data/case_grades/compute_final_case_grades.R

   1 ## load in the data
   2 #################################
   3
   4 case.sessions  <- 15
   5 myuw <- read.csv("../myuw-COM_482_A_autumn_2020_students.csv", stringsAsFactors=FALSE)
   6
   7 ## class-level variables
   8 question.grades <- c("GOOD"=100, "FAIR"=100-(50/3.3), "BAD"=100-(50/(3.3)*2))
   9 missed.question.penalty <- (50/3.3) * 0.2 ## 1/5 of a full point on the GPA scale
  10
  11 setwd("../")
  12 source("track_participation.R")
  13 setwd("case_grades")
  14
  15 rownames(d) <- d$discord.name
  16
  17 ## show the distribution of assessments
  18 table(call.list.full$assessment)
  19 prop.table(table(call.list.full$assessment))
  20 table(call.list.full$answered)
  21 prop.table(table(call.list.full$answered))
  22
  23 total.questions.asked <- nrow(call.list.full)
  24
  25 ## create new column with number of questions present
  26 d$prop.asked <- d$num.calls / d$num.present
  27
  28 ## generate statistics using these new variables
  29 prop.asks.quantiles <- quantile(d$prop.asked, probs=seq(0,1, 0.01))
  30 prop.asks.quantiles <- prop.asks.quantiles[!duplicated(prop.asks.quantiles)]
  31
  32 ## this is generating broken stuff but it's not used for anything
  33 d$prop.asked.quant <- cut(d$prop.asked, breaks=prop.asks.quantiles,
  34     labels=names(prop.asks.quantiles)[1:(length(prop.asks.quantiles)-1)])
  35
  36 ## generate grades
  37 ##########################################################
  38
  39 d$part.grade <- NA
  40
  41 ## print the median number of questions for (a) everybody and (b)
  42 ## people that have been present 75% of the time
  43 median(d$num.calls[d$days.absent < 0.25*case.sessions])
  44 median(d$num.calls)
  45
  46 questions.cutoff <- median(d$num.calls)
  47
  48 ## helper function to generate average grade minus number of missing
  49 gen.part.grade <- function (x.discord.name) {
  50     q.scores <- question.grades[call.list$assessment[call.list$discord.name == x.discord.name]]
  51     base.score <- mean(q.scores, na.rm=TRUE)
  52
  53     ## number of missing days
  54     missing.days <- nrow(missing.in.class[missing.in.class$discord.name == x.discord.name,])
  55
  56     ## return the final score
  57     data.frame(discord.name=x.discord.name,
  58                part.grade=(base.score - missing.days * missed.question.penalty))
  59 }
  60
  61 tmp <- do.call("rbind", lapply(d$discord.name[d$num.calls >= questions.cutoff], gen.part.grade))
  62
  63 d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
  64
  65 ## next handle the folks *under* the median
  66
  67 ## first we handle the zeros
  68 ## step 1: first double check the people who have zeros and ensure that they didn't "just" get unlucky"
  69 d[d$num.calls == 0,]
  70
  71 ## set those people to 0 :(
  72 d$part.grade[d$num.calls == 0] <- 0
  73
  74 ## step 2 is to handle folks who got unlucky in the normal way
  75 tmp <- do.call("rbind", lapply(d$discord.name[is.na(d$part.grade) & d$prop.asked <= median(d$prop.asked)], gen.part.grade))
  76 d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
  77
  78 ## the people who are left are lucky and still undercounted so we'll penalize them
  79 d[is.na(d$part.grade),]
  80 penalized.discord.names <- d$discord.name[is.na(d$part.grade)]
  81
  82 ## generate the baseline participation grades as per the process above
  83 tmp <- do.call("rbind", lapply(penalized.discord.names, gen.part.grade))
  84 d[as.character(tmp$discord.name), "part.grade"] <- tmp$part.grade
  85
  86 ## now add "zeros" for every questions that is below the normal
  87 d[as.character(penalized.discord.names),"part.grade"] <- ((
  88     (questions.cutoff - d[as.character(penalized.discord.names),"num.calls"] * 0) +
  89     (d[as.character(penalized.discord.names),"num.calls"] * d[as.character(penalized.discord.names),"part.grade"]) )
  90     / questions.cutoff)
  91
  92 d[as.character(penalized.discord.names),]
  93
  94 ## map part grades back to 4.0 letter scale and points
  95 d$part.4point <-round((d$part.grade / (50/3.3)) - 2.6, 2)
  96
  97 d[sort.list(d$prop.asked), c("discord.name", "num.calls", "num.present",
  98                              "prop.asked", "prop.asked.quant", "part.grade", "part.4point",
  99                              "days.absent")]
 100
 101 d[sort.list(d$part.4point), c("discord.name", "num.calls", "num.present",
 102                              "prop.asked", "prop.asked.quant", "part.grade", "part.4point",
 103                              "days.absent")]
 104
 105
 106 ## writing out data
 107 quantile(d$num.calls, probs=(0:100*0.01))
 108 d.print <- merge(d, myuw[,c("StudentNo", "FirstName", "LastName", "UWNetID")],
 109            by.x="student.num", by.y="StudentNo")
 110 write.csv(d.print, file="final_participation_grades.csv")
 111
 112 library(rmarkdown)
 113
 114 for (x.discord.name in d$discord.name) {
 115     render(input="student_report_template.Rmd",
 116            output_format="html_document",
 117            output_file=paste("student_reports/",
 118                              d.print$UWNetID[d.print$discord.name == x.discord.name],
 119                              sep=""))
 120 }