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Examining First-Time-in-College Student Enrollment Patterns and Performance in Select Courses Following Developmental Education Redesign in Florida


by Toby J Park, Chenoa S. Woods, David A. Tandberg, Keith Richard, Oguzcan Cig, Shouping Hu & Tamara Bertrand Jones - October 26, 2016

In this research note, we compare enrollment and passing rates of developmental courses and gateway (introductory college-level) courses for entering cohorts of first-time-in-college (FTIC) students before and after a major developmental education reform was implemented in Florida. Overall, we find that more FTIC students are taking and passing gateway courses in the post-reform era than before.

The year 2014 may well have been a watershed year in developmental education (DE) policy in Florida and beyond. In most states, first-time-in-college (FTIC) students identified as academically underprepared via placement tests are required to take developmental, or remedial, courses prior to enrolling in college-level (credit-bearing) coursework. Once seen as a necessary way to provide academic support to underprepared FTIC students, DE has come under scrutiny in recent years due to the large number of FTIC students identified as needing DE, its high cost, questions over the ability of placement tests to accurately assign FTIC students to courses, and disturbingly low college completion rates for FTIC students in DE courses. In response, a number of states have been exploring policies that allow for more flexibility in course placement and/or provide other ways to help accelerate FTIC students through DE, such as redesigned DE courses. In Florida, however, policymakers took a more drastic approach.


Through Senate Bill 1720 (2013), which was originally passed in 2013 and went into effect in 2014, the Florida legislature made DE optional for many FTIC students enrolling in the 28 Florida colleges in the Florida College System (FCS, formerly the Florida Community College System), and mandated that DE be offered through a variety of delivery modalities. Under the new legislation, FTIC students who entered 9th grade in a Florida public school in the 2003-04 school year and beyond are considered college ready, provided they earned a standard high school diploma, and are exempt from taking DE. As such, the law prohibits requiring placement testing or DE courses for these FTIC students. The law also exempts active duty members of the military from placement testing and developmental coursework. Further, the second part of the law requires that colleges offer instruction in ways that are thought to move FTIC students quickly into college credit courses, including co-requisite, accelerated, modularized, and contextualized delivery modes, as well as enhanced advising and other forms of supplemental support.


Recent estimates indicate that over half of all students across the country seeking an associate’s degree require at least one developmental course (Bailey, Jeong, & Cho, 2010; Complete College America, 2012), and the National Center for Education Statistics, in 2010, found that 1.7 million beginning students required at least one DE course each year. At the same time, over $3 billion are spent each year providing DE (Alliance for Excellent Education, 2011). In Florida alone, 70% of FTIC community college students are enrolled in at least one developmental course, which cost $154 million during the 2009-10 academic year (Underhill, 2013). However, only a small fraction of students who have taken DE actually go on to earn college-level credentials (Complete College America, 2012). In addition, studies have shown that approximately one-fourth of students assessed via placement tests are mis-assigned, mainly by being unnecessarily placed into DE courses, which could delay students’ educational progress (Scott-Clayton, Crosta, & Belfield, 2014). Thus, many argue that the Florida DE reform could improve overall student success rates by making DE optional and altering the structure of DE courses, while others have concerns over the extent to which students will be able to make accurate enrollment decisions that best prepare them for academic success. Our earlier work has shown that the law has changed both DE programs and practices across the FCS institutions (Park, Tandberg, Hu, & Hankerson, 2016) and student course choices (Hu et al., 2016; Park et al., 2016); now, in this paper, we utilize student records to explore student outcomes following the legislation.


In this research note, we focus on the first part of the Florida DE reform: how, for many FTIC students in 2014, DE was optional regardless of prior academic preparation. As FTIC students can now bypass DE, we compared enrollment and passing rates of DE courses and gateway courses for entering cohorts of FTIC students before and after the reform was implemented. Studying changes in these metrics provides insight into how the policy is related to key measures of student success. This is important as the issue of DE reform is not a phenomenon unique to Florida. State legislatures across the country are struggling to find ways to promote student attainment of educational credentials. Our findings, then, may be useful for both state policy makers and college leaders when they consider DE reforms.


RESEARCH DESIGN


To understand whether developmental education reform has improved FTIC student success in FCS colleges, we compared enrollment rates in both developmental education and gateway courses before and after the reform, as well as pass rates in both kinds of courses. As the legislation only gave exempt FTIC students the option to exercise additional choice in their course enrollment options, we compared only those FTIC students identified as exempt in 2014 to those FTIC students in 2013 who would have likely been exempt had the legislation been implemented in 2013. Data provided to us from the Florida Department of Education allowed us to make this comparison and provides additional indicators for student background characteristics and measures of prior high school academic preparation.


ANALYTIC SAMPLE CONSTRUCTION


We utilized data from the Florida Education Data Warehouse (FL-EDW), which provides longitudinal data on individual students from public K-12 schools as well as the 28 institutions in the FCS. Our data consist of two cohorts of first time in college (FTIC) students who initially enrolled in the FCS in fall 2013 or fall 2014.  We began with 68,440 FTIC students for fall 2013 and 68,315 FTIC students for fall 2014. In order to compare only those FTIC students with the option to bypass DE (exempt FTIC students) in fall 2014 to FTIC students who would have been exempt in fall 2013 had the legislation been implemented earlier, we turned to high school transcript data.


For fall 2013, likely exempt FTIC students were coded as those FTIC students who graduated from a Florida public high school in the spring of 2006 or later. The legislation defined exempt status in 2014 as any student who entered ninth grade in 2003-2004 and graduated from a Florida public school. For fall 2013, we assumed on-time graduation from high school and backed up the ninth grade year to 2002-2003, thus arriving at our spring 2006 or later high school graduation as our simulated exempt group. For fall 2014, a variable indicating “exempt” status was part of the FCS data; we used all FTIC students that were not coded as “exempt-no” by the FCS data. Thus, our analytic sample consisted of 44,240 FTIC students for fall 2013 and 46,123 FTIC students for fall 2014, or roughly 64.6 percent and 67.5 percent of the total FTIC cohort for fall 2013 and fall 2014, respectively.


DEPENDENT VARIABLES


We constructed dichotomous indicators of whether FTIC students (a) enrolled in DE courses (modeling mathematics, reading, and writing separately), (b) passed DE courses with a C- or better (modeling the three DE subjects separately), (c) enrolled in gateway courses (modeling ENC 1101: English Composition I and MAT 1033: Intermediate Algebra separately1), and (d) passed gateway courses (modeling the two gateway courses separately). Descriptive statistics for these enrollment and pass rates, disaggregated by cohort, are provided in Table 1. With regard to gateway courses in particular, we note that the FCS has common course numbering across all institutions and that the gateway English course across the system is English Composition I (ENC 1101), and the traditional gateway mathematics course is Intermediate Algebra (MAT1033). There are, however, additional mathematics and statistics courses that can fulfill the gateway requirement, depending on a student’s intended major. Investigating outcomes in these courses will be covered in a future analysis.


Table 1. Descriptive Statistics, by Cohort

    
 

Fall 2013

Fall 2014

 

n

%

n

%

Student Characteristics (S)

 

 

 

 

Race/Ethnicity

    

  Black

9,454

21.4

10,023

21.7

  Hispanic

14,651

33.1

16,080

34.9

  White

17,448

39.4

17,427

37.8

  Other

2,687

6.1

2,593

5.6

Female

22,795

51.5

24,037

52.1

Free or Reduced Lunch Eligibility

20,980

47.4

23,571

51.1

High School Academic Preparation (HS)

    

Took Algebra 2

36,559

82.6

36,610

79.4

Took Trigonometry

2,050

4.6

2,339

5.1

Took Other Advanced Math

10,148

22.9

9,429

20.4

Earned AP English credit

6,263

14.2

5,641

12.2

Earned Honors English credit

24,571

55.5

24,616

53.4

N

44,240

 

46,123

 

 


INDEPENDENT VARIABLES


Our independent variable of interest was a simple indicator for whether a student was in the fall 2014 cohort. To account for any differences in the composition of the two cohorts, we controlled for a number of student background characteristics (S) and high school academic preparation (HS) factors that have also been shown to affect educational outcomes (Coleman, Hoffer, & Kilgore, 1982; Hearn, 1988; Kuh, Kinzie, Buckley, Bridges, & Hayek, 2007; Sewell, Haller, & Ohlendorf, 1970). In terms of student background characteristics, we included indicators for race/ethnicity, gender, and economic capacity. White was used as the reference category for race/ethnicity with indicators representing FTIC students from Black, Hispanic, and other racial/ethnic backgrounds. We collapsed Asian American, Pacific Islander, multiracial, and unknown races into the “other” category due to the relatively small representation of these groups in our analytic sample. The reference category for gender was male and we used free/reduced lunch eligibility in high school as a proxy for economic capacity. High school academic preparation includes the following dummy variables: whether a student earned credit in algebra 2, trigonometry, another advanced math course, advanced placement English, and/or honors English. Descriptive statistics for these student background characteristics and measures of high school academic preparation, disaggregated by cohort, are provided in Table 2.


Table 2. Enrollment Rates and Passing Rates

 

 

 

 

 

Fall 2013

Fall 2014

 

n

%

n

%

Developmental Education Enrollment and Success

 

 

 

 

Took Developmental Math

16,220

36.7

8,252

17.9

Took Developmental Reading

8,504

19.2

3,233

7.0

Took Developmental Writing

7,230

16.3

4,222

9.2

Passed Developmental Math

9,671

59.6

4,764

57.7

Passed Developmental Reading

6,733

79.2

2,392

74.0

Passed Developmental Writing

5,545

76.7

3,072

72.8

     

Gateway Education Enrollment and Success

    

Took Gateway ENC 1101

23,685

53.5

29,811

64.6

Took Gateway MAT 1033

10,020

22.7

16,614

36.0

Passed Gateway ENC 1101

17,781

75.1

20,841

69.9

Passed Gateway MAT 1033

6,277

62.6

8,444

50.8

 


MODEL SPECIFICATION


Across our different outcome measures, we utilized the following logistic regression equation:


logit(yijt) = α + β(2014t) + θ(Sijt) + γ(HSijt) + δj  


Under this specification, yijt represents one of outcome measures (taking/passing DE and gateway courses, by subject) for student i, at FCS institution j, in cohort t. Our independent variable is the dichotomous indicator of being in the post-implementation cohort (2014); S and HS are vectors of student characteristics and high school academic preparation indicators as outlined above. Finally, we included FCS institutional fixed effects (δj) to account for unobserved heterogeneity across institutions.


RESULTS


We present our results in several sections, one for each of our outcomes. For ease of interpretation, we present our results in Table 3 in terms of changes in predicted probability of our outcome measure in the case of a statistically significant difference between fall 2013 and fall 2014. Full model results, including point estimates, are available from the authors.


Table 3. Predicted Probabilities of Enrollment and Passing Rates

 

 

 

 

 

 

Enrollment Rates

 

Passing Rates

 

 

2013

2014

Difference

 

2013

2014

Difference

Developmental Education (DE)

       
 

Mathematics

34.4%

15.2%

-19.2***

 

60.1%

58.1%

-1.9

 

Reading

16.2%

5.5%

-10.8***

 

80.4%

75.4%

-5.1***

 

Writing

12.8%

6.7%

-6.0***

 

78.0%

74.1%

-3.9***

Gateway

       
 

ENC1101

53.7%

65.6%

11.9***

 

76.0%

70.8%

-5.2***

 

MAT 1033

21.9%

35.4%

13.5***

 

63.3%

50.8%

-12.5***

Notes: Predicted probabilities based on models that include the full array of student characteristics (S) and high school academic preparation (HS). Statistically significant differences are indicated by *p<.05, **p<0.01, ***p<0.001.

 


DEVELOPMENT EDUCATION COURSE ENROLLMENT RATES


The predicted probability of enrolling in DE mathematics, reading, and writing all decreased in 2014: 19.2 percentage points for mathematics, 10.7 percentage points for reading, and 6.1 percentage points for writing.


DEVELOPMENT EDUCATION COURSE-BASED PASSING RATES


Among FTIC students enrolled in developmental courses, the predicted probability of successfully passing DE decreased slightly for reading (5.1 percentage points) and writing (3.9 percentage points), while the difference for DE math was not statistically significant.


GATEWAY COURSE ENROLLMENT RATES


The predicted probability for enrolling in gateway courses increased for both ENC 1101 (11.9 percentage points) and MAT 1033 (13.5 percentage points).


GATEWAY COURSE-BASED PASSING RATES


Among FTIC students enrolled, the predicted probability for gateway pass rates, however, decreased in both ENC 1101 (5.2 percentage points) and MAT 1033 (12.5 percentage points).


GATEWAY COHORT-BASED PASSING RATES


Another way to examine the gateway course passing rates is to examine the same outcome, but for all FTIC students in each cohort, not just the FTIC students enrolled in gateway courses. This cohort-based gateway completion rate can tell us whether, overall, more FTIC students are passing gateway courses following the reform. In terms of cohort-based gateway passing rates, both subjects show increases: 5.3 percentage points for ENC 1101 and 3.7 percentage points for MAT 1033.


Table 4. Predicted Probabilities of Gateway Cohort-Based Passing Rates

 

  

2013

2014

Difference

 

ENC 1101

39.8%

44.6%

5.4***

 

MAT 1033

12.8%

16.5%

3.8***

Notes: Predicted probabilities based on models that include the full array of student characteristics (S) and high school academic preparation (HS). Statistically significant differences are indicated by: *p<.05, **p<0.01, ***p<0.001.


SUMMARY AND DISCUSSION


Given the option to bypass DE regardless of academic preparation, many FTIC students choose to do so, often in favor of enrolling directly in gateway courses. These changes in enrollment pathways are statistically significant, even after accounting for student characteristics and academic preparation. At the same time, there is a mixed message regarding overall FTIC student success. While DE course-based passing rates declined slightly, the declines in gateway course-based pass rates are larger, and both are statistically significant. However, when examining cohort-based gateway pass rates, a different story emerges: more FTIC students are taking and passing gateway courses in the post-reform era than before.  Put differently, even though course-based passing rates have declined, there are enough FTIC students electing to take gateway courses and being successful that the overall cohort-based pass rates have increased. Further, while not presented here, descriptive statistics disaggregated by FTIC student characteristics largely show a similar story: declines in DE course enrollment, increases in gateway course enrollment, declines in course-based passing rates for gateway courses, yet increases in cohort-based passing rates (see www.centerforpostsecondarysuccess.org for more details).


These findings have several possible explanations and implications. The fact that DE passing rates have declined slightly could be a result of FTIC students who recognize that they need DE in order to be successful, electing to take DE even when it is optional. Thus, the FTIC students in DE courses in 2014 are those who are likely less academically prepared on measures beyond what our data are able to capture. As such, it is not surprising that the DE course-based pass rates have declined slightly. In fact, one could argue that it is surprising that DE pass rates have not declined more. At the same time, the fact that gateway course passing rates have declined raises questions over whether FTIC students are able to self-place into courses accurately. In other words, if FTIC students taking gateway courses in fall 2014 are those who see themselves as academically prepared for such course work, we should expect to see no difference in passing rates. Instead, we see sharp declines, even after controlling for the measures of academic preparation that our data provide. This raises concerns that others have voiced over whether FTIC students are able to accurately assess their ability and take courses where they will be successful without preparatory DE courses. Overall, however, it is encouraging that more FTIC students, on the whole, are taking and passing gateway courses following the DE reform, giving credence to the notion that by giving FTIC students the option to self-place, we will see more cohort-based success in gateway courses.


In disaggregating the results by subject area, a more nuanced story appears. More FTIC students are bypassing developmental math than reading or writing, yet the changes in pass rates across all developmental courses are fairly similar. At the same time, increases in gateway course enrollments are slightly higher for math, but the decrease in pass rates for gateway math is twice the size of the decrease in gateway English. One possible explanation for this is that FTIC students are less able to gauge their ability in math than in English and are rushing into math gateway courses unprepared, only to fail the gateway course. This could also explain why the net gain in the total percentage of the FTIC students passing gateway math, while positive, is still smaller than gateway English. In future analyses, we intend to disaggregate the results further, by student demographic characteristics and by ability level, in order to determine if the reform is differentially affecting certain student subgroups. Descriptive statistics for course enrollment and passing rates for different student groups can be found online at www.centerforpostsecondarysuccess.org. In general, we find declines in enrollment in DE courses, increases in gateway course enrollment, declines in passing rates for gateway courses, yet increases in cohort-based passing rates for all student subgroups.


Despite having access to nearly 100,000 student records, these analyses are limited to the availability of specific data points. One thing we do not know is the advice given to FTIC students from their advisor on which courses to take. We plan to conduct future analyses using data from specific institutions in the FCS that collect these data in order to provide a more complete picture of the effect of the policy. In addition, we are also actively conducting site visits at a number of FCS institutions where we interview administrators, faculty, advisors, and FTIC students to better understand how FTIC students make decisions in an environment of increased choice. Understanding the mechanism behind student choice, the why a student bypasses DE, is paramount to a full analysis of the reform. Finally, future analyses are planned to explore the second part of the law, offering DE through a variety of instructional strategies, and how this is related to student success. The Florida legislature has drastically changed how DE is offered and for whom it is required. While early analyses provide a cautiously optimistic outlook, only through additional studies will we be able to more fully understand the impact of making DE optional.


For more information about our overall project investigating the Florida DE Reform, please visit the authors’ website: www.centerforpostsecondarysuccess.org.


Note


1. The FCS has common course numbering across all institutions. The gateway English course across the system is English Composition I (ENC 1101), and the traditional gateway mathematics course is Intermediate Algebra (MAT1033). There are, however, additional mathematics and statistics courses that can fulfill the gateway requirement, depending on a student’s intended major. Investigating outcomes in these courses will be covered in a future analysis.


References


Alliance for Excellent Education (May 2011). Saving now and saving later: How high school reform can reduce the nation’s wasted remediation dollars. Washington, DC. Retrieved from: http://eric.ed.gov/?id=ED520329


Bailey, T., Jeong, D. W., & Cho, S. W. (2010). Referral, enrollment, and completion in developmental education sequences in community colleges. Economics of Education Review, 29(2), 255–270.


Coleman, J., Hoffer, T., & Kilgore, S. (1982). Cognitive outcomes in public and private schools. Sociology of Education, 55(2), 65–76.


Complete College America. (2012). Remediation: Higher education’s bridge to nowhere. Washington, DC. Retrieved from: http://www.completecollege.org/docs/CCA-Remediation-final.pdf


S. 1720, 113th Cong. (2013).


Hearn, J. C. (1988). Attendance at higher-cost colleges: Ascribed, socioeconomic, and academic influences on student enrollment patterns. Economics of Education Review, 7(1), 65–76.


Hu, S., Park, T., Woods, C., Richard, K., Tandberg, D. A., & Bertrand Jones, T. (2016). Probability of success: Evaluation of Florida’s developmental education redesign based on cohorts of first-time-in-college students from 2009-10 to 2014-15. Tallahassee, FL: Center for Postsecondary Success.


Kuh, G. D., Kinzie, J., Buckley, J., Bridges, B., & Hayek, J. (2007). Piecing together the student success puzzle: Research, propositions, and recommendations. ASHE Higher Education Report, 32(5). San Francisco, CA: Jossey-Bass.


National Center for Educational Statistics (2010). Digest of education statistics: Table 241. Washington, D.C.: U.S. Department of Education. Retrieved from https://nces.ed.gov/programs/digest/d10/tables/dt10_241.asp


Park, T., Tandberg, D., Hu, S., & Hankerson, D. (2016). One policy, disparate reactions: Institutional responses in Florida's developmental education reform. Community College Journal of Research and Practice, 40(10), 824–837.


Park, T., Woods, C., Tandberg, D., Hu, S., Bertrand Jones, T., & Richard, K. (2016). When developmental education is optional, what will students do? Analysis of survey data on student course enrollment decisions in an environment of increased choice. Innovative Higher Education, 41(3), 221–236.


Scott-Clayton, J., Crosta, P. M., & Belfield, C. R. (2014). Improving the targeting of treatment evidence from college remediation. Educational Evaluation and Policy Analysis, 36(3), 371–393.


Sewell, W. H., Haller, A. O., & Ohlendorf, G. W. (1970). The educational and early occupational status attainment process: Replication and revision. American Sociological Review, 35(6), 1014–1027.


Underhill, B. (2013, Feb. 20). College remediation. Presentation at the Florida Senate the 2013 Regular Session Appropriations Subcommittee on Education Meeting, Tallahassee, FL. Retrieved from http://www.flsenate.gov/PublishedContent/Committees/2012-2014/AED/MeetingRecords/MeetingPacket_2056.pdf




Cite This Article as: Teachers College Record, Date Published: October 26, 2016
https://www.tcrecord.org ID Number: 21699, Date Accessed: 12/3/2021 3:12:32 AM

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About the Author
  • Toby Park
    Florida State University
    E-mail Author
    TOBY J. PARK is an associate director of the Center for Postsecondary Success and an assistant professor of education policy at Florida State University. His primary research utilizes quasi-experimental methods and large statewide datasets to investigate student outcomes in postsecondary education and explore potential policy initiatives that could improve student success. His recent publications include articles in Innovated Higher Education and Teachers College Record.
  • Chenoa Woods
    Florida State University
    E-mail Author
    CHENOA S. WOORDS is a research faculty member in the Center for Postsecondary Success at Florida State University. Her primary research interests include college access, choice, and success with an emphasis on precollege counseling and preparation. Her recent publications include an article in Journal of Education for Students Placed at Risk and a chapter in the book High School to College Transition Research Studies.
  • David Tandberg
    Florida State University
    E-mail Author
    DAVID TANDBERG is a principal policy analyst with the State Higher Education Executive Officers Organization and formerly an associate professor of higher education at Florida State University. His research interests center on state higher education policy, politics, and finance. Recent publications include articles in The Journal of Higher Education and Community College Journal of Research and Practice.
  • Keith Richard
    Florida State University
    E-mail Author
    KEITH RICHARD is a graduate research assistant in the Center for Postsecondary Success and a doctoral candidate in the Sociology department at Florida State University. His research interests include community college reform, sociology of education, and social inequalities. His recent publications include an article in Innovative Higher Education.
  • Oguzcan Cig
    Florida State University
    E-mail Author
    OGUZCAN CIG is a researcher in the Office of Quality Assurance and Reporting and an adjunct instructor in Educational Leadership and Policy program. His research interests include early cognitive development, teacher education, and racial and ethnic disparities in education. His recent publications include articles in Research in Special Education Needs and Computers in Human Behavior.
  • Shouping Hu
    Florida State University
    E-mail Author
    SHOUPING HU is the founding director of the Center for Postsecondary Success and the Louis W. and Elizabeth N. Bender endowed professor of higher education at Florida State University. His research interests include student postsecondary readiness, outcomes, and success and public policy. His recent publications include articles in Journal of Higher Education and Review of Higher Education.
  • Tamara Bertrand Jones
    Florida State University
    E-mail Author
    TAMARA BERTRAND JONES is an associate director of the Center for Postsecondary Success and an associate professor of higher education at Florida State University. Her research uses culturally responsive frameworks to examine the sociocultural contexts of evaluation and education for underrepresented populations in academia. Her recent publications include an article in Journal of Negro Education.
 
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