coldcallbot/assessment_and_tracking/compute_final_case_grades.R

## load in the data
#################################
myuw <- read.csv("../data/2022_winter_COM_481_A_students.csv", stringsAsFactors=FALSE)

current.dir <- getwd()
source("../assessment_and_tracking/track_participation.R")
setwd(current.dir)

rownames(d) <- d$unique.name
call.list$timestamp <- as.Date(call.list$timestamp)

## class-level variables
question.grades <- c("PLUS"=100, "CHECK"=100-(50/3.3), "MINUS"=100-(50/(3.3)*2))
missed.question.penalty <- (50/3.3) * 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)
absence.threshold <- 4 ## TODO talk about this

## 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
## first these are the people were were not called simply because they got unlucky

 ## this is the 95% percentile based on simulation in simulation.R
questions.cutoff <- 4

## show the distribution of assessments
table(call.list$assessment)
prop.table(table(call.list$assessment))

table(call.list.full$answered)
prop.table(table(call.list.full$answered))

total.questions.asked <- nrow(call.list)

## find out how man questions folks have present/absent for
##########################################################
calls.per.day <- data.frame(day=as.Date(names(table(call.list$timestamp))),
                            questions.asked=as.numeric(table(call.list$timestamp)))

## function to return the numbers of calls present for or zero if they
## were absent
calls.for.student.day <- function (day, student.id) {
    if (any(absence$unique.name == student.id & absence$date.absent == day)) {
        return(0)
    } else {
        return(calls.per.day$questions.asked[calls.per.day$day == day])
    }
}

compute.questions.present.for.student <- function (student.id) {
    sum(unlist(lapply(unique(calls.per.day$day), calls.for.student.day, student.id)))
}

## create new column with number of questions present
d$q.present <- unlist(lapply(d$unique.name, compute.questions.present.for.student))
d$prop.asked <- d$num.calls / d$q.present

## generate statistics using these new variables
prop.asks.quantiles <- quantile(d$prop.asked, probs=seq(0,1, 0.01))
prop.asks.quantiles <- prop.asks.quantiles[!duplicated(prop.asks.quantiles)]

d$prop.asked.quant <- cut(d$prop.asked, right=FALSE, breaks=c(prop.asks.quantiles, 1),
    labels=names(prop.asks.quantiles)[1:(length(prop.asks.quantiles))])

## generate grades
##########################################################

## print the median number of questions for (a) everybody and (b)
## people that have been present 75% of the time
median(d$num.calls)

## 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]]
    base.score <- mean(q.scores, na.rm=TRUE)

    ## number of missing days
    missing.in.class.days <- nrow(missing.in.class[missing.in.class$unique.name == x.unique.name,])

    ## return the final score
    data.frame(unique.name=x.unique.name,
               base.grade=base.score,
               missing.in.class.days=missing.in.class.days)
}


## create the base grades which do NOT include missing questions
tmp <- do.call("rbind", lapply(d$unique.name, gen.part.grade))
d <- merge(d, tmp)
rownames(d) <- d$unique.name

## apply the penality for number of days we called on them and they were gone
d$part.grade <- d$base.grade - d$missing.in.class.days * missed.question.penalty
d$part.grade.orig <- d$part.grade

## first we handle the zeros
## step 1: first double check the people who have zeros and ensure that they didn't "just" get unlucky"
d[d$num.calls == 0,]

## set those people to 0 :(
d[d$num.calls == 0]
d$part.grade[d$num.calls == 0] <- 0

## step 2: identify the people who were were not asked "enough" questions but were unlucky/lucky
## penalized.unique.names <- d$unique.name[d$num.calls < median(d$num.calls) & d$absences > median(d$absences)]

## first these are the people were were not called simply because they got unlucky
d[d$num.calls < questions.cutoff & d$absences < absence.threshold,]

## first these are the people were were not called simply because they got unlucky
penalized.unique.names <- d$unique.name[d$num.calls < questions.cutoff & d$absences > absence.threshold]
d[d$unique.name %in% penalized.unique.names,]

## now add "zeros" for every questions that is below the normal
d[as.character(penalized.unique.names),"part.grade"] <- ((
    (questions.cutoff - d[as.character(penalized.unique.names),"num.calls"] * 0) +
    (d[as.character(penalized.unique.names),"num.calls"] * d[as.character(penalized.unique.names),"part.grade"]) )
    / questions.cutoff)

d[as.character(penalized.unique.names),]

## TODO ensure this is right. i think it is
## map part grades back to 4.0 letter scale and points
d$part.4point <- round((d$part.grade / (50/3.3)) - 2.6, 2)

d[sort.list(d$part.4point, decreasing=TRUE),
  c("unique.name", "short.name", "num.calls", "absences", "part.4point")]


## writing out data to CSV
d.print <- merge(d, myuw[,c("StudentNo", "FirstName", "LastName", "UWNetID")],
                 by.x="unique.name", by.y="StudentNo")
write.csv(d.print, file="../data/final_participation_grades.csv")

library(rmarkdown)

for (id in d$unique.name) {
    render(input="student_report_template.Rmd",
           output_format="html_document",
           output_file=paste("../data/case_grades/",
                             d.print$unique.name[d.print$unique.name == id],
                             sep=""))
}