges (experience, review, practice) and focus on your high complexity ones to ensure you get an A.

The order of application for community badges:

• to make up missing experience badges

• to make up for missing review or practice badges to earn the breadth bonus

• to upgrade review to practice to meet a threshold

• toward the community badge bonus

To calculate your final grade at the end of the semester, a script will count your badges and logged event bonuses. The script can output as a yaml file, which is like a dictionary, for an example here we will use a dictionary.

see cspt docs for CLI version

example_student = {'experience' :22, 'lab': 13, 'review': 0,'practice': 24,
                   'explore': 0,
                   'build' :0,
                 'community': 0,
                 'hack':0,
                 'unstuck': 0,
                 'descriptive': 0,
                 'early': 0,
                  'question':0 }

badges_comm_applied = grade_calculation.community_apply(example_student)
badges_comm_applied

grade_calculation.calculate_grade(badges_comm_applied)

grade_calculation.calculate_grade(badges_comm_applied,True)