Search

Community colleges are the postsecondary entry point for thousands of students each year in the United States. Over 80 percent of these students indicate a desire to earn a bachelor���s degree or higher (Horn & Skomsvold, 2011). However, according to studies by the National Student Clearinghouse, only about 15 percent of all students who start at two-year public colleges earn a bachelor���s degree within six years (Shapiro et al., 2012). Although the expected pathway for community college students seeking a bachelor���s degree includes earning an associate degree, little is known about the impact of earning an associate degree on bachelor���s degree completion. This paper thus seeks to answer the following question: Are community college students who earn an associate degree before transferring to a four-year college more likely to earn a bachelor���s degree? Using data on students in one state who entered community college and then transferred, we find large, positive apparent impacts of earning the transfer-oriented (e.g., Associate in Arts) associate degree on the probability of earning a bachelor���s degree within four, five, and six years. However, we do not find any apparent impact associated with earning one of the workforce-oriented (e.g., Associate in Applied Science) degrees that are awarded by programs typically designed for direct labor market entry. This is an important distinction, as all associate degrees are not equal in their potential impacts on future baccalaureate completion.

At an annual cost of roughly $7 billion nationally, remedial coursework is one of the single largest interventions intended to improve outcomes for underprepared college students. But like a costly medical treatment with non-trivial side effects, the value of remediation overall depends upon whether those most likely to benefit can be identified in advance. This NBER working paper uses administrative data and a rich predictive model to examine the accuracy of remedial screening tests, either instead of or in addition to using high school transcript data to determine remedial assignment. The authors find that roughly one in four test-takers in math and one in three test-takers in English are severely mis-assigned under current test-based policies, with mis-assignments to remediation much more common than mis-assignments to college-level coursework. Using high school transcript information—either instead of or in addition to test scores—could significantly reduce the prevalence of assignment errors. Further, the choice of screening device has significant implications for the racial and gender composition of both remedial and college-level courses. Finally, if institutions took account of students’ high school performance, they could remediate substantially fewer students without lowering success rates in college-level courses.

Community colleges are under pressure to increase completion rates and efficiency despite limited evidence of the economic consequences of different reform strategies. We introduce an economic model of student course pathways linked to college expenditures and revenues. Using detailed data from a single college, we calculate baseline efficiency and differences in efficiency for students who follow different pathways. We simulate changes in output, expenditures, revenues, net revenues, and efficiency assuming that the college meets particular performance targets. Findings indicate substantial differences in efficiency across pathways and significant differences in efficiency across strategies to help students complete college. They also suggest that increasing the completion rate is difficult and typically requires additional resources beyond the costs of implementing particular strategies. The model has wide practical application for community colleges.

This study examines the relationship between community college enrollment patterns and two successful student outcomes—credential completion and transfer to a four-year institution. It also introduces a new way of visualizing the various attendance patterns of community college students. Patterns of enrollment intensity (full-time or part-time status) and continuity (enrolling in consecutive terms or skipping one or more terms) are graphed and then clustered according to their salient features. Using data on cohorts of first-time community college students at five colleges in a single state, the study finds that, over an 18-semester period, ten patterns of attendance account for nearly half the students, with the two most common patterns characterized by enrolling in one semester full-time or one semester part-time. Among the remaining students who persisted, there is astounding variation in their patterns of enrollment. Clustering these patterns reveals two relationships: the first is a positive association between enrollment continuity and earning a community college credential, and the second is a positive association between enrollment intensity and likelihood of transfer.

In the United States, community colleges serve nearly half of the 18 million students enrolled in postsecondary education. However, it has only been the last decade or so where these public, two-year institutions have claimed substantial attention from the research community. This dissertation consists of three essays that focus on aspects of the community college student pathway and feature analyses relevant to research, college, and state stakeholders. The first essay evaluates the effectiveness of structured transfer pathways for Associate in Arts and Associate in Science degrees in North Carolina (called pre-major programs). It asks how these programs impact student behavior and the postsecondary outcomes of earning a community college credential, transferring to a four-year institution, and earning a baccalaureate degree compared to students enrolled in conventional, less structured associate degree programs. The paper employs an instrumental variables technique that exploits exogenous variation in student exposure to the pre-major program opportunity. Among first-time in college students, reduced-form estimates suggest that pre-major programs have a negative intent-to-treat effect on earning the intended community college credential among students enrolled in institutions that offer pre-majors. However, the program offer does not appear to have an effect on four-year credential outcomes. A plausible explanation for the findings is not that structured programs are ineffective, but rather, there likely is a failure in the policies between two-year and four-year colleges that govern the transfer of credits. Alternatively, the programs may simply be too ``light touch" to result in detectable impacts. The second essay examines the relationship between community college enrollment patterns and two successful student outcomes -- credential completion and transfer to a four-year institution. It also introduces a new way to visualize the various attendance patterns of community college students. Patterns of enrollment intensity (full- or part-time status) and continuity (enrolling in consecutive terms or skipping one or more terms) are graphed and then clustered according to their salient features. Using data on cohorts of first-time community college students at five colleges in a single state, the study finds that over an 18-semester period, ten patterns of attendance account for nearly half the students, with the two most common patterns characterized by enrolling in one semester full time or one semester part time. Among the remaining students who persisted, there is astounding variation in their patterns of enrollment. Clustering reveals two relationships: the first is a positive association between enrollment continuity and earning a community college credential and the second is a positive association between enrollment intensity and the likelihood of transfer. The third essay discusses an economic model for community college pathways. In a departure from cost models that use cross-sectional data to relate college expenditures to student outcomes, this paper takes a longitudinal cohort approach to estimate pathway costs. It suggests a model for estimating costs, revenues, and efficiency metrics for cohorts of students progressing through a community college. The framework is then used to simulate how economic metrics change as intermediate student and institutional goals are accomplished, with a special emphasis on informing colleges engaging in reform processes. It is argued that goals with the greatest efficiency (such as increasing completion rates for students who have earned 30 credits but have not earned a credential) should be preferred when budget consciousness is prioritized. Efficiency is a central theme running through the essays. In the first essay, structured transfer pathways are not found to be more efficient (in terms of student progression) than unstructured pathways, likely due to policy weaknesses. The second essay highlights the scattered enrollment patterns generated by community college students, many of which are not efficient pathways for completing college. The third essay explicitly measures the expenditures and outputs to understand efficiency quantitatively and to see how college reforms may improve efficiency.

Community college programs are designed around regularity. Degrees and certificates are often branded with an expected or “regular” completion time (e.g., two-year associate degree, one-year certificate), and suggested course sequences often assume this underlying time structure. In reality, however, relatively few students finish their course sequences and programs of study so quickly. Instead, students generate a diverse array of attendance patterns that may include periods of non-enrollment and shifts in part-time/fulltime status. Indeed, the notion of a regular enrollment pattern is quickly challenged through analysis of data on how students actually attend community college. Using a large dataset, this report (based on a longer paper—see Crosta, 2013) reveals the diversity of enrollment patterns among community college students as they progress along their education pathways. It also demonstrates a novel graphical technique for displaying large numbers of student enrollment patterns in terms of continuity (whether or not a student enrolls in a given semester) and intensity (whether a student enrolls part-time or full-time).

Students who drop out of community college are expensive. Over a five-year period, federal, state, and local authorities spent about $4 billion on community college students who began as first-time, full-time, degree-seeking students but did not return for a second year of school (Schneider and Yin, 2011). In an era where the policy focus is on accountability and efficiency, improving first-year persistence is a critical goal. For colleges to develop effective dropout prevention strategies, it is necessary to have a clear picture of who these early dropouts are. This report aims to identify distinguishing characteristics of this group by analyzing six years of transcript data on 14,429 first-time college students who in 2005 and 2006 enrolled at one of five community colleges in a single state. Of these students, 28 percent never returned to the same college after their first semester. This outcome represents the most common enrollment and exit pattern among students in the sample—more students dropped out en masse after the first term than at any other time. The majority of these students never attended any college again. This analysis departs from other work on dropout and persistence in two important ways: It includes part-time students (who constitute a majority of first-time enrollees at community colleges), and it focuses on very early dropouts—those who enrolled for one term of study but never returned to the same college for another term. For the purposes of this report, these early dropouts are compared with a group called early persisters (67 percent of students in the sample)—those who enrolled at least twice in the first four enrollment terms (fall, spring, summer, and fall).

This paper uses student-level data from a statewide community college system to examine the validity of placement tests and high school information in predicting course grades and college performance. It considers the ACCUPLACER and COMPASS placement tests, using two quantitative and two literacy tests from each battery. The authors find that placement tests do not yield strong predictions of how students will perform in college. Placement test scores are positively—but weakly—associated with college grade point average (GPA). The correlation disappears when high school GPA is controlled for. Placement test scores are positively associated with college credit accumulation even after controlling for high school GPA. After three to five semesters, a student with a placement test score in the highest quartile has on average nine credits more than a student with a placement test score in the lowest quartile. In contrast, high school GPAs are useful for predicting many aspects of students' college performance. High school GPA has a strong association with college GPA; students' college GPAs are approximately 0.6 units below their high school GPAs. High school GPA also has a strong association with college credit accumulation. A student whose high school GPA is one grade higher will have accumulate approximately four extra credits per semester. Other information from high school transcripts is modestly useful; this includes number of math and English courses taken in high school, honors courses, number of F grades, and number of credits. This high school information is not independently useful beyond high school GPA, and collectively it explains less variation in college performance. The authors also calculate accuracy rates and four validity metrics for placement tests. They find high "severe" error rates using the placement test cutoffs. The severe error rate for English is 27 to 33 percent; i.e., three out of every ten students is severely misassigned. For math, the severe error rates are lower but still nontrivial. Using high school GPA instead of placement tests reduces the severe error rates by half across both English and math.

Colleges and state higher education agencies too often lack accurate information about the socioeconomic status (SES) of their students. Information on family income, education levels, and other demographics is available for students who fill out a Free Application for Federal Financial Aid (FAFSA), but at most colleges only a portion of students do so. This presents a problem for efforts to better understand patterns and determinants of student success, since research shows that SES is a key factor in college access and attainment (Adelman, 1999; Cabrera, Burkum, and La Nasa, 2005; Long, 2004; Pascarella and Terenzini, 2005; Rouse, 1995). The paper is organized as follows: Following this introduction, the second section describes how we linked students with block groups to estimate their SES, defined in terms of household income, education, and occupation. We did this analysis using data from both the 1990 and 2000 Censuses. In the third section, we used cluster analysis to combine Census block groups into 15 demographically distinctive “community clusters,” and then matched each student with a cluster. Doing this enabled us to classify students according to a richer set of information than just income, education, and occupation. These community clusters will be useful to the WSBCTC and its member colleges in gauging their “market penetration” in various demographic sectors of the state, and in identifying particular communities where students may face barriers to college access. Given the interest of the state in ensuring access to college by students from low-income families, the fourth section shows how the community clusters break out by SES. We conclude with some suggestions for ways that state agencies and colleges in other states can use this methodology.

Traditionally, community colleges were judged on their number of enrollments and their ability to provide postsecondary education to a wide variety of students. Recently, however, state and federal policymakers have become increasingly concerned with student outcomes, and some states have even begun to consider linking the funding of community colleges to their performance on student outcome measures. In 1990, Congress passed the Student Right-to- Know (SRK) and Campus Security Act. It requires that all colleges report graduation rates to the National Center for Education Statistics (NCES) in order for their students to receive federal financial aid. These Student Right-to-Know graduation rates are part of the Integrated Postsecondary Education Data System (IPEDS). The SRK rates are the only performance measures available for virtually every undergraduate institution in the nation, including community colleges, but critics assert that the rates understate the success of community colleges in several important ways.

This paper examines the validity of the Student Right-to-Know (SRK) graduation rates as measures of community college performance. The SRK rates are the only performance measures available for every undergraduate institution in the U.S. Many community college educators argue that the SRK rates give an inaccurate picture of community college outcomes. Using data from national longitudinal surveys of college students, we examined criticisms commonly leveled against the SRK measures and found that the SRK rates do indeed yield a biased and potentially misleading picture of individual community college student outcomes. We then analyzed the usefulness of the SRK rate as a measure of relative institutional performance. Specifically, we considered whether using different measures of performance would result in substantially different rankings of Florida’s 28 community colleges. Contrary to initial expectations, we found that the relative performance of the colleges did not change substantially as different students or outcomes were used. Even after we adjusted for student characteristics that might affect outcomes, the college rankings were still fairly stable.

This paper presents findings from a study of the experiences and outcomes of older and younger community college students. We developed a discrete-time hazard model using longitudinal transcript data on a cohort of first-time community college students in Florida to compare the impact of enrollment pathways (such as remediation) and enrollment milestones (such as attaining a certain number of credits) on educational outcomes of older students—those who entered college for the first time at age 25 or later—with those of traditional-age students. Results suggest that reaching milestones such as obtaining 20 credits or completing 50 percent of a program is a more important positive factor affecting graduation probabilities for younger students than it is for older students. We also found that although enrollment in remedial courses decreases the odds of graduating for all students, older students who enroll in remediation are less negatively affected than are younger ones who do the same.

This paper presents findings from a new study of the experiences and outcomes of older community college students - those who enter college for the first time at age 25 or later. We estimate a discrete-time hazard model using transcript data on a cohort of first-time community college students in Florida to compare the effect of enrollment pathways on educational outcomes of older students with those of traditional age students. Results suggest that reaching milestones such as fall-to-fall retention, obtaining 20 credits or completing 50% of the program is a more important positive factor affecting graduation probabilities for younger students than it is for older students. We also find that although remediation decreases the odds of graduating in any given term, older students who enroll in remediation are less negatively impacted than younger ones who do.