I keep my entire data directory in git and I'd recommend that you do
too. Just make sure you don't commit and publish student records into
the public git repository. I usually just keep a separate branch for
classes.

Daily Process
================================

1. Open your terminal (on Windows, this will likely be powershell in anaconda)

2. Change into the directory with the coldcall scripts.

3. Download new data with: `python download_student_info.py`

   This will download the latest version of absence data into `data/optout_poll_data.tsv` as well as th student information into `data/student_information.tsv`.

   If you noticed any changes you need to make (e.g., the same preferred names, incorrectly entered absences, etc) you should edit the Google sheets and then running the download again with the same script.

4. When you're ready, fun the main script in the same directory: python coldcallbot-manual.py
   
   This will both:
   
   - output a paper list in terminal. I often redirect this to a file like: `python coldcallbot-manual.py > data/paper_call_list-2024-09-26.txt` or similar.
   - Create the computed call list in the `data/` folder

During case, I take notes on student answers on paper during class (typically I
only note down non "GOOD" answers) and then add these to the sheet
immediately after class.

After class each day, you need to open up "call_list-YYYY-MM-DD.tsv"
and edit the two columns in which you store the results of the
case. The first columns `answered` means that the person responded and
answered the question (i.e., they were present in the room but away
from their computer and unresponsive). This is almost always TRUE but
would be FALSE if the student were missing.

The assessment column should be is "GOOD", "SATISFACTORY", "POOR", "NO
MEANINGFUL ANSWER" or "ABSENT" but you can do whatever makes sense in
this and we can work with it when it comes to grading. Just make sure
you are consistent!

Details on my rubric is here:

https://wiki.communitydata.science/User:Benjamin_Mako_Hill/Assessment#Rubric_for_case_discussion_answers


Assessment and Tracking
======================================

These scripts rely on a file in this repository called
`data/student_information.csv` which I have set to be downloaded
automatically from a Google form using the download script.

I don't expect that these will necessary work without
modification. It's a good idea to go line-by-line through these to
make sure they are doing what *you* want and that you agree with the
assessment logic built into this.

For reference, that file has the following column labels (this is the
full header, in order):

    Timestamp
    Your UW student number
    Name you'd like to go by in class
    Your Wikipedia username
    Your username on the class Discord server
    Preferred pronouns
    Anything else you'd like me to know?

The scripts in this directory are meant to be run or sourced *from*
the data directory. As in:

    $ cd ../data
    $ R --no-save < ../assessment_and_tracking/track_participation.R

There are three files in that directory:

track_enrolled.R:

    This file keeps track of who is in Discord, who is enrolled for
    the class, etc. This helps me remove people from the
    student_informaiton.csv spreadsheet who are have dropped the
    class, deal with users who change their Discord name, and other
    things that the scripts can't deal with automatically.

    This all need to be dealt with manually, one way or
    another. Sometimes by modifying the script, sometimes by modifying
    the files in the data/ directory.

    This requires an additional file called
    `myuw-COM_482_A_autumn_2020_students.csv` which is just the saved
    CSV from https://my.uw.edu which includes the full class list. I
    download this one manually.

track_participation.R:

    This file generates histograms and other basic information about
    the distribution of participation and absences. I've typically run
    this weekly after a few weeks of the class and share these images
    with students at least once or twice in the quarter.

    This file is also sourced by compute_final_case_grades.R.

compute_final_case_grades.R:

    You can find a narrative summary of my assessment process here:

    https://wiki.communitydata.science/User:Benjamin_Mako_Hill/Assessment#Overall_case_discussion_grade

    This also requires the registration file (something like
    `myuw-COM_482_A_autumn_2020_students.csv`) which is described
    above.

    To run this script, you will need to create the following subdirectories:

    data/case_grades
    data/case_grades/student_reports


One final note: A bunch of things in these scripts assumes a UW 4.0
grade scale. I don't think it should be hard to map these onto some
other scale, but that's an exercise I'll leave up to those that want
to do this.

   
 5. after class, update the call list in the data folder to remove lines for any call that didn't happen (or you don't want to count) and update the assessments: