Community College Student Persistence During the COVID-19 Crisis of Spring 2020

Abstract

Objective: This quantitative study examines the impact of the COVID-19 pandemic on students’ persistence at a minority-serving, open-access, public, urban community college in New York City. Specifically, the project looked at factors associated with mid-semester college withdrawals during spring 2020 when the college shifted to remote instruction due to the COVID-19 pandemic. Method: Utilizing data from three spring semesters (spring 2018, 2019, and 2020), four logistic regression models tested the marginal effects of student background and college program factors on mid-semester withdrawal and the moderating effect of spring 2020, the COVID-19 outbreak semester. Results: Findings indicated that the withdrawal rates were higher for new students, men, minoritized students, and part-time students across all three spring semesters. Spring 2020 disproportionally affected part-time students, men, Black students, as well as readmitted students. The greatest increase in the probability of mid-semester college withdrawal was observed for Black men who had been enrolled part-time in spring 2020. Belonging to a highly structured full-time study program protected students from leaving mid-semester, although this protection was weaker in spring 2020 and spring 2019 compared to spring 2018. Contributions: The research highlights the equity gap for Black men at the college and points to additional factors contributing to mid-semester college attrition. The work provides insights into factors that worsened during the COVID-19 pandemic. The study thereby contributes to understanding short-term risk factors for vulnerable student populations and adds to the body of literature on crisis situations in higher education.

Keywords

college drop-out gender disparity logistic regression COVID-19 pandemic equity gap part-time students

Nationwide, over one-third of first-year students in public 2-year institutions leave college after their first year. For African American first-year students, that attrition rate is over 45%. These national rates have been stable for years (National Student Clearinghouse [NSC], 2020b). According to the 2020 Signature Report by the National Student Clearinghouse, only 39.2% of first-time, full-time freshmen who entered college in 2014 at a public 2-year institution graduated with a degree within 6 years (Causey et al., 2020).

Research shows that factors associated with academic failure and college attrition for community college students include inequalities students face before entering college, such as social and economic barriers, and inequalities they face in college, such as limited support and guidance. They are more likely than students in 4-year institutions to be first-generation college students (Redford & Mulvaney Hoyer, 2017), to come from low-income families (Carnevale & Strothel, 2013), and to belong to a minoritized demographic (M. J. Bailey & Dynarski, 2011; Goldrick-Rab, 2010; Kena et al, 2016; McNair et al., 2016, 2020). Attrition factors are also linked to difficulty adjusting to college demands and lack of support in college (T. Bailey et al., 2015; Felix & Gonzalez, 2020; Holzer, 2018; Karp, 2011; Karp et al., 2008; Rosenbaum et al., 2009).

Much of the research in this area has focused on medium- to long-term retention and graduation rates. For this analysis, I focused instead on a short-term but highly problematic behavior—stopping class attendance in the middle of the semester. I understood this behavior to indicate a heightened vulnerability among students struggling in college and hypothesized that such vulnerability may have been elevated during the onset of the COVID-19 pandemic in spring 2020. My hypothesis assumed that students most vulnerable to dropping out are likely to be disadvantaged on (at least) two fronts, and that they are more likely to have barriers or biases to overcome to attend college and find suboptimal guidance and support structures in college (McNair et al., 2020). Therefore, I incorporated student background characteristics and college program factors into the analysis. Measures of student background included first-generation college status, financial aid, minority status,¹ and gender. Measures of college program factors included degree status, number of credits enrolled, and an indicator for the student’s participation in a highly structured, advising heavy program called Accelerated Study in Associate Programs (ASAP). The analysis site was a minority-serving open-access public community college located in New York City classified as Associate’s College: High Transfer High Tradition.²

The findings revealed that new students were much more likely to stop attending classes than students who had been successful in a prior semester. Younger students, men, and minoritized students were more likely to leave compared to older students, women, and white students. Part-time students were at a much higher risk of leaving mid-semester. During the pandemic crisis, part-time students, readmitted students, men, and Black students became more vulnerable to leaving. The vulnerability to leaving increased most for Black men attending part-time in spring 2020. The ASAP program protected students from leaving mid-semester, including in spring 2020, although the protection varied by semester and was reduced compared to spring 2018.

This study brings to light inequities that are associated with mid-semester attrition more generally, and thereby shifts the conversation to heightened vulnerability, disparity gaps, short-term risks, and intervention possibilities. Second, the “natural experiment” of the COVID-19 spring 2020 semester allowed for testing the effect of a crisis situation on vulnerable student groups, and thereby the findings contribute to a body of literature concerned with crisis situations in higher education.

Research Questions

The study was guided by the following research questions:

RQ1: Which student characteristics and college program factors contribute to mid-semester withdrawal from college?

RQ2: Did students withdraw from college mid-semester at a higher rate in spring 2020 (during the onset of COVID-19) than in prior spring terms?

RQ3: Which student characteristics and college program factors had more substantial impacts on mid-semester college withdrawals in spring 2020, compared to withdrawals in previous semesters?

Conceptual Framework and Literature Review

Multi-Dimensional Inequities

This study is guided by the assumption that community college student success is compromised by inequities students face before entering college and inequities they encounter in college. Students often start college with academic, social, and economic challenges and then attend institutions that do not provide the costly academic and student supports they need.

Many of these inequities that students faced before starting college are well documented. Compared to freshmen at 4-year institutions, they tend to come from poorer families (Dougherty et al., 2017; Vasquez et al., 2019) and have grown up in more impoverished neighborhoods (Wade, 2017) where they attended underserved high schools (M. J. Bailey & Dynarski, 2011; Green, 2006; McNair et al., 2016; Snellman et al., 2015). They are more likely to belong to an underrepresented minority and belong to the first generation in their families to attend college (American Association of Community Colleges [AACC], 2020; Berkner & Choy, 2008; Dowd & Bensimon, 2015; Goldrick-Rab, 2010; Kena et al., 2016; Redford & Mulvaney Hoyer, 2017; Wood et al., 2015). They may have also been more likely to have experienced structural racism throughout their upbringing (Carnevale & Strothel, 2013; McNair et al., 2020; Pattillo, 2015; Shedd, 2015). For all these reasons, community college students tend to be more doubtful about belonging in college (Krumrei-Mancuso et al., 2013; Martinez & Munsch, 2019; Museus et al., 2017), struggle with academic work (Acevedo-Gil et al., 2015; Logue, 2016; Monaghan & Attewell, 2015), deal with competing life demands (Vasquez et al., 2019), and therefore be more vulnerable to dropping out before they have the chance to succeed. Research has confirmed that community college attrition is associated with low-income status, first-generation college status, and belonging to an underrepresented minority (Atherton, 2014; Berliner, 2013; Collier & Morgan, 2008; NSC, 2020a; Redford & Mulvaney Hoyer, 2017; Sirin, 2005; Sparkman et al., 2012; Soria & Stebleton, 2012). A growing body of literature has found that many of these disadvantages are even more pronounced for men and especially for men of color (Buchmann & DiPrete, 2006; Buchmann et al., 2008; Harris & Wood, 2013, 2016; Person et al., 2017; Strayhorn, 2017; Wood & Newman, 2017). Black, Latino, and Asian American men show lower retention and completion rates in 2-year institutions compared to their female counterparts (Bush & Bush, 2010; Causey et al., 2020; Flowers, 2006; Harris et al., 2017; Hatch et al., 2016; McNair et al., 2020; Sáenz & Ponjuan, 2009). Research finds that strong college ambitions in disadvantaged youth at high school graduation can mitigate some of these risk factors, but that lack of socioeconomic and academic resources and attending college on a part-time basis remain strong factors predicting the students’ inability to hold steady in their college plans even for those with high ambitions (Alexander et al., 2008).

Community college students are further disadvantaged by attending institutions that provide less support than 4-year institutions. Open-access public 2-year institutions often lack the financial funding that is available at selective institutions and they often are not structured around college student support in the same way as 4-year institutions (Complete College America, 2016; Kahlenberg, 2015; Strumbos et al., 2018). Strumbos et al. (2018) pointed out that oftentimes, community colleges lack the coordination between academic and support departments and student advisors have very high caseloads. They do not have the resources to counteract the students’ challenges with programs that are typically human-resource intensive and designed to guide students through their studies. T. Bailey et al. (2015) observed that the academic and support services at community colleges are comparable to a cafeteria-style setup by which students can pick from many options and pursue a variety of goals, which can be confusing. This lack of personalized advising affects the students’ ability to understand what is expected of them and how to ask for help when they need it (Karp & Bork, 2014). Such a lack of structured guidance has been associated with early drop-out or prolonged enrollment and excessive credit accumulation (Deil-Amen & Rosenbaum, 2003; Rosenbaum et al., 2009). Finally, literature that is more recent is surfacing a reproduction of inequities in college through biased student support practices that leave minoritized students behind. (Felix & Gonzalez, 2020; Dowd & Bensimon, 2015; Maldonado, 2019; McNair et al., 2020; Serrano, 2022; White & Dache, 2020).

Effective Strategies of Preventing Dropout

Some of the strategies that have been found effective in preventing early drop-outs and failure include channeling the academic choices that students make, providing guided pathways that link courses to majors and majors to careers (T. Bailey et al., 2015; Jenkins et al., 2017; Scott-Clayton, 2011), and coaching students to develop goal-oriented study habits (Pechac & Slantcheva-Durst, 2019). Full-time attendance and receiving financial grant aid positively affected persistence for community college students especially if a student received a Pell Grant during the first 2 years of college (Fike & Fike, 2008; McKinney & Novak, 2012). The effect of Pell Grant aid awards in preventing dropout was found to be even greater for minoritized students (Chen & DesJardins, 2010). Watson and Chen (2019) reviewed a program called the Educational Opportunity Fund program (EOF) in New Jersey, which provides financial assistance to low-income students, and found positive effects on student retention. As mentioned earlier, The City University of New York’s ASAP program is another successful example that has implemented many of these strategies. Students in the ASAP program are required to be eligible for federal financial aid, must attend college full-time, and meet with their advisor on a regular basis. The program includes consolidated course scheduling and cohort course-taking, career and employment counseling, and tutoring (Strumbos et al., 2018). With these structures in place, students receive sustained support and often attend classes in groups with fellow ASAP students. Early studies documented the ASAP program’s strong positive effect on student retention and increased graduation rates (Kolenovic et al., 2013; Scrivener et al., 2012). Kolenovic et al. (2013) analyzed the retention and graduation rates of the first ASAP cohort and found that they consistently outperformed a comparable student cohort not enrolled in ASAP. The program has been adopted outside New York and continues to produce significantly higher graduation rates than national averages (CUNY ASAP, 2021; Miller & Weiss, 2021; Weiss et al., 2019).

How the COVID Crisis Affects Retention

Observations bringing to light the multidimensional inequities of community college students reveal how social inequality and breakdowns in resources (material and social-emotional) negatively impact student success. Many spring 2020 students included in this study came from homes with crowded living space and limited access to technology, lived in multi-generational dwellings, and 42% of their families lived on an annual household income of below $30,000 a year.³ Over half of the underrepresented minority⁴ degree-seeking students were first-generation college students (52%). The median household income for these first-generation students,—was $23,711.

The literature shows that strategies that increase student success tend to be interpersonal, highly structured, and human-resource intensive (T. Bailey et al., 2015; Jenkins et al., 2017; Kolenovic et al., 2013; Scott-Clayton, 2011; Scrivener et al., 2012;. Strumbos et al., 2018). The lockdown in March of 2020 interrupted any such advising and services for students and moved them onto remote platforms or could only be offered over the phone. Furthermore, only a small fraction of faculty members at this college was familiar with software and instructional techniques specific to online teaching. It is likely that the lockdown and switch to remote learning in spring 2020 compromised the quality of advising and teaching and added additional stressors to students and their families. In other words, I assumed that this crisis further disadvantaged students, especially those with limited resources at home, and compromised the quality of student support they received. With all this in mind, I designed this study to examine the impact of student background characteristics and college factors on mid-semester college withdrawal before and during the pandemic. I included demographic data, first-generation college status, and financial aid data to measure student background characteristics. For program factors, I included new student status, degree type, credits enrolled, and participation in the highly structured ASAP program.

Method

Study Context

In March 2020, after the outbreak of COVID-19 was declared a global pandemic, infection rates, hospitalization rates, and death rates were rising rapidly in New York City.⁵ Virtually all public and private schools and colleges in New York City shut down to in-person classes and faculty and staff were sent home to teach and work remotely. For the college included in this study, all teaching activities came to a stop on March 12, 2020. Starting on March 19, 2020, students had no choice but to continue with the spring semester from home. Before the lockdown, students were attending mostly in-person classes several days a week and commuting from the surrounding areas. Only 18 fully-online courses were offered in spring 2020 prior to the lockdown, with a total of 403 students enrolled, which was less than 1% of all course enrollment on campus. After the lockdown, the college went fully remote. The abrupt change in college life, alongside the obvious health threat and economic uncertainties for students and their families, was confusing and chaotic and constituted an unprecedented crisis. I hypothesized that this situation contributed to a higher attrition rate compared to the mid-semester attrition rates in prior spring semesters.

Data and Sample

The study utilized student data for three spring semesters—spring 2018, spring 2019, and spring 2020—in order to compare changes in persistence over time and to identify the possible added impact of spring 2020 on mid-semester withdrawal. A student may have been enrolled in one, two, or all three of the spring semesters at the institution of analysis. For any given student in the dataset, sex, race and ethnicity, and first-generation college status were static across all three semesters, age was specific to the semester, and so were all other covariates such as new student status, degree type, credits enrolled, ASAP program participation, and financial aid status. The 3-year data set consisted of 34,552 associate degree-seeking students with roughly equal numbers each year, although spring 2020 had the lowest enrollment of the three semesters (reflecting a declining enrollment trend in this college in recent years). Missing data was not an issue, with the exception of first-generation college student status. The source for this data was the Free Application for Federal Student Aid records (FAFSA for short), which was only available for students who filed a FAFSA application. To account for this, I created a categorical variable for first-generation college student status with three values: “yes,” “no,” and “unknown.” “Unknown” was then included as a control variable in the logit models.

Dependent Variables

The student behavior of interest in this analysis was mid-semester college withdrawal of students in a given spring semester. Students were counted as withdrawn from college mid-semester when they had enrolled at the start of the semester, had been verified to be attending classes during the first few weeks of school, and then withdrew from all of their courses before final grades were submitted. The college’s student-record system captured this behavior with a term-withdrawal code. The mid-semester withdrawal rate averaged 7.51% across the three spring semesters.

Independent Variables

The selection of covariates included controls for degree type, new student status, and factors that reflected the theoretical framework. First-generation college status, race and ethnicity, and sex were included to examine potential equity gaps for first-generation college students, minoritized students, and for men—including men of color. Factors hypothesized to mitigate the risk of dropping out included ASAP participation and full-time attendance. Financial aid application and Pell Grant eligibility were also included in the analysis. Table 1 displays descriptive statistics. Close to 53% of the students were female. Students of color were roughly equally represented (with the exception of American Indian or Native Alaskan) with 26.77% Black, 28.51% Hispanic, and 28.90% Asian or Pacific Islander. The college enrolled 14.77% white students and an additional 1.6% of the students who identified as American Indians or Native Alaskan. The median student age was 21 years old. Over three quarters of students had applied for federal financial aid (77.67%) and 53.79% were Pell Grant recipients. Many part-time students had also applied for financial aid (63%) and 34.48% of part-time students had been awarded some Pell Grant money. Thirty-six percent of the students were first-generation college students. However, parents’ educational level was unknown for 32% of the population. The data set included 7.41% freshmen, 6.24% transfer students, 7.37% readmitted students, and the remaining 78.98% were students who had continued from the prior fall. The majority of students were enrolled in AS and AA programs (82.25%) with the intention of transferring to a 4-year institution upon completion, and 17.75% were enrolled in an AAS degree program for career and technical training. For 64.78% of the students, enrollment was full-time with a median of 12 credits enrolled in a given spring semester. Finally, 22.12% were enrolled in the ASAP program.⁶

Table 1.

Descriptive Statistics of Dependent and Independent Variables.

	Categories	N	%	Median	M	SD
Dependent variable
Mid-semester college withdrawal	Yes	2,594	7.51
Mid-semester college withdrawal	No	31,958	92.49
Semesters	Spring 2018	12,232	35.40
	Spring 2019	11,452	33.14
	Spring 2020	10,868	31.45
Demographics and financial aid
Sex	Male	16,255	47.05
Sex	Female	18,297	52.95
Race/ethnicity	Black	9,248	26.77
	Hispanic	9,850	28.51
	Asian or Pacific Islander	9,984	28.90
	White	5,105	14.77
	American Indian and Native Alaskan	365	1.06
Age				21	23.81	7.0
Applied for financial aid	Yes	26,835	77.67
Applied for financial aid	No	7,717	22.33
Pell Grant recipient	Yes	18,585	53.79
Pell Grant recipient	No	15,967	46.21
First generation student	Yes	12,537	36.28
Parent with college education	Yes	10,861	31.43
Parents’ education unknown	Yes	11,154	32.28
Student type, credit load, and ASAP
New student status	Freshmen	2,561	7.41
	Transfer	2,157	6.24
	Readmit	2,545	7.37
	Not new, continuing from prior fall	27,289	78.98
Type of associate’s degree	AAS	6,133	17.75
Type of associate’s degree	AS/AA	28,419	82.25
Full-time status (12 or more credits enrolled)	Yes	22,384	64.78
Full-time status (12 or more credits enrolled)	No	12,168	35.22
Number of credits enrolled				12	11.25	4.03
ASAP	Yes	7,644	22.12
ASAP	No	26,908	77.88

Statistical Models

In order to answer RQ1 and RQ2 (What student characteristics and program factors are associated with a mid-semester college withdrawal? Were students withdrawing at a higher rate in spring 2020 than in prior terms?), I used percent changes of the outcome over time and for the various student characteristics and program factors. I tested these associations further in one logistic regression model and reported the marginal effects these covariates had on the probability of students leaving mid-semester over these three spring semesters combined. The differences in the marginal effects of spring 2020 and spring 2019 to spring 2018 were also calculated in this model to report the added effect of the COVID-19 breakout semester on the probability of mid-semester attrition. To answer RQ3 (Which student characteristics and college program factors had more substantial impacts on mid-semester college withdrawals in Spring 2020, compared to withdrawals in previous semesters?), I ran separate logistic regression models for spring 2018, 2019, and 2020 and reported the differences in the marginal effects on the probability of students leaving mid-semester prior to COVID-19 and in the COVID-19 spring term.

Robust Standard Errors were calculated for all logistic regression models in order to deal with heteroskedasticity.

A logistic regression predicts the natural log of the odds of a binary outcome such as dropping out. The logistic regression model follows this formula:

L o g [\frac{π_{i}}{(1 - π_{i})}] = α + β_{1} * x_{1} + β_{2} * x_{2} + β_{3} * x_{3} \dots + β_{k} * x_{k}

(1)

In Formula (1), $π_{i}$ is the probability that a student withdrew from college mid-semester. The coefficients $β$ are estimates of the effects of each of the independent variables ( $x_{1} t o x_{k})$ on the log odds of the outcome, holding the other predictors constant.

Four logit models were calculated that followed the general outline of Formula (2).

\begin{array}{l} L o g [\frac{π_{i}}{(1 - π_{i})}] = α + β_{1} * S p r i n g t e r m s + β_{2} * D e m o g r a p h i c s + \\ β_{3} * F i r s t G e n e r a t i o n C o l l e g e S t a t u s + β_{4} * F i n a n c i a l A i d + \\ β_{5} * N e w S t u d e n t + β_{56} * D e g r e e S t a t u s + β_{7} * A S A P p a r t i c i p a t i o o n + \\ β_{8} * C r e d i t s e n r o l l e d . \end{array}

(2)

Marginal effects

Marginal effects allow comparisons of the impact of the covariates and group membership across models and time on the probability of P(y = 1), that is the outcome to be true (Breen et al, 2018; Long & Mustillo, 2021; Mood, 2010).

Marginal effects follow this formula

β_{x 1} f (β_{x i})

(3)

In Formula (3) $β_{x 1}$ is the estimated log odds ratio for variable $x_{1}$ and $β_{x i}$ is the value of the logit (which is the linear combination of values on variable x and their estimated coefficients $β$ ) for the ith observation. The expression $f (β_{x i})$ is the probability distribution function of the logistic distribution with regards to $β_{x i}$ (Mood, 2010, p. 75).

I used the marginal effects commands in STATA, which provide the 95% confidence interval along with a test of the null hypothesis that the average prediction is zero. STATA does this using the delta method to compute standard errors (Long & Freese, 2014, p. 141).

Limitations of the Study

Undoubtedly, the stressors unleashed by the COVID-19 crisis affected students’ ability to focus on college classes in multiple ways. This study was not designed to analyze the multitude of these factors. For example, we did not address the impact of neighborhoods and family composition. Nor were we able to incorporate data related to pandemic-linked changes to the private lives of the students, such as illness and unemployment. Rather, this study is limited to individual demographic factors and academic and college program data.

Results

Descriptive Statistics

The semester-to-semester trend shown in Table 2 revealed that the spring 2020 semester had a higher mid-semester withdrawal rate (8.9%) compared to the prior two spring terms, although the rates were trending up slightly from 2018 to 2019 (spring 2018: 6.5% and spring 2019: 7.2%). My hypothesis was that this jump in 2020 was in part due to the COVID-19 pandemic situation. Table 2 presents observed percentages of mid-semester drop-out rates for student demographics, financial aid, and academic and college program factors without controls. Numbers bolded indicate a higher-than-average increase (two percentage points or more) in mid-semester withdrawal-rates comparing spring 2020 and spring 2019. The table shows that higher withdrawal rates in spring 2020 were observed for men, readmitted students, transfer students, part-time students, students not enrolled in the ASAP program, students who had not applied for financial aid (FAFSA), and students without Pell Grant. Both, Black and White students had higher rates in spring 2020. Full-time students and ASAP students had considerably lower rates of withdrawal from college mid-semester in any of the spring semesters analyzed.

Table 2.

Percentages of Mid-Semester College Withdrawals by Student Characteristics.

	A	B	C	D
	All three semesters combined	Spring 2018	Spring 2019	Spring 2020
	% withdrew	% withdrew	% withdrew	% withdrew
All degree students	7.5	6.5	7.2	8.9
Male	8.9	7.6	8.2	11.0
Female	6.3	5.6	6.2	7.2
Black	8.2	7.2	7.6	10.0
Hispanic	8.2	7.3	7.8	9.6
Asian or Pacific Islander	6.3	5.0	6.3	7.6
White	7.4	6.9	6.6	9.0
American Indian and Native Alaskan	6.6	7.4	8.6	3.4 ^a
Applied for financial aid	6.9	6.3	6.6	8.0
Did not apply for financial aid	9.5	7.6	9.1	12.0
Pell Grant	6.0	5.1	5.8	7.2
No Pell Grant	9.3	8.4	8.8	10.9
First generation student	7.2	6.4	7.0	8.4
Parent with college education	6.7	6.0	6.1	8.1
Freshmen	11.1	8.8	12.2	13.0
Transfers	9.8	9.4	9.0	11.1
Readmits	13.0	11.2	12.3	16.1
Continuing students	6.5	5.6	6.1	7.8
AAS	8.0	7.1	7.7	9.5
AS/AA	7.4	6.4	7.1	8.8
ASAP	3.4	2.1	3.4	4.4
Not ASAP	8.7	7.6	8.3	10.4
Full-time degree students	5.6	5.2	5.4	6.4
Part-time degree students	11.0	9.3	10.5	13.2

Note. Numbers bolded indicate a difference of two percentage points or higher between spring 2019 and 2020.

Only four students in this category withdrew from college mid-semester in spring 2020.

Logistic Regression Results

Tables 3 and 4 show the logit model summary statistics, the marginal effects, and their significance levels for four separate models. Table 3 shows the results of the logistic regression model 1, which combined all three spring terms into one model. The marginal effects show that the probability of leaving mid-semester was higher for men, Black and Hispanic students, younger students, first-generation college students, and new and readmitted students as compared to women, White students, older students, students whose parents had a college degree, and continuing students. Students in the ASAP program, Pell Grant recipients, and students with more credits enrolled had lower probabilities of withdrawing compared to students not enrolled in ASAP, part-time students, and students not receiving Pell Grant. Interestingly, the probability of leaving mid-semester increased by two percentage points for students who applied for FAFSA. Perhaps this factor’s unique contribution is an indicator for financial need, which negatively affected the vulnerability of students. Finally, spring 2020 (and to a lesser extent spring 2019) increased the probability of mid-semester withdrawal as compared to spring 2018. To a large extent, the results from model 1 confirm the descriptive statistics revealed in Table 2, with the exception of financial aid application.

Table 3.

Logit Model 1 on Mid-Semester Term Withdrawal From College.

Variables	Model 1: Spring 2018, 2019, and 2020 combined
Variables	Marginal effects	pValues
Spring 2019	0.008	.017
Spring 2020	0.023	.000
Male	0.023	.000
Black	0.011	.015
Hispanic	0.010	.027
Asian or Pacific Islander	−0.005	.251
Native American	−0.011	.354
Age	−0.004	.000
First-generation college student	0.009	.009
Parental education unknown	0.017	.000
Applied for FAFSA	0.020	.000
Pell Grant awarded	−0.007	.042
First-time freshman	0.050	.000
Transfer student	0.034	.000
Readmitted student	0.027	.000
AAS degree	−0.001	.729
ASAP student	−0.033	.000
Credits enrolled at start of the semester	−0.009	.000
Model χ²	1,608.20***
Degree of freedom	19
N	34,552

Note. Average partial effects were used for categorical covariates and marginal effects at mean was used for continuous covariates. Age squared was included in the equation to control for the nonlinear effect of age.

***

p value = .000.

Table 4.

Logit Models 2 to 4 on Mid-Semester Term Withdrawal From College.

Variables	Spring 2018		Spring 2019		Spring 2020
Variables	Marginal effects	p-Value	Marginal effects	p-Value	Marginal effects	p-Value
Male	0.017	.000	0.016	.001	0.035	.000
Black	0.007	.355	0.009	.202	0.017	.045
Hispanic	0.009	.205	0.012	.095	0.007	.366
Asian or Pacific Islander	−0.013	.052	0.002	.746	−0.002	.760
Native American	0.002	.919	0.014	.546	−0.053	.002
Age	−0.003	.000	−0.004	.000	−0.005	.000
First-generation student	0.008	.120	0.011	.054	0.007	.297
Parental education unknown	0.014	.025	0.020	.003	0.018	.015
Applied for FAFSA	0.030	.000	0.018	.008	0.009	.263
Pell Grant awarded	−0.020	.000	−0.005	.423	0.006	.398
First-time freshman	0.034	.001	0.071	.000	0.046	.000
Transfer student	0.038	.001	0.030	.016	0.031	.008
Readmitted student	0.023	.008	0.026	.007	0.036	.001
AAS degree	0.001	.883	−0.001	.851	−0.004	.561
ASAP student	−0.044	.000	−0.027	.000	−0.028	.000
Credits enrolled at start of the semester	−0.006	.000	−0.009	.000	−0.011	.000
Model χ²	419.80***		525.53***		677.18***
Degree of freedom	17		17		17
N	12,232		11,452		10,868

***

p-Value = .000.

The impact of spring 2020

Table 4 shows the semester specific logistic regression results for models 2 to 4 and reveals that spring 2020 increased the probability of leaving mid-semester for male students, Black students, and readmitted students. In other words, their vulnerability increased with the crisis. The crisis also moderated the impact of credits enrolled. For every additional credit enrolled in spring 2020, the probability of leaving declined by 1.1 percentage point, whereas in the prior semesters that protection was lower, especially in spring 2018. In other words, the crisis intensified the vulnerability of students attending part-time. American Indian and Native Alaskan students showed significant lower probabilities for mid-semester attrition in spring 2020. However, this student group was very small and this finding cannot be generalized. Finally, the ASAP effect of preventing mid-semester attrition was weaker in spring 2020 and spring 2019 as compared to spring 2018. Therefore, we cannot assume that spring 2020 had a substantial impact on the program’s ability to prevent attrition. New students and younger students had statistically significant marginal effects in all three semesters, but spring 2020 did not elevate these vulnerabilities. Pell Grant aid was a preventive factor in spring 2018 only and became insignificant in 2019 and 2020. FAFSA status increased the probability of leaving mid-semester in the two springs prior to COVID-19 but showed no such effect in spring 2020. First generation college status had no significant effect by year. Finally, pursuing an AAS career degree as compared to an AA or AS transfer degree made no difference in the probability of withdrawing.

The probability of dropping out in the middle of the semester increased the most for Black men attending part-time. Table 5 shows the average predicted probability to withdraw mid-semester for students enrolled in six credits. The probabilities are shown for all students (i.e., the average for all students enrolled in six credits) and separately for all men enrolled in six credits, for all Black students enrolled in six credits, and all Black men enrolled in six credits. Comparing these numbers by years the data shows an added impact of spring 2020 on leaving. Among all students enrolled in six credits, Black men in spring 2020 had the highest increase in the probability of leaving compared to spring 2018, and also compared to spring 2019.

Table 5.

Average Predicted Probability to Withdraw Mid-Semester for Students Enrolled in Six Credits.

	All	All men	All Black students	Black men
Spring 2018	0.112	0.126	0.122	0.137
Spring 2019	0.142	0.158	0.147	0.163
Spring 2020	0.179	0.214	0.201	0.239
Difference 2018–2020	0.067	0.088	0.079	0.102
Difference 2019–2020	0.037	0.056	0.054	0.076

This vulnerability for part-time students to drop out and how it was elevated in spring 2020 is illustrated in Graphs 1 and 2. Graph 1 shows the differences between 2019 and 2020, and Graph 2 shows the differences between 2018 and 2020 by enrolled credits and for the subgroups. The largest difference between part-time and full-time status on the probability of dropping out was found for Black men in both graphs.

Graph 1.

Differences in the probability to withdraw mid-semester between spring 2019 and 2020 by credits enrolled.

Graph 2.

Differences in the probability to withdraw mid-semester between spring 2018 and 2020 by credits enrolled.

Conclusions

The study confirmed several inequities intensified during the outbreak of COVID-19 in spring 2020. Men showed a consistently greater risk of leaving compared to women, confirming the gender disparity previously documented in the literature. The spring 2020 crisis heightened the disparity for Black men and for part-time students. Readmitted students also found themselves more likely to stop attending college during the crisis. For the most part, students who were readmitted had a history of academic failure (academic probation or dismissals) and were a vulnerable student population even without the crisis. With and without the crisis, new and younger students were more vulnerable to leaving compared to continuing students and older students, which confirms observations made elsewhere that the first semester and first year is a particularly difficult time for students (Adelman, 2006; Craig & Ward, 2008; Hawley & Harris, 2005). However, I did not find any strong effects of first-generation college status. The financial aid status was inconclusive. While the literature points to findings that show how Pell Grant money can be helpful in preventing attrition, in this analysis I found this to be the case only some of the time.

Finally, the analysis confirmed the effectiveness of the ASAP program in preventing students from dropping out even though that protection was declining in spring 2019, and weaker in spring 2020 than in spring 2018. The lowering of the ASAP advantage was statistically significant even though students in this program were still less likely to leave mid-semester than those who were not in this program.

Recommendations for Policymakers and Institutions

The findings confirm what many administrators and policymakers in community colleges across the nation already know. When students start college in a spring semester and only enroll part-time they are at a higher risk of not being successful. I also confirmed a gender disparity that leaves men more vulnerable to leaving during a crisis. The crisis of spring 2020 also negatively affected Black students. Black men who were enrolled part-time showed the highest probability of leaving in COVID-19 spring 2020. On the other hand, students who were enrolled full-time and in the ASAP program weathered the COVID-19 storm better than part-time students, and also better than other full-time students with financial aid who were not enrolled in this program. The findings support policies that discourage part-time study for minoritized students and new and young college freshmen, and that steer them toward financial-aid funded full-time programs coupled with intrusive advising and guided pathway elements. Aside from the costly ASAP-like support services, other ways to enhance student support may be effective. For example, students may benefit from course withdrawal monitoring practices coupled with built-in advisor outreach for students who drop below full-time status. Catching such behavior in mid-semester and before a student leaves altogether might positively affect the student’s chance of success. The findings also support the importance of programs that pay special attention to support for men in community colleges and students who may be minoritized based on race and other factors. To provide effective support, recent findings of the assessment of such programs for Black men in community colleges in California suggest that colleges need to systematically change policies informed by an equity-mindedness that acknowledges how structural racism is produced by current practices on campus (Felix & Gonzalez, 2020). In this effort, close attention needs to be paid to not re-enforce stereotypes and deficit-based beliefs which further marginalizes or victimizes vulnerable students, including Black men. Instead, colleges need to become student-ready institutions that understand current practices to be the problem rather than the students (Malcom-Piqueux & Bensimon, 2017; McNair et al., 2016, 2020).

Recommendations for Future Research

Over the last decade, a significant number of community colleges have implemented guided pathways and early-warning systems to enable advisors’ outreach to struggling students and match students to interventions such as tutoring and counseling (Jenkins et al., 2017; Tampke, 2013; Villano et al., 2018). Studies of such early-warning systems’ effects on mid-semester withdrawals and college persistence would be useful. Special focus could be paid on their efficacy for vulnerable students, such as students who are new to college and those who drop from full-time to part-time status.

Additional research is needed to examine the medium- to long-term effects of COVID-19 on community college students and on their progress toward degree completion. Recent reports by the National Student Clearinghouse (NSC) and the Community College Research Center (CCRC) show that enrollment dropped most drastically for community colleges in fall 2020 and continued to drop significantly in fall 2021. In fall 2020, the decline in enrollment was most dramatic for men (Marcus, 2021; NSC, 2021; Sedmak, 2020). An analysis of U.S. Census Bureau household survey data in summer 2020 revealed that more than 40% of households reported that a prospective student is cancelling all plans for community college (CCRC, 2020). Among the most affected are those from low socioeconomic backgrounds and minoritized students (Lanahan, 2021). Questions addressed by such research should include the role of outside-college, pandemic-related stressors on the students’ ability to remain in college and to make progress. Special attention could be paid to men of color and other minoritized student groups. To add a qualitative dimension to the quantitative findings presented here, future research should investigate changes in students’ perspectives toward college in the wake of the COVID crisis.

Footnotes

Acknowledgments and Credits

I would like to thank my Professor Dr. Paul Attewell for his encouragement and insightful feedback. I would also like to thank Dr. Maggie Fay for her invaluable feedback to an earlier draft of the manuscript. Last but not least, I would like to thank Ms. Amaris Matos for her comments that strengthened my writing.

IRB File #2020-0444

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Elisabeth Lackner

Notes

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