Milestone analysis S22
buildHeaders <- function(numberOfQuestions){
colHeaders = c()
#build headers
for (n in seq(numberOfQuestions)){
colHeaders = c(colHeaders,paste("q",as.character(n),sep = ""))
}
return(colHeaders)
}
variationWithinQuestionTypes <- function(m1,pat){
#compare each question with the average
totcol = ncol(m1)
numcol = (totcol-2-10)/2
aveQ = data.frame( matrix(ncol=3 ,nrow=0) )
indivQ = data.frame( matrix(ncol=0 ,nrow=nrow(m1)) )
for (q in seq(10,totcol-2,2)){
#q is the number, q-1 is the tilte
qtitle = colnames(m1)[q-1]
if ( grepl(pat,qtitle)){
thisQ = c(
colnames(m1)[q-1],
mean(m1[,q],na.rm=TRUE),
length( na.omit(m1[,q]))
)
aveQ = rbind(aveQ,thisQ)
indivQ[qtitle] = m1[,q]
}
}
results = list("indi" = indivQ, "ave" = aveQ)
return(results)
}
printVariationWithinQuestions <- function(indivQ,ndig){
#library(Hmisc)
#for some reason I cant calc the correlation when theres too many NA
#res2<-rcorr(as.matrix(indivQ),type = "pearson")
#flattenCorrMatrix(res2$r, res2$P)
totlen = length(colnames(indivQ))
#pval = matrix(1:totlen, nrow = totlen, ncol = totlen)
pval = data.frame( matrix(ncol=totlen ,nrow=0) )
for (i in seq(1,totlen)){
thisline = c()
for (j in seq(1,totlen)){
if ( j < i){
thisline = c(thisline," ")
}else{
#p = as.numeric(t.test(indivQ[,i],indivQ[,j])$p.value)
#need a tryCatch just in case we are comparing questions without attempts
#tryCatch(
# expr = {
p = format(round( t.test(indivQ[,i],indivQ[,j])$p.value,ndig), nsmall = ndig)
if( p < 0.05){ p = paste(p,"*",sep = "")}
# },
# error = {
# p = "nope"
# }
#)
thisline = c(thisline,p)
}
}
pval = rbind(pval,thisline)
}
colnames(pval) = colnames(indivQ)
rownames(pval) = colnames(indivQ)
return(pval)
#t.test(indivQ$q8_4,indivQ$q8_5)
}
buildDF_fromMilestone <- function(m1,numberOfQuestions){
#Build df with just answers
totcol = ncol(m1)
m1df = data.frame( matrix(ncol=numberOfQuestions+3,nrow=0) )
colHeaders = c(buildHeaders(numberOfQuestions),"total")
studNames = c()
attNumb = c()
#loop over students or row
for (st in seq(1,nrow(m1)) ){
#build attemptNumb and studNames. theyll be added as columns later
attNumb = c(attNumb,m1[st,8])
studNames = c(studNames,m1[st,1])
#empty the score array and build it up as it finds each question
thisSt = rep(NA,numberOfQuestions)
#loop over columns to find nonempty scores
for (q in seq(10,totcol-2,2)){
score = m1[st,q]
if ( !is.na( score ) ){
questionNumber = colnames(m1)[q-1]
questionNumber = unlist(strsplit(questionNumber,"_"))[1]
questionNumber = as.numeric( gsub("^q","",questionNumber))
thisSt[questionNumber] = score
}
}
#studNames = c(studNames,m1[st,1])
thisSt = c(thisSt, m1[st,totcol])
m1df = rbind(m1df,thisSt)
}
colnames(m1df) = colHeaders
m1df = rbind(m1df,colMeans(m1df))
m1df$studName = c(studNames,NA)
m1df$attNumb = c(attNumb,NA)
return(m1df)
}
buildStats <- function(m1df,numberOfQuestions,att){
m1dfat1 = data.frame( matrix(ncol=numberOfQuestions,nrow=0) )
m1dfat1sd = data.frame( matrix(ncol=numberOfQuestions,nrow=0) )
st100 = subset(m1df,m1df$total> 79.99 & m1df$attNumb == att)
m1dfat1 = rbind(m1dfat1, colMeans(st100[,1:numberOfQuestions]) )
m1dfat1sd = rbind(m1dfat1sd, apply(st100[,1:numberOfQuestions],2,sd) )
st80 = subset(m1df,m1df$total> 69.99 & m1df$total < 79.99 & m1df$attNumb == att)
m1dfat1 = rbind(m1dfat1, colMeans(st80[,1:numberOfQuestions]) )
m1dfat1sd = rbind(m1dfat1sd, apply(st80[,1:numberOfQuestions],2,sd) )
st0 = subset(m1df,m1df$total< 69.99 & m1df$attNumb == att)
m1dfat1 = rbind(m1dfat1, colMeans(st0[,1:numberOfQuestions]) )
m1dfat1sd = rbind(m1dfat1sd, apply(st0[,1:numberOfQuestions],2,sd) )
colnames(m1dfat1) = buildHeaders(numberOfQuestions)
colnames(m1dfat1sd) =buildHeaders(numberOfQuestions)
rownames(m1dfat1) = c("Avg > 80", "Avg 70><80","Avg < 70")
rownames(m1dfat1sd) = c("SD > 80", "SD 70><80","SD < 70")
#return(list(m1dfat1,m1dfat1sd))
ltot = length(
subset(m1df,m1df$attNumb == att)[,1]
)
l100 = length(st100[,1])
l80 = length(st80[,1])
l0 = length(st0[,1])
useStats = c(ltot,l100,l80,l0)
results = list("ave" = m1dfat1, "sd" = m1dfat1sd, "usestats" = useStats)
return(results)
}
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
roundThisScore <- function(score){
if (score <70){ r = 0
} else if (score <80){ r = 80
} else if (score >80){ r = 100 }
return(r)
}
flowAndSettling <- function(m1df){
#check students who did not attempt a 3rd and got lower than 80
studs = unique(m1df$studName)
studs = studs[!is.na(studs)]
settle = data.frame( matrix(ncol=3,nrow=0) )
flow = data.frame(matrix(ncol=3,nrow=0))
for (stud in studs){
thisStDF = m1df[which(m1df$studName == stud),]
#if att number lower than 3 and score lower than 80
maxNum = max(thisStDF$attNumb)
maxScore = max(thisStDF$total)
if ( maxNum < 3 & maxScore < 80 ){
settle = rbind(settle,c(stud,maxNum,maxScore))
}
sc1 = roundThisScore( max(m1df[which(m1df$studName == stud & m1df$attNumb < 2),]$total) )
sc2 = roundThisScore( max(m1df[which(m1df$studName == stud & m1df$attNumb < 3),]$total) )
sc3 = roundThisScore( max(m1df[which(m1df$studName == stud & m1df$attNumb < 4),]$total) )
flow = rbind(flow,c(sc1,sc2,sc3))
}
colnames(flow) = c("one","two","three")
flow2 = data.frame(matrix(ncol=3,nrow=3))
flow2[,1]=table(flow$one)
flow2[,2]=table(flow$two)
flow2[,3]=table(flow$three)
colnames(flow2) = c("After 1st","After 2nd","After 3rd")
colnames(settle) = c("name","MaxAttempt","MaxScore")
all = list("settle" = settle,"flow" = flow2)
return(all)
}
fullCourseReport <- function(m1,nquest,semester,milenumber){
m1df = buildDF_fromMilestone(m1,nquest)
results = flowAndSettling(m1df)
flow = results$flow
pct1st = flow[,1]/sum(flow[,1])*100
pct2nd = flow[,2]/sum(flow[,2])*100
pct3rd = flow[,3]/sum(flow[,3])*100
stackAndGrouped = data.frame(matrix(ncol = 4,nrow=0))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct1st[1],semester,"First"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct1st[2],semester,"First"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct1st[3],semester,"First"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct2nd[1],semester,"Second"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct2nd[2],semester,"Second"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct2nd[3],semester,"Second"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct3rd[1],semester,"Third"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct3rd[2],semester,"Third"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct3rd[3],semester,"Third"))
pct1st = paste(signif( pct1st,digits = 4),"%",sep = "")
pct2nd = paste(signif( pct2nd,digits = 4),"%",sep = "")
pct3rd = paste(signif( pct3rd,digits = 4),"%",sep = "")
flow$`After 1st` = paste( flow$`After 1st`, pct1st,sep = " / ")
flow$`After 2nd` = paste( flow$`After 2nd`, pct2nd,sep = " / ")
flow$`After 3rd` = paste( flow$`After 3rd`, pct3rd,sep = " / ")
scores = c("0","80","100")
flow = cbind(scores,flow)
knitr::kable(flow)
row.names(results$flow) = c("0","80","100")
barplot(as.matrix(results$flow),col=c("red","yellow","green"),main =paste("Milestone ", milenumber,": students scores best of 3 attempts"),legend = rownames(results$flow))
}#lets first load the files
if (Sys.info()["sysname"] == "Windows"){
m1 = read.csv("G:/My Drive/Teaching/Grades_and_SRT/Spring2022/newm1.csv",header = TRUE)
m2 = read.csv("G:/My Drive/Teaching/Grades_and_SRT/Spring2022/newm2.csv",header = TRUE)
m3 = read.csv("G:/My Drive/Teaching/Grades_and_SRT/Spring2022/newm3.csv",header = TRUE)
m4 = read.csv("G:/My Drive/Teaching/Grades_and_SRT/Spring2022/newm4.csv",header = TRUE)
m5 = read.csv("G:/My Drive/Teaching/Grades_and_SRT/Spring2022/newm5.csv",header = TRUE)
}else{
m1 = read.csv("~/Teaching/Grades_and_SRT/Spring2022/newm1.csv",header = TRUE)
m2 = read.csv("~/Teaching/Grades_and_SRT/Spring2022/newm2.csv",header = TRUE)
m3 = read.csv("~/Teaching/Grades_and_SRT/Spring2022/newm3.csv",header = TRUE)
m4 = read.csv("~/Teaching/Grades_and_SRT/Spring2022/newm4.csv",header = TRUE)
m5 = read.csv("~/Teaching/Grades_and_SRT/Spring2022/newm5.csv",header = TRUE)
}1 Milestone 1
fullCourseReport(m1,12,"S22","1")
1.1 M1 - Attempt 1 averages
m1df = buildDF_fromMilestone(m1,12)
# First attempt stats
results = buildStats(m1df,12,1)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 85 | 38 (45%) | 4 (4.7%) | 43 (51%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 7.94 | 8.11 | 8.18 | 7.65 | 8.12 | 7.23 | 7.89 | 7.22 | 7.50 | 6.14 | 7.67 | 6.58 |
| Avg 70><80 | 8.33 | 7.98 | 7.30 | 8.16 | 7.41 | 6.25 | 4.16 | 6.02 | 6.95 | 2.08 | 6.25 | 4.16 |
| Avg < 70 | 6.82 | 6.88 | 6.25 | 5.59 | 6.34 | 2.71 | 2.71 | 5.09 | 4.49 | 0.97 | 1.74 | 0.77 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.19 | 0.69 | 0.57 | 1.23 | 0.70 | 2.85 | 1.89 | 1.26 | 1.93 | 3.72 | 2.28 | 3.44 |
| SD 70><80 | 0.00 | 0.69 | 2.08 | 0.35 | 1.85 | 4.16 | 4.81 | 0.93 | 1.13 | 4.16 | 4.16 | 4.81 |
| SD < 70 | 2.05 | 2.12 | 2.45 | 2.03 | 1.92 | 3.95 | 3.95 | 1.41 | 3.32 | 2.70 | 3.43 | 2.45 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
1.2 M1 - Attempt 2 averages
# First attempt stats
results = buildStats(m1df,12,2)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 43 | 18 (42%) | 10 (23%) | 15 (35%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 8.33 | 8.33 | 8.22 | 7.21 | 8.19 | 7.87 | 7.87 | 6.95 | 8.34 | 6.02 | 7.87 | 5.55 |
| Avg 70><80 | 7.66 | 8.19 | 7.92 | 7.15 | 7.60 | 5.00 | 6.66 | 6.76 | 5.56 | 3.33 | 6.66 | 2.50 |
| Avg < 70 | 6.89 | 6.85 | 6.95 | 5.00 | 6.92 | 2.78 | 3.33 | 5.44 | 3.52 | 3.33 | 3.33 | 0.56 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 0.00 | 0.00 | 0.49 | 1.77 | 0.66 | 1.96 | 1.96 | 1.16 | 0.00 | 3.84 | 1.96 | 4.04 |
| SD 70><80 | 1.16 | 0.44 | 1.32 | 1.27 | 1.05 | 4.30 | 3.51 | 1.07 | 3.93 | 4.30 | 3.51 | 4.02 |
| SD < 70 | 1.24 | 1.61 | 2.03 | 1.90 | 1.56 | 4.06 | 4.22 | 1.40 | 3.63 | 4.22 | 4.22 | 2.15 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 2nd attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
2 Milestone 2
List of questions
- Heat capacity
- Solution preparation
- Predict acid/base/neutral
- Calculate pH of weak species
- Rank by pH
- Calculate pH of strong
- Titration 4 graphs: ID where we are A-P
- Build buffers
- Qualitative solubility: Le Chatelier
- Titration: find equivalence point
- Titration: Composition oalong the curve
- Quantitative solubility
fullCourseReport(m2,12,"S22","2")
2.1 M2 - Attempt 1 averages
m1df = buildDF_fromMilestone(m2,12)
# First attempt stats
results = buildStats(m1df,12,1)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 83 | 46 (55%) | 15 (18%) | 22 (27%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 7.91 | 7.98 | 7.74 | 7.61 | 7.79 | 7.61 | 7.06 | 7.61 | 7.58 | 7.15 | 7.85 | 6.70 |
| Avg 70><80 | 7.59 | 7.23 | 6.42 | 6.12 | 5.55 | 5.56 | 5.55 | 5.69 | 6.48 | 6.94 | 7.54 | 3.89 |
| Avg < 70 | 6.94 | 4.93 | 5.60 | 5.69 | 3.03 | 1.90 | 2.27 | 3.69 | 5.05 | 3.79 | 6.12 | 0.76 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.17 | 1.72 | 1.04 | 2.38 | 2.08 | 2.38 | 2.46 | 1.97 | 1.23 | 2.09 | 0.89 | 3.34 |
| SD 70><80 | 1.65 | 2.93 | 1.38 | 3.82 | 4.06 | 4.07 | 3.75 | 1.84 | 2.50 | 2.57 | 1.74 | 4.30 |
| SD < 70 | 1.87 | 4.20 | 1.47 | 3.98 | 4.10 | 3.58 | 2.79 | 2.80 | 2.63 | 3.38 | 2.73 | 2.45 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
2.2 M2 - Attempt 2 averages
# First attempt stats
results = buildStats(m1df,12,2)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 34 | 11 (32%) | 10 (29%) | 13 (38%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 7.57 | 7.58 | 7.50 | 7.58 | 8.33 | 8.34 | 7.95 | 5.68 | 7.32 | 6.82 | 7.98 | 6.82 |
| Avg 70><80 | 7.77 | 5.00 | 6.86 | 8.34 | 6.66 | 7.51 | 6.66 | 6.25 | 5.69 | 5.41 | 6.14 | 2.50 |
| Avg < 70 | 6.41 | 4.49 | 5.99 | 4.49 | 3.20 | 1.92 | 3.52 | 3.36 | 5.77 | 4.16 | 6.57 | 2.56 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.30 | 2.51 | 0.77 | 2.51 | 0.00 | 0.00 | 1.26 | 2.65 | 1.87 | 3.37 | 0.86 | 3.37 |
| SD 70><80 | 1.17 | 4.31 | 1.25 | 0.00 | 3.51 | 2.64 | 2.91 | 2.60 | 2.12 | 3.43 | 2.72 | 4.02 |
| SD < 70 | 2.63 | 4.33 | 1.72 | 4.33 | 4.22 | 3.66 | 3.74 | 2.89 | 2.39 | 2.95 | 1.69 | 4.00 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 2nd attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
3 Milestone 3
List of questions
- Calc Hess Law
- Signs of heat, H, and entropy
- Temperature dependence (no graph)
- Interpret K and DG
- Kinetic vs Thermo stability
- G all graphs
- Qualitative solubility
- Calculate the pH of any mixture ac/base
- Titration: find composition along the curves
- Stoichiometry combustion
- Lewis structures and vibration
- Rank EM radiation
fullCourseReport(m3,12,"S22","4")
3.1 M3 - Attempt 1 averages
m1df = buildDF_fromMilestone(m3,12)
# First attempt stats
results = buildStats(m1df,12,1)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 78 | 50 (64%) | 13 (17%) | 15 (19%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 8.16 | 7.75 | 7.89 | 7.83 | 7.50 | 8.02 | 7.51 | 6.17 | 8.06 | 8.33 | 7.47 | 8.12 |
| Avg 70><80 | 7.69 | 7.13 | 6.41 | 7.69 | 5.45 | 7.85 | 5.67 | 1.92 | 7.56 | 3.84 | 6.50 | 7.91 |
| Avg < 70 | 5.00 | 6.80 | 5.92 | 6.11 | 4.72 | 6.01 | 4.45 | 1.11 | 6.37 | 3.33 | 4.84 | 5.93 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.18 | 1.26 | 1.17 | 2.00 | 2.52 | 0.69 | 1.40 | 3.70 | 0.91 | 0.00 | 1.32 | 1.24 |
| SD 70><80 | 2.31 | 1.41 | 2.86 | 2.31 | 3.95 | 1.01 | 2.57 | 3.66 | 1.16 | 4.32 | 1.58 | 1.04 |
| SD < 70 | 4.22 | 1.33 | 2.32 | 3.81 | 4.13 | 2.02 | 2.74 | 2.93 | 1.63 | 4.22 | 1.93 | 3.46 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
3.2 M3 - Attempt 2 averages
# First attempt stats
results = buildStats(m1df,12,2)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 25 | 12 (48%) | 6 (24%) | 7 (28%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 8.33 | 7.20 | 8.10 | 7.64 | 6.25 | 7.73 | 7.41 | 5.56 | 7.70 | 6.94 | 8.03 | 8.34 |
| Avg 70><80 | 6.94 | 7.77 | 6.48 | 8.33 | 5.55 | 7.54 | 6.95 | 1.39 | 6.95 | 2.78 | 6.94 | 7.41 |
| Avg < 70 | 4.76 | 6.82 | 5.55 | 5.95 | 3.57 | 7.45 | 5.56 | 0.00 | 6.35 | 2.38 | 5.44 | 7.55 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 0.00 | 1.59 | 0.80 | 2.40 | 3.77 | 0.95 | 1.81 | 4.11 | 1.60 | 3.24 | 0.54 | 0.00 |
| SD 70><80 | 3.40 | 1.01 | 2.27 | 0.00 | 4.30 | 1.23 | 1.52 | 3.40 | 2.33 | 4.30 | 1.39 | 2.27 |
| SD < 70 | 4.45 | 2.19 | 2.78 | 4.06 | 4.45 | 1.28 | 3.21 | 0.00 | 2.76 | 4.06 | 1.66 | 2.10 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 2nd attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
4 Milestone 4
List of questions
- Balance redox reactions
- Parts of a battery
- Nernst: adding things to cathode and anode
- Calculate DeltaG from E
- Quantiative Electrodeposition
- ID products of electrolysis
- Organic redox
- Signs of heat, H, and S
- Quantitative solubility
- All titrations: composition along the curve
- Predict pH of any acid/base mixture
- Predict acid, base or neutral
- Solution preparation
- Ions in water
Number of students in the three grade categories at different attempts
fullCourseReport(m4,14,"S22","4")
4.1 M4 - Attempt 1 averages
m1df = buildDF_fromMilestone(m4,14)
# First attempt stats
results = buildStats(m1df,14,1)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 76 | 32 (42%) | 19 (25%) | 25 (33%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 6.60 | 7.08 | 6.47 | 6.25 | 6.47 | 7.04 | 6.41 | 6.68 | 5.13 | 6.14 | 6.25 | 6.64 | 6.92 | 6.34 |
| Avg 70><80 | 6.44 | 6.95 | 6.31 | 6.01 | 5.26 | 3.76 | 5.14 | 6.57 | 3.01 | 4.89 | 3.76 | 6.14 | 5.26 | 5.15 |
| Avg < 70 | 3.92 | 6.00 | 5.77 | 3.14 | 2.57 | 2.43 | 3.81 | 6.12 | 1.14 | 3.14 | 2.57 | 5.17 | 3.71 | 2.77 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.07 | 0.22 | 0.96 | 2.40 | 2.11 | 0.63 | 1.28 | 1.09 | 3.26 | 2.26 | 2.40 | 0.63 | 1.26 | 1.30 |
| SD 70><80 | 0.68 | 0.48 | 1.20 | 2.67 | 3.23 | 3.67 | 2.28 | 1.26 | 3.62 | 3.20 | 3.66 | 1.05 | 3.23 | 2.14 |
| SD < 70 | 2.26 | 1.33 | 1.33 | 3.62 | 3.50 | 3.22 | 2.75 | 1.47 | 2.67 | 3.15 | 3.50 | 1.40 | 3.64 | 2.35 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
4.2 Variation within question groups
Q6
results = variationWithinQuestionTypes(m4,"q6_")
indivQ = results$indi
aveQ = results$ave
boxplot(indivQ)
library("psych")
knitr::kable(describe(indivQ))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| q6_1 | 1 | 9 | 5.163889 | 3.152854 | 7.150 | 5.163889 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.8239226 | -1.2887377 | 1.0509512 |
| q6_2 | 2 | 15 | 3.575000 | 3.309807 | 3.575 | 3.575000 | 5.300295 | 0.000 | 7.15 | 7.150 | 0.0000000 | -1.9111111 | 0.8545885 |
| q6_3 | 3 | 10 | 5.720000 | 2.499661 | 7.150 | 6.256250 | 0.000000 | 0.000 | 7.15 | 7.150 | -1.1935618 | -0.0746281 | 0.7904622 |
| q6_4 | 4 | 11 | 4.550000 | 3.607354 | 7.150 | 4.766667 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.4914204 | -1.9079103 | 1.0876580 |
| q6_5 | 5 | 12 | 4.766667 | 3.520417 | 7.150 | 5.005000 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.6205863 | -1.7395833 | 1.0162567 |
| q6_6 | 6 | 13 | 6.325000 | 2.141942 | 7.150 | 6.825000 | 0.000000 | 0.000 | 7.15 | 7.150 | -2.0951253 | 3.0764400 | 0.5940679 |
| q6_7 | 7 | 5 | 5.720000 | 1.958108 | 7.150 | 5.720000 | 0.000000 | 3.575 | 7.15 | 3.575 | -0.2921187 | -2.2533333 | 0.8756926 |
| q6_8 | 8 | 7 | 5.107143 | 3.488843 | 7.150 | 5.107143 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.7528372 | -1.6040816 | 1.3186586 |
| q6_9 | 9 | 9 | 5.561111 | 3.152854 | 7.150 | 5.561111 | 0.000000 | 0.000 | 7.15 | 7.150 | -1.1198947 | -0.7989418 | 1.0509512 |
| q6_10 | 10 | 9 | 3.972222 | 3.317460 | 3.575 | 3.972222 | 5.300295 | 0.000 | 7.15 | 7.150 | -0.1819657 | -1.9406097 | 1.1058199 |
| q6_11 | 11 | 7 | 5.107143 | 3.488843 | 7.150 | 5.107143 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.7528372 | -1.6040816 | 1.3186586 |
| q6_12 | 12 | 11 | 5.200000 | 3.339761 | 7.150 | 5.561111 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.8846579 | -1.3126722 | 1.0069757 |
| q6_13 | 13 | 9 | 4.766667 | 3.575000 | 7.150 | 4.766667 | 0.000000 | 0.000 | 7.15 | 7.150 | -0.5925926 | -1.8148148 | 1.1916667 |
| q6_14 | 14 | 5 | 2.860000 | 3.916216 | 0.000 | 2.860000 | 0.000000 | 0.000 | 7.15 | 7.150 | 0.2921187 | -2.2533333 | 1.7513852 |
Q9
results = variationWithinQuestionTypes(m4,"q9_")
indivQ = results$indi
aveQ = results$ave
boxplot(indivQ)
library("psych")
knitr::kable(describe(indivQ))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| q9_1 | 1 | 26 | 4.393846 | 3.542432 | 7.14 | 4.543636 | 0.000000 | 0 | 7.14 | 7.14 | -0.4472406 | -1.867419 | 0.6947281 |
| q9_2 | 2 | 25 | 3.141600 | 3.617287 | 0.00 | 3.060000 | 0.000000 | 0 | 7.14 | 7.14 | 0.2273881 | -2.024540 | 0.7234574 |
| q9_3 | 3 | 18 | 3.570000 | 3.673500 | 3.57 | 3.570000 | 5.292882 | 0 | 7.14 | 7.14 | 0.0000000 | -2.108025 | 0.8658522 |
| q9_4 | 4 | 21 | 3.400000 | 3.654012 | 0.00 | 3.360000 | 0.000000 | 0 | 7.14 | 7.14 | 0.0886175 | -2.084725 | 0.7973707 |
| q9_5 | 5 | 16 | 3.570000 | 3.687080 | 3.57 | 3.570000 | 5.292882 | 0 | 7.14 | 7.14 | 0.0000000 | -2.121094 | 0.9217700 |
| q9_6 | 6 | 26 | 2.746154 | 3.542432 | 0.00 | 2.596364 | 0.000000 | 0 | 7.14 | 7.14 | 0.4472406 | -1.867419 | 0.6947281 |
4.3 M4 - Attempt 2 averages
m1df = buildDF_fromMilestone(m4,14)
# First attempt stats
results = buildStats(m1df,14,2)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| Two | 42 | 18 (43%) | 14 (33%) | 10 (24%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 6.18 | 7.14 | 6.66 | 5.95 | 6.74 | 4.97 | 6.22 | 6.55 | 5.95 | 6.35 | 6.74 | 6.26 | 6.35 | 6.63 |
| Avg 70><80 | 5.83 | 6.88 | 6.63 | 5.10 | 6.63 | 5.36 | 5.96 | 6.23 | 2.04 | 4.34 | 4.59 | 6.01 | 4.08 | 4.77 |
| Avg < 70 | 5.10 | 5.89 | 5.43 | 5.71 | 4.28 | 2.86 | 2.86 | 5.78 | 1.43 | 5.35 | 3.57 | 4.92 | 3.57 | 3.82 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.46 | 0.00 | 0.98 | 2.74 | 1.68 | 3.28 | 1.66 | 1.06 | 2.74 | 1.96 | 1.68 | 1.24 | 2.31 | 1.02 |
| SD 70><80 | 1.23 | 0.55 | 1.20 | 3.35 | 1.91 | 2.72 | 2.04 | 1.76 | 3.35 | 3.48 | 3.55 | 0.86 | 3.67 | 2.29 |
| SD < 70 | 1.80 | 1.79 | 2.00 | 3.01 | 3.69 | 3.29 | 2.93 | 1.44 | 3.01 | 2.52 | 3.76 | 0.90 | 3.76 | 2.01 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
5 Milestone 5
List of questions
- Nernst: adding things to cathode and anode
- ID products of electrolysis
- Organic redox
Number of students in the three grade categories at different attempts
fullCourseReport(m5,14,"S22","5")
5.1 M5 - Attempt 1 averages
m1df = buildDF_fromMilestone(m5,14)
# First attempt stats
results = buildStats(m1df,14,1)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| One | 73 | 43 (59%) | 16 (22%) | 14 (19%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 6.76 | 6.90 | 6.15 | 6.92 | 6.40 | 7.02 | 6.31 | 6.89 | 6.84 | 6.48 | 6.31 | 6.92 | 5.48 | 6.72 |
| Avg 70><80 | 5.51 | 7.06 | 4.47 | 6.25 | 5.27 | 6.92 | 5.80 | 3.79 | 6.34 | 5.21 | 4.46 | 4.09 | 4.02 | 5.81 |
| Avg < 70 | 4.57 | 5.34 | 1.53 | 4.42 | 3.76 | 5.67 | 1.53 | 2.29 | 5.20 | 4.59 | 4.21 | 4.42 | 1.53 | 5.62 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 1.06 | 0.96 | 2.51 | 0.87 | 1.34 | 0.31 | 2.32 | 1.21 | 0.67 | 1.31 | 1.57 | 0.65 | 3.05 | 0.63 |
| SD 70><80 | 2.30 | 0.36 | 3.58 | 1.19 | 1.84 | 0.40 | 2.88 | 3.56 | 1.16 | 2.49 | 2.52 | 3.10 | 3.66 | 0.90 |
| SD < 70 | 2.52 | 1.98 | 3.04 | 1.58 | 2.23 | 1.84 | 3.04 | 3.32 | 1.74 | 2.37 | 2.39 | 2.93 | 3.04 | 0.83 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))
5.2 Variation within question groups
Q7
results = variationWithinQuestionTypes(m5,"q7_")
indivQ = results$indi
aveQ = results$ave
boxplot(indivQ)
library("psych")
knitr::kable(describe(indivQ))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| q7_1 | 1 | 32 | 4.016250 | 3.598675 | 7.14 | 4.119231 | 0 | 0 | 7.14 | 7.14 | -0.2402577 | -2.0019376 | 0.6361619 |
| q7_2 | 2 | 26 | 5.766923 | 2.869698 | 7.14 | 6.166364 | 0 | 0 | 7.14 | 7.14 | -1.4722289 | 0.1787123 | 0.5627941 |
| q7_3 | 3 | 25 | 5.712000 | 2.914893 | 7.14 | 6.120000 | 0 | 0 | 7.14 | 7.14 | -1.4109061 | -0.0048000 | 0.5829786 |
| q7_4 | 4 | 33 | 4.543636 | 3.487920 | 7.14 | 4.760000 | 0 | 0 | 7.14 | 7.14 | -0.5413726 | -1.7574446 | 0.6071689 |
Q3
results = variationWithinQuestionTypes(m5,"q3_")
indivQ = results$indi
aveQ = results$ave
boxplot(indivQ)
library("psych")
knitr::kable(describe(indivQ))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| q3_4 | 1 | 31 | 3.690323 | 3.632204 | 7.15 | 3.718000 | 0 | 0 | 7.15 | 7.15 | -0.0614517 | -2.059573 | 0.6523630 |
| q3_1 | 2 | 30 | 5.243333 | 3.215902 | 7.15 | 5.660417 | 0 | 0 | 7.15 | 7.15 | -1.0029674 | -1.024924 | 0.5871406 |
| q3_2 | 3 | 32 | 4.915625 | 3.367143 | 7.15 | 5.225000 | 0 | 0 | 7.15 | 7.15 | -0.7714139 | -1.447248 | 0.5952324 |
| q3_3 | 4 | 23 | 4.663043 | 3.481941 | 7.15 | 4.892105 | 0 | 0 | 7.15 | 7.15 | -0.5977914 | -1.711468 | 0.7260349 |
5.3 M5 - Attempt 2 averages
m1df = buildDF_fromMilestone(m5,14)
# First attempt stats
results = buildStats(m1df,14,2)
m1dfat1 = results$ave
m1dfat1sd = results$sd| Attempt | Total students | Sudents with score > 80 | Sudents with score 70-80 | Students with score < 70% |
|---|---|---|---|---|
| Two | 29 | 14 (48%) | 4 (14%) | 11 (38%) |
Table of averages per question and their standard deviation
library(knitr)
knitr::kable(m1dfat1,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Avg > 80 | 6.98 | 6.89 | 6.13 | 6.46 | 5.47 | 7.01 | 6.63 | 7.14 | 6.63 | 6.12 | 5.48 | 5.86 | 4.59 | 6.49 |
| Avg 70><80 | 6.06 | 6.70 | 3.58 | 7.14 | 5.80 | 6.69 | 5.35 | 1.78 | 5.00 | 4.76 | 4.46 | 4.76 | 5.35 | 6.44 |
| Avg < 70 | 6.43 | 6.73 | 0.65 | 5.84 | 5.53 | 6.82 | 1.30 | 3.25 | 5.97 | 5.19 | 2.43 | 4.65 | 1.30 | 6.31 |
knitr::kable(m1dfat1sd,digits=2)| q1 | q2 | q3 | q4 | q5 | q6 | q7 | q8 | q9 | q10 | q11 | q12 | q13 | q14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SD > 80 | 0.46 | 0.65 | 2.60 | 1.45 | 1.71 | 0.32 | 1.91 | 0.00 | 0.71 | 2.23 | 2.47 | 2.58 | 3.55 | 1.03 |
| SD 70><80 | 1.70 | 0.89 | 4.13 | 0.00 | 0.89 | 0.89 | 3.57 | 3.57 | 2.47 | 1.94 | 2.30 | 3.37 | 3.57 | 1.01 |
| SD < 70 | 1.35 | 0.97 | 2.16 | 1.24 | 1.20 | 0.45 | 2.89 | 3.73 | 1.07 | 2.78 | 2.43 | 3.12 | 2.89 | 0.77 |
matplot(t(m1dfat1),type="b",ylim=c(0,9),xlab = "Question number",ylab = "Average score/8.33",main="Average score per question on 1st attempt")
legend("bottomright",legend = c("1:>80","2:70-80","3:<70"))