## load in the data
#################################
-myuw <- read.csv("../data/2022_winter_COM_481_A_students.csv", stringsAsFactors=FALSE)
+myuw <- read.csv("../data/2024_autumn_COMMLD_570_A_joint_students.csv", stringsAsFactors=FALSE)
current.dir <- getwd()
source("../assessment_and_tracking/track_participation.R")
## class-level variables
gpa.point.value <- 50/(4 - 0.7)
-question.grades <- c("PLUS"=100, "CHECK"=100-gpa.point.value, "MINUS"=100-(gpa.point.value*2))
+## question.grades <- c("GOOD"=100, "FAIR"=100-gpa.point.value, "BAD"=100-(gpa.point.value*2))
+question.grades <- c("GOOD"=100, "SATISFACTORY"=100-gpa.point.value, "POOR"=100-(gpa.point.value*2), "NO MEANINGFUL ANSWER"=0)
missed.question.penalty <- gpa.point.value * 0.2 ## 1/5 of a full point on the GPA scale
## inspect set the absence threashold
ggplot(d) + aes(x=absences) + geom_histogram(binwidth=1, fill="white",color="black")
absence.threshold <- median(d$absences)
-
## inspect and set the questions cutoff
## questions.cutoff <- median(d$num.calls)
## median(d$num.calls)
## questions.cutoff <- nrow(call.list) / nrow(d) ## TODO talk about this
## this is the 95% percentile based on simulation in simulation.R
-questions.cutoff <- 4
+questions.cutoff <- 15
## show the distribution of assessments
table(call.list$assessment)
## helper function to generate average grade minus number of missing
gen.part.grade <- function (x.unique.name) {
q.scores <- question.grades[call.list$assessment[call.list$unique.name == x.unique.name]]
+ print(q.scores)
base.score <- mean(q.scores, na.rm=TRUE)
## number of missing days
missing.in.class.days=missing.in.class.days)
}
-
## create the base grades which do NOT include missing questions
tmp <- do.call("rbind", lapply(d$unique.name, gen.part.grade))
d <- merge(d, tmp)
from coldcall import ColdCall
from datetime import datetime
from csv import DictReader
+from random import sample
import json
+import argparse
+
+parser = argparse.ArgumentParser(description='run the coldcall bot manually to create a coldcall list')
+
+parser.add_argument('-n', '--num', dest="num_calls", default=100, const=100, type=int, nargs='?',
+ help="how many students should be called")
+
+parser.add_argument('-s', '--shuffle', dest="shuffle_roster", action="store_true",
+ help="select without replacement (i.e., call each person once with n equal to the group size)")
+
+args = parser.parse_args()
current_time = datetime.today()
with open("configuration.json") as config_file:
students_present = [s for s in registered_students if s not in missing_today]
# print("Students present:", students_present) # useful for debug
-for i in range(100):
- selected_student = cc.select_student_from_list(students_present)
+def print_selected(selected_student):
+ if "print_index" in globals():
+ global print_index
+ else:
+ global print_index
+ print_index = 1
try:
preferred_name = preferred_names[selected_student]
pronouns = preferred_pronouns[selected_student]
else:
pronouns = "[unknown pronouns]"
-
- print(f"{i + 1}. {preferred_name} :: {pronouns} :: {full_names[selected_student]} :: {selected_student}")
+
+ print(f"{print_index}. {preferred_name} :: {pronouns} :: {full_names[selected_student]} :: {selected_student}")
cc.record_coldcall(selected_student)
+ print_index += 1 ## increase the index
+
+# if we're in suffle mode
+shuffle = args.shuffle_roster
+
+print_index = 1
+
+if shuffle:
+ for selected_student in sample(students_present, len(students_present)):
+ print_selected(selected_student)
+else:
+ num_calls = args.num_calls
+ for i in range(num_calls):
+ selected_student = cc.select_student_from_list(students_present)
+ print_selected(selected_student)