120 lines
4.6 KiB
R
120 lines
4.6 KiB
R
## load in the data
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#################################
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case.sessions <- 15
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myuw <- read.csv("myuw-COM_482_A_autumn_2020_students.csv", stringsAsFactors=FALSE)
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## class-level variables
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question.grades <- c("GOOD"=100, "FAIR"=100-(50/3.3), "BAD"=100-(50/(3.3)*2))
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missed.question.penalty <- (50/3.3) * 0.2 ## 1/5 of a full point on the GPA scale
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source("../assessment_and_tracking/track_participation.R")
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setwd("case_grades")
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rownames(d) <- d$discord.name
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## show the distribution of assessments
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table(call.list.full$assessment)
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prop.table(table(call.list.full$assessment))
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table(call.list.full$answered)
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prop.table(table(call.list.full$answered))
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total.questions.asked <- nrow(call.list.full)
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## create new column with number of questions present
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d$prop.asked <- d$num.calls / d$num.present
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## generate statistics using these new variables
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prop.asks.quantiles <- quantile(d$prop.asked, probs=seq(0,1, 0.01))
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prop.asks.quantiles <- prop.asks.quantiles[!duplicated(prop.asks.quantiles)]
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## this is generating broken stuff but it's not used for anything
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d$prop.asked.quant <- cut(d$prop.asked, breaks=prop.asks.quantiles,
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labels=names(prop.asks.quantiles)[1:(length(prop.asks.quantiles)-1)])
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## generate grades
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##########################################################
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d$part.grade <- NA
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## print the median number of questions for (a) everybody and (b)
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## people that have been present 75% of the time
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median(d$num.calls[d$days.absent < 0.25*case.sessions])
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median(d$num.calls)
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questions.cutoff <- median(d$num.calls)
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## helper function to generate average grade minus number of missing
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gen.part.grade <- function (x.discord.name) {
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q.scores <- question.grades[call.list$assessment[call.list$discord.name == x.discord.name]]
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base.score <- mean(q.scores, na.rm=TRUE)
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## number of missing days
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missing.days <- nrow(missing.in.class[missing.in.class$discord.name == x.discord.name,])
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## return the final score
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data.frame(discord.name=x.discord.name,
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part.grade=(base.score - missing.days * missed.question.penalty))
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}
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tmp <- do.call("rbind", lapply(d$discord.name[d$num.calls >= questions.cutoff], gen.part.grade))
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d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
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## next handle the folks *under* the median
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## first we handle the zeros
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## step 1: first double check the people who have zeros and ensure that they didn't "just" get unlucky"
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d[d$num.calls == 0,]
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## set those people to 0 :(
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d$part.grade[d$num.calls == 0] <- 0
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## step 2 is to handle folks who got unlucky in the normal way
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tmp <- do.call("rbind", lapply(d$discord.name[is.na(d$part.grade) & d$prop.asked <= median(d$prop.asked)], gen.part.grade))
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d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
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## the people who are left are lucky and still undercounted so we'll penalize them
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d[is.na(d$part.grade),]
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penalized.discord.names <- d$discord.name[is.na(d$part.grade)]
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## generate the baseline participation grades as per the process above
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tmp <- do.call("rbind", lapply(penalized.discord.names, gen.part.grade))
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d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
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## now add "zeros" for every questions that is below the normal
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d[as.character(penalized.discord.names),"part.grade"] <- ((
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(questions.cutoff - d[as.character(penalized.discord.names),"num.calls"] * 0) +
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(d[as.character(penalized.discord.names),"num.calls"] * d[as.character(penalized.discord.names),"part.grade"]) )
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/ questions.cutoff)
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d[as.character(penalized.discord.names),]
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## map part grades back to 4.0 letter scale and points
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d$part.4point <-round((d$part.grade / (50/3.3)) - 2.6, 2)
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d[sort.list(d$prop.asked), c("discord.name", "num.calls", "num.present",
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"prop.asked", "prop.asked.quant", "part.grade", "part.4point",
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"days.absent")]
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d[sort.list(d$part.4point), c("discord.name", "num.calls", "num.present",
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"prop.asked", "prop.asked.quant", "part.grade", "part.4point",
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"days.absent")]
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## writing out data
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quantile(d$num.calls, probs=(0:100*0.01))
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d.print <- merge(d, myuw[,c("StudentNo", "FirstName", "LastName", "UWNetID")],
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by.x="student.num", by.y="StudentNo")
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write.csv(d.print, file="final_participation_grades.csv")
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library(rmarkdown)
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for (x.discord.name in d$discord.name) {
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render(input="../../assessment_and_tracking/student_report_template.Rmd",
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output_format="html_document",
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output_file=paste("../data/case_grades/student_reports/",
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d.print$UWNetID[d.print$discord.name == x.discord.name],
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sep=""))
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}
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