Setting up the Discord Bot ====================================== I run the Discord boy from my laptop. It requires the discord Python module available in PyPi and installable like: $ pip3 install discord I don't have details on how I set up my own Discord bot and/or invited it to my server but I hope you'll add to this file as you do this and figure out what needs to happen. Using the Cold Call Bot ====================================== 1. All students must have the role "Student" in Discord. If they do not have the roll, they will not be called upon. 2. The "classroom" is the "Classroom Voice" channel. This is currently hard coded. 3. The bot has only one command: "$next" which calls a person and records this information in the logs. You can run this command in any channel that the bot has access to (e.g., #bot-commands) but I do it a public channel called "#classroom-questions" so that students can watch it operate. Daily Process ====================================== You need to start the bot from the laptop each day. I do that by: $ ./coldcallboy.py The bot will run in the terminal, print out data as it works including detailed weights as it goes, and it will record data into files in the /data/ directory. After class, you will have two new files created that will be named like this (with today's date): attendance-2020-10-05.tsv call_list-2020-10-05.tsv Each day, you need to open up "call_list-YYYY-MM-DD.tsv" and edit the final two columns. 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 final column `assessment` is GOOD, FAIR, or BAD in my rubric. I've detailed what that means on this page: https://wiki.communitydata.science/User:Benjamin_Mako_Hill/Assessment#Rubric_for_case_discussion_answers 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. I keep my entire data directory in git and I'd recommend that you do too. 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. 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 a 1-line wget command. 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.