Introduction

In 2022, forty percent of first year college students were entering community colleges. (Census Bureau, 2023) Community colleges as we know them today became a fixture of the US educational system during the mid-20th century where they increased Americans’ access to an affordable education. (Drury, 2000) One of the primary purposes of all colleges and universities has been to foster young adults’ participation in the civic life of the country. With lower tuition fees and often guaranteed admission, community colleges expand the reach of this goal to a broader and more diverse portion of the population. At present, there is a sizeable gap between research specifically focused on civic engagement among students attending community colleges and research on their peers at four-year institutions. The aim of this project is to explore the impact of community college students’ experiences and involvement on campus on their likelihood to become engaged citizens post-graduation.

The following will explore three research questions:

  1. What relationship is there, if any, between extracurricular participation on campus (through groups, clubs) and students’ sense of being part of a larger community?
  2. What relationship is there, if any, between students’ participation and/or leadership in extracurricular activities like groups, clubs, or service learning, and students’ future plans to work with others for social and political change after graduation?
  3. What relationship is there, if any, between students’ taking political science courses and their belief that they can have an impact on the country as a whole?

Results from this study could be used to encourage policy makers to direct funding to more community colleges, along with guidance on how those funds might be best used to increase civic engagement. It might also be used to encourage greater philanthropic contributions to colleges to foster greater participation in our political system, or partnerships between non-profits focused on civic engagement and community colleges. Many community colleges are public institutions, getting their funding directly through state budgets; data on ways to improve student involvement with the larger community can be particularly helpful.

Previous research indicates that the inclusion of service learning in community college curricula does improve student engagement in the community. (Prentice & Robinson, 2007) Other research indicates that political education is also correlated increased participation by college students. (Pritzker et al., 2015) Results could also be used to adjust standard community college curriculum and programming; for example, if there is a clear relationship between students’ sense of their ability to impact the country and their participation in coursework focused on civic engagement (through service learning or more traditional classroom learning), colleges may be encouraged to include these as required courses for all students.

For this study, I used data gathered by the Community College Civic Outcomes Survey (ICPSR 36961), using a publicly available dataset through the University of Michigan. The original study was run through the Center for the Study of Community Colleges in 2017, and last updated in April of 2018. Some variables of interest will include participation and/or leadership in campus clubs or organizations, participation in service learning outside the classroom, taking or not taking coursework that includes civic engagement such as political science, government, or service learning, students’ sense of membership in the campus community, students’ sense of ability to impact the country, and students future plans to actually engage political or social change efforts. Data is at the individual level, with each observation reflecting the responses of one student. Data was gathered from eight community colleges around the country, selected to generate a wide cross section that would be representative of community college students in the US.

I used the following libraries in R: tidyverse, gt, and gtExtras.

Data Cleaning

The full dataset was originally an R set that I converted to a csv file for easier organizing. It contained 1168 observations and 118 columns.

dim(full_data)
## [1] 1168  118

Once the data was loaded, I began my data cleaning process by changing the variable names to lowercase letters for ease of re-typing.

colnames(full_data) <- str_to_lower(colnames(full_data))

I then gathered the variables I wanted to use for the project: students experience in service learning, whether or not students had taken a political science class, students participation in campus leadership, students sense of themselves as part of the campus community, students belief they could have an impact on the country after graduation, and how frequently they felt they might act to promote social change after graduation. I also included enrollment status, age, and ethnicity to help contextualize the dataset.

The entire dataset contained 188 variables. For the purpose of this study I chose 10 overall. Each question uses two to four variables. Because the data was gathered through surveys, they are all categorical variables. Some used a Likert scale, ranging from “strongly agree” to “strongly disagree”, some used a range of time from “never” to “daily”, and one was a simple “yes/no” question.

project_data =
  full_data%>%
  select(college_service_learning,
         college_poly_sci,
         college_leadership_role,
         college_group_partic,
         see_myself_part_of_campus,
         see_myself_having_impact,
         frequency_promote_change,
         enrollment_status,
         age,
         ethnicity)

Having narrowed down my data, I checked to see if there were any NAs. I found 122 NAs overall, most in the service learning variable (32 out of 1158 observations). I opted to remove the NAs because, relative to the overall number of observations, their absence had only a nominal impact on the results. The remaining data cleaning was done at the start of each research question.

project_data %>%
  map_dbl(~ sum(is.na(.)))
##  college_service_learning          college_poly_sci   college_leadership_role 
##                        34                        20                        23 
##      college_group_partic see_myself_part_of_campus  see_myself_having_impact 
##                        18                         8                        10 
##  frequency_promote_change         enrollment_status                       age 
##                         9                        10                        12 
##                 ethnicity 
##                        32
project_data =
  na.omit(project_data)

Before moving into exploring the data, I created a simple theme to use for all charts throughout the report. The theme uses Helvetica font, and modifies the size of the text to better fit on the screen.

my_theme=
  theme_light()+
  theme(
    plot.title = element_text(face = "bold",
                              size = 16,
                              family = "Helvetica",
                              color = "darkslategray"),
    axis.text = element_text(size = 10, face = "italic", 
                             family = "Helvetica"),
    legend.title = element_text(size = 10, face = "bold",
                                family = "Helvetica"))

To better contextualize my research questions, I looked at the general composition of the participants.

enrollment_chart=
  project_data%>%
  select(enrollment_status)%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=enrollment_status),
           fill = "#556b2f")
 
enrollment_chart

Participants were fairly evenly split between part time and full time students, 530 described themselves as part time, and 628 described themselves as full time.

age_chart=
  project_data%>%
  select(age)%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=age),
           fill = "lightblue")
 
age_chart

Unsurprisingly, students 20-24 made up the largest age group, with 322 respondents. This was followed by students 19 or below, 233 respondents, then 195 students 35-49, 172 between 25 and 29, 118 students between 30 and 34, and 116 students 50 or above. The fact that these data are from community colleges, which often have wider age diversity, is likely a factor in the wide range of ages of participants.

ethnicity_chart=
  project_data%>%
  select(ethnicity)%>%
  ggplot()+
  coord_flip()+
  my_theme+
  geom_bar(aes(x=ethnicity),
           fill = "#2f4f4f")

ethnicity_chart

Finally, I looked at the race of the participants. The survey developers offered nine categories: American Indian or Alaska Native, Asian, Black or African American, Hispanic/Latino(a), Native Hawiian or other Pacific Islander, White, two or more races, unknown, and non-resident alien. The largest portion of respondents were white (717), followed by hispanic (113), and two or more races (102).

Research Questions

Students’ Sense of Community

My first question is, what relationship is there, if any, between extracurricular participation on campus (through groups, clubs, or on-campus jobs) and students’ sense of being part of a larger community? In other words, do students who are more involved on campus feel more connected? This is of particular relevance in a community college setting, where students are more likely to be part time or commuters. My prediction was that students who are more involved in campus life would be more likely to respond “agree” or “strongly agree” to their feeling of being part of the community.

Descriptive statistics

Before beginning my analysis, I looked at these specific variables to understand their breakdown.

community_desc=
  project_data %>%
  select(college_service_learning, college_leadership_role, college_group_partic,see_myself_part_of_campus)

Survey developers asked students a number of questions regarding their participation in various activities. The following was asked as: Since entering this college, how frequently have you engaged in: service learning outside of class?

The chart below shows that the vast majority of students have not participated in service learning. this makes those who have participated a unique minority.

community_desc%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=college_service_learning),
           fill = "lightblue")+
  labs(title= "Responses to Service Learning during college",
       subtitle ="Question: how frequently have you engage in service learning as part of a class?",
       x= "Frequency of Service Learning")

Respondents were similarly asked about leadership roles in college. The original question was:

Since entering this college, how frequently have you participated in: performed a leadership role in an organization or club?

As with service learning, the large majority of participants had not participated in any leadership on campus. These students may, therefore, offer a unique insight in the effect of this type of activity on campus community.

community_desc%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x= college_leadership_role),
           fill = "#556b2f")+
  labs(title= "Responses to Leadership Role During College",
       subtitle ="Question: how frequently have you perform a leadership role in an organization or club?",
       x= "Frequency of Leadership Roles")

Survey takers were also asked if they had been part of a group or organization on campus:

Since entering this college, how frequently have you participated in: participated in a group or organization?

As is clear in the chart below, a majority of students had not participated in any clubs or groups. However, compared to service learning and leadership, a larger number responded that they had some involvement, with many reporting weekly engagement. Participation in a club is often less involved or time-consuming than leadership or service work, so it is valuable to see if it, too, has an impact on students’ sense of belonging to the campus community.

community_desc%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=college_group_partic),
           fill= "#2f4f4f")+
  labs(title= "Responses to College Group Participation",
       subtitle = "Question:how frequently have you participate in a group or organization?",
       x= "Frequency of group participation")

Finally, for this question I also looked at students’ overal response to feeling part of the campus community. The original survey question was:

I see myself: as part of the campus community.

Notably, although on-campus involvement is not in the majority, the largest share of responses were “agree”.

community_desc%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=see_myself_part_of_campus),
           fill= "#4682B4")+
  labs(title= "Participants Sense of Being Part of Campus",
       subtitle= "Responses to: I see myself as part of the campus community.",
       x = "Frequency of responses by category")

Adressing the research question

This led me to tackle the actual research question. To do so, I needed to clean up this data subset by first removing any unnecessary characters and then converting the values into a format I could use. The variable about students sense of being part of the campus was changed into a factor variable retaining the original response names (“Strongly Agree”, “Agree”, etc). For the other three variables (leadership, service learning, and group participation) I removed the response words (“never”, “annually” etc) and retained the numbers, which were then switched from a character value to a numeric one.

community = 
  project_data %>%
  select(college_service_learning, college_leadership_role, college_group_partic,see_myself_part_of_campus)%>%
  mutate(see_myself_part_of_campus = str_remove(see_myself_part_of_campus, "1|2|3|4|5|6"))%>%
  mutate(see_myself_part_of_campus = str_remove(see_myself_part_of_campus, "\\("))%>%
  mutate(see_myself_part_of_campus = str_remove(see_myself_part_of_campus, "\\)"))%>%
  mutate(see_myself_part_of_campus= as.factor(see_myself_part_of_campus))%>%
  mutate(service_learn = str_remove(college_service_learning,
                                    "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(college_leader = str_remove(college_leadership_role,
                                     "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(group_partic = str_remove(college_group_partic, 
                                   "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(service_learn = str_remove(service_learn, "\\("))%>%
  mutate(service_learn = str_remove(service_learn, "\\)"))%>%
  mutate(college_leader = str_remove(college_leader, "\\("))%>%
  mutate(college_leader = str_remove(college_leader, "\\)"))%>%
  
  mutate(group_partic = str_remove(group_partic, "\\("))%>%
  mutate(group_partic = str_remove(service_learn, "\\)"))%>%
  mutate(service_learn = as.numeric(service_learn))%>%
  mutate(college_leader= as.numeric(college_leader))%>%
  mutate(group_partic = as.numeric(group_partic))%>%
  mutate(service = ifelse(service_learn >= 4, 1,0))%>%
  mutate(leader = ifelse(college_leader >= 4,
                         1,0))%>%
  mutate(participate = ifelse(group_partic>=4,
                              1,0))%>%
  mutate(service= as.factor(service))%>%
  mutate(leader = as.factor(leader))%>%
  mutate(participate = as.factor(participate))

Having cleaned my data, I then organized it so that the numeric responses to the three independent variables would be organized into the categories of the dependent. That is, within each response category to feeling part of the community, the resulting table would show what the average frequency was of participating in extra-curricular activities. This produced a simple table, which I then used to create a clear visualization. For the vizualisation, I used the R library “gt”.

connection_table =
  community%>%
  mutate(see_myself_part_of_campus=
           fct_relevel(see_myself_part_of_campus,
                       " Strongly Agree",
                       " Agree",
                       " Neither Agree nor Disagree",
                       " Disagree",
                       " Strongly Disagree"))%>%
  group_by(see_myself_part_of_campus)%>%
  summarize(across(c(service_learn, college_leader, group_partic), mean))
connection_viz=
  connection_table%>%
  gt()%>%
  cols_label(
    see_myself_part_of_campus = "Response",
    service_learn= "Service learning",
    college_leader= "Involved in leadership",
    group_partic= "Club participation"
  )%>%
  cols_align(align= "left") %>%
  tab_header(title= "Students' Sense of Community",
             subtitle = md("Response to *I see myself as part of the campus community* by participation in service learning, campus leadership, or participation in clubs"))%>%
  tab_source_note(source_note = "Source: Community College Civic Outcomes Survey, 2018")%>%
  fmt_number(columns = service_learn,
             decimals = 2)%>%
  fmt_number(columns = college_leader,
             decimals = 2)%>%
  fmt_number(columns = group_partic,
           decimals = 2)%>%
  gt_plt_bar(colum = service_learn,
             keep_column = F,
             color = "lightblue",
             scale_type = "number",
             accuracy = 0.01)%>%
  gt_plt_bar(colum = college_leader,
             keep_column = F,
             color = "#556b2f",
             scale_type = "number",
             accuracy = 0.01)%>%
  gt_plt_bar(colum = group_partic,
             keep_column = F,
             color = "#2f4f4f",
             scale_type = "number",
             accuracy = 0.01)

connection_viz
Students' Sense of Community
Response to I see myself as part of the campus community by participation in service learning, campus leadership, or participation in clubs
Response Service learning Involved in leadership Club participation
Strongly Agree 3.13 3.39 3.13
Agree 2.41 2.44 2.41
Neither Agree nor Disagree 1.85 2.03 1.85
Disagree 1.92 2.08 1.92
Strongly Disagree 1.52 1.67 1.52
Source: Community College Civic Outcomes Survey, 2018

The chart above highlights that students who participate in extracurricular activities like club leadership, club membership, or service learning, were more likely to select “strongly agree” to their sense of being a part of the campus community. Among the students who selected “strongly agree” to that prompt, the mean response to frequency of service learning, college leadership, and group participation was above 3, corresponding to at least semi-annual involvement on campus.

Plans to Promote Change

My next research question had to do with students’ life after graduation. My question was, what relationship is there, if any, between students’ participation and/or leadership in extracurricular activities like groups, clubs, or service learning, and students’ future plans to work with others for social and political change after graduation? My predicted result was that students who participated in extracurricular activities would be more likely to plan to work with other for change in after graduation.

This used the same three independent variables as above, service learning, leadership, and group participation. The dependent variable was how frequently students planned to work to promote social or political change. The original question read as follows:

After leaving this college, how frequently do you plan to do the following: work with others to promote social or political change?

In general, most students only expected working with others “occasionally”.

project_data%>%
  select(frequency_promote_change)%>%
  mutate(frequency_promote_change= fct_relevel(frequency_promote_change,
                                               " Almost Always",
                                               " Frequently",
                                               " Occasionally",
                                               " Hardly Ever"))%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=frequency_promote_change),
           fill= "lightblue")+
  labs(title= "Plans to work for change after graduation",
       subtitle = "How frequently do you plan to work with others to promote change?",
       x = "Frequency")

Because I did the majority of my data cleaning on the subset for the first question, I needed to repeat the procedure with this second data subset. I followed the same process, using stringr functions to turn the dependent variable (frequency of promoting change) into a factor, and the independent variables into numeric values.

future_plans=
  project_data%>%
  select(college_service_learning, college_leadership_role, college_group_partic,frequency_promote_change)%>%
  mutate(frequency_promote_change = str_remove(frequency_promote_change, "1|2|3|4|5|6"))%>%
  mutate(frequency_promote_change = str_remove(frequency_promote_change, "\\("))%>%
  mutate(frequency_promote_change = str_remove(frequency_promote_change, "\\)"))%>%
  mutate(frequency_promote_change= as.factor(frequency_promote_change))%>%
  mutate(service_learn = str_remove(college_service_learning,
                                    "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(college_leader = str_remove(college_leadership_role,
                                     "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(group_partic = str_remove(college_group_partic, 
                                   "Never|Annually|Semi-Annually|Monthly|Weekly|Daily"))%>%
  mutate(service_learn = str_remove(service_learn, "\\("))%>%
  mutate(service_learn = str_remove(service_learn, "\\)"))%>%
  mutate(college_leader = str_remove(college_leader, "\\("))%>%
  mutate(college_leader = str_remove(college_leader, "\\)"))%>%
  
  mutate(group_partic = str_remove(group_partic, "\\("))%>%
  mutate(group_partic = str_remove(service_learn, "\\)"))%>%
  mutate(service_learn = as.numeric(service_learn, na.rm=T))%>%
  mutate(college_leader= as.numeric(college_leader, na.rm = T))%>%
  mutate(group_partic = as.numeric(group_partic,  na.rm = T))%>%
  mutate(service = ifelse(service_learn >= 4, 1,0))%>%
  mutate(leader = ifelse(college_leader >= 4,
                         1,0))%>%
  mutate(participate = ifelse(group_partic>=4,
                              1,0))%>%
  mutate(service= as.factor(service))%>%
  mutate(leader = as.factor(leader))%>%
  mutate(participate = as.factor(participate))

Having cleaned my data, I composed the table, using the same method as in the first question.

future_table =
  future_plans%>%
  group_by(frequency_promote_change)%>%
  mutate(frequency_promote_change= fct_relevel(frequency_promote_change,
                                               " Almost Always",
                                               " Frequently",
                                               " Occasionally",
                                               " Hardly Ever"))%>%
  summarize(across(c(service_learn, college_leader, group_partic), mean))

Once my table was ready, I used the “gt” package to create a clear visualization of my analysis.

future_viz=
  future_table%>%
  gt()%>%
  cols_label(
    frequency_promote_change = "Response",
    service_learn= "Service learning",
    college_leader= "Involved in leadership",
    group_partic= "Club participation"
  )%>%
  cols_align(align= "left") %>%
  tab_header(title= "Students Desire to Promote Change",
             subtitle = md("Response to *After leaving this college, how frequently do you plan to do the following: work with others to promote social or political change* by participation in service learning, campus leadership, or participation in clubs"))%>%
  tab_source_note(source_note = "Source: Community College Civic Outcomes Survey, 2018")%>%
  fmt_number(columns = service_learn,
             decimals = 2) %>%
  fmt_number(columns = college_leader,
             decimals = 2)%>%
  fmt_number(columns = group_partic,
             decimals = 2)%>%
  gt_plt_bar(colum = service_learn,
             keep_column = F,
             color = "lightblue",
             scale_type = "number",
             accuracy= 0.01)%>%
  gt_plt_bar(colum = college_leader,
             keep_column = F,
             color = "#556b2f",
             scale_type = "number",
             accuracy= 0.01)%>%
  gt_plt_bar(colum = group_partic,
             keep_column = F,
             color = "#2f4f4f",
             scale_type = "number",
             accuracy= 0.01)


future_viz
Students Desire to Promote Change
Response to After leaving this college, how frequently do you plan to do the following: work with others to promote social or political change by participation in service learning, campus leadership, or participation in clubs
Response Service learning Involved in leadership Club participation
Almost Always 2.93 3.18 2.93
Frequently 2.49 2.59 2.49
Occasionally 1.98 2.16 1.98
Hardly Ever 1.69 1.71 1.69
Source: Community College Civic Outcomes Survey, 2018

The results from this question indicate some connection between participation in extracurricular activities and students plans to work with others to promote change. Among students who answered “almost always”, the mean response to how frequently participate in service learning and club participation was just under 3 (3 being “semi-annually”), and slightly above 3 for college leadership. In other words, students who participate in at least semi-annual extracurricular activities were more likely to expect that they would “almost always” work with others for social or political change. This supports my hypothesis, although less strongly than in the previous question.

Impact of Civics Courses

My final question was: What relationship is there, if any, between students’ taking political science or government courses and their belief that they can have an impact on the country as a whole? This was of interest given previous data indicating that government or political science courses were linked to increased participation in the local community.

poly_sci_chart=
  project_data%>%
  select(college_poly_sci)%>%
  mutate(age= as.factor(college_poly_sci))%>%
  group_by(college_poly_sci)%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=college_poly_sci),
           fill = "lightblue")+
  labs(title = "Student participation in poli-sci or civics courses",
       subtitle = "Question: Have you taken a political science or government course?",
       x = "Frequency")
 
poly_sci_chart

The original survey asked a yes/no question about taking a course in political science or government:

Since entering this college, have you participated in any of the following activities? (yes/no): Taken a political science or government course”

I was interested in seeing if taking this type of course would lead students to feel they have more ability to impact the country. Ultimately, civic education should foster students’ sense of agency in the political sphere, so understanding the value of explicit political or government education could help develop curriculum for future use. 306 students responded that they had taken such a course, while the majority of students, 775, had not.

This independent variable was combined with the question about students’ sense of having an impact on the world, originally asked as:

I see myself: as an individual who can have an impact on what happens in this country.

The responses to this question ranged from “strongly agree” to “strongly disagree”.

project_data %>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=see_myself_having_impact),
           fill = "#2f4f4f")+
  labs(title= "Distribution of responses to impact question",
       subtitle = "Question: I see myself as an individual who can have an impact",
       x = "Frequency")

As with the first two questions, I began with cleaning the subset of data I chose to work with, using a similar procedure that included removing unncessary characters and converting the dependent variable into a factor.

impact = 
  project_data%>%
  select(college_poly_sci,
         see_myself_having_impact)%>%
  mutate(college_poly_sci = str_remove(college_poly_sci,
                                    "1|2"))%>%
  mutate(college_poly_sci = str_remove(college_poly_sci, "\\("))%>%
  mutate(college_poly_sci = str_remove(college_poly_sci, "\\)"))%>%
  mutate(see_myself_having_impact= str_remove(see_myself_having_impact, "1|2|3|4|5"))%>%
  mutate(see_myself_having_impact = str_remove(see_myself_having_impact, "\\("))%>%
  mutate(see_myself_having_impact = str_remove(see_myself_having_impact, "\\)"))%>%
  mutate(see_myself_having_impact= as.factor(see_myself_having_impact))

I then made sure the responses would appear in the correct order, and renamed the independent variable for ease of labeling.

impact_viz=
  impact%>%
  mutate(see_myself_having_impact = fct_relevel (see_myself_having_impact,
                                                " Strongly Disagree",
                                                " Disagree",
                                                " Neither Agree nor Disagree",
                                                " Agree",
                                                " Strongly Agree"))%>%
  rename("Political_Science" = college_poly_sci)

I used a simple “ggplot” bar plot to analyse the two variables together.

impact_viz%>%
  ggplot()+
  my_theme+
  geom_bar(aes(x=see_myself_having_impact,
               fill = Political_Science),
           position= "dodge")+
  scale_fill_manual(values= c("#6495ED", "#3CB371"))+
  labs(title = "Students sense of ability to impact the country",
       subtitle = "I see myself: as an individual who can have an impact on what happens in the country.",
       x= "Response Frequency")

The chart above indicates that students who take political science or government classes may feel more able to influence the country at large. With this analysis alone, however, it is unclear the extent of the impact of the course as compared to those students who didn’t take a political science or governemnt course.

Conclusion

These three questions indicate that there is some relationship between regular extracurricular activities like leadership, service learning, and club participation and students’ community engagement. Students who participated in these activities an average of semi-annually were more likely to report “strongly agree” when asked if they felt connected to their campus community. When asked about their plans to work on social or political change after graduation, students who responded “almost always” were involved in extracurricular activities between annually and semi-annually. The third research question was less clear, but indicated that students who had taken a political science or government class were more likely to agree or strongly agree that they could have an impact on the country. However, this finding requires additional analysis to confirm.

Limitations

There are a few limitations to this study: first, the age of this dataset is such that it does not account for the impact of covid on community college instructional methods and enrollment. However, results are likely to still be relevant in the post-covid era of education. Second, the data is from just eight colleges and despite a rigorous survey and sample development process may still not be representative of community college students overall.

References

Bureau, U. C. (2023, April). Community College Month: April 2023. Census.gov. https://www.census.gov/newsroom/stories/community-college-month.html

Drury, R. (2000). Community Colleges in America: A Historical Perspective. Inquiry, 8(1). https://files.eric.ed.gov/fulltext/EJ876835.pdf

Prentice, M., & Robinson, G. (2007). Linking Service Learning and Civic Engagement in Community College Students. American Association of Community Colleges. https://files.eric.ed.gov/fulltext/ED503445.pdf

Pritzker, S., Springer, M., & McBride, A. (2015). Learning to Vote: Informing Political Participation Among College Students. Journal of Community Engagement and Scholarship, 8(1). https://doi.org/10.54656/vnhp5824