X-Git-Url: https://code.communitydata.science/coldcallbot-discord.git/blobdiff_plain/3955a6bfcc0bd424fcf069f05d866c75315ee16c..743e0a39f3f56beab45e22845cd5117a5e316506:/data/case_grades/compute_final_case_grades.R

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