CHEM1331 Performance Analysis
Xavier Prat-Resina
2022-08-08
#lets first load the files
if (Sys.info()["sysname"] == "Windows"){
}else{
#F23
m1f23 = read.csv("~/Teaching/Grades_and_SRT/Fall2023/m1.csv",header = TRUE)
m2f23 = read.csv("~/Teaching/Grades_and_SRT/Fall2023/m2.csv",header = TRUE)
m3f23 = read.csv("~/Teaching/Grades_and_SRT/Fall2023/m3.csv",header = TRUE)
m4f23 = read.csv("~/Teaching/Grades_and_SRT/Fall2023/m4.csv",header = TRUE)
gradesf23 = read.csv("~/Teaching/Grades_and_SRT/Fall2023/chem1331_f23_grades_canvas.csv",header = TRUE)
#F22
m1f22 = read.csv("~/Teaching/Grades_and_SRT/Fall2022/m1.csv",header = TRUE)
m2f22 = read.csv("~/Teaching/Grades_and_SRT/Fall2022/m2.csv",header = TRUE)
m3f22 = read.csv("~/Teaching/Grades_and_SRT/Fall2022/m3.csv",header = TRUE)
m4f22 = read.csv("~/Teaching/Grades_and_SRT/Fall2022/m4.csv",header = TRUE)
gradesf22 = read.csv("~/Teaching/Grades_and_SRT/Fall2022/chem1331_f22_grades_canvas.csv",header = TRUE)
#F21
m1f21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone1.csv",header = TRUE)
m2f21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone2.csv",header = TRUE)
m3f21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone3.csv",header = TRUE)
m4f21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone4.csv",header = TRUE)
#ex1 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/Exam_1_scores.csv",header = TRUE)
#ex2 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/Exam_2_scores.csv",header = TRUE)
gradesf21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/chem1331_f21_finalgrades.csv",header = TRUE)
gradesf21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/chem1331_f21_finalgrades_canvas.csv",header = TRUE)
#demo21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/")
#use this file to compare among years
videof21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/videowatching_resultsf21.csv",header = TRUE)
#this video analytics has all the videos of the course
videof21_all = read.csv("~/Teaching/Grades_and_SRT/Fall2021/videowatchingAnalytics.csv",header = TRUE)
#m1practicef21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone1_practice.csv",header = TRUE)
#m2practicef21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone2_practice.csv",header = TRUE)
#m3practicef21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone3_practice.csv",header = TRUE)
#m4practicef21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/milestone4_practice.csv",header = TRUE)
#F19
m1f19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone1.csv",header = TRUE)
m2f19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone2.csv",header = TRUE)
m3f19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone3.csv",header = TRUE)
m4f19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone4.csv",header = TRUE)
gradesf19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/chem1331f19_gradebook.csv",header = TRUE)
#demo19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/DISCOVER_chem1331_f19.csv")
videof19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/videowatching_resultsf19.csv",header = TRUE)
#m1practicef19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone1_practice.csv",header = TRUE)
#m2practicef19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone2_practice.csv",header = TRUE)
#m3practicef19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone3_practice.csv",header = TRUE)
#m4practicef19 = read.csv("~/Teaching/Grades_and_SRT/Fall2019/CHEM1331/milestone4_practice.csv",header = TRUE)
gradesf18 = read.csv("~/Teaching/Grades_and_SRT/Fall2018/chem1331_f18.csv",header = TRUE)
gradesf18mood = read.csv("~/Teaching/Grades_and_SRT/Fall2018/c1_f18_moodle_gradebook.csv",header = TRUE)
m1f18 = read.csv("~/Teaching/Grades_and_SRT/Fall2018/milestone1.csv",header = TRUE)
videof18 = read.csv("~/Teaching/Grades_and_SRT/Fall2018/videowatching_resultsf18.csv",header = TRUE)
}1 Course Grade Distribution
As you will see below, since we’re using different assignments, the grade distribution is not comparable. The main takeaways are
- In Fall2018 we weren’t sure if we were pushing students too much. When we thought that our Exam1 was too harsh we went easier. Specially in the Final. The final exam was too easy which explains the overall grade being much higher.
- The low number of DFW students in F18 can also be explained by the fact that the milestones had no numerical questions. These are well known to bring down the grade. We realized though that it made no sense to “kick the can down the road”, so we brought dimensional analysis back in F19.
- Probably our Exam1 in F19 was too hard and Exam1 in F21 was too easy. I think the final exam in those last two years is where we want it to be.
- Exam1 is still an unknown, I think in F21 it was too easy and it made students overconfident
- In terms of grade distribution, we probably want to be somewhere between F19 and F21
nf18 = length(gradesf18$Student)
nf19 = length(gradesf19$Student)
nf21 = length(gradesf21$Student)
nf22 = length(gradesf22$Student)
nf23 = length(gradesf23$Student)
#revmove D-
gradesf18$Unposted.Final.Grade = gsub('D-','D', gradesf18$Unposted.Final.Grade)
gradesf19$Unposted.Final.Grade = gsub('D-','D', gradesf19$Unposted.Final.Grade)
gradesf21$Unposted.Final.Grade = gsub('D-','D', gradesf21$Unposted.Final.Grade)
gradesf22$Unposted.Final.Grade = gsub('D-','D', gradesf22$Unposted.Final.Grade)
gradesf23$Unposted.Final.Grade = gsub('D-','D', gradesf23$Unposted.Final.Grade)
#remove column to avoid confusion
gradesf18$Final.Grade = NULL
gradesf19$Final.Grade = NULL
gradesf21$Final.Grade = NULL
gradesf22$Final.Grade = NULL
gradesf23$Final.Grade = NULL
gradesf18$simpleLetter = gsub('\\+','',
gsub('-','',gradesf18$Unposted.Current.Grade))
gradesf19$simpleLetter = gsub('\\+','',
gsub('-','',gradesf19$Unposted.Current.Grade))
gradesf21$simpleLetter = gsub('\\+','',
gsub('-','',gradesf21$Unposted.Current.Grade))
gradesf22$simpleLetter = gsub('\\+','',
gsub('-','',gradesf22$Unposted.Current.Grade))
gradesf23$simpleLetter = gsub('\\+','',
gsub('-','',gradesf23$Unposted.Current.Grade))
allLetters = as.data.frame.matrix( rbind(
table(gradesf18$Unposted.Final.Grade)/length(gradesf18$Unposted.Final.Grade)*100,
table(gradesf19$Unposted.Final.Grade)/length(gradesf19$Unposted.Final.Grade)*100,
table(gradesf21$Unposted.Final.Grade)/length(gradesf21$Unposted.Final.Grade)*100,
table(gradesf22$Unposted.Final.Grade)/length(gradesf22$Unposted.Final.Grade)*100,
table(gradesf23$Unposted.Final.Grade)/length(gradesf23$Unposted.Final.Grade)*100
#table(gradesf21$LetterGrade)/length(gradesf21$LetterGrade)*100
) )
allStats = as.data.frame.matrix( rbind(
summary(gradesf18$Final.Score),
summary(gradesf19$Final.Score),
summary(gradesf21$Final.Score),
summary(gradesf22$Final.Score),
summary(gradesf23$Final.Score)
))
#allStats = cbind(allStats,allLetters)
allStats = cbind(Nstudents = c(
length(gradesf18$Student),
length(gradesf19$Student),
length(gradesf21$Student),
length(gradesf22$Student),
length(gradesf23$Student)
),allStats)
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
row.names(allLetters) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allLetters,caption = "Final Grade Letter Percents",digits = 1)| A | A- | B | B- | B+ | C | C- | C+ | D | F | |
|---|---|---|---|---|---|---|---|---|---|---|
| Fall18 | 28.7 | 22.1 | 14.9 | 6.6 | 16.0 | 2.8 | 1.1 | 2.8 | 2.2 | 2.8 |
| Fall19 | 9.3 | 14.3 | 15.4 | 17.0 | 20.9 | 5.5 | 3.3 | 8.8 | 3.3 | 2.2 |
| Fall21 | 21.0 | 16.9 | 13.8 | 4.6 | 17.4 | 4.1 | 6.2 | 6.7 | 5.1 | 4.1 |
| Fall22 | 17.3 | 12.5 | 14.9 | 14.9 | 19.2 | 5.3 | 4.3 | 7.7 | 0.5 | 3.4 |
| Fall23 | 21.2 | 25.8 | 11.3 | 8.6 | 11.9 | 5.3 | 4.6 | 6.6 | 0.7 | 4.0 |
| Nstudents | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|---|
| Fall18 | 181 | 26.8 | 85.4 | 90.0 | 87.5 | 93.6 | 97.8 |
| Fall19 | 182 | 26.2 | 76.8 | 81.2 | 80.2 | 85.7 | 94.6 |
| Fall21 | 195 | 20.1 | 79.7 | 87.3 | 84.0 | 92.3 | 99.1 |
| Fall22 | 208 | 37.4 | 80.5 | 86.2 | 84.6 | 91.8 | 97.9 |
| Fall23 | 151 | 18.6 | 81.8 | 89.2 | 85.7 | 92.6 | 97.9 |
1.1 Withdrawn students
The statistics shown above are only the students who were still registered at the end of the semester. Some students withdrew from the course, we are going to use the number of students who took milestone 1 as the number of students who started to compare with the number of students who finished.
- In 2018, 181 students took milestone 1 and 181 finished
- In 2019, 191 students took milestone 1 and 182 finished
- In 2021, 211 students took milestone 1 and 195 finished.
- In 2022, 220 students took milestone 1 and 208 finished.
- In 2023, 163 students took milestone 1 and 151 finished.
Pay attention to this high number of withdrawls. This means that the overall grade average would have been much lower if all these people who dropped stayed until the end.
2 Open Ended Exams Grade Distribution
gradesf18[which(gradesf18$Exam.1..335345.==0),]$Exam.1..335345. = NA
gradesf21[which(gradesf21$Exam..1..2161998.==0),]$Exam..1..2161998. = NA
gradesf21[which(gradesf21$Exam..1..2161998.=="EX"),]$Exam..1..2161998. = NA
gradesf21$Exam..1..2161998. = as.numeric(gradesf21$Exam..1..2161998.)/25*100
gradesf22[which(gradesf22$Open.Ended.Exam..1..2592814. == 0),]$Open.Ended.Exam..1..2592814. = NA
gradesf22$Open.Ended.Exam..1..2592814. = as.numeric(gradesf22$Open.Ended.Exam..1..2592814.)/25*100
#gradesf23[which(gradesf23$Open.Ended.Exam..1..3262287. == 0),]$Open.Ended.Exam..1..3262287. = NA
gradesf23$Open.Ended.Exam..1..3262287. = as.numeric(gradesf23$Open.Ended.Exam..1..3262287.)/25*100
allStats = as.data.frame.matrix( rbind(
summary(gradesf18$Exam.1..335345.),
summary(gradesf19$Open.Ended.Written.Exam.1..816325.),
summary(gradesf21$Exam..1..2161998.),
summary(gradesf22$Open.Ended.Exam..1..2592814.),
summary(gradesf23$Open.Ended.Exam..1..3262287.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
allStats$`NA's` = NULL
knitr::kable(allStats,caption = "Exam 1 Statistics",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| Fall18 | 16.0 | 52.0 | 66.0 | 64.2 | 76.0 | 96.0 |
| Fall19 | 13.1 | 44.3 | 58.3 | 57.4 | 72.3 | 95.2 |
| Fall21 | 12.0 | 68.0 | 81.2 | 78.4 | 92.2 | 100.0 |
| Fall22 | 10.0 | 41.0 | 58.0 | 57.0 | 74.0 | 100.0 |
| Fall23 | 8.0 | 48.0 | 65.0 | 64.9 | 86.0 | 98.0 |
#EXAM 2
gradesf18[which(gradesf18$Exam.2..358394.==0),]$Exam.2..358394. = NA
gradesf19[which(gradesf19$Open.Ended.Exam.2..836125.==0),]$Open.Ended.Exam.2..836125. = NA
gradesf21[which(gradesf21$Exam..2..2171776.==0),]$Exam..2..2171776. = NA
gradesf21$Exam..2..2171776. = as.numeric(gradesf21$Exam..2..2171776.)/25*100
gradesf22[which(gradesf22$Open.Ended.Exam..2..2592815. == 0),]$Open.Ended.Exam..2..2592815. = NA
#out of 26 of all numbers :)
gradesf22$Open.Ended.Exam..2..2592815. = as.numeric(gradesf22$Open.Ended.Exam..2..2592815.)/26*100
gradesf23[which(gradesf23$Open.Ended.Exam..2..3262288. == 0),]$Open.Ended.Exam..2..3262288. = NA
gradesf23$Open.Ended.Exam..2..3262288. = as.numeric(gradesf23$Open.Ended.Exam..2..3262288.)/25*100
allStats = as.data.frame.matrix( rbind(
summary(gradesf18$Exam.2..358394.),
summary(gradesf19$Open.Ended.Exam.2..836125.),
summary(gradesf21$Exam..2..2171776.),
summary(gradesf22$Open.Ended.Exam..2..2592815.),
summary(gradesf23$Open.Ended.Exam..2..3262288.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
#allStats$`NA's` = NULL
knitr::kable(allStats,caption = "Exam 2 Statistics",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| Fall18 | 19.0 | 59.5 | 75.0 | 70.5 | 84.0 | 98.0 | 5 |
| Fall19 | 19.0 | 49.0 | 67.0 | 63.8 | 78.0 | 100.0 | 1 |
| Fall21 | 9.0 | 51.4 | 65.4 | 63.6 | 78.3 | 98.5 | 2 |
| Fall22 | 6.7 | 42.0 | 58.0 | 58.1 | 78.8 | 100.0 | 2 |
| Fall23 | 6.0 | 43.2 | 60.0 | 58.6 | 76.0 | 94.0 | 1 |
#EXAM3
gradesf18[which(gradesf18$Exam.3..395005. == 0),]$Exam.3..395005. = NA
gradesf19[which(gradesf19$Open.Ended.Exam.3..875116. == 0),]$Open.Ended.Exam.3..875116. = NA
gradesf21[which(gradesf21$Exam..3..2184781. == 0),]$Exam..3..2184781. = NA
gradesf21$Exam..3..2184781. = gradesf21$Exam..3..2184781./25*100
gradesf22[which(gradesf22$Open.Ended.Exam..3..2592816. == 0),]$Open.Ended.Exam..3..2592816. = NA
gradesf22$Open.Ended.Exam..3..2592816. = gradesf22$Open.Ended.Exam..3..2592816./25*100
gradesf23[which(gradesf23$Open.Ended.Exam..3..3262289. == 0),]$Open.Ended.Exam..3..3262289. = NA
gradesf23$Open.Ended.Exam..3..3262289. = gradesf23$Open.Ended.Exam..3..3262289./25*100
allStats = as.data.frame.matrix( rbind(
summary(gradesf18$Final.exam..408510.),
summary(gradesf19$Open.Ended.Exam.3..875116.),
summary(gradesf21$Exam..3..2184781.),
summary(gradesf22$Open.Ended.Exam..3..2592816.),
summary(gradesf23$Open.Ended.Exam..3..3262289.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allStats,caption = "Exam 3 Statistics",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| Fall18 | 48.9 | 72.4 | 81.4 | 79.9 | 88.3 | 99 | 5 |
| Fall19 | 2.5 | 42.5 | 55.0 | 56.4 | 72.5 | 95 | 5 |
| Fall21 | 9.3 | 38.0 | 52.8 | 51.2 | 64.7 | 99 | 11 |
| Fall22 | 21.0 | 50.0 | 63.0 | 62.1 | 76.3 | 98 | 5 |
| Fall23 | 12.0 | 48.0 | 64.0 | 62.5 | 81.2 | 98 | 7 |
#FINAL EXAM
gradesf19[which(gradesf19$Final.exam.Unposted.Current.Score == 0),]$Final.exam.Unposted.Current.Score = NA
gradesf21[which(gradesf21$Final.Written.Exam.Current.Score == 0),]$Final.Written.Exam.Current.Score = NA
#gradesf22[which(gradesf22$Final.Written.Exam.Current.Score == 0),]$Final.Written.Exam.Current.Score = NA
allStats = as.data.frame.matrix( rbind(
summary(gradesf18$Final.exam..408510.),
summary(gradesf19$Final.exam.Unposted.Current.Score),
summary(gradesf21$Final.Written.Exam.Current.Score),
summary(gradesf22$Final.Written.Exam.Current.Score),
summary(gradesf23$Final.Written.Exam.Current.Score)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allStats,caption = "Final Exam Statistics",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| Fall18 | 48.9 | 72.4 | 81.4 | 79.9 | 88.3 | 99.0 | 5 |
| Fall19 | 33.7 | 55.3 | 64.8 | 65.3 | 75.7 | 97.3 | 5 |
| Fall21 | 14.7 | 49.5 | 63.7 | 63.5 | 76.0 | 100.0 | 5 |
| Fall22 | 40.3 | 60.4 | 67.2 | 68.7 | 77.9 | 93.9 | 1 |
| Fall23 | 0.0 | 57.6 | 66.9 | 65.2 | 75.8 | 93.6 | 0 |
3 Milestone Grades Distribution
3.1 M1 on ACF scale
- First attempt
#filter for at1
m1f19_att1 = m1f19[which(m1f19$attempt == "1"),]
m1f21_att1 = m1f21[which(m1f21$attempt == "1"),]
m1f22_att1 = m1f22[which(m1f22$attempt == "1"),]
m1f23_att1 = m1f23[which(m1f23$attempt == "1"),]
makeTable = function(m1f19_att1, m1f21_att1, m1f22_att1, m1f23_att1 ){
n19 = length(m1f19_att1$score)
nA19 = length(m1f19_att1[which(m1f19_att1$score > 79.99),]$score)
nC19 = length(m1f19_att1[which(m1f19_att1$score > 69.99 & m1f19_att1$score < 79.99),]$score)
nF19 = length(m1f19_att1[which(m1f19_att1$score < 70.00),]$score)
n21 = length(m1f21_att1$score)
nA21 = length(m1f21_att1[which(m1f21_att1$score > 79.99),]$score)
nC21 = length(m1f21_att1[which(m1f21_att1$score > 69.99 & m1f21_att1$score < 79.99),]$score)
nF21 = length(m1f21_att1[which(m1f21_att1$score < 70.00),]$score)
n22 = length(m1f22_att1$score)
nA22 = length(m1f22_att1[which(m1f22_att1$score > 79.99),]$score)
nC22 = length(m1f22_att1[which(m1f22_att1$score > 69.99 & m1f22_att1$score < 79.99),]$score)
nF22 = length(m1f22_att1[which(m1f22_att1$score < 70.00),]$score)
n23 = length(m1f23_att1$score)
nA23 = length(m1f23_att1[which(m1f23_att1$score > 79.99),]$score)
nC23 = length(m1f23_att1[which(m1f23_att1$score > 69.99 & m1f23_att1$score < 79.99),]$score)
nF23 = length(m1f23_att1[which(m1f23_att1$score < 70.00),]$score)
combined_summary = bind_rows(describe(m1f19_att1$score),describe(m1f21_att1$score),describe(m1f22_att1$score),describe(m1f23_att1$score))
combined_summary <- subset(combined_summary, select = c("n", "mean", "sd", "median", "min","max"))
colnames(combined_summary)[colnames(combined_summary) == "n"] <- "students"
rownames(combined_summary) = c("F19","F21","F22","F23")
colnames(combined_summary)[0] <- "Semester"
combined_summary$`# / % A scores` = c(
paste0( nA19, " / ", round(nA19/n19*100, digits = 1),"%"),
paste0( nA21, " / ", round(nA21/n21*100, digits = 1),"%"),
paste0( nA22, " / ", round(nA22/n22*100, digits = 1),"%"),
paste0( nA23, " / ", round(nA23/n23*100, digits = 1),"%")
)
combined_summary$`# / % C scores` = c(
paste0( nC19, " / ", round(nC19/n19*100, digits = 1),"%"),
paste0( nC21, " / ", round(nC21/n21*100, digits = 1),"%"),
paste0( nC22, " / ", round(nC22/n22*100, digits = 1),"%"),
paste0( nC23, " / ", round(nC23/n23*100, digits = 1),"%")
)
combined_summary$`# / % F scores` = c(
paste0( nF19, " / ", round(nF19/n19*100, digits = 1),"%"),
paste0( nF21, " / ", round(nF21/n21*100, digits = 1),"%"),
paste0( nF22, " / ", round(nF22/n22*100, digits = 1),"%"),
paste0( nF23, " / ", round(nF23/n23*100, digits = 1),"%")
)
table <- kable(combined_summary, format = "markdown")
return(table)
}
# Apply some additional formatting if needed
#table <- table %>%
# kable_styling(full_width = FALSE) %>%
# add_header_above(c("Statistic" = 1, "Value" = 1)) %>%
# row_spec(0, bold = TRUE)
table = makeTable(m1f19_att1, m1f21_att1, m1f22_att1, m1f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 191 | 86.59701 | 11.47493 | 89.28000 | 35.92683 | 100 | 150 / 78.5% | 23 / 12% | 18 / 9.4% |
| F21 | 211 | 84.70227 | 13.83789 | 87.80250 | 11.11667 | 100 | 152 / 72% | 33 / 15.6% | 26 / 12.3% |
| F22 | 220 | 82.94547 | 13.94072 | 85.58783 | 40.71200 | 100 | 144 / 65.5% | 35 / 15.9% | 41 / 18.6% |
| F23 | 163 | 82.12047 | 16.51057 | 87.83817 | 23.49350 | 100 | 107 / 65.6% | 22 / 13.5% | 34 / 20.9% |
- Second attempt
m1f19_att1 = m1f19[which(m1f19$attempt == "1" | m1f19$attempt == "2" ),]
m1f21_att1 = m1f21[which(m1f21$attempt == "1" | m1f21$attempt == "2" ),]
m1f22_att1 = m1f22[which(m1f22$attempt == "1" | m1f22$attempt == "2" ),]
m1f23_att1 = m1f23[which(m1f23$attempt == "1" | m1f23$attempt == "2" ),]
chooseHighest = function(df){
df <- df %>%
group_by(id) %>%
filter(score == max(score))
return(df)
}
m1f19_att1 = chooseHighest(m1f19_att1)
m1f21_att1 = chooseHighest(m1f21_att1)
m1f22_att1 = chooseHighest(m1f22_att1)
m1f23_att1 = chooseHighest(m1f23_att1)
table = makeTable(m1f19_att1,
m1f21_att1,
m1f22_att1,
m1f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 191 | 88.45648 | 8.922470 | 90.00200 | 58.64667 | 100 | 168 / 88% | 15 / 7.9% | 8 / 4.2% |
| F21 | 211 | 87.57826 | 11.124503 | 89.43867 | 27.53433 | 100 | 177 / 83.9% | 23 / 10.9% | 11 / 5.2% |
| F22 | 220 | 86.62394 | 9.930089 | 88.33600 | 54.61150 | 100 | 173 / 78.6% | 33 / 15% | 14 / 6.4% |
| F23 | 163 | 86.75313 | 10.975111 | 89.29000 | 23.49350 | 100 | 128 / 78.5% | 24 / 14.7% | 11 / 6.7% |
- Third Attempt
m1f19_att1 = m1f19[which(m1f19$attempt == "1" | m1f19$attempt == "2" | m1f19$attempt == "3" ),]
m1f21_att1 = m1f21[which(m1f21$attempt == "1" | m1f21$attempt == "2" | m1f21$attempt == "3" ),]
m1f22_att1 = m1f22[which(m1f22$attempt == "1" | m1f22$attempt == "2" | m1f22$attempt == "3" ),]
m1f23_att1 = m1f23[which(m1f23$attempt == "1" | m1f23$attempt == "2" | m1f23$attempt == "3" ),]
m1f19_att1 = chooseHighest(m1f19_att1)
m1f21_att1 = chooseHighest(m1f21_att1)
m1f22_att1 = chooseHighest(m1f22_att1)
m1f23_att1 = chooseHighest(m1f23_att1)
table = makeTable(m1f19_att1,
m1f21_att1,
m1f22_att1,
m1f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 191 | 89.05406 | 8.190662 | 90.28167 | 58.64667 | 100 | 175 / 91.6% | 11 / 5.8% | 5 / 2.6% |
| F21 | 211 | 88.15861 | 10.280442 | 89.43867 | 27.53433 | 100 | 180 / 85.3% | 26 / 12.3% | 5 / 2.4% |
| F22 | 220 | 88.12103 | 8.473910 | 88.89125 | 55.81883 | 100 | 194 / 88.2% | 19 / 8.6% | 7 / 3.2% |
| F23 | 163 | 88.18607 | 10.009504 | 89.60533 | 23.49350 | 100 | 142 / 87.1% | 13 / 8% | 8 / 4.9% |
3.2 M2 on ACF scale
- First attempt
m2f19_att1 = m2f19[which(m2f19$attempt == "1" ),]
m2f21_att1 = m2f21[which(m2f21$attempt == "1" ),]
m2f22_att1 = m2f22[which(m2f22$attempt == "1" ),]
m2f23_att1 = m2f23[which(m2f23$attempt == "1" ),]
m2f19_att1 = chooseHighest(m2f19_att1)
m2f21_att1 = chooseHighest(m2f21_att1)
m2f22_att1 = chooseHighest(m2f22_att1)
m2f23_att1 = chooseHighest(m2f23_att1)
table = makeTable(m2f19_att1,
m2f21_att1,
m2f22_att1,
m2f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 190 | 87.24709 | 10.81401 | 90.35901 | 39.07243 | 100 | 152 / 80% | 22 / 11.6% | 16 / 8.4% |
| F21 | 205 | 77.56799 | 14.75559 | 79.77267 | 37.10763 | 100 | 102 / 49.8% | 51 / 24.9% | 52 / 25.4% |
| F22 | 216 | 81.86074 | 12.90459 | 85.01722 | 38.42122 | 100 | 136 / 63% | 40 / 18.5% | 40 / 18.5% |
| F23 | 158 | 82.34789 | 14.76793 | 85.93283 | 29.16567 | 100 | 110 / 69.6% | 17 / 10.8% | 31 / 19.6% |
- Second attempt
m2f19_att1 = m2f19[which(m2f19$attempt == "1" | m2f19$attempt == "2" ),]
m2f21_att1 = m2f21[which(m2f21$attempt == "1" | m2f21$attempt == "2" ),]
m2f22_att1 = m2f22[which(m2f22$attempt == "1" | m2f22$attempt == "2" ),]
m2f23_att1 = m2f23[which(m2f23$attempt == "1" | m2f23$attempt == "2" ),]
m2f19_att1 = chooseHighest(m2f19_att1)
m2f21_att1 = chooseHighest(m2f21_att1)
m2f22_att1 = chooseHighest(m2f22_att1)
m2f23_att1 = chooseHighest(m2f23_att1)
table = makeTable(m2f19_att1,
m2f21_att1,
m2f22_att1,
m2f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 190 | 88.71861 | 9.371188 | 90.43986 | 39.07243 | 100 | 171 / 90% | 10 / 5.3% | 9 / 4.7% |
| F21 | 205 | 81.02490 | 12.313678 | 83.10267 | 37.10763 | 100 | 128 / 62.4% | 43 / 21% | 34 / 16.6% |
| F22 | 216 | 85.05377 | 10.152792 | 86.92933 | 46.45111 | 100 | 165 / 76.4% | 31 / 14.4% | 20 / 9.3% |
| F23 | 158 | 85.05217 | 11.825039 | 87.05144 | 30.41511 | 100 | 121 / 76.6% | 20 / 12.7% | 17 / 10.8% |
- Third Attempt
m2f19_att1 = m2f19[which(m2f19$attempt == "1" | m2f19$attempt == "2" | m2f19$attempt == "3" ),]
m2f21_att1 = m2f21[which(m2f21$attempt == "1" | m2f21$attempt == "2" | m2f21$attempt == "3" ),]
m2f22_att1 = m2f22[which(m2f22$attempt == "1" | m2f22$attempt == "2" | m2f22$attempt == "3" ),]
m2f23_att1 = m2f23[which(m2f23$attempt == "1" | m2f23$attempt == "2" | m2f23$attempt == "3" ),]
m2f19_att1 = chooseHighest(m2f19_att1)
m2f21_att1 = chooseHighest(m2f21_att1)
m2f22_att1 = chooseHighest(m2f22_att1)
m2f23_att1 = chooseHighest(m2f23_att1)
table = makeTable(m2f19_att1,
m2f21_att1,
m2f22_att1,
m2f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 190 | 89.20468 | 8.464771 | 90.43986 | 39.07243 | 100 | 175 / 92.1% | 10 / 5.3% | 5 / 2.6% |
| F21 | 205 | 82.64865 | 10.762921 | 84.18644 | 37.10763 | 100 | 140 / 68.3% | 41 / 20% | 24 / 11.7% |
| F22 | 216 | 86.50969 | 8.566437 | 87.95989 | 53.28894 | 100 | 183 / 84.7% | 21 / 9.7% | 12 / 5.6% |
| F23 | 158 | 86.04928 | 10.614212 | 87.43781 | 30.41511 | 100 | 128 / 81% | 19 / 12% | 11 / 7% |
3.3 M3 on ACF scale
- First attempt
m3f19_att1 = m3f19[which(m3f19$attempt == "1" ),]
m3f21_att1 = m3f21[which(m3f21$attempt == "1" ),]
m3f22_att1 = m3f22[which(m3f22$attempt == "1" ),]
m3f23_att1 = m3f23[which(m3f23$attempt == "1" ),]
m3f19_att1 = chooseHighest(m3f19_att1)
m3f21_att1 = chooseHighest(m3f21_att1)
m3f22_att1 = chooseHighest(m3f22_att1)
m3f23_att1 = chooseHighest(m3f23_att1)
table = makeTable(m3f19_att1,
m3f21_att1,
m3f22_att1,
m3f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 183 | 87.08412 | 9.75140 | 88.51711 | 49.98278 | 100 | 151 / 82.5% | 21 / 11.5% | 11 / 6% |
| F21 | 195 | 81.63402 | 15.31543 | 85.99000 | 24.37333 | 100 | 122 / 62.6% | 30 / 15.4% | 43 / 22.1% |
| F22 | 210 | 79.51752 | 16.25780 | 84.11167 | 9.18000 | 100 | 128 / 61% | 33 / 15.7% | 49 / 23.3% |
| F23 | 151 | 82.07987 | 13.73418 | 86.29667 | 43.91667 | 100 | 104 / 68.9% | 17 / 11.3% | 30 / 19.9% |
- Second attempt
m3f19_att1 = m3f19[which(m3f19$attempt == "1" | m3f19$attempt == "2" ),]
m3f21_att1 = m3f21[which(m3f21$attempt == "1" | m3f21$attempt == "2" ),]
m3f22_att1 = m3f22[which(m3f22$attempt == "1" | m3f22$attempt == "2" ),]
m3f23_att1 = m3f23[which(m3f23$attempt == "1" | m3f23$attempt == "2" ),]
m3f19_att1 = chooseHighest(m3f19_att1)
m3f21_att1 = chooseHighest(m3f21_att1)
m3f22_att1 = chooseHighest(m3f22_att1)
m3f23_att1 = chooseHighest(m3f23_att1)
table = makeTable(m3f19_att1,
m3f21_att1,
m3f22_att1,
m3f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 183 | 88.55890 | 7.485728 | 88.65200 | 53.23144 | 100 | 166 / 90.7% | 14 / 7.7% | 3 / 1.6% |
| F21 | 195 | 84.64072 | 11.961618 | 87.65000 | 45.64333 | 100 | 141 / 72.3% | 28 / 14.4% | 26 / 13.3% |
| F22 | 210 | 83.22498 | 12.068709 | 85.37167 | 37.95333 | 100 | 147 / 70% | 34 / 16.2% | 29 / 13.8% |
| F23 | 151 | 85.22079 | 10.182821 | 86.61667 | 44.83333 | 100 | 122 / 80.8% | 18 / 11.9% | 11 / 7.3% |
- Third Attempt
m3f19_att1 = m3f19[which(m3f19$attempt == "1" | m3f19$attempt == "2" | m3f19$attempt == "3" ),]
m3f21_att1 = m3f21[which(m3f21$attempt == "1" | m3f21$attempt == "2" | m3f21$attempt == "3" ),]
m3f22_att1 = m3f22[which(m3f22$attempt == "1" | m3f22$attempt == "2" | m3f22$attempt == "3" ),]
m3f23_att1 = m3f23[which(m3f23$attempt == "1" | m3f23$attempt == "2" | m3f23$attempt == "3" ),]
m3f19_att1 = chooseHighest(m3f19_att1)
m3f21_att1 = chooseHighest(m3f21_att1)
m3f22_att1 = chooseHighest(m3f22_att1)
m3f23_att1 = chooseHighest(m3f23_att1)
table = makeTable(m3f19_att1,
m3f21_att1,
m3f22_att1,
m3f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 183 | 88.84171 | 7.287616 | 88.88267 | 53.23144 | 100 | 171 / 93.4% | 9 / 4.9% | 3 / 1.6% |
| F21 | 195 | 86.26802 | 10.446492 | 87.77333 | 45.64333 | 100 | 156 / 80% | 27 / 13.8% | 12 / 6.2% |
| F22 | 210 | 84.88897 | 10.332357 | 86.15333 | 37.95333 | 100 | 162 / 77.1% | 33 / 15.7% | 15 / 7.1% |
| F23 | 151 | 85.81885 | 9.479026 | 87.04190 | 44.83333 | 100 | 127 / 84.1% | 16 / 10.6% | 8 / 5.3% |
3.4 M4 on ACF scale
- First attempt
m4f19_att1 = m4f19[which(m4f19$attempt == "1" ),]
m4f21_att1 = m4f21[which(m4f21$attempt == "1" ),]
m4f22_att1 = m4f22[which(m4f22$attempt == "1" ),]
m4f23_att1 = m4f23[which(m4f23$attempt == "1" ),]
m4f19_att1 = chooseHighest(m4f19_att1)
m4f21_att1 = chooseHighest(m4f21_att1)
m4f22_att1 = chooseHighest(m4f22_att1)
m4f23_att1 = chooseHighest(m4f23_att1)
table = makeTable(m4f19_att1,
m4f21_att1,
m4f22_att1,
m4f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 177 | 79.27583 | 14.73094 | 81.64083 | 38.43000 | 100 | 96 / 54.2% | 37 / 20.9% | 44 / 24.9% |
| F21 | 187 | 82.10934 | 13.27249 | 83.88033 | 47.22000 | 100 | 116 / 62% | 39 / 20.9% | 33 / 17.6% |
| F22 | 206 | 79.04682 | 14.39811 | 81.80700 | 26.94200 | 100 | 114 / 55.3% | 41 / 19.9% | 53 / 25.7% |
| F23 | 148 | 80.92414 | 14.35564 | 84.57617 | 44.43833 | 100 | 91 / 61.5% | 28 / 18.9% | 29 / 19.6% |
- Second attempt
m4f19_att1 = m4f19[which(m4f19$attempt == "1" | m4f19$attempt == "2" ),]
m4f21_att1 = m4f21[which(m4f21$attempt == "1" | m4f21$attempt == "2" ),]
m4f22_att1 = m4f22[which(m4f22$attempt == "1" | m4f22$attempt == "2" ),]
m4f23_att1 = m4f23[which(m4f23$attempt == "1" | m4f23$attempt == "2" ),]
m4f19_att1 = chooseHighest(m4f19_att1)
m4f21_att1 = chooseHighest(m4f21_att1)
m4f22_att1 = chooseHighest(m4f22_att1)
m4f23_att1 = chooseHighest(m4f23_att1)
table = makeTable(m4f19_att1,
m4f21_att1,
m4f22_att1,
m4f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 177 | 83.85381 | 11.883539 | 86.58267 | 45.94233 | 100 | 125 / 70.6% | 30 / 16.9% | 22 / 12.4% |
| F21 | 187 | 84.93125 | 11.022564 | 86.11333 | 51.10067 | 100 | 139 / 74.3% | 29 / 15.5% | 19 / 10.2% |
| F22 | 206 | 83.85520 | 10.368770 | 84.99433 | 46.66800 | 100 | 146 / 70.9% | 43 / 20.9% | 18 / 8.7% |
| F23 | 148 | 85.15438 | 9.762059 | 86.24550 | 46.65800 | 100 | 112 / 75.7% | 24 / 16.2% | 12 / 8.1% |
- Third Attempt
m4f19_att1 = m4f19[which(m4f19$attempt == "1" | m4f19$attempt == "2" | m4f19$attempt == "3" ),]
m4f21_att1 = m4f21[which(m4f21$attempt == "1" | m4f21$attempt == "2" | m4f21$attempt == "3" ),]
m4f22_att1 = m4f22[which(m4f22$attempt == "1" | m4f22$attempt == "2" | m4f22$attempt == "3" ),]
m4f23_att1 = m4f23[which(m4f23$attempt == "1" | m4f23$attempt == "2" | m4f23$attempt == "3" ),]
m4f19_att1 = chooseHighest(m4f19_att1)
m4f21_att1 = chooseHighest(m4f21_att1)
m4f22_att1 = chooseHighest(m4f22_att1)
m4f23_att1 = chooseHighest(m4f23_att1)
table = makeTable(m4f19_att1,
m4f21_att1,
m4f22_att1,
m4f23_att1 )
table| students | mean | sd | median | min | max | # / % A scores | # / % C scores | # / % F scores | |
|---|---|---|---|---|---|---|---|---|---|
| F19 | 177 | 85.56451 | 10.407627 | 87.21733 | 45.94233 | 100 | 141 / 79.7% | 22 / 12.4% | 14 / 7.9% |
| F21 | 187 | 86.17210 | 9.920564 | 86.94967 | 51.10067 | 100 | 149 / 79.7% | 26 / 13.9% | 12 / 6.4% |
| F22 | 206 | 84.98724 | 9.406832 | 86.10917 | 46.66800 | 100 | 159 / 77.2% | 37 / 18% | 11 / 5.3% |
| F23 | 148 | 85.99251 | 8.627701 | 86.24800 | 46.65800 | 100 | 118 / 79.7% | 24 / 16.2% | 6 / 4.1% |
3.5 Students who stopped trying
Milestone 1
#count
countGiveUp = function(df){
# Create a table of name counts
name_counts <- table(df$id)
# Create a logical vector indicating names that appear three times or more
to_remove <- df$id %in% names(name_counts[name_counts >= 3])
# Create a logical vector indicating names with at least one score > 80
to_remove_scores <- df$id %in% unique(df$id[df$score > 80.00])
# Combine the two logical vectors using OR to get the final rows to remove
final_to_remove <- to_remove | to_remove_scores
# Filter the dataframe to keep only the valid rows
df <- df[!final_to_remove, ]
df = subset(df, select = c("name", "id", "attempt", "score"))
return(df)
}
giveUp19 = countGiveUp(m1f19)
giveUp21 = countGiveUp(m1f21)
giveUp22 = countGiveUp(m1f22)
giveUp23 = countGiveUp(m1f23)
name_counts <- table(giveUp19$id)
twice19 <- length(names(name_counts[name_counts == 2]))
once19 <- length(names(name_counts[name_counts == 1]))
name_counts <- table(giveUp21$id)
twice21 <- length(names(name_counts[name_counts == 2]))
once21 <- length(names(name_counts[name_counts == 1]))
name_counts <- table(giveUp22$id)
twice22 <- length(names(name_counts[name_counts == 2]))
once22 <- length(names(name_counts[name_counts == 1]))
name_counts <- table(giveUp23$id)
twice23 <- length(names(name_counts[name_counts == 2]))
once23 <- length(names(name_counts[name_counts == 1]))
writeAttemptTable <- function(numbers, titles, table_title) {
if(length(numbers) != length(titles)) {
stop("Length of numbers and titles must be the same.")
}
df <- data.frame(matrix(nrow = 1, ncol = length(numbers)))
colnames(df) <- titles
df[1,] <- numbers
#attr(df, "title") <- table_title
return(df)
}
#M1
numbers <- c(
length(unique(giveUp19$id)),
length(unique(giveUp21$id)),
length(unique(giveUp22$id)),
length(unique(giveUp23$id))
)
titles <- c("F19", "F21", "F22", "F23")
table <- writeAttemptTable(numbers, titles,"Students who settled for a lower score without trying 3 attempts. Milestone 1")
#M2
giveUp19 = countGiveUp(m2f19)
giveUp21 = countGiveUp(m2f21)
giveUp22 = countGiveUp(m2f22)
giveUp23 = countGiveUp(m2f23)
numbers <- c(
length(unique(giveUp19$id)),
length(unique(giveUp21$id)),
length(unique(giveUp22$id)),
length(unique(giveUp23$id))
)
titles <- c("F19", "F21", "F22", "F23")
table2 <- writeAttemptTable(numbers, titles,"Students who settled for a lower score without trying 3 attempts. Milestone 2")
table = rbind(table, table2)
#M3
giveUp19 = countGiveUp(m3f19)
giveUp21 = countGiveUp(m3f21)
giveUp22 = countGiveUp(m3f22)
giveUp23 = countGiveUp(m3f23)
numbers <- c(
length(unique(giveUp19$id)),
length(unique(giveUp21$id)),
length(unique(giveUp22$id)),
length(unique(giveUp23$id))
)
titles <- c("F19", "F21", "F22", "F23")
table2 <- writeAttemptTable(numbers, titles,"Students who settled for a lower score without trying 3 attempts. Milestone 3")
table = rbind(table, table2)
#M4
giveUp19 = countGiveUp(m4f19)
giveUp21 = countGiveUp(m4f21)
giveUp22 = countGiveUp(m4f22)
giveUp23 = countGiveUp(m4f23)
numbers <- c(
length(unique(giveUp19$id)),
length(unique(giveUp21$id)),
length(unique(giveUp22$id)),
length(unique(giveUp23$id))
)
titles <- c("F19", "F21", "F22", "F23")
table2 <- writeAttemptTable(numbers, titles,"Students who settled for a lower score without trying 3 attempts. Milestone 4")
table = rbind(table, table2)
rownames(table) = c(
"Milestone 1",
"Milestone 2",
"Milestone 3",
"Milestone 4"
)
knitr::kable(table, caption="Students who settled for a lower score without trying 3 attempts.")| F19 | F21 | F22 | F23 | |
|---|---|---|---|---|
| Milestone 1 | 14 | 23 | 13 | 12 |
| Milestone 2 | 9 | 32 | 16 | 14 |
| Milestone 3 | 7 | 23 | 19 | 14 |
| Milestone 4 | 14 | 25 | 31 | 18 |
- In 2019, 14 students settled for a lower score, without trying the 3 attempts.
- In 2021, 25 students settled for a lower score, without trying the 3 attempts.
- In 2022, 31 students settled for a lower score, without trying the 3 attempts.
- In 2023, 18 students settled for a lower score, without trying the 3 attempts.
3.6 Statistics of raw scores
f18m1 = suppressWarnings(as.numeric( gradesf18mood$Quiz.Milestone.1..Do.not.take.it.outside.of.class..Requires.Respondus.LockDown.Browser..Real.))
f18m2 = suppressWarnings(as.numeric( gradesf18mood$Quiz.Milestone.2...Do.not.take.it.outside.of.class...Requires.Respondus.LockDown.Browser..Real.))
f18m3 = suppressWarnings(as.numeric( gradesf18mood$Quiz.Milestone.3...Do.not.take.it.outside.of.class..Requires.Respondus.LockDown.Browser..Real.))
f18m4 = suppressWarnings(as.numeric( gradesf18mood$Quiz.Milestone.4...Do.not.take.it.outside.of.class..Requires.Respondus.LockDown.Browser..Real.))
allStats = as.data.frame.matrix( rbind(
summary(f18m1),
summary(gradesf19$Milestone.1.Quiz..Requires.Respondus.LockDown.Browser..798227.),
summary(gradesf21$Milestone.1..Requires.Respondus.LockDown.Browser..1911986.),
summary(gradesf22$Milestone.1..Requires.Respondus.LockDown.Browser..2592695.),
summary(gradesf23$Milestone.1..Requires.Respondus.LockDown.Browser..3262166.)
))
allStats$`NA's` = NULL
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allStats,caption = "Milestone 1 raw score",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| Fall18 | 51.2 | 88.2 | 94.0 | 92.3 | 98.4 | 100 |
| Fall19 | 71.9 | 84.5 | 90.5 | 89.9 | 95.9 | 100 |
| Fall21 | 51.2 | 84.4 | 89.8 | 89.4 | 96.0 | 100 |
| Fall22 | 61.2 | 84.0 | 89.2 | 89.1 | 94.5 | 100 |
| Fall23 | 23.5 | 85.1 | 90.0 | 89.2 | 95.6 | 100 |
allStats = as.data.frame.matrix( rbind(
summary(f18m2),
summary(gradesf19$Milestone.2.Quiz..Requires.Respondus.LockDown.Browser..823364.),
summary(gradesf21$Milestone.2..Requires.Respondus.LockDown.Browser..1912009.),
summary(gradesf22$Milestone.2..Requires.Respondus.LockDown.Browser..2592715.),
summary(gradesf23$Milestone.2..Requires.Respondus.LockDown.Browser..3262143.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
allStats$`NA's` = NULL
knitr::kable(allStats,caption = "Milestone 2 raw score",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| Fall18 | 55.6 | 84.4 | 91.6 | 90.0 | 96.0 | 100 |
| Fall19 | 67.0 | 85.6 | 90.6 | 90.1 | 95.7 | 100 |
| Fall21 | 37.1 | 79.3 | 84.9 | 84.1 | 90.1 | 100 |
| Fall22 | 53.3 | 82.8 | 88.0 | 87.2 | 92.4 | 100 |
| Fall23 | 30.4 | 81.7 | 88.2 | 87.2 | 94.2 | 100 |
allStats = as.data.frame.matrix( rbind(
summary(f18m3),
summary(gradesf19$Milestone.3.Quiz..Requires.Respondus.LockDown.Browser..841979.),
summary(gradesf21$Milestone.3..Requires.Respondus.LockDown.Browser..2192128.),
summary(gradesf22$Milestone.3..Requires.Respondus.LockDown.Browser..2592728.),
summary(gradesf23$Milestone.3..Requires.Respondus.LockDown.Browser..3262146.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allStats,caption = "Milestone 3 raw score",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| Fall18 | 35.4 | 86.8 | 92.4 | 90.5 | 96.7 | 100 | 3 |
| Fall19 | 64.4 | 84.2 | 89.5 | 89.2 | 94.4 | 100 | 2 |
| Fall21 | 48.4 | 82.3 | 88.0 | 87.4 | 94.1 | 100 | 4 |
| Fall22 | 38.2 | 81.4 | 86.3 | 85.8 | 91.8 | 100 | 1 |
| Fall23 | 44.8 | 82.2 | 87.2 | 86.4 | 92.1 | 100 | 3 |
allStats = as.data.frame.matrix( rbind(
summary(f18m4),
summary(gradesf19$Milestone.4.Quiz..Requires.Respondus.LockDown.Browser..860755.),
summary(gradesf21$Milestone.4..Requires.Respondus.LockDown.Browser..2216543.),
summary(gradesf22$Milestone.4..Requires.Respondus.LockDown.Browser..2592732.),
summary(gradesf23$Milestone.4..Requires.Respondus.LockDown.Browser..3262167.)
))
row.names(allStats) = c("Fall18","Fall19","Fall21","Fall22","Fall23")
knitr::kable(allStats,caption = "Milestone 4 raw score",digits = 1)| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| Fall18 | 44.0 | 82.6 | 88.9 | 87.2 | 94.6 | 100 | 5 |
| Fall19 | 46.4 | 83.4 | 87.5 | 86.4 | 92.2 | 100 | 5 |
| Fall21 | 51.1 | 81.7 | 87.0 | 86.4 | 94.4 | 100 | 8 |
| Fall22 | 46.7 | 80.6 | 86.4 | 85.3 | 91.4 | 100 | 3 |
| Fall23 | 46.7 | 81.6 | 86.2 | 86.0 | 91.7 | 100 | 3 |
- Passing grades as they attempt milestones
library(ggplot2)
buildHeaders <- function(numberOfQuestions){
colHeaders = c()
#build headers
for (n in seq(numberOfQuestions)){
colHeaders = c(colHeaders,paste("q",as.character(n),sep = ""))
}
return(colHeaders)
}
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)
}
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)
}
compileStackAndGrouped = function(m1f21,numb_m1f21,m1f19,numb_m1f19){
m1df = buildDF_fromMilestone(m1f21,numb_m1f21)
results = flowAndSettling(m1df)
flow = results$flow
pct1st = flow[,1]/sum(flow[,1])*100
pct2nd = flow[,2]/sum(flow[,2])*100
pct3rd = flow[,3]/sum(flow[,3])*100
stackAndGrouped = data.frame(matrix(ncol = 4,nrow=0))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct1st[1],"F21","First"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct1st[2],"F21","First"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct1st[3],"F21","First"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct2nd[1],"F21","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct2nd[2],"F21","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct2nd[3],"F21","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct3rd[1],"F21","Third"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct3rd[2],"F21","Third"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct3rd[3],"F21","Third"))
m1df19 = buildDF_fromMilestone(m1f19,numb_m1f19)
results19 = flowAndSettling(m1df19)
flow19 = results19$flow
pct1st = flow19[,1]/sum(flow19[,1])*100
pct2nd = flow19[,2]/sum(flow19[,2])*100
pct3rd = flow19[,3]/sum(flow19[,3])*100
stackAndGrouped = rbind(stackAndGrouped,c("F",pct1st[1],"F19","First"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct1st[2],"F19","First"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct1st[3],"F19","First"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct2nd[1],"F19","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct2nd[2],"F19","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct2nd[3],"F19","Second"))
stackAndGrouped = rbind(stackAndGrouped,c("F",pct3rd[1],"F19","Third"))
stackAndGrouped = rbind(stackAndGrouped,c("C",pct3rd[2],"F19","Third"))
stackAndGrouped = rbind(stackAndGrouped,c("A",pct3rd[3],"F19","Third"))
colnames(stackAndGrouped) = c("Grade","Pct","Year","Attempt")
stackAndGrouped$Pct = as.numeric(stackAndGrouped$Pct)
return(stackAndGrouped)
}
stackAndGrouped = compileStackAndGrouped(m1f21,12,m1f19,12)
ggplot() + geom_bar(data=stackAndGrouped, aes(y = Pct, x = Year, fill = Grade), stat="identity",
position='stack') +
theme_bw() +
ggtitle("Milestone 1: F19 vs F21")+
scale_fill_manual(values = c("green","yellow","red" )) +
facet_grid( ~ Attempt)
stackAndGrouped = compileStackAndGrouped(m2f21,11,m2f19,12)
ggplot() + geom_bar(data=stackAndGrouped, aes(y = Pct, x = Year, fill = Grade), stat="identity",
position='stack') +
theme_bw() +
ggtitle("Milestone 2: F19 vs F21")+
scale_fill_manual(values = c("green","yellow","red" )) +
facet_grid( ~ Attempt)
stackAndGrouped = compileStackAndGrouped(m3f21,13,m3f19,12)
ggplot() + geom_bar(data=stackAndGrouped, aes(y = Pct, x = Year, fill = Grade), stat="identity",
position='stack') +
theme_bw() +
ggtitle("Milestone 3: F19 vs F21")+
scale_fill_manual(values = c("green","yellow","red" )) +
facet_grid( ~ Attempt)
stackAndGrouped = compileStackAndGrouped(m4f21,12,m4f19,12)
ggplot() + geom_bar(data=stackAndGrouped, aes(y = Pct, x = Year, fill = Grade), stat="identity",
position='stack') +
theme_bw() +
ggtitle("Milestone 4: F19 vs F21")+
scale_fill_manual(values = c("green","yellow","red" )) +
facet_grid( ~ Attempt)
4 Preclass
- Students not turning in preclass through the semester
f18preclass = gradesf18[,grepl("class",names(gradesf18)) & grepl("X",names(gradesf18)) & !grepl("Score",names(gradesf18))]
f19preclass = gradesf19[,grepl("class",names(gradesf19)) & grepl("X",names(gradesf19)) & !grepl("Score",names(gradesf19))]
f21preclass = gradesf21[,grepl("class",names(gradesf21)) & grepl("X",names(gradesf21)) & !grepl("Score",names(gradesf21))]
gradesf18$missPreclass = rowSums( f18preclass == 0, na.rm = TRUE)
gradesf19$missPreclass = rowSums( f19preclass == 0, na.rm = TRUE)
gradesf21$missPreclass = rowSums( f21preclass == 0, na.rm = TRUE)
l = length(colnames(f21preclass))
#f21preclass[,l] = NULL
#f21preclass[,l-1] = NULL
plot(0+colSums(f18preclass == 0, na.rm = TRUE),type="l",col="black",ylim=c(0,40),xlab = "class day",ylab = "students missing preclass")
lines(0+colSums(f19preclass == 0,na.rm = TRUE),type="l",col="red")
lines(0+colSums(f21preclass < 1,na.rm = TRUE),type="l",col="green")
legend(1, 40, legend=c( paste("F2018 n=",nf18),
paste("F2019 n=",nf19),
paste("F2021 n=",nf21)), col=c("black", "red","green"),lty=1:1, cex=0.8)
title("Number of students missing each Preclass")
- Repeating offenders
par(mfrow=c(1,3),
oma = c(5,4,0,0) + 0.1,
mar = c(0,0,1,1) + 0.1)
hist(rowSums( f18preclass == 0,na.rm = TRUE),breaks=10,xlim = c(0,15),ylim=c(0,140),main="Fall18",xlab="Number of preclass missed",ylab="Number of students")
hist(rowSums( f19preclass == 0,na.rm = TRUE),breaks=10,xlim = c(0,15),ylim=c(0,140),main="Fall19",xlab="Number of preclass missed",ylab="Number of students")
hist(rowSums( f21preclass == 0,na.rm = TRUE),breaks=10,xlim = c(0,15),ylim=c(0,140),main="Fall21",xlab="Number of preclass missed",ylab="Number of students")
5 Homework
- Students not completing the homework is a sign of students giving up or completely disengaged. Notice that because of the “drop the lowest” policy some students just don’t attempt the last one because they’re happy with their grade. I’m wondering if the fact that the grade is out of 12 and not out of 100 loses sense to them. For example, the 70% of 12 is 8.4 which is not as dramatic.
f18hw = gradesf18[,grepl("Thu",names(gradesf18))]
f19hw = gradesf19[,grepl("Homework",names(gradesf19)) & !grepl("Score",names(gradesf19)) & !grepl("Homework.13th.week",names(gradesf19))]
for (i in 1:ncol(f19hw)){ f19hw[,i] = as.numeric(f19hw[,i])/12*100 }
f21hw = gradesf21[,grepl("Thu",names(gradesf21))]
for (i in 1:ncol(f21hw)){ f21hw[,i] = as.numeric(f21hw[,i])/12*100 }
hwfail = data.frame(matrix(ncol = 0,nrow=3))
for (i in 1:ncol(f21hw)){
hwfail[ paste("hw",i)]=c(
sum( f18hw[,i] < 70,na.rm = TRUE),
sum( f19hw[,i] < 70,na.rm = TRUE),
sum( f21hw[,i] < 70,na.rm = TRUE)
)
}
row.names(hwfail) = c("Fall18","Fall19","Fall21")
knitr::kable(hwfail,caption = "Number of students with homework scores below 70",digits = 1)| hw 1 | hw 2 | hw 3 | hw 4 | hw 5 | hw 6 | hw 7 | hw 8 | hw 9 | |
|---|---|---|---|---|---|---|---|---|---|
| Fall18 | 2 | 5 | 1 | 9 | 9 | 3 | 2 | 3 | 11 |
| Fall19 | 1 | 4 | 4 | 15 | 44 | 4 | 6 | 3 | 21 |
| Fall21 | 3 | 18 | 11 | 29 | 26 | 15 | 15 | 29 | 30 |
hwfail = data.frame(matrix(ncol = 0,nrow=3))
for (i in 1:ncol(f21hw)){
hwfail[ paste("hw",i)]=c(
sum( f18hw[,i] < 50,na.rm = TRUE),
sum( f19hw[,i] < 50,na.rm = TRUE),
sum( f21hw[,i] < 50,na.rm = TRUE)
)
}
row.names(hwfail) = c("Fall18","Fall19","Fall21")
knitr::kable(hwfail,caption = "Number of students with homework scores below 50",digits = 1)| hw 1 | hw 2 | hw 3 | hw 4 | hw 5 | hw 6 | hw 7 | hw 8 | hw 9 | |
|---|---|---|---|---|---|---|---|---|---|
| Fall18 | 0 | 4 | 0 | 4 | 3 | 2 | 0 | 1 | 5 |
| Fall19 | 0 | 4 | 2 | 4 | 11 | 2 | 3 | 1 | 8 |
| Fall21 | 1 | 8 | 2 | 18 | 10 | 7 | 8 | 17 | 18 |
6 Lab reports
- Except for one, lab reports are out of ten. The table shows how many students got scores below 5
f18report = gradesf18[, grepl("Reporting",names(gradesf18)) &
!grepl("Score",names(gradesf18))
]
f19report = gradesf19[, grepl("Reporting",names(gradesf19)) &
!grepl("Score",names(gradesf19))
]
f21report = gradesf21[,grepl("Reporting",names(gradesf21))]
for (i in 1:ncol(f18report)){
f18report[,i] = as.numeric(f18report[,i])
thismax = max(f18report[,i],na.rm = TRUE)
f18report[,i] = f18report[,i]/thismax*100
}
for (i in 1:ncol(f19report)){
f19report[,i] = as.numeric(f19report[,i])
thismax = max(f19report[,i],na.rm = TRUE)
f19report[,i] = f19report[,i]/thismax*100
}
for (i in 1:ncol(f21report)){
f21report[,i] = as.numeric(f21report[,i])
thismax = max(f21report[,i],na.rm = TRUE)
f21report[,i] = f21report[,i]/thismax*100
}
f21report = f21report[,order(names(f21report))]
f21report = f21report[,c(1,4,5,6,7,8,9,10,11,2,3)]
reportfail = data.frame(matrix(ncol = 0,nrow=3))
for (i in 1:ncol(f21report)){
reportfail[ paste("Report",i)]=c(
sum( f18report[,i] < 50,na.rm = TRUE),
sum( f19report[,i] < 50,na.rm = TRUE),
sum( f21report[,i] < 50,na.rm = TRUE)
)
}
row.names(reportfail) = c("Fall18","Fall19","Fall21")
knitr::kable(reportfail,caption = "Number of students with report scores below 50",digits = 1)| Report 1 | Report 2 | Report 3 | Report 4 | Report 5 | Report 6 | Report 7 | Report 8 | Report 9 | Report 10 | Report 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fall18 | 2 | 1 | 5 | 2 | 2 | 4 | 3 | 3 | 3 | 4 | 6 |
| Fall19 | 2 | 11 | 26 | 16 | 10 | 10 | 17 | 10 | 18 | 13 | 14 |
| Fall21 | 7 | 51 | 30 | 48 | 17 | 24 | 36 | 30 | 48 | 43 | 27 |
7 Prelab
Missing a prelab is big deal because you are not allowed to turn in your report and the moment you miss more than two labs you automatically fail the course.
f18prelab = gradesf18[,grepl("Prelab",names(gradesf18)) & !grepl("Score",names(gradesf18))]
f19prelab = gradesf19[,grepl("Prelab",names(gradesf19)) & !grepl("Score",names(gradesf19))]
f21prelab = gradesf21[,grepl("Prelab",names(gradesf21)) & !grepl("Score",names(gradesf21))]
gradesf18$missPrelab = rowSums( f18prelab == 0, na.rm = TRUE)
gradesf19$missPrelab = rowSums( f19prelab == 0, na.rm = TRUE)
gradesf21$missPrelab = rowSums( f21prelab == 0, na.rm = TRUE)
plot(0+colSums(f18prelab == 0, na.rm = TRUE),type="l",col="black",ylim=c(0,8),xlab = "Lab day",ylab = "students missing prelab")
lines(0+colSums(f19prelab == 0,na.rm = TRUE),type="l",col="red")
lines(0+colSums(f21prelab < 1,na.rm = TRUE),type="l",col="green")
legend("topright", legend=c( paste("F2018 n=",nf18),
paste("F2019 n=",nf19),
paste("F2021 n=",nf21)), col=c("black", "red","green"),lty=1:1, cex=0.8)
title("Number of students missing each Prelab")
8 Video Watching
- What days students are not watching the videos. The data only shows up to Module 3 (the first two months of the course). I need to compile all the video watching data from F19 and F18, but Kaltura changed the format so it’s harder to get the data for the date ranges that I need.
allVideo = data.frame(matrix(ncol = 0,nrow=3))
j18 = nrow(videof18)
j19 = nrow(videof19)
j21 = nrow(videof21)
#f19 has the least number of columns, I need more data. The last two columns are averages
for (i in 1:(ncol(videof19)-2)){
allVideo[paste("video",i)] = c(
videof18[j18,i+1],
videof19[j19,i+1],
videof21[j21,i+6]
)
}
row.names(allVideo) = c("Fall18","Fall19","Fall21")
tAllVideo = t(allVideo)
matplot(tAllVideo,type="l",xlab="Video instance",ylab="Number students not watching it",main="Students who do not watch that video")
legend("topright",legend = colnames(tAllVideo),col=1:3,lty=1:3)
- Distibution of repeating offenders: Are there a lot of students who consistently do not watch the videos. Fall21 stands out again.
par(mfrow=c(1,3),
oma = c(5,4,0,0) + 0.1,
mar = c(0,0,1,1) + 0.1)
hist(videof18$TotalMissed,xlim = c(0,50),ylim=c(0,140),main="Fall18",xlab="Number of videos missed",ylab="Number of students")
hist(videof19$TotalMissed,xlim = c(0,50),ylim=c(0,140),main="Fall19",xlab="Number of videos missed",ylab="Number of students")
hist(videof21$VideosMissed,xlim = c(0,50),ylim=c(0,140),main="Fall21",xlab="Number of videos missed",ylab="Number of students")
9 What Correlates With Course Grades
9.1 Semester Exams
plot( gradesf18$Exam.1..335345., gradesf18$Unposted.Final.Score ,type='p',xlab='Exam1/100',ylab='Final Grade/100',main='Final grade vs Exam1')
abline(lm(gradesf18$Unposted.Final.Score ~ gradesf18$Exam.1..335345.))
points( gradesf19$Open.Ended.Written.Exam.1..816325., gradesf19$Unposted.Final.Score,col="red")
abline(lm(gradesf19$Unposted.Final.Score ~ gradesf19$Open.Ended.Written.Exam.1..816325.),col="red")
points( gradesf21$Exam..1..2161998., gradesf21$Unposted.Final.Score,col="green")
#abline(lm(
# gradesf21$Unposted.Final.Score[!is.na(gradesf21$Exam..1..2161998.)] ~
# gradesf21$Exam..1..2161998.[!is.na(gradesf21$Exam..1..2161998.)]/25*100),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
plot( gradesf18$Exam.2..358394., gradesf18$Unposted.Final.Score,type='p',xlab='Exam2/100',ylab='Final Grade/100',main='Final grade vs Exam2')
abline(lm( gradesf18$Unposted.Final.Score ~ gradesf18$Exam.2..358394.))
points( gradesf19$Open.Ended.Exam.2..836125., gradesf19$Unposted.Final.Score, col="red")
abline(lm( gradesf19$Unposted.Final.Score ~ gradesf19$Open.Ended.Exam.2..836125. ),col="red")
points( gradesf21$Exam..2..2171776.,gradesf21$Unposted.Final.Score, col="green")
abline(lm( gradesf21$Unposted.Final.Score ~ gradesf21$Exam..2..2171776.), col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
plot( gradesf18$Exam.3..395005., gradesf18$Unposted.Final.Score, type='p',xlab='Exam3/100',ylab='Final Grade/100',main='Final grade vs Exam3')
abline(lm( gradesf18$Unposted.Final.Score ~ gradesf18$Exam.3..395005.))
points( gradesf19$Open.Ended.Exam.3..875116., gradesf19$Unposted.Final.Score, col="red")
abline( lm( gradesf19$Unposted.Final.Score ~ gradesf19$Open.Ended.Exam.3..875116.),col="red")
points( gradesf21$Exam..3..2184781., gradesf21$Unposted.Final.Score, col="green")
abline( lm( gradesf21$Unposted.Final.Score ~ gradesf21$Exam..3..2184781.),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
plot( gradesf18$Final.exam..408510., gradesf18$Unposted.Final.Score, type='p',xlab='Final Exam/100',ylab='Final Grade/100',main='Final grade vs Final exam',ylim=c(50,100))
abline( lm( gradesf18$Unposted.Final.Score ~ gradesf18$Final.exam..408510. ))
points( gradesf19$Final.exam.Unposted.Current.Score, gradesf19$Unposted.Final.Score, col="red")
abline( lm( gradesf19$Unposted.Final.Score ~ gradesf19$Final.exam.Unposted.Current.Score), col="red")
points( gradesf21$Final.Written.Exam.Current.Score, gradesf21$Unposted.Current.Score, col="green")
abline(lm( gradesf21$Unposted.Final.Score ~ gradesf21$Final.Written.Exam.Current.Score),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
9.2 Milestone Exams and Course Grade
- A graph of milestone exams is not useful because most students get 100/80/0 so it makes no sense to plot it in an xy coordinate. The table below showing what “A”,“B”,“C”.. students get as average is more informative.
9.3 Homework
f18hw$ave = rowMeans(f18hw)
f19hw$ave = rowMeans(f19hw)
f21hw$ave = rowMeans(f21hw)
plot(f18hw$ave, gradesf18$Unposted.Final.Score,type='p', xlab='Ave HW/100',ylab='Final Grade/100',main='Final Grade vs Average Homework',ylim=c(0,100))
abline( lm(gradesf18$Unposted.Final.Score ~ f18hw$ave ))
points(f19hw$ave, gradesf19$Unposted.Current.Score,col="red")
abline( lm(gradesf19$Unposted.Final.Score ~ f19hw$ave ),col="red")
points(f21hw$ave, gradesf21$Unposted.Current.Score,col="green")
abline( lm(gradesf21$Unposted.Final.Score ~ f21hw$ave ),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
9.4 Lab reports
f18report$ave = rowMeans(f18report)
f19report$ave = rowMeans(f19report)
f21report$ave = rowMeans(f21report)
plot(f18report$ave, gradesf18$Unposted.Final.Score,type='p', xlab='Ave Lab Report/100',ylab='Final Grade/100',main='Final Grade vs Average Lab Report',ylim=c(0,100))
abline( lm(gradesf18$Unposted.Final.Score ~ f18report$ave ))
points(f19report$ave, gradesf19$Unposted.Current.Score,col="red")
abline( lm(gradesf19$Unposted.Final.Score ~ f19report$ave ),col="red")
points(f21report$ave, gradesf21$Unposted.Current.Score,col="green")
abline( lm(gradesf21$Unposted.Final.Score ~ f21report$ave ),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
9.5 Prelab
It looks like in 2018 we didn’t require the 90% prelab score to to be in the lab. This shows as Fall2018 has the prelab averages all over the place. Fall2019 and Fall2021 however shows pretty much everyone with an average above 90%.
plot( gradesf18$Lab..Prelab.Quiz.Unposted.Final.Score, gradesf18$Unposted.Final.Score,type='p', xlab='Ave Prelab /100',ylab='Final Grade/100',main='Final Grade vs Average Prelab',ylim=c(0,100) )
abline(lm( gradesf18$Unposted.Final.Score ~ gradesf18$Lab..Prelab.Quiz.Unposted.Final.Score))
points(gradesf19$Lab..Prelab.Quiz.Unposted.Final.Score, gradesf19$Unposted.Current.Score,col="red")
abline( lm(gradesf19$Unposted.Final.Score ~ gradesf19$Lab..Prelab.Quiz.Unposted.Final.Score ),col="red")
points(gradesf21$Pre.Lab.Assignments.Unposted.Final.Score, gradesf21$Unposted.Current.Score,col="green")
abline( lm(gradesf21$Unposted.Final.Score ~ gradesf21$Pre.Lab.Assignments.Unposted.Final.Score ),col="green")
legend("bottomright",legend = c("F18","F19","F21"),col=c("black","red","green"),lty=1:1, cex=0.8)
9.6 Tables of Behavioral charactersitics of students based on grade
- Fall 2018
getMeanAndSD = function(df,score){
results = data.frame(matrix(ncol=1,nrow=5))
i = 1
for (let in c("A","B","C","D","F")){
thisMean = sprintf("%.2f", mean( df[which(grepl(let,df$Unposted.Final.Grade)),][[score]], na.rm = TRUE))
thisSD = sprintf("%.1f", sd( df[which(grepl(let,df$Unposted.Final.Grade)),][[score]], na.rm = TRUE))
results[i,1] = paste(thisMean,'+/-',thisSD)
i=i+1
}
return(results)
}
f18ave = data.frame(matrix(ncol=0,nrow = 5))
f18ave["Written Exams "] = getMeanAndSD(gradesf18,"Science.Practice.Exercises.Unposted.Final.Score")
f18ave["Final Exam"] = getMeanAndSD(gradesf18,"Final.exam.Unposted.Current.Score")
f18ave["Milestone "] = getMeanAndSD(gradesf18,"Milestones.Unposted.Current.Score")
f18ave["HW "] = getMeanAndSD(gradesf18,"Homework.Current.Score")
f18ave["Lab reports"] = getMeanAndSD(gradesf18,"Lab..Reporting.Unposted.Current.Score")
f18ave["Miss Preclass"] = getMeanAndSD(gradesf18,"missPreclass")
f18ave["Miss Prelab"] = getMeanAndSD(gradesf18,"missPrelab")
row.names(f18ave)=c("A students","B students","C students","D students","F students")
knitr::kable(f18ave,caption="Fall 2018: Average +/- Standard Deviation Based on Grade Performance")| Written Exams | Final Exam | Milestone | HW | Lab reports | Miss Preclass | Miss Prelab | |
|---|---|---|---|---|---|---|---|
| A students | 81.51 +/- 6.6 | 87.55 +/- 6.0 | 100.00 +/- 0.0 | 97.69 +/- 2.5 | 92.12 +/- 3.8 | 1.58 +/- 1.9 | 0.04 +/- 0.3 |
| B students | 62.77 +/- 9.8 | 74.05 +/- 7.3 | 98.82 +/- 3.0 | 94.33 +/- 5.1 | 88.05 +/- 5.5 | 2.99 +/- 2.7 | 0.12 +/- 0.4 |
| C students | 48.98 +/- 7.2 | 61.31 +/- 7.0 | 85.42 +/- 10.3 | 87.27 +/- 7.3 | 84.94 +/- 7.5 | 3.17 +/- 2.5 | 0.08 +/- 0.3 |
| D students | 43.92 +/- 11.3 | 59.91 +/- 6.3 | 63.75 +/- 13.1 | 79.92 +/- 14.8 | 71.19 +/- 10.8 | 5.25 +/- 3.9 | 1.00 +/- 1.2 |
| F students | 35.88 +/- 10.7 | NaN +/- NA | 46.05 +/- 25.3 | 62.19 +/- 17.2 | 59.30 +/- 14.9 | 14.40 +/- 5.2 | 2.40 +/- 1.5 |
f19ave = data.frame(matrix(ncol=0,nrow = 5))
f19ave["Written Exams "] = getMeanAndSD(gradesf19,"Open.Ended.Written.Exams.Unposted.Final.Score")
f19ave["Final Exam"] = getMeanAndSD(gradesf19,"Final.exam.Unposted.Final.Score")
f19ave["Milestone "] = getMeanAndSD(gradesf19,"Milestones.Unposted.Current.Score")
f19ave["HW "] = getMeanAndSD(gradesf19,"Homework.Current.Score")
f19ave["Lab reports"] = getMeanAndSD(gradesf19,"Lab..Reporting.Unposted.Current.Score")
f19ave["Miss Preclass"] = getMeanAndSD(gradesf19,"missPreclass")
f19ave["Miss Prelab"] = getMeanAndSD(gradesf19,"missPrelab")
row.names(f19ave)=c("A students","B students","C students","D students","F students")
knitr::kable(f19ave,caption="Fall 2019: Average +/- Standard Deviation Based on Grade Performance")| Written Exams | Final Exam | Milestone | HW | Lab reports | Miss Preclass | Miss Prelab | |
|---|---|---|---|---|---|---|---|
| A students | 84.14 +/- 6.0 | 82.22 +/- 8.0 | 100.00 +/- 0.0 | 98.25 +/- 1.6 | 88.44 +/- 6.6 | 1.77 +/- 1.9 | 0.07 +/- 0.3 |
| B students | 64.24 +/- 9.7 | 64.08 +/- 9.4 | 99.74 +/- 1.1 | 93.92 +/- 4.8 | 81.71 +/- 9.5 | 2.33 +/- 2.0 | 0.08 +/- 0.3 |
| C students | 47.63 +/- 10.8 | 46.57 +/- 13.8 | 92.34 +/- 8.6 | 91.34 +/- 6.4 | 73.07 +/- 11.8 | 3.00 +/- 2.7 | 0.09 +/- 0.4 |
| D students | 37.11 +/- 12.5 | 45.38 +/- 7.8 | 70.83 +/- 10.2 | 77.24 +/- 14.3 | 57.39 +/- 23.2 | 4.00 +/- 4.0 | 0.33 +/- 0.8 |
| F students | 33.85 +/- 12.9 | 9.96 +/- 19.9 | 68.75 +/- 33.8 | 64.80 +/- 16.5 | 41.30 +/- 16.4 | 12.25 +/- 5.9 | 1.75 +/- 3.5 |
f21ave = data.frame(matrix(ncol=0,nrow = 5))
f21ave["Written Exams "] = getMeanAndSD(gradesf21,"Open.Ended.Semester.Exams.Unposted.Final.Score")
f21ave["Final Exam"] = getMeanAndSD(gradesf21,"Final.Written.Exam.Unposted.Final.Score")
f21ave["Milestone "] = getMeanAndSD(gradesf21,"Milestones.Unposted.Current.Score")
f21ave["HW "] = getMeanAndSD(gradesf21,"Homework.Current.Score")
f21ave["Lab reports"] = getMeanAndSD(gradesf21,"Lab.Reports.Unposted.Current.Score")
f21ave["Miss Preclass"] = getMeanAndSD(gradesf21,"missPreclass")
f21ave["Miss Prelab"] = getMeanAndSD(gradesf21,"missPrelab")
f21ave["Miss Videos"] = getMeanAndSD(gradesf21,"VideosMissed")
row.names(f21ave)=c("A students","B students","C students","D students","F students")
knitr::kable(f21ave,caption="Fall 2021: Average +/- Standard Deviation Based on Grade Performance")| Written Exams | Final Exam | Milestone | HW | Lab reports | Miss Preclass | Miss Prelab | Miss Videos | |
|---|---|---|---|---|---|---|---|---|
| A students | 86.60 +/- 6.8 | 78.75 +/- 11.3 | 99.66 +/- 1.3 | 96.71 +/- 3.5 | 91.30 +/- 4.7 | 1.84 +/- 1.9 | 0.01 +/- 0.1 | 8.41 +/- 11.5 |
| B students | 70.36 +/- 8.1 | 58.90 +/- 11.5 | 98.57 +/- 3.2 | 91.26 +/- 5.1 | 78.68 +/- 10.2 | 2.91 +/- 2.2 | 0.07 +/- 0.3 | 19.09 +/- 15.9 |
| C students | 54.11 +/- 13.3 | 46.36 +/- 13.8 | 85.61 +/- 9.4 | 82.54 +/- 9.6 | 65.32 +/- 13.6 | 4.39 +/- 2.6 | 0.12 +/- 0.3 | 25.64 +/- 15.9 |
| D students | 45.06 +/- 7.7 | 44.44 +/- 7.8 | 66.00 +/- 17.0 | 73.20 +/- 10.3 | 52.70 +/- 18.1 | 5.50 +/- 3.7 | 0.30 +/- 0.7 | 35.50 +/- 18.7 |
| F students | 34.22 +/- 20.1 | 17.25 +/- 27.5 | 30.00 +/- 19.6 | 52.11 +/- 26.7 | 40.13 +/- 21.2 | 9.88 +/- 5.1 | 1.50 +/- 1.9 | 44.38 +/- 21.3 |
Missing four videos is approximately equivalent to missing one day of class
10 Statistical significance
10.1 Written Exams
#install.packages("ggpubr")
library(ggpubr)
plotGGbox = function(df,myx,myy,mytitle,myylab){
maxy = max(df[[myy]])
ggboxplot(df, x = myx, y = myy,
title = mytitle,
color = myx, add = "jitter", legend="none",ylab = myylab) + rotate_x_text(angle = 45) +
geom_hline( yintercept = mean(df[[myy]]), linetype = 2) +
stat_compare_means(method = "anova", label.y = maxy*1.10) +
stat_compare_means(label = "p.format", size=2.5, method = "t.test", ref.group = ".all.",label.y = maxy*1.05)
}
gradesf18 = gradesf18[order(gradesf18$simpleLetter),]
gradesf19 = gradesf19[order(gradesf19$simpleLetter),]
gradesf21 = gradesf21[order(gradesf21$simpleLetter),]
#Written exams
print( plotGGbox(gradesf18,"simpleLetter","Science.Practice.Exercises.Unposted.Final.Score","Fall18: Written Semester Exams:Wrapped Letters","Average Semester Exams"))
print( plotGGbox(gradesf18,"Unposted.Final.Grade","Science.Practice.Exercises.Unposted.Final.Score","Fall18: Written Semester Exams:All Letters","Average Semester Exams"))
print( plotGGbox(gradesf19,"simpleLetter","Open.Ended.Written.Exams.Unposted.Final.Score","Fall19: Written Semester Exams:Wrapped Letters","Average Semester Exams"))
print(plotGGbox(gradesf19,"Unposted.Final.Grade","Open.Ended.Written.Exams.Unposted.Final.Score","Fall19: Written Semester Exams:All Letters","Average Semester Exams"))
print( plotGGbox(gradesf21,"simpleLetter","Open.Ended.Semester.Exams.Unposted.Current.Score","Fall21: Written Semester Exams:Wrapped Letters","Average Semester Exams"))
print(plotGGbox(gradesf21,"Unposted.Final.Grade","Open.Ended.Semester.Exams.Unposted.Current.Score","Fall21: Written Semester Exams:All Letters","Average Semester Exams"))
10.2 Milestone exams
print( plotGGbox(gradesf18,"simpleLetter","Milestones.Unposted.Current.Score","Fall18: Milestone Avg","Avg Milestone grade") )
print( plotGGbox(gradesf19,"simpleLetter","Milestones.Unposted.Current.Score","Fall19: Milestone Avg","Avg Milestone grade") )
print( plotGGbox(gradesf21,"simpleLetter","Milestones.Unposted.Current.Score","Fall21: Milestone Avg","Avg Milestone grade") )
10.3 HW
print( plotGGbox(gradesf18,"simpleLetter","Homework.Current.Score","Fall18: Homework Avg","Avg Homework grade") )
print( plotGGbox(gradesf19,"simpleLetter","Homework.Current.Score","Fall19: Homework Avg","Avg Homework grade") )
print( plotGGbox(gradesf21,"simpleLetter","Homework.Current.Score","Fall21: Homework Avg","Avg Homework grade") )
10.4 Lab reports
print( plotGGbox(gradesf18,"simpleLetter","Lab..Reporting.Unposted.Current.Score","Fall18: Lab reports Avg","Avg Lab reports grade") )
print( plotGGbox(gradesf19,"simpleLetter","Lab..Reporting.Unposted.Current.Score","Fall19: Lab reports Avg","Avg Lab reports grade") )
print( plotGGbox(gradesf21,"simpleLetter","Lab.Reports.Unposted.Current.Score","Fall21: Lab reports Avg","Avg Lab reports grade") )
10.5 Missed preclass
print( plotGGbox(gradesf18,"simpleLetter","missPreclass","Fall18: Missed preclass instances","Missed preclass") )
print( plotGGbox(gradesf19,"simpleLetter","missPreclass","Fall19: Missed preclass instances","Missed preclass") )
print( plotGGbox(gradesf21,"simpleLetter","missPreclass","Fall21: Missed preclass instances","Missed preclass") )
10.6 Missed prelab
print( plotGGbox(gradesf18,"simpleLetter","missPrelab","Fall18: Missed prelab instances","Missed preclass") )
print( plotGGbox(gradesf19,"simpleLetter","missPrelab","Fall19: Missed prelab instances","Missed preclass") )
print( plotGGbox(gradesf21,"simpleLetter","missPrelab","Fall21: Missed prelab instances","Missed preclass") )
11 Good milestone practices
12 Bundling students: Clustering based on effort and performance
Too often, educators conjugate students as “they” as if they were a homogeneous group. When trying to use a finer granularity we may label “good students” and “bad students” based on performance. However, even among these two groups the charactersitics are not similar. In fact, as instructors we want to identify the group of students that we can help the most. I propose that we cluster students along two axis, performance and effort, yielding at least four different groups:
WE-LP (wrong/low effor and low performance)
HE-LP (high effort and low performance)
LE-AP (low/lazy effort - acceptable performance)
HE-AP (high effort - acceptable performance)
12.1 Measuring effort
We have several indicators that when combined can be used to measure effort. We will start simple and look at what Canvas reports as Canvas participation
part_f21 = read.csv("~/Teaching/Grades_and_SRT/Fall2021/participation_f21.csv",header = TRUE )
plot(part_f21$Overall.course.grade,part_f21$Participations)
