pygtracker

Package Overview

pygtracker is a Python package that allows UBC MDS lecturers to record, analyze and adjust grades for students in a particular program. Users can record grades from each course, generate a summary report to determine which class is more challenging than the rest, or identify students who may need help. Finally, the package can suggest ways to adjust the grades for students to ensure the average grades match predefined benchmarks.

Python Ecosystem

There are existing packages to manage students' grades in the Python ecosystem. For example, grades-report is a package purely generating statistics from all grades. In addition, edx-bulk-grades aids lecturers in grading and modifying students' grades in bulk. However, these packages are lacking useful features that are unique to the MDS program, such as identifying students / courses that are much different from their peers or adjusting grades to make sure they conform to MDS standard. As a result, we come up with this useful tool which helps UBC MDS lecturers have a better control of student performance.

Features

This package contains seven functions: register_courses, record_grades, calculate_final_grade, generate_course_statistics, rank_courses, rank_students and suggest_grade_adjustment.

The main components of this package are:

• Register courses
  • Read/store the courses information as a dataframe
• Record grades for students
  • Read/store the students' grades for each assessment as a dataframe
• Calculate the final grade
  • Provide the final grade for each student in each course using the courses information and students' grade
• Generate course statistics
  • Provide grade statistics on the courses, including mean, 1st quantile, median and 3rd quantile
• Rank courses
  • Provide the rankings of courses based on courses' average grades
• Rank students
  • Provide the rankings of students based on their average grades for the selected course (or the whole program).
• Suggest grade adjustment
  • Suggest grade adjustments for any course based on predefined benchmarks

Installation

$ pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple pygtracker

Dependencies

• You can find all dependencies in pyproject.toml file

Usage

A toy dataset is used to showcase the usage of the functions. Any csv file containing the grades and courses for the UBC MDS program may be used.

1. you will need to import the pygtracker package, create an instance of the class, and store courses, grades as data frames by function register_courses() and record_grades().

# import packages 
from pygtracker import pygtracker 
import os 

# call an instance of the class
tracker = pygtracker.GradeTracker()

# read in the toy dataset 
courses = pd.read_csv(os.path.join(".", "tests", "test_data", "course_info.csv"))
grades = pd.read_csv(os.path.join(".", "tests", "test_data", "student_info.csv"))

# read and stores data as a dataframe 
tracker.register_courses(courses)
tracker.record_grades(grades)

2. By specifying a list of courses, function generate_course_statistics() would allow you to create a data frame with summary statistics, including mean, 1st-quantile, median and 3rd-quantile of grades so far.

# generates a dataframe with summary statistics for a specified course 
tracker.generate_course_statistics(["511"])

	course_id	mean	1st-quantile	median	3rd-quantile
0	511	87.87	86.91	88	88.96

3. Function rank_courses() generates a data frame of course ranking based on specified method (default is mean) in descending order by default. This would allow the user to find which course students perform better and which they may struggle with so far.

# generates a dataframe for course ranking by grade 
tracker.rank_courses()

	course_id	grade
0	522	91.29
1	511	87.87

4. To look into more details on performance of individual students, function rank_students() would generate a data frame with student ranking by grades based on specified courses (default is all courses completed so far) in descending order. Thus, the students who may need more help or guidance could be found.

# generates a dataframe with student ranking by grade 
tracker.rank_students()

	student_id	grade	rank
0	joel	91.81	1.0
1	tom	90.09	2.0
2	mike	88.29	3.0

5. Lastly, based on function suggest_grade_adjustment(), the user is able to find adjusting suggestions by setting benchmark of the whole course, labs, or quizzes of that course, such that the final grades meet or exceed these benchmarks.

# calculates the adjustment for a course for a specified benchmark 
tracker.suggest_grade_adjustment("511", benchmark_course=100)

	course_id	student_id	feedback	lab1	lab2	lab3	lab4	milestone1	milestone2	milestone3	milestone4	quiz1	quiz2
0	511	joel	0	100	100	100	100	0	0	0	0	100	100
1	511	mike	0	100	100	100	100	0	0	0	0	100	100
2	511	tiff	0	100	100	100	100	0	0	0	0	100	100
3	511	tom	0	100	100	100	100	0	0	0	0	100	100

Documentation

The official documentation is hosted on Read the Docs: https://pygtracker.readthedocs.io/en/latest/

Contributors

We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.

• Javairia Raza
• Jianru Deng
• Tran Doan Khanh Vu
• Yanhua Chen

Credits

This package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from the pyOpenSci/cookiecutter-pyopensci project template and the audreyr/cookiecutter-pypackage.