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Failing at Remediation? College Remedial Coursetaking, Failure and Long-Term Student Outcomes

Abstract

Colleges offer remedial coursework to help students enrolling in post-secondary education who are not adequately prepared to succeed in college-level courses. Despite the prevalence of remediation, previous research presents contradictory findings regarding its short- and long-term effects. This paper uses a doubly robust inverse probability weighting strategy to examine whether the degree completion and wage outcomes associated with remedial education vary by passing or failing remedial coursework. Using the NLSY Postsecondary Transcript-1997 data, we find that almost 30% of remedial course takers fail a remedial course. Students who took and passed their remedial coursework at both 2-year and 4-year colleges were more likely to graduate from college than similar students who did not take remediation. For both 2-year and 4-year college entrants, students who failed remedial coursework were less likely to obtain a bachelor’s degree and, among degree receivers, took longer to graduate. Students who entered 2-year or 4-year colleges and who failed remedial coursework earned lower wages over time compared to similar students who never took remediation. Among 4-year college entrants, these wage differences seem to be explained completely by degree completion. However, wage differences for 2-year college entrants still remain after accounting for degree receipt. Our findings thus suggest that while many students may benefit from remedial education, a substantial number of students struggle with remedial coursework and fail to realize the intended benefits.

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Notes

  1. While prior studies have examined remedial course by subject matter (e.g. English versus math), our paper focuses on the 2-year and 2-year contrast. Given issues of statistical power, this precludes separating our sample by course subject.

  2. We use averages from 5 years of post-college wage data where possible, but in cases where this is not possible, we use averages of three or 4 years of data. For example, if a student’s last term was in 2006 or earlier, we use the 5 years from 2007 through 2011 to calculate their average hourly wages. If a student’s last term was in 2007, we use the 4 years from 2008 through 2011. For students whose last term was in 2008, we use the 3 years from 2009 through 2011. For these analyses, we do not use respondents with fewer than 3 years of earnings, which means that our wage analyses exclude students whose last reported college term was in 2009 or later. Supplemental analyses that include zero wages show similar findings.

  3. Note that the subgroups for students who enter a 2-year or 4-year college does not add up to the total sample of students in the study (n = 3646). Table 1 provides descriptive statistics for the study sample prior to multiple imputation, and students who were missing information for primary institution type were not included in the descriptive statistics but are included in the analytic models after multiple imputation.

  4. The comparison in Model 1 (comparing students who took remediation to students who have never taken remediation) most closely corresponds to previous research.

  5. Appendix Table 7 provides information about a fourth comparison that is potentially of interest: those who pass and fail remediation. In addition to the other covariates in Tables 4 and 5, Table 7 also controls for the number of remedial courses taken to ensure that these differences are not driven by differences in the number of remedial courses taken.

  6. Supplemental analyses examined remedial coursetaking and failure to predict whether 2-year college entrants earned an associate’s degree to ensure that the patterns are similar to those we report for bachelor’s degree completion. While the magnitude of the differences is somewhat smaller, the overall pattern is the same.

  7. In addition to the covariates for the Panels A-C, all three models in Panel D also control for both associate’s and bachelor’s degree completion.

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Funding

Research reported in this publication was supported by the NLSY 1997 Postsecondary Research Network funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under Award Number 5R01HD061551-02 and by the Population Research Center at the University of Texas at Austin, which receives core support from the National Institute of Child Health and Human Development under the Award Number 5 R24 HD042849. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Appendix

Appendix

Doubly Robust Inverse Probability Weighting

In the first step of doubly robust IPW, we estimate treatment propensities (P) for each student. Using covariates discussed in the paper, a propensity score is estimated for each student. An individual variable does not have to be a statistically significant predictor of treatment to be included in the propensity model since the objective is for students in the treated and control categories to be balanced on the covariates. The propensity scores are estimated using a multivariate logistic regression model predicting the probability of a student receiving the treatment (i.e., not taking a remedial course, taking and passing a remedial course, and taking and failing a remedial course). All covariates discussed in the paper were included in the multiple logistic regression equation to predict the probability of treatment:

$$Pr\left( {Remedial\,Group} \right)_{i} = { }\alpha_{i} + \beta_{k} {\varvec{X}}_{ki} + \user2{ }\varepsilon_{i}$$
(2)

Equation (2) predicts the probability of student i being in one of three groups: never took a remedial course, took and passed remedial coursework, and took and failed a remedial course. \({{\varvec{X}}}_{ki}\) is a vector of control variables.

We estimate each student’s predicted probability of being in each of the remedial groups in Eq. (2), and use these probabilities to create inverse probability weights. For each treatment category t (never took remediation, took and passed remediation, or took and failed remediation), we define our inverse probability weight as:

$$W = 1/\hat{P}_{t}$$
(3)

where \({\widehat{P}}_{t}\) is the predicted probability that a student received the treatment that he or she received.

For doubly robust IPW estimators, the same covariates used to estimate the probability weights for Eq. (2) are also included as controls in a linear probability model predicting our degree and wage outcomes. We estimate two sets of these models; the first set predicts bachelor’s degree completion in any field and the second set predicts the average wage outcomes. Thus, our first model predicts whether students complete a bachelor’s degree as a function of being in one of the three remedial groups: never took remediation, took and passed remediation, and took and failed remediation:

$$\Pr \left( {Bachelors} \right)_{i} = { }\alpha_{i} + { }\beta_{1} Remedial_{i} + { }\beta_{k} {\varvec{X}}_{ki} + \user2{ }\varepsilon_{i}$$
(4)

where Remediali is a dummy variable equal to one if a student ever took remediation and zero otherwise and Xi is a vector of background controls for doubly robust estimates. To estimate the relationship between failing remedial coursework and our other outcomes, we estimate additional models that take the same general form as (4), but instead of Remediali, we use a dummy variable equal to one if a student took and passed their remedial coursework and zero otherwise, or alternatively a dummy variable equal to one if a student took and failed their remedial coursework and zero otherwise. The error term, \({\varepsilon }_{i}\) captures characteristics not accounted in the model that influence the outcome variable. We estimate these models separately for students who entered a 2-year and 4-year college and we use similar models to predict average post-college wages for the latest 5 years (2007 through 2011) (Tables

Table 6 Linear probability models (LPM) predicting failure among remedial course takers

6,

Table 7 Doubly robust estimates of outcomes associated with remedial course failure relative to passing remedial coursework

7).

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Sanabria, T., Penner, A. & Domina, T. Failing at Remediation? College Remedial Coursetaking, Failure and Long-Term Student Outcomes. Res High Educ 61, 459–484 (2020). https://doi.org/10.1007/s11162-020-09590-z

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Keywords

  • Remediation
  • Higher education
  • Degree completion
  • Wages
  • Inverse probability weighting