X-Git-Url: https://code.communitydata.science/coldcallbot-discord.git/blobdiff_plain/743e0a39f3f56beab45e22845cd5117a5e316506:/data/case_grades/compute_final_case_grades.R..9c4f81c30ac7c23cf1dfad7af54d1f12d4ba4d58:/assessment_and_tracking/static/gitweb.css

diff --git a/data/case_grades/compute_final_case_grades.R b/data/case_grades/compute_final_case_grades.R
deleted file mode 100644
index e11b1a9..0000000
--- a/data/case_grades/compute_final_case_grades.R
+++ /dev/null
@@ -1,120 +0,0 @@
-## 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=""))
-}