change assessment code for BSOC 2024

diff --git a/assessment_and_tracking/compute_final_case_grades.R b/assessment_and_tracking/compute_final_case_grades.R
index 93d6d1f782b1e79d0aee2f2d956a13bca474e015..e35505269fa3f7149da622ed0362b4ea66125860 100644 (file)
--- a/assessment_and_tracking/compute_final_case_grades.R
+++ b/assessment_and_tracking/compute_final_case_grades.R
@@ -1,6 +1,6 @@
 ## load in the data
 #################################
 ## load in the data
 #################################

-myuw <- read.csv("../data/2022_winter_COM_481_A_students.csv", stringsAsFactors=FALSE)
+myuw <- read.csv("../data/2024_autumn_COMMLD_570_A_joint_students.csv", stringsAsFactors=FALSE)

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


@@ -11,20 +11,20 @@ call.list$timestamp <- as.Date(call.list$timestamp)
 
 ## class-level variables
 gpa.point.value <- 50/(4 - 0.7)
 
 ## class-level variables
 gpa.point.value <- 50/(4 - 0.7)

-question.grades <- c("PLUS"=100, "CHECK"=100-gpa.point.value, "MINUS"=100-(gpa.point.value*2))
+## question.grades <- c("GOOD"=100, "FAIR"=100-gpa.point.value, "BAD"=100-(gpa.point.value*2))
+question.grades <- c("GOOD"=100, "SATISFACTORY"=100-gpa.point.value, "POOR"=100-(gpa.point.value*2), "NO MEANINGFUL ANSWER"=0)

 missed.question.penalty <- gpa.point.value * 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)
 
 missed.question.penalty <- gpa.point.value * 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)
 

-

 ## 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
 ## this is the 95% percentile based on simulation in simulation.R
 ## 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
 ## this is the 95% percentile based on simulation in simulation.R

-questions.cutoff <- 4
+questions.cutoff <- 15

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


@@ -78,6 +78,7 @@ 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$unique.name == x.unique.name]]
 ## helper function to generate average grade minus number of missing
 gen.part.grade <- function (x.unique.name) {
     q.scores <- question.grades[call.list$assessment[call.list$unique.name == x.unique.name]]

+    print(q.scores)

     base.score <- mean(q.scores, na.rm=TRUE)
 
     ## number of missing days
     base.score <- mean(q.scores, na.rm=TRUE)
 
     ## number of missing days


@@ -89,7 +90,6 @@ gen.part.grade <- function (x.unique.name) {
                missing.in.class.days=missing.in.class.days)
 }
 
                missing.in.class.days=missing.in.class.days)
 }
 

-

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