Compressed Remediation and Developmental Education

Compressed Remediation and Developmental Education: A Critical Review of Research

Alexandros M. Goudas    (Working Paper No. 16)    May 2021

In 2009, President Obama initiated a postsecondary completion agenda that was officially entitled the American Graduation Initiative (Field, 2015; Lester, 2014). Interest groups, philanthropic organizations, and researchers promptly responded to assist with efforts to improve graduation rates (Bailey & Cho, 2010). With the support of funding by federal grants and charitable organizations, research and interest groups began studying and promoting accelerated remediation and developmental education (R/DE) models because they believed traditional R/DE was ineffective and therefore needed to be reduced by various means (Bailey et al., 2009, 2010; Complete College America, 2012; Education Commission of the States, 2015).

After a decade of investigation into variations of acceleration models conducted across the nation, the most popular and widely implemented extant acceleration frameworks could be classified in two broad categories: corequisite and compressed models. The corequisite model involves pairing a prerequisite R/DE course, which is traditionally a stand-alone course offered prior to college-level coursework, with a gateway college-level course, both of which students take concurrently. The most well-known corequisite model has been the Accelerated Learning Program (ALP) (Cho et al., 2012; Jenkins et al., 2010). Compressed courses, on the other hand, are typically two or more stand-alone R/DE courses that are taken in sequential semesters but are combined into one compressed class, usually by doubling each course’s time and offering it in two sequential halves of a semester, or by creating a new and typically longer course to replace the two. The California Acceleration Project (CAP) outlines some of the most popular models involving compressed courses, and CAP is being promoted as a model for other states and institutions nationwide (Hern, 2010, 2011; Hern & Snell, 2014; Hern et al., 2020).

Some researchers and proponents of compressed course models have argued that they increase gateway pass rates compared to prerequisite R/DE courses (Edgecombe, 2011; Edgecombe et al., 2013a, 2013b; Edgecombe et al., 2014; Hern, 2010, 2011; Hern & Snell, 2014; Hern et al., 2020; Hodara & Jaggars, 2014; Jaggars et al., 2014; Zachry Rutschow & Mayer, 2018). However, several problems with much of this research base exist: a preponderance of confounding factors; mismatched study time periods among prerequisite R/DE and accelerated analytic groups; and a dearth of randomized controlled trials and quasi-experimental designs.

More importantly, after a decade of research on various forms of their implementation, R/DE acceleration models have not resulted in long-term increases in completion. In fact, Jaggars and Bickerstaff (2018) concluded that acceleration will do little to increase graduation, and completion rates at two-year colleges have not increased in the past decade in spite of widespread reforms such as acceleration (Shapiro et al., 2019). Nonetheless, policymakers use disparate and dubious positive results from R/DE accelerated models in studies to mandate the elimination of prerequisite traditional R/DE in state systems and institutions nationwide (Education Commission of the States, 2015; Zachry Rutschow & Mayer, 2018). The conflicting nature of the research and overall lack of positive outcomes suggest the need for further investigation into the efficacy of acceleration and whether results warrant a widespread adoption.

To address these issues, in this paper I provide a synthesis of the extant research on compressed acceleration R/DE models and summarize their outcomes. Further, I describe the general limitations of the research and highlight probable confounding factors. I conclude with a discussion on whether any increases in outcomes from models on compressed R/DE justify the widespread implementation of acceleration and the accompanying decisions to remove or severely restrict prerequisite R/DE options in institutions and state systems.


Beginning over a decade ago, as one of many responses to the Great Recession, newly elected President Obama declared that one of the nation’s goals should be to increase postsecondary completion. Termed the American Graduation Initiative, the completion agenda set its sights on addressing unmet needs in the workforce. One of the centerpieces of this declaration involved community colleges. Notably, it had been several decades since a sitting president called attention to the important role two-year public colleges play in higher education (Lester, 2014). However, Obama’s proposal narrowed the traditional community college mission from access and equity to workforce training, arguing, “‘We will not fill those jobs, or even keep those jobs here in America, without the training offered by community colleges’” (Liasson, 2009, para. 9).

As a result of the American Graduation Initiative’s focus on completion, the field of remediation and developmental education (R/DE) drew increased scrutiny from scholars, research centers, and interest groups that saw an opportunity for reforms that could result in higher completion rates, especially at two-year public colleges (Bailey et al., 2009, 2010; Bailey et al., 2015; Bailey et al., 2016; Complete College America, 2012; Jaggars & Bickerstaff, 2018; Vandal, 2014, 2015). Policymakers highlighted three areas in R/DE that they deemed could be improved at the nation’s two-year publics in order to increase completion: multiple measures for assessment and placement, accelerated coursework, and guided pathways.

Based on early research into Achieving the Dream colleges and the progression and outcomes of a sample of students in R/DE, Bailey (2008) and Bailey et al. (2009, 2010), from the Community College Research Center (CCRC), produced seminal papers that set the groundwork for future research and reforms at community colleges. Even before the completion agenda had been declared, Bailey (2008) had outlined a plan for important areas of research for various reforms to increase completion rates at two-year public colleges, including such reforms as more comprehensive assessment and placement, the acceleration or streamlining of R/DE coursework, and a change in curricular designs to include a pathways approach, all of which foreshadowed the three primary reform approaches most institutions and state systems adopted several years later and continue to expand today. Bailey summarized his recommendations in a statement that portended massive and widespread reform movements in R/DE:

I suggest a broad developmental education reform agenda based on a comprehensive approach to assessment, more rigorous research that explicitly tracks students with weak academic skills through their early experiences at community colleges, a blurring of the distinction between developmental and “college-level” students that could improve pedagogy for both groups of students, and strategies to streamline developmental programs and accelerate students’ progress toward engagement in college-level work. (abstract)

The three primary reforms in Bailey’s (2008) outline have been honed over time as researchers and interest groups began to study and apply models that included these approaches. Eventually they became popularized in the field under these three headings: multiple measures for placement; models of acceleration such as corequisites; and a specific type of guided pathways called mathematics pathways (Dougherty et al., 2017; Ganga et al., 2018). Each reform progressed through stages that have included an initial theory, a process of development with studied models, and a more widespread implementation across the nation.

Regarding acceleration, the initial reform’s theory and scholarship focused on three successful incipient models of acceleration to provide institutions with examples for how to accelerate at-risk students: the Accelerated Learning Project (ALP) and other corequisite models; fast start models such as the Community College of Denver’s FastStart (CCD) and the City University of New York’s CUNY Start; and general compressed acceleration models, as in the model at Chabot College, the basis for California Acceleration Project (CAP), and the Charles A. Dana Center Mathematics Pathways (Charles A. Dana Center, 2020; Cho et al., 2012; Edgecombe, 2011; Edgecombe et al., 2013a, 2013b; Edgecombe et al., 2014; Hodara & Jaggars, 2014; Jaggars et al., 2014; Jaggars et al., 2015; Jenkins et al., 2010; Rodriguez et al., 2017; Scrivener et al., 2018; Zachry Rutschow & Mayer, 2018; Zachry Rutschow et al., 2019).

In what later became termed corequisites, ALP spearheaded accelerated models in English composition and was studied using more advanced quasi-experimental research designs (Cho et al., 2012; Jenkins et al., 2010). The initial program at the Community College of Baltimore County accepted student volunteers from beneath the cutoff for college-level composition and had them take two 3-credit courses simultaneously from the same instructor: a college-level English composition course and an R/DE co-requisite companion course (i.e., as opposed to the traditional prerequisite R/DE course before college English) (Adams et al., 2009).

Results demonstrated that initial pass rates in the first and second college composition courses were higher in the intervention groups, but weaknesses in ALP’s design and flaws in the research such as the confounding factors of volunteer participants, imputed data, and non-random placement have cast doubts on the generalizability of the outcomes in these studies (Bailey et al., 2016; Cho et al., 2012; Jenkins et al., 2010). Nevertheless, well-funded interest groups such as Complete College America (2012) began promoting the wholesale conversion of R/DE into corequisites shortly after research displayed promising results, pushing nationwide corequisite reform in several state systems (Denley, 2017; Education Commission of the States, 2015; Vandal, 2014, 2015).

Aside from the corequisite model, some states and institutions have experimented with a compressed model of acceleration in which students take assigned prerequisite R/DE courses in one semester prior to enrolling in college-level courses. In one of the earliest examples, the CCD implemented a module-based compressed semester of R/DE courses called FastStart, and research showed modest improvement for students in gateway completion (Edgecombe, 2011; Edgecombe et al., 2013a). Similarly, the CUNY Start model offers all R/DE courses in a low-cost single semester of compressed and accelerated coursework, and research has found slightly higher rates of enrollment and pass rates in subsequent college courses, along with higher graduation due to enrollment in a different program (Scrivener et al., 2018; Weiss et al., 2021).

Other more popular compressed accelerated models, including Chabot College’s model and the Dana Center’s Mathematics Pathways program, have implemented types of accelerated R/DE English and math models with modest results. Some research on similar models have shown that students in compressed R/DE courses who take them slightly outperform students who take traditional prerequisite R/DE coursework (Edgecombe et al., 2014; Jaggars et al., 2015). Additionally, Zachry Rutschow et al. (2019) and Schudde and Meiselman (2019) also found modest increases in success rates for compressed models over traditional R/DE.

However, in spite of researchers’ and interest groups’ conclusions that R/DE compressed acceleration is effective based on a limited number of papers, numerous serious confounds exist in the literature, the whole of which might lead a reasonable policymaker to determine that full implementation of the model might be hasty or premature. More critically, contrary to numerous claims based on theory and practice, none of the research to date has proven to increase two-year college completion rates as a result of any compressed models’ reform efforts. Much more in-depth investigations of the research must take place before solid conclusions can be drawn.

Conceptual Framework: Early Momentum Metrics

The most common and implicit conceptual framework serving as the foundation for nearly all acceleration research is the idea that students who start sooner in higher-level coursework in college, and who complete more courses and credits successfully and more rapidly, will ultimately attain higher completion and graduation rates compared to those who do not begin college as such. Though prior meta-reviews touched on this concept (Scott & Conrad, 1991), the most recent iteration of this conceptual framework was outlined in a paper in which the authors referred to the concept of early momentum (Jenkins & Bailey, 2017). The authors did not label it a theory per se, but they used it as theoretical basis for the implementation and promotion of recent R/DE reforms. They outlined what they view as three important early momentum metrics (EMMs): credit attainment, gateway course pass rates (first-year, college-level courses), and program participation. The authors further argued that colleges should focus on increasing these metrics because doing so will increase graduation rates subsequently.

A later paper expanded on EMMs by coining and recommending the use and application of a new term for this same conceptual framework, key performance indicators (KPIs): “CCRC recommends using a set of early momentum key performance indicators (KPIs)—leading indicators, measurable in one year, of longer term outcomes—to assess the impacts of whole-college reforms” (Jenkins et al., 2018, p. 29). Evidence for this idea is limited, and more is being conducted (Belfield et al., 2019), but the conceptual frameworks of EMMs and KPIs, as well as their proposed use in two-year college reforms, are commonly accepted by scholars and policymakers, and they are utilized as critical theoretical underpinnings for studying and promoting R/DE compressed course reforms. It is the basis upon which nearly all modern community college reform research is founded (e.g., Bailey et al., 2009, 2010, 2015, 2016).

As applied to compressed course programs, there are several means by which this model may theoretically increase early momentum. For instance, an instructor of compressed R/DE students in an accelerated model may treat students differently because they are expected to learn faster. Many students in compressed R/DE models are in effect receiving double the time on task during the week but half the number of weeks. The compressed model’s outcomes may also result in pass rate increases due to a lack of attrition from fewer exit points since two semesters of prerequisite R/DE courses are compacted into one. All of these potential factors, among others, may contribute to students in the compressed model attaining higher initial metrics such as increased pass rates, higher credit attainment, and lower attrition. Therefore, since this conceptual framework postulates that students who are able to begin their college tenure with increased metrics should be retained at higher rates, it could be expected that a compressed model might ultimately demonstrate that the initial increase in outcomes will positively affect more important metrics such as associate degrees, certificates, and transfer rates. The logic of this theoretical chain of events is the foundation of the EMM and KPI conceptual framework.

An Overview of Findings on Recent Compressed Acceleration Models

Compressed course models can be separated into several categories: English only, mathematics only, and combined or more holistic models that integrate both disciplines into a program. Regardless of the type, there are notably few compressed R/DE models that have been studied using more rigorous analyses that include statistical controls or a randomized controlled trial (RCT).

One of the first compressed models researchers studied was a combined R/DE English model in Chabot College in California (Hern, 2010, 2011). Two quantitative analyses showed moderate increases in college-level take and pass rates for program participants (Jaggars et al., 2015; Edgecombe et al., 2014). First, in what is possibly the most positive results from any compressed course model studied, in an analysis that used propensity score matching (PSM), Jaggars et al. (2015) found that program students passed a gatekeeper English course at a rate of 17 percentage points higher than the control group using two different analytic samples that tracked students for 1 year and 3 years respectively. Participants also had just over four more college credits attained after 3 years (p. 16). Another study, Edgecombe et al. (2014), also used PSM to analyze the same Chabot College compressed course dataset but extended the analytic sample time frame. After tracking students for 5 years, the PSM output showed that participants’ college-level pass rates were 17–22 percentage points higher, with nearly 5 more transferable credits earned, 4.5 more college-level credits earned, and no difference graduation rates (p. 35).

A more recent study employed PSM as well to assess various accelerated integrated reading and writing courses in Texas (Paulson & Van Overschelde, 2021). Results demonstrated that the traditional stand-alone and separated R/DE courses were more effective. In fact, the authors found that the PSM results “demonstrated significantly greater outcomes for students who took non-accelerated separate developmental reading and writing courses than for matched students who took an accelerated and integrated course” (abstract). 

In mathematics, both the Dana Center Math Pathway and the Community College of Denver’s FastStart (CCD) programs have been studied using an RCT, statistical regressions, or PSM analyses. For research on FastStart, both Edgecombe et al. (2013a) found that CCD’s participants in the model with the most controls were over 50% more likely to pass the gateway math course after tracking students for 2 years or 6 terms. Models with fewer controls showed a reduction in pass rates for participants of approximately 20%. The raw number of students in the intervention group passing the gateway math course increased about 10 to 15 percentage points (p. 40). Jaggars et al. (2015) found that program participants were 11 percentage points more likely to pass their gateway math course. However, the sample was restricted to 133 in the intervention group compared to 1,222 student in the control group (p. 10). Both studies found slightly increased college-level credits.

Zachry Rutschow et al. (2019) summarized Dana Center Math Pathways results from an RCT conducted at four Texas community colleges. Schudde and Meiselman (2019) and Schudde and Keisler (2019) provided follow-up data from other institutions that implemented the same program with later cohorts. These studies found marginal increases in pass rates for students participating in the program. The most reliable results can be found in the RCT due to the more robust controls as a result of the randomization process, but even after 3 semesters in the RCT, program students had passed college-level math at a rate of only 6.8 percentage points higher (25.3% versus 18.5%) (Zachry Rutschow et al., 2019, p. 57). This positive finding was attenuated by the program group having higher withdrawal rates, and there were virtually no other substantive benefits to the program in terms of higher credits earned, transfer, or completion. Schudde and Meiselman (2019) found that in spite of significantly different subgroups in the program group analytic sample (e.g., much higher proportions of females and white students), after statistical controls, students in the program group were approximately 5 percentage points more likely to complete the college-level math course after being tracked for 2 years (p. 7), along with negligible increases in college-level credits and pass rates for racial subgroups over time.

For combined English and mathematics compressed models, researchers have highlighted a program involving the City University of New York, CUNY Start, but others have also studied various accelerated and semi-compressed models of English and mathematics courses implemented by several CUNY colleges. First, Hodara and Jaggars (2014) used PSM to compare students who took compressed R/DE English and accelerated math to a similar group who did not, and they found slightly positive results for pass rates in both compressed math and English participants (3 and 6 percentage points higher, respectively) (p. 265). This finding was also offset by negative pass rates in those college-level courses, but the authors argued that this was due to a higher number of students being placed into the college-level courses.

Second, Scrivener et al. (2018) and Weiss et al. (2021) studied CUNY Start, a model that compresses all R/DE coursework into one holistic semester prior to students enrolling in college-level coursework. The funding structure for this program allows students to pay only $75 for the entire semester’s coursework (Weiss et al., 2021, p. 6). In the Weiss et al. (2021) RCT analysis, authors found that the program increased credit completion marginally and had a small effect on 3-year graduation rates, largely due to higher rates of participation in the CUNY Accelerated Study in Associate Programs (ASAP), a holistic, effective, and well-funded and program.

Lastly, the California Acceleration Project (CAP) is an all-encompassing name for a project involving various acceleration in California’s community colleges, but current reports on this model is limited to descriptive outcomes on the number of institutions implementing compressed R/DE and some overall numbers of student subgroups involved. These reports lack statistical controls or matched group analyses (Hern & Snell, 2014; Hern et al., 2020).

Confounding Factors and Limitations in the Most Rigorous Research on Compressed Acceleration Models

The corpus of literature supporting the R/DE compressed course model is restricted to a handful of studies involving an even smaller number of programs. The limitations in these studies also contribute to a general lack of firm findings. Edgecombe (2011) recognized this early on in a summary of acceleration research: “While the empirical basis for acceleration is not as strong as is desirable, existing evidence suggests that there are a variety of models of course redesign and mainstreaming that community colleges can employ to enhance student outcomes” (abstract). In the decade after this statement, more research has been produced to support these few models, but the foundation of research is still quite limited and contains severe confounding factors and limitations in the studies’ methodologies that limit their applicability, transferability, or generalizability to other students, institutions, and state systems.

The research design with the greatest chance at establishing causality and thus increase generalizability—the RCT—is limited in the compressed research base to two models, CUNY Start and the Dana Center Mathematics Pathways program. Unfortunately, CUNY Start’s design may not be representative of compressed R/DE courses in general due to its more unique, holistic, and well-funded framework. In other words, the sheer number of variables occurring simultaneously in CUNY Start may not allow a researcher to claim that the compressed component is causing the relatively modest increases in outcomes. Furthermore, as recent researchers concluded, the primary positive result from the model’s RCT, slightly increased graduation, was likely due to later enrollment in CUNY ASAP (Weiss et al., 2021).

The Dana Center Mathematics Pathways program, studied in the other RCT, contains a significant limitation due to the type of intervention as well. As Zachry Rutschow et al. (2019) highlighted, the Dana Center’s RCT combined two interventions simultaneously, both compression and a move away from traditional remedial math curriculum, meaning it involved “changes in both math content and instructional methods in developmental education and college-level courses while also accelerating developmental students’ progress into college-level math” (p. ES-2). Therefore, the relatively modest increase in college-level pass rates (6.8 percentage points after 3 semesters) might have been as a result of a curriculum change from prerequisite algebra to statistics and quantitative reasoning (p. 30).

As corollary evidence for this possibility, a recent statewide change in Tennessee employed a math corequisite model that also entailed a curricular shift away from the more traditional prealgebra coursework. Authors who studied the reform package found that the 15 percentage point increases in gateway math pass rates were “were largely driven by efforts to guide students to take math courses aligned with the requirements for their program rather than placing most students into the algebra-calculus track by default” (Ran & Lin, 2019, abstract). Both compressed models studied with the most rigorous and reliable study design, the RCT, cannot therefore be generalized to the larger population due to the complex nature of the interventions. Compression may not have caused positive outcomes.

The Community College of Denver’s FastStart (CCD) model’s primary confound is the relatively low number of students in the program group (n = 133) (Jaggars et al., 2015, p. 10). Also, Jaggars et al. (2015) revealed that CCD’s “accelerated students were encouraged to take a student success course during or prior to their program semester, and 50% of them did so, as compared with only 20% of their peers” (p. 11). Further confounding the study, faculty participating in FastStart had “one-on-one classroom observations,” “forums for faculty reflection,” and a “dedicated case manager and faculty professional development” (p. 8). Though the PSM analyses Jaggars et al. used controlled for these potential confounds, this statistical technique only controls for student characteristics and is limited even then (King & Nielsen, 2019). The low number of students and the special design of FastStart are perhaps the most troublesome confounding factors limiting its generalizability in terms of a compressed model.

Another serious confounding factor contained in most other research on compressed R/DE models, including those with PSM analyses, is the mismatched time frames of the two groups being studied. As noted earlier, perhaps the most positive outcome from a compressed course model boasted a 17–22 percentage point increase in pass rates in the gatekeeper English at Chabot College (Edgecombe et al., 2014; Jaggars et al., 2015). The problem is that students in the compressed course models had more opportunities to complete the college-level English course because they had the chance to enroll in those courses up to one or two semesters earlier.

In the more rigorous studies on compressed models (Edgecombe et al., 2014; Hodara & Jaggars, 2014; Jaggars et al., 2015), a significant confound occurs when researchers analyze the same time frame for both analytic groups, though the stand-alone traditional remedial course sequence may be two semesters and the compressed course one. Two-semester sequences create “exit points,” meaning chances that students will not return to college (i.e., attrition). Attrition rates are stubbornly consistent at two-year public colleges: On average, about 25% of students do not return the second semester and 50% do not return the second year (National Student Clearinghouse Research Center, 2015). Study designs that do not adjust for this will always find positive outcomes for accelerated models since there are more exit points in traditional R/DE.

For the compressed course model at Chabot College, this time frame analysis discrepancy meant that students in the two-semester, stand-alone prerequisite R/DE course data had only 2 years in which to enroll in and complete the college-level English course, whereas students in the compressed version had 2.5 years. The extra half year, in addition to other confounding factors, may have resulted in the intervention groups’ higher gateway completion rates of 17–22 percentage points (Edgecombe et al., 2014; Jaggars et al., 2015).

Another corollary study’s methodology offers evidence that this time frame discrepancy could have been analyzed using a differential time frame to account for the difference in opportunity. Noble and Sawyer (2013) compared R/DE graduation rates to nonremedial students, but shifted the time frame for R/DE students, concluding that “developmental students typically completed a Bachelor’s degree in six years at a rate similar to or higher than that of non-developmental students in five years” (p. ii).

Other methodological limitations confound the research on Chabot College. For example, placement decisions may have skewed results in favor of the program group because first, “students placed themselves into one of the two options” (Jaggars et al., 2015, p. 9); and second, advisors who coached students may have had an impact on the quality of the students in the intervention groups. Additionally, Jaggars et al. noted that “faculty believed that accelerated students tended to be more motivated” (p. 9). Finally, the original compressed course was enveloped in a learning community model, and faculty involved in the redesign were also highly motivated and participated in extensive professional development (Edgecombe et al., 2014).

Is the Evidence on Compressed Course Models Robust Enough to Warrant the Wholesale Elimination of Prerequisite Stand-Alone Remediation?

It is critical to note that in none of the research on R/DE compressed course frameworks exists evidence for increased completion at two- or four-year institutions. At best, the various models studied have resulted in modest and temporary increases in first-year gateway pass rates and college-level credits. With a limited number of studies carried out on over 2 decades of evidence thus far, R/DE compressed course model results fade over time and do not show increased graduation metrics at community colleges, higher transfer rates, or improved university completion, as both the early momentum metrics and key performance indicators conceptual frameworks theorize should have occurred. It is also important to note that the sole reason for implementing the reform of acceleration, which began with the seminal papers from the CCRC (Bailey et al., 2009, 2010), was to rise to and meet the challenge of the American Graduation Initiative of 2009 proposed by President Obama (Bailey & Cho, 2010). Compressed course models and other acceleration models have failed to deliver that promise.

In spite of the lack of success with increasing graduation rates, a few proponents of recent postsecondary reforms are calling for the elimination of prerequisite stand-alone R/DE based on various arguments ranging from its perceived ineffectiveness to assertions of its role in systemic racism (Complete College America, 2012, 2021; Hern et al., 2020; Logue, 2021; Vandal, 2014, 2015). Because the scope and depth of research on the R/DE compressed course model is limited to two RCTs (one of which is a holistic model that may not technically be categorized as a compressed model, CCD’s FastStart), and to a handful of studies using PSM statistical analyses with flawed methodologies and potentially severe limitations, in addition to a few descriptive reports as in CAP reports, it is difficult to conclude that its corpus of literature supports this.


Remarkably, experts who have been involved in reforms at the national level, even the very scholars involved with studies on compressed and other accelerated models, have concluded that this particular reform’s efficacy is decidedly limited in its ability to improve the most important outcomes for students at community colleges. Jaggars and Bickerstaff (2018), both lead researchers at CCRC, in a chapter on R/DE in the 2018 book Higher Education: Handbook of Theory and Research, referred to the most popular reforms of the past decade and concluded,

Research suggests that the most popular reform models (including multiple measures assessment and placement, math pathways, and the co-requisite approach) will indeed improve students’ rate of success in college-level math and English, but they are unlikely to substantially improve graduation rates. (p. 496)

Furthermore, core CCRC researchers Bailey et al. (2013), in response to criticism of the CCRC’s seminal research (Bailey et al., 2009, 2010), stated unequivocally, “We do not advocate—nor do we believe that the results of our research support—the elimination of developmental education, the placing of all students into college courses, or the wholesale conversion of developmental education into a co-requisite model” (p. 2).

These profound statements from the very proponents and scholars involved in most of the rigorous research on acceleration present a challenge for policymakers and practitioners who wish to replace R/DE in its entirety with various acceleration models. Considering the fact that two National Center for Education Statistics datasets—2003 and 2011—have shown that R/DE two-year college graduation rates are nearly identical to nonremedial rates (Chen, 2016; Chen et al., 2020), in addition to other research that has shown traditional stand-alone R/DE is effective (Attewell et al., 2006; Bahr, 2008, 2010; Boatman & Long, 2010; Paulson & Van Overschelde, 2021; Sanabria et al., 2020; Saw, 2019), these data, combined with key researchers’ recent admissions, should give pause to reform advocates who believe compressed R/DE is a silver bullet for low graduation rates.



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