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Thank you thank you from the bottom of my heart. I was told I had a math deficiency when I was in kindergarten. Your app honestly makes me excited for the next chapter haha! However, unknown confounders may influence selection into DCMP and the outcomes. We assess the sensitivity of our estimates to potential unobserved confounders by simulating an unobserved confounder on program assignment and outcome.

Following a procedure outlined by Ichino, Mealli, and Nannicini , we estimate whether the findings are robust to the inclusion of a simulated unobserved binary covariate that relates to both DCMP assignment and the outcome. We include a greater description of this method and our results in Appendix B online. Table 2 presents descriptive statistics of our treatment and control groups.

To facilitate exploration of differences in the subgroups of interest and whether PSM reduced differences between treatment and control groups, the table allows for the comparison of covariate means and standard deviations before and after matching. Before matching, the treatment group showed marked differences in observable characteristics from both control groups we describe predictors of placement into DCMP in the Results section.

After matching, the differences between the treatment and two-and-three semester control group were largely diminished.

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After matching, observable differences between DCMP students and those assigned to the one-course traditional dev-ed sequence virtually disappeared; the only remaining difference was that control group students were slightly more likely to have filed FAFSAs. The remaining covariate imbalance after matching, though minimal, bolsters support for using Ho et al.

Table 2 also shows the treatment and control group means and standard deviations of the outcomes before and after matching. The results suggest that students in DCMP were less likely to enroll in and pass college algebra than their peers in either control group but more likely to enroll in and pass nonalgebra college math and enroll in any college-level math overall , particularly by the end of Year 3.

However, as we present below, the patterns from the doubly robust estimation strategy are more conservative, likely because they allow us to further adjust for covariates in the model. Table 3 presents the results from probit models predicting participation in DCMP. For each set of analyses, we present the coefficients log-odds and, for ease of interpretation, the marginal effects, along with standard errors.

The results provide insight into the factors that predict placement into DCMP for students with remedial needs after controlling for other student background and institutional characteristics. Enrollment in dev-ed writing increases the probability of placement into DCMP by 2. There are no significant relationships between other dev-ed coursework and DCMP placement when using the one-semester control group.

For each outcome, the table shows the average marginal effect—the predicted change in probability for students in DCMP compared with each control group, while holding all covariates at their mean—and the control group mean for the outcome. We first describe the results for the two- or three-semester sequence control group, followed by the one-semester sequence control group.

Table 4 , Column 1, presents our preferred results, comparing DCMP students with those in a two- or three-semester traditional developmental math sequence. We anticipated a positive relationship between the treatment and college math enrollment in the next semester. However, after doubly controlling for student and institutional characteristics, there was little evidence of a relationship between DCMP status and college math course enrollment measures at the end of Year 1. By the end of the next term, DCMP students also took, on average, 1.

Two years later, in spring , DCMP students surpassed the two- to three-semester sequence control group in college math enrollment and completion. Enrolling in and passing nonalgebra college math coursework explained the majority of the increase in college math completion—participation in DCMP was associated with a At the same time, participating in DCMP has a small negative relationship with taking and passing college algebra, lowering the probability of each by about 1 percentage point.

Although students in the treatment group did not appear more likely to earn an associate degree by the end of 3 years of follow up, they did experience a significant positive boost in the number of college-level credits earned. Our second control group includes students in the one-semester traditional dev-ed math sequence see Table 4 , Column 2. Although they tended to be more academically prepared than DCMP students, they were on a similar academic trajectory in terms of sequence. Both groups should have been eligible to complete their dev-ed requirement in one term, although DCMP explicitly encourages immediate enrollment in college math.

By the end of the academic year, DCMP participants were more likely to have enrolled in and passed a college math course than their peers in the one-course traditional sequence, though participating in DCMP appeared to have a particularly strong association with nonalgebra college math enrollment. DCMP participation also has a negative, though smaller, association with taking and passing college algebra. Of course, if college math completion is an important college milestone, as suggested by the community college literature, enrolling in and passing any type of college math moves students closer to their degree attainment goals.

Compared with students in the one-course sequence of traditional dev-ed math, participating in DCMP was associated with a We performed a number of robustness checks to examine whether our results are sensitive to additional observable variables and to a potential unobserved confounder.

Appendix A describes and presents results from three alternative model specifications, which suggest our main results are largely robust to alternative model specifications. Appendix B presents an overview, along with results and implications, of a sensitivity analysis for unobserved confounders Ichino et al. Several Year 3 results appear robust to even a confounder with a very strong relationship with assignment to treatment or the outcome.

To make the goals of DCMP feasible, dev-ed students—who constitute a substantial portion of community college enrollees—must gain momentum in their pathway by passing dev-ed math and enrolling in a gateway math course. We examined the relationship between DCMP and measures of college milestones, comparing DCMP students with those enrolled in a traditional dev-ed sequence.

Second, we estimated the relationship between DCMP participation and important outcomes in Year 1, like passing the current dev-ed math course, enrolling in and passing college-level math, and the number of college-level credits accumulated, and similar outcomes plus associate degree attainment by the end of Year 3. Unsurprisingly, the effects are the largest when comparing DCMP students with those in longer dev-ed sequences requiring two to three courses.

By the end of Year 3, participating in the DCMP model is positively and significantly associated with taking and passing college-level math.

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Early milestones like increased probabilities of passing dev-ed math and persistence appear to have a domino effect on other important milestones, such as number of degree-bearing credits. While we still see a positive relationship between DCMP participation and accumulation of college credits by the end of Year 3, we do not observe a relationship between DCMP status and associate degree attainment.

Only 4. We highlight patterns of effects but are unable to untangle the mechanisms driving them. There are several potential consequences for students, though we cannot yet observe all of them with only 3 years of follow-up. First, we might see an increase in associate degree attainment, as more students get past a key barrier to major requirements, passing college math.

Second, students in DCMP may be less likely to pursue algebra-intensive majors because it would require taking an additional entry-level math course, college algebra, for those who took a nonalgebra course. Furthermore, the benefit of increasing the number of students making progress toward a degree likely outweighs the potential loss of time and money for a small number of students switching to an algebra-intensive major.

Of course, we can only speculate at this point, and we hope to see additional research on the implications of nonalgebra math coursework on degree attainment and major selection.

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This study suggests that an accelerated curriculum coupled with an emphasis on yearlong math experiences may be able to help community college students reach important milestones in their college careers, especially compared to students placed into a traditional dev-ed sequence of two or more courses. One area for continued exploration in the model is further improving early college-level math enrollment during the 1st year , since the gains over the control groups appear to occur after that point in this sample.

The results have important implications for students across the nation. The DCMP model continues to expand across the country as more colleges—in both the 2- and 4-year sectors—aim to efficiently prepare students for college-ready coursework and reform college math to align with the skillsets demanded by careers. Although we focus on DCMP, it is one of many reforms to dev-ed math.

As institutions invest their limited resources in these interventions, evaluation of reforms and programmatic changes is crucial. The approach we take here—examining the relationship between an alternative dev-ed pathway and college milestones by comparing its effects with those of traditional dev-ed pathways—offers valuable information necessary for assessing reforms to dev-ed. As additional dev-ed reforms are implemented across the country, evaluation can illuminate whether and which reform models are effective. The content is the sole responsibility of the authors and does not represent the official views of the National Institutes of Health, the Texas Association of Community Colleges, or the State of Texas.

The reference list includes references from the main text and the online appendixes. Her research examines the impact of educational policies and practices on college student outcomes, with a particular interest in broad-access postsecondary institutions. Her work focuses on public finance, health, and education outcomes, with a focus on policy outcomes.

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