Library created by Michael Riad Zaky mriadzaky@fordham.edu (2020), with Daniel Leeds, Gary Weiss, Mavis Zhang at Fordham University. Library free for use provided you cite https://github.com/MichaelRZ/EDMLib in any resulting publications. Library free for redistribution provided you retain the author attributions above.
New: more details as well as a GUI for the library can be found at https://edmlib.ml/. More library documentation found at https://edmlib.ml/edmlib/index.html.
Usage:
The library takes in grade data in the form of a CSV file or Pandas dataframe and manipulates the data according to certain constant column names for student IDs, class IDs, and a student’s grade in that class. These constants can be changed as need be, and by default work with Fordham’s data.
Importing the library (while edmlib.py is in the same directory):
import edmlib
from edmlib import gradeData
from edmlib import classCorrelationData
Using the gradeData class:
Initialization
data = gradeData(‘filename.csv’)
# or data = gradeData(pandasDataFrame)
Filter to class data that has a gpa deviation larger than X (ex. 0.2):
data.filterByGpaDeviationMoreThan(X)
Filter to specific classes or majors:
data.reduceToMultipleMajorsOrClasses(majorsToFilterTo, classesToFilterTo)
# where 'majorsToFilterTo' is a list of majors matching the class major column and 'classesToFilterTo'
# is a list of classes matching the class defining column
Export correlation file:
data.exportCorrelationsWithAvailableClasses('outputfile.csv')
# Has format of "course1", "course2", "corr" (correlation between 0-1), "P-value" (value between 0-1),
# "#students" (number of students shared between these classes).
Change constants to match your file:
data.defineWorkingColumns(finalGradeInClassColumn, classDefiningColumn, courseNumberAndTerm, studentIdColumn, classMajorColumn)
See the demo file for example usage, as well as https://edmlib.ml/edmlib/index.html for more library documentation.