In the last two years, online education has considerably evolved and it has been drawing more students. Nearly 460 schools offered online bachelor’s and graduate courses in 2013, according to the latest annual survey by Babson College in Massachusetts, which has been tracking the spread of online education for 11 years. And the proportion of all students, undergraduate and graduate, taking at least one online course hit a high of 33.5 percent in 2013, Babson reports.
Gonzaga University’s Virtual Campus first opened its doors three years ago, managing several graduate programs (M.A. in Organizational Leadership, M.A. in Communication and Leadership Studies, Master of Science in Nursing, M.A. in Sport and Athletic Administration, M.A in Theology and Leadership) and a doctoral program (Doctorate of Nursing Practice).
For the purposes of the study, we have compiled and used data on current graduate and doctoral students pursuing their online degrees. We will be exploring the geographic spread of online graduate students throughout the United States, their degree enrollment, average students GPAs, as well as active duty military students. R studio has been used to create the graphs and visualize the data.
Questions Posed
Throughout the data analysis, we intend to shed some light to the following questions:
How are Gonzaga online graduate students geographically spread throughout the United States?
What are the enrollment numbers for each online graduate degree?
How does active duty military enrollment compare to total online enrollment?
How does military students’ GPA compare to the non-military students’ GPA?
How do military students’ MAT scores compare to the non-military students’ scores?
The majority of the Gonzaga online graduate students are found in the Northwest with a focus in the state of Washington. This is just as we assumed because of Gonzaga being located in Washington. The surrounding states, Oregon, Idaho, Montana, and California, then hold the next highest amount of students with totals between 39 and 93. The rest of the students seem to be evenly distributed throughout the rest of the country.
StudentState=gusd%>%group_by(State)%>%summarize(Count=length(State))%>%filter(!is.na(State))
GeoStates <- gvisGeoChart(StudentState, "State", "Count",
options=list(region="US",
displayMode="regions",
resolution="provinces",
width=600, height=400,colorAxis="{colors:['lightblue', 'blue', 'red','green','orange']}"))
plot(GeoStates)With the Military Enrollment, as predicted, there was also a high concentration of students from the state of Washington because of the air force base Fairchild being located just outside of Spokane, WA. The next highest numbers come from states where there are a high number of armed forces bases including Virginia and Illinois.
Mil=gumil%>%group_by(State)%>%summarize(Count=length(State))%>%filter(!is.na(State))
head(Mil[order(-Mil$Count),],20)%>%dimple(x ="State", y = "Count", type = "bar",width=1000, height=750) %>%
xAxis(type = "addCategoryAxis") %>%
add_title( html = "<h4>Military Student Personel Enrollment By State</h4>")As the graph below indicates, the majority of the military student - heavy states have a higher GPA than non-military students. One of the main reasons for this discrepancy is the fact that Gonzaga programs create a more lenient and flexible learning environment that allows military students to succeed in their classes, without interfering with their career, such as leave of absence policies and assignment deadline extension. As a result, Gonzaga Virtual Campus is ranked #4 in Best Colleges for Military and Veterans in the West.
milvnon1=gusd%>%group_by(State, Status)%>%summarise(Ave.GPA=mean(GPA))%>%
filter(!is.na(Ave.GPA))
dimple(data = milvnon1,
x = c("State","Status"), y = "Ave.GPA",
groups = "Status", type = "bar", width = 950, height = 600
) %>%
add_legend( x = 50, y = 10, width = 100, height = 50,
horizontalAlign = "left"
) %>%
default_colors()Gonzaga graduate students typically hold a high GPA, as the map also indicates. One of the main reasons is that students need a GPA of higher than 3.0 in order to be able to graduate with their degree. As the map demonstrates, Iowa holds the lowest GPA, while Connecticut holds the highest GPA (4.0)
SGPA1=gusd%>%group_by(State)%>%summarise(Ave.GPA=mean(GPA))%>%filter(!is.na(Ave.GPA))
SGPA1=data.frame(SGPA1)
GeoStates1 <- gvisGeoChart(SGPA1, "State",colorvar = "Ave.GPA",
options=list(title = "Mill Score vs State", region="US", displayMode="regions", resolution="provinces",colorAxis="{colors:['gold','blue']}", width=600, height=400))
GeoTable1 <- gvisTable(SGPA1,
options=list(width=220, height=300))
GT1 <- gvisMerge(GeoStates1,GeoTable1, horizontal=TRUE)
plot(GT1) |
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print(GT1,file="GT1.html")milvnon2=gusd%>%group_by(State, Status)%>%summarise(AVE.MIL=mean(MILLER.ANALOGIES.RAW.SCORE))%>%
filter(!is.na(AVE.MIL))
dimple(data = milvnon2,
x = c("State","Status"), y = "AVE.MIL",
groups = "Status", type = "bar", width = 950, height = 600
) %>%
add_legend( x = 50, y = 10, width = 100, height = 50,
horizontalAlign = "left"
) %>%
default_colors()SMIL=gusd%>%group_by(State)%>%summarise(AVE.MIL=mean(MILLER.ANALOGIES.RAW.SCORE))%>%
filter(!is.na(AVE.MIL))
SMIL=data.frame(SMIL)
GeoStates2 <- gvisGeoChart(SMIL, "State",colorvar = "AVE.MIL",
options=list(title = "Miller Score vs State", region="US", displayMode="regions", resolution="provinces",colorAxis="{colors:['gold','blue']}", width=600, height=400))
GeoTable2 <- gvisTable(SMIL,
options=list(width=220, height=300))
GT2 <- gvisMerge(GeoStates2,GeoTable2, horizontal=TRUE)
plot(GT2) |
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This graph displays the minimal change in GPA depending on which degree is chosen. It ranges from the highest being 3.8 in Master in Arts and Organizational Leadership.
DEGvGPA=gusd%>%group_by(Degree)%>%summarise(AVE.GPA=mean(GPA))%>%
filter(!is.na(AVE.GPA))
dimple(data = DEGvGPA,
x = c("Degree"), y = "AVE.GPA",
type = "bar", width = 950, height = 600
) %>%
add_legend( x = 50, y = 10, width = 100, height = 50,
horizontalAlign = "left"
) %>%
default_colors()%>%
add_title( html = "<h4>Student Degree vs GPA</h4>")Looking at the bar chart below we see a more defined graph of the the programs students are currently enrolled in vs. their current GPA. Resident Nurses in Family Practice scored the highest average GPA at 3.9 while the lowest program, Resident Nurse Pyso-Mental Health, average a GPA of 3.0.
PRGMvGPA=prgm%>%group_by(PROGRAM)%>%summarise(AVE.GPA=mean(GPA))%>%
filter(!is.na(AVE.GPA))
dimple(data = PRGMvGPA,
x = c("PROGRAM"), y = "AVE.GPA", groups = "PROGRAM",
type = "bar", width = 850, height = 900
) %>%
default_colors()%>%
add_title( html = "<h4>Student Program vs GPA</h4>")Here, we see the bar chart showing a breakdown of current student enroll by their chosen program.
PRGMvENR=prgm%>%group_by(PROGRAM)%>%summarise(Count=length(GPA))
dimple(data = PRGMvENR,
x = c("PROGRAM"), y = "Count", groups = "PROGRAM",
type = "bar", width = 850, height = 900
) %>%
default_colors()%>%
add_title( html = "<h4>Student Program by Enrollment</h4>")One of the limitations of the study was the fact that we were unable to find data on international students taking graduate online classes at Gonzaga. This could viewed as an opportunity for further research and study. In addition, due to the sensitive nature of the data, we were unable to fully take advantage of the data.
Through these graphs and charts we were able to tell where the majority of the online graduate student body is located (WA), which program is the most popular (MA in Org Lead.) and see how GPA correlates with different variables and factors.