## Self-Test

Using what you have learnt so far, obtain descriptive statistics and draw histograms of first-year exam —scores, computer literacy, numeracy and lectures attended.

//import data

//set factor for the variable uni
rexam\$uni<-factor(rexam\$uni, levels = c(0:1), labels = c(“Duncetown University”, “Sussex University”))

//obtain descriptive statistics
describe(rexam[,c(“exam”, “computer”, “lectures”, “numeracy”)])
stat.desc(rexam[, c(“exam”, “computer”, “lectures”, “numeracy”)], basic = FALSE, norm = TRUE)

//round to 3 decimal digits
round(stat.desc(rexam[,c(“exam”, “computer”, “lectures”, “numeracy”)], basic = FALSE, norm = TRUE), digits = 3)

we can interpret absolute values of kurt.2SE and skew.2SE greater than 1, 1.29, and 1.65 as significant p < .05, p < .01, and p < .001, respectively.

//histogram with normal curve on it (using exam score as example)
exam exam
exam + stat_function(fun = dnorm, args = list(mean = mean(rexam\$exam, na.rm = TRUE), sd = sd(rexam\$exam, na.rm = TRUE)), colour = “black”, size = 1)

Repeat these analyses for the computer literacy and percentage of lectures attended and interpret the results.

//descriptive stats based on groups
by(cbind(data=rexam\$computer,data=rexam\$lectures), rexam\$uni, describe)

//normality test based on groups
by(rexam\$lectures, rexam\$uni, stat.desc, basic = FALSE, norm = TRUE)

//histogram based on groups using lectures as an example
dunceData<-subset(rexam, rexam\$uni=="Duncetown University") sussexData<-subset(rexam, rexam\$uni=="Sussex University") hist.lectures.duncetown <- ggplot(dunceData, aes(lectures)) + theme(legend.position = "none") + geom_histogram(aes(y = ..density..), fill = "white", colour = "black", binwidth = 1) + labs(x = "Lectures", y = "Density") + stat_function(fun=dnorm, args=list(mean = mean(dunceData\$lectures, na.rm = TRUE), sd = sd(dunceData\$lectures, na.rm = TRUE)), colour = "blue", size=1)

hist.lectures.duncetown

hist.lectures.sussex <- ggplot(sussexData, aes(lectures)) + theme(legend.position = "none") + geom_histogram(aes(y = ..density..), fill = "white", colour = "black", binwidth = 1) + labs(x = "Lectures", y = "Density") + stat_function(fun=dnorm, args=list(mean = mean(sussexData\$lectures, na.rm = TRUE), sd = sd(sussexData\$lectures, na.rm = TRUE)), colour = "blue", size=1)

hist.lectures.sussex

//import data

lecturerData\$job<-factor(lecturerData\$job, levels = c(1:2), labels = c(“Lecturer”, “Student”))

//1
bar <- ggplot(lecturerData, aes(job, friends)) bar + stat_summary(fun.y = mean, geom = "bar", fill = "grey", colour = "Black") + stat_summary(fun.data = mean_cl_normal, geom = "pointrange") + labs(x = "job", y = "friends")

//2
bar <- ggplot(lecturerData, aes(job, alcohol)) bar + stat_summary(fun.y = mean, geom = "bar", fill = "grey", colour = "Black") + stat_summary(fun.data = mean_cl_normal, geom = "pointrange") + labs(x = "job", y = "alcohol")

//3
bar <- ggplot(lecturerData, aes(job, income)) bar + stat_summary(fun.y = mean, geom = "bar", fill = "grey", colour = "Black") + stat_summary(fun.data = mean_cl_normal, geom = "pointrange") + labs(x = "job", y = "income")

//4
bar <- ggplot(lecturerData, aes(job, neurotic)) bar + stat_summary(fun.y = mean, geom = "bar", fill = "grey", colour = "Black") + stat_summary(fun.data = mean_cl_normal, geom = "pointrange") + labs(x = "job", y = "neurotic")

//5
scatter <- ggplot(lecturerData, aes(alcohol, neurotic)) scatter + geom_point() + geom_smooth(method ="lm", aes(fill = job), colour = "black", se = F)

//import data

infidelityData\$Gender<-factor(infidelityData\$Gender, levels = c(1:2), labels = c("Male", "Female"))

//wide data into the long format
infidelity<-melt(infidelityData, id = c("Gender"), measured = c("Partner", "Self"))

//rename variables
names(infidelity)<-c("Gender", "Target", "Bullets")

//plot1
bar <- ggplot(infidelity, aes(Target, Bullets, fill = Gender)) bar + stat_summary(fun.y = mean, geom = "bar", position="dodge") + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.90), width = 0.2) + labs(x = "Target", y = "Bullets", fill = "Gender")

//plot2
bar + stat_summary(fun.y = mean, geom = “bar”) + stat_summary(fun.data = mean_cl_normal, geom = “errorbar”, width = 0.2) + facet_wrap( ~ Gender) + labs(x = “Target”, y = “Bullets”) + theme(legend.position = “none”)

## Ten Contributions to Timeline

My 10 Contributions to Timeline:

Item Start Date End Date Headline Media
1 2006 TPACK http://files.eric.ed.gov/fulltext/EJ868626.pdf
2  1992 1994  SMART Board 585 http://education.smarttech.com/en/products/hardware
3  2007 Flipped Instruction in Chemistry Class http://www.viddler.com/v/8bac8f58
5  1991  Edutopia  http://www.edutopia.org/
6  2008  Edmodo  https://www.edmodo.com/
8  2003  Second Life  http://secondlife.com/
9  2009  Prezi  http://prezi.com/
10  2005  Gliffy  https://www.gliffy.com/

## One-Dimensional Man and Pessimism_10

Selwyn’s (2011) article on technology pessimism called for a realistic approach to re-looking at the field of educational technology. He was opposed to either techno-optimism or techno-fatalism. As an educational researcher on digital technologies, one, he argued, should expect nothing and investigate the actual use of technology by teachers and students. This critical voice connected me to the seminal work One-Dimensional Man written by Herbert Marcuse.

In One-Dimensional Man, Marcuse argued that the capitalist society, in praise of consumerism, created in citizens many false needs through mass media and advertisement. Citizens gradually lost their power to negate destructive patterns in pursuit of satisfying unnecessary wants. By quenching purchasing thirst, people did not feel more free, but got stuck deeper into the existing structure. Both Selwyn and Marcuse were advocates of negative thinking as a refusal for naivete.

Here, I would argue that corporations and marketing companies contribute more to the current status of techno-optimism than do academia. It is true that the majority of educational researchers in the field of educational technology have been excited about the digital potentials. However, one drastic difference between business world and academic world is the different weight put on persuasive rhetoric and empirical evidence. For technology corporations, selling state-of-the-art devices to schools requires more appealing sales pitch and convincing imaginations than hard evidence. For a researcher to brag about the impact of certain digital gadgets on classrooms, he/she has to establish empirical evidence with strong internal and external validity and various replications in different places.

If we, as researchers, are still willing to be “stubborn” in the empirical tradition, the corporate zeal for technologies will always face and be counterbalanced with the academic coolness. This stubbornness, I believe, will prevent us from being a one-dimensional man without critical awareness.