Content
Please rate how much these components of content help you learn. Feel free to contextualize your answers in the free response space below each prompt.
Answer Options:
I have not experienced this (N/A = 0); Highly Ineffective (1); Ineffective (2); Neutral (3); Effective (4); Highly Effective (5)
######### Descriptive Stats ##########
#select structure vars and subtract 1 to make a meaningful zero.
content.vars<-rawAMAPData[,125:128]
#describe structure vars
summarytools::freq(content.vars)## Frequencies
## content.vars$c_appliedeg
## Label: Topically-Relevant (Applied) Examples
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 1 1.09 1.09 1.01 1.01
## 2 1 1.09 2.17 1.01 2.02
## 3 1 1.09 3.26 1.01 3.03
## 4 20 21.74 25.00 20.20 23.23
## 5 69 75.00 100.00 69.70 92.93
## <NA> 7 7.07 100.00
## Total 99 100.00 100.00 100.00 100.00
##
## content.vars$c_discussion
## Label: Class Discussion for Methods Literature
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 7 7.53 7.53 7.07 7.07
## 1 3 3.23 10.75 3.03 10.10
## 2 11 11.83 22.58 11.11 21.21
## 3 10 10.75 33.33 10.10 31.31
## 4 32 34.41 67.74 32.32 63.64
## 5 30 32.26 100.00 30.30 93.94
## <NA> 6 6.06 100.00
## Total 99 100.00 100.00 100.00 100.00
##
## content.vars$c_prstmethod
## Label: Students Present Methods Paper
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 20 21.51 21.51 20.20 20.20
## 1 11 11.83 33.33 11.11 31.31
## 2 9 9.68 43.01 9.09 40.40
## 3 16 17.20 60.22 16.16 56.57
## 4 22 23.66 83.87 22.22 78.79
## 5 15 16.13 100.00 15.15 93.94
## <NA> 6 6.06 100.00
## Total 99 100.00 100.00 100.00 100.00
##
## content.vars$c_replicate
## Label: Students Replicate Analysis in Paper
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 46 49.46 49.46 46.46 46.46
## 1 2 2.15 51.61 2.02 48.48
## 2 3 3.23 54.84 3.03 51.52
## 3 10 10.75 65.59 10.10 61.62
## 4 15 16.13 81.72 15.15 76.77
## 5 17 18.28 100.00 17.17 93.94
## <NA> 6 6.06 100.00
## Total 99 100.00 100.00 100.00 100.00#remove NAs
content.vars<-na_if(content.vars, 0)
psych::describe(content.vars)## vars n mean sd median trimmed mad min max range skew kurtosis
## c_appliedeg 1 91 4.73 0.54 5 4.82 0.00 2 5 3 -2.22 6.12
## c_discussion 2 86 3.87 1.14 4 4.00 1.48 1 5 4 -0.85 -0.25
## c_prstmethod 3 73 3.29 1.34 4 3.36 1.48 1 5 4 -0.39 -1.03
## c_replicate 4 47 3.89 1.11 4 4.03 1.48 1 5 4 -0.83 -0.03
## se
## c_appliedeg 0.06
## c_discussion 0.12
## c_prstmethod 0.16
## c_replicate 0.16Specific items are listed as plot titles
Plot Content
######### Plot setup ##########
#define colors
custom.col <- c("#c4bfc0","#555960","#6f727b", "#ebd99f","#daaa00","#8e6f3e")
custom.col.noIE <- c("#c4bfc0","#6f727b", "#ebd99f","#daaa00","#8e6f3e")
#define style
plot.style = list(
geom_bar(show.legend = FALSE),
scale_fill_manual(values = custom.col,na.translate = FALSE),
labs(y="Freq.", x= ""),
scale_x_discrete(limits = c("0", "1", "2", "3", "4", "5"),
labels=c("0" = 'N/A', "1" = 'Highly Ineffective', "2" = 'Ineffective',
"3" = 'Neutral', "4" = 'Effective', "5" = 'Highly Effective')),
scale_y_continuous(breaks=(seq(0, 70, 10)), limits = c(0, 70))
)
plot.style2 = list(
geom_bar(show.legend = FALSE),
scale_fill_manual(values = custom.col.noIE,na.translate = FALSE),
labs(y="Freq.", x= ""),
scale_x_discrete(limits = c("0", "1", "2", "3", "4", "5"),
labels=c("0" = 'N/A', "1" = 'Highly Ineffective', "2" = 'Ineffective',
"3" = 'Neutral', "4" = 'Effective', "5" = 'Highly Effective')),
scale_y_continuous(breaks=(seq(0, 70, 10)), limits = c(0, 70))
)
######### Plots ##########
c1 <- ggplot(data = subset(rawAMAPData, !is.na(c_appliedeg)),
aes(x=factor(c_appliedeg), fill=factor(c_appliedeg))) +
labs(title = "Applied examples") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
plot.style2
c2 <- ggplot(data = subset(rawAMAPData, !is.na(c_discussion)),
aes(x=factor(c_discussion), fill=factor(c_discussion))) +
labs(title = "Class discusses lit using method") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
plot.style
c3 <- ggplot(data = subset(rawAMAPData, !is.na(c_prstmethod)),
aes(x=factor(c_prstmethod), fill=factor(c_prstmethod))) +
labs(title = "Students present articles") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
plot.style
c4 <- ggplot(data = subset(rawAMAPData, !is.na(c_replicate)),
aes(x=factor(c_replicate), fill=factor(c_replicate))) +
labs(title = "Students replicate published lit") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
plot.style
grid.arrange(c1, c2, ncol=2)
grid.arrange(c3, c4, ncol=2)
Regarding content, using topically-relevant applied examples was highly effective. Having class discussions after reading literature using the method being taught was also highly rated as effective or highly effective. Students were more mixed in the effectiveness of presenting articles or the methods from published literature. Relatively few students were asked to replicate the analysis from published literature, but when they did this exercise was seen as overall effective.
Follow-up qualitative questions are currently being coded
Think about the best methods course you have taken with regard to content. What stands out as particularly effective? This may be something we asked about above, or something else we missed.
Think about the worst methods course you have taken with regard to content. What stands out as particularly ineffective? This may be something we asked about above, or something else we missed.
