EFFECT OF REMEDIAL MATH-TAKING IN COLLEGE ON DEGREE ATTAINMENT

EFFECT OF REMEDIAL MATH-TAKING IN COLLEGE ON DEGREE ATTAINMENT

1MEGHAN A. CLOVIS, 2MIDO CHANG

1,2Florida International University, Miami, Florida, USA E-mail: 1mclovis@fiu.edu, 2midchang@fiu.edu

Abstract - The purpose of this study was to examine the effect of remedial math course taking on degree attainment using a subsample of the National Center for Education Statistics' 2002 Educational Longitudinal Study.Study results indicated the remedial math-taking was a significant predictor and negatively correlated with degree attainment. Students were 1.4 times more likely to graduate if they did not take remedial math in college.

Keywords - Remedial Math, College Degree Attainment, Logistic Regression

I. BACKGROUND OF THE STUDY

Approximately 75% of all postsecondary institutions offer at least one level of remediation in reading, writing, and/or mathematics (NCES, 2010; Parsad& Lewis, 2003). Estimates of the numbers of students enrolling in these remedial courses range from 25% to as high as 75% of all incoming freshman (Bonham & Boylan, 2012). Additionally, more students require remedial math courses than reading and/or writing (Bettinger& Long, 2005; Donovan &Wheland, 2008; Fike&Fike, 2012). To have any hope of attaining a degree, which is so highly sought after, students must successfully negotiate these remedial courses. Higher education institutions have been accepting underprepared students for over 150 years and have continually developed services to meet the needs of diverse student populations with varying skill sets (Boylan, Bonham, & White, 1999; Casazza, 1999). The purpose of remedial coursework is not only to prepare students with the necessary skills to be successful in college-level courses, but also to "reduce disparities between disadvantaged and advantaged groups" that may exist beyond academic skill gaps (Bahr, 2007, p. 695).

The number of students requiring remediation varies greatly by state, institution type, and area of remediation needed. In 2009-10, it was estimated that 75.3% of all 4-year public institutions offered remedial education (NCES, 2010). The National Center for Education Statistics (NCES) reported that in 2007-08, 36% of all college freshman had taken at least one remedial course. If these data are subdivided by type of institution, the percentages look slightly different: 42% at 2-year public institutions, 39% at 4year, non-doctorate granting public institutions, and 24% at 4-year doctorate granting public institutions. The percentages of students taking remedial courses was largest (40%)(NCES, 2011, p. 70). According to the National Governors Association (2010), approximately 40% of students entering postsecondary education will require remediation and this number increases to 60% at the community

college level. Furthermore, due to increasing demand, the cost of offering remediation in college is increasing. According to an August 2006 issue brief from the Alliance for Excellent Education, the estimated cost of remedial education in community colleges alone was $3.7 billion.

Despite the increased need for, and prevalence of, remediation, numerous studies show that many students do not successfully complete their coursework. Specifically, less than 30% of students pass all remedial math courses in which they enroll (Attewell, Lavin, Domina, & Levey, 2006; Bahr, 2010).The literature is not consistent regarding the effects of taking remedial math on degree completion. Whether it positively impacts degree completion is questionable, particularly at the junior college level (Crisp& Delgado, 2014; Bahr, 2008; Melguizo, Bos, & Prather, 2011; Quarles& Davis,2017; Shields &O'Dwyer, 2017; Valentine, Konstantopoulos, &Goldrick-Rab, 2017; Xu&Dadgar, 2018). The lack of success of remedial mathematics students has prompted numerous revisions to the teaching and learning process in these courses through massive redesign efforts. These redesigns have had mixed results (Bahr, 2007; Bettinger& Long, 2005; Bonham & Boylan, 2012; Illich, Hagan, &McCallister, 2004; Okimoto, & Heck, 2015; Pretlow & Washington, 2011).

The impact of remedial education programs on retention and success in college is questionable. Despite years of course redesign and the implementation of various instructional formats, student success rates have not been significantly altered on a large scale. So, what are we missing? The answer may not be in the effectiveness of the courses in addressing basic skill gaps alone, but in the characteristics of the students who are enrolled in these courses. Given the apparent lower success and completion rates of students in remedial math courses, further investigations into the effect of remedial math taking on degree attainment is warranted.

Proceedings of ISER 198th International Conference, Milan, Italy, 26th-27th April, 2019 21

II. METHOD

Effect of Remedial Math-Taking in College on Degree Attainment

III. RESULTS

This study focused on the effect of remedial mathtaking in college on degree attainment (the dependent variable) for students who attended 4-year postsecondary institution(s). The study utilized a three-stage binary logistic regression. The initial sample of participants was selected from the final data collection wave using highest known degree attained, known postsecondary institution(s) attended, and known number of remedial math courses taken in college.

Data The data used in this study were obtained from the public use data file of Educational Longitudinal Study of 2002 (ELS:2002) sponsored by the National Center for Education Statistics. ELS:2002 is a nationally representative longitudinal study of students' transition from high school into postsecondary education and the workforce.

Preliminary Analysis As shown in Table 1, the study conducted descriptive statistics, Chi-square tests, and t-tests. Chi-square tests of association showed significant differences between graduates and non-graduates for all categorical variables except high school urbanity. Students who attained a degree were more likely to be female and to have participated in a college prep program. Graduates were less likely to be Black or Hispanic, to be first-generation, to come from a single-parent home, and to have taken remedial math in college. Although significant differences in proportions were found, effect sizes were small (ranging from .08 to .21). Independent sample t-tests showed significant mean differences between graduates and non-graduates in family income as well as math and reading proficiency. An examination of the mean difference and effect size (d) reveals medium effect sizes for these differences.

Table 1. Descriptives, Chi-Square, and T-Tests

Degree Attained

Pre-dictor

No

Yes

Freq. (%)

Freq. (%)

2 (df)

/V

Sex

Male

572 (55.2) 1348 (45.8) 27.17* (1)

.08

Female 464 (44.8) 1595 (54.2)

Race

Black

224 (21.6) 257 (8.7) 166.46* (3)

.21

Hispanic 139 (13.4) 231 (7.8)

Asian

78 (7.5)

345 (11.7)

White

595 (57.4) 2110 (71.7)

First Gen.

No degree 510 (51.3)

2010 (71.7) 136.10* (1)

-.19

Degree 484 (48.7) 795 (28.3)

Single Parent No

746 (75.0) 2356 (84.0) 39.84* (1)

-.10

Yes

249 (25.0) 449 (16.0)

Urban HS No

630 (60.8) 1832 (62.2) .67 (1)

-.01

Yes

406 (39.2) 1111 (37.8)

College Prep No

428 (42.0) 781 (26.9) 81.23* (1)

.14

Yes

591 (58.0) 2127 (73.1)

Reme-dial No

712 (68.7) 2481 (84.3) 117.28* (1)

-.17

Yes

324 (31.3) 462 (15.7)

Mean (SD) Mean (SD) t-test (df)

d

Income

9.16 (2.23) 10.09 (2.05) [-11.89*] (1693.22) -.44

Math IRT

39.34 (10.78) 46.29 (9.65) [-18.19*] (1625.04) -.70

Read IRT

31.36 (9.10) 36.19 (7.73) [-15.14*] (1562.81) -.60

Notes. *p < .001. d, , V = effect size.[ ] = equal variances

not assumed and Cochran & Cox test statistic used.

Logistic Regression In logistic regression, student demographic predictor variables entered in Block 1 included sex, race (Black, Hispanic, and Asian), parental income,

generational status (first-generation college students), and high school family composition (single-parent home). White, male students, students who lived in a two-parent home, and students who were not first-

Proceedings of ISER 198th International Conference, Milan, Italy, 26th-27th April, 2019 22

Effect of Remedial Math-Taking in College on Degree Attainment

generation were used as reference groups. High school urbanity, college preparatory program participation, and math and reading proficiency scores were entered in Block 2. Students attending urban schools and those who did not participate in college prep were reference groups. Remedial mathtaking was entered in Block 3 with no remedial math as the reference group.

Prior to running the analysis, the assumption of continuous variables being linear on the logit was investigated using the Box-Tidwell test, with each variable centered at one. The assumption was violated for family income. To investigate the effect of this violation on regression outcomes income was transformed using the natural logarithm and the BoxTidwell transformation (centered at 1). Separate regression analyses were performed predicting degree attainment and the corrected Akaike's Information Criterion (AICc) was used to evaluate model fit. No significant change in model fit was found using either transformation and the original variable was used in the final regression analysis (see Table 2).

Table 2. Box-Tidwell Test Results and AICc Comparison of

Transformations

B

SE

2

Income

-.97

.24

16.35**

BT_Income .38

.08

23.77**

Math

.02

.06

.11

BT_Math

.01

.01

.59

Reading

.13

.07

3.84

BT_Read

-.02

.02

.89

Income Comparison (N = 3979)

-2LL

AICc

i

Original

4422.16 4426.17 7.89

BT

4414.27 4418.28 Min

LN

4461.46 4465.46 47.19

The first model provided a statistically significant

prediction

of

degree

attainment,

2 7, N 3792 328.90,p< .001. The predictive

accuracy of the model was 74.8% (pseudo R2 = .12).

All demographic variables were significant predictors

of degree attainment except single-parent household.

Odds ratios indicated that female and Asian students

were more likely to attain a degree; income was

positively correlated with degree attainment; Black,

Hispanic, and first-generation college students were

less likely to attain a degree.

The addition of high school-level variables in the

second model provided a statistically significant

prediction

of

degree

attainment,

2 11, N 3792 515.77 , p< .001. The predictive

accuracy of the model increased slightly from 74.8% to 75.9% and pseudo R2 from .12 to .19. College prep participation and math proficiency were significant high school-level predictors; students who

participated in college prep were more likely to graduate and math proficiency was positively correlated with degree attainment. Sex, Black, Asian, income, and first-generation remained a significant predictor, but Hispanic did not. The model was assessed using the change in the restricted log likelihood and Model 2 did show a significant improvement in model fit over Model 1

2 4 186.88, p .001 .

The addition of remedial math-taking in the third

model also provided a statistically significant

prediction

of

degree

attainment,

2 12, N 3792 530.84, p< .001. The predictive

accuracy and pseudo R2 remained relatively

unchanged. However, remedial math-taking was a

significant predictor and was negatively correlated

with degree attainment. Sex, Black, Asian, income,

first-generation, college prep, and math proficiency

remained significant predictors. The model was

assessed using the change in the log likelihood and

Model 3 showed a significantly improved model fit

over Model 2, 2 1 15.07, p .001 .

DISCUSSION

Demographic variables included sex, race, family income, generational status, and family composition. These variables combined provided a statistically significant prediction of degree attainment for Model 1. High school preparations variables included urbanity, college preparatory program participation, and math and reading proficiency scores. The addition of these variables also provided a statistically significant prediction of degree attainment for Model 2. However, the addition of high school preparations did not improve the model fit over Model 1. Adding remedial math-taking in college in Model 3 provided a statistically significant prediction of degree attainment and Model 3 showed a significant improvement over Model 2.

The final models indicated that female students were 1.8 times more likely to graduate than males. Sex had the largest odds ratio of any significant predictors in the final model. Students who were not Black were 1.6 times more likely to graduate. Asian students were 1.6 times more likely to graduate. Family income was significant and was positively correlated with degree attainment. Students were 1.6 times more likely to graduate if they were not a first-generation college student. Hispanic race and family composition were not a significant predictor.

High school urbanity was not significant. College prep was significant. Students were 1.5 times more likely to graduate if they had participated in college prep in high school. Math proficiency was a

Proceedings of ISER 198th International Conference, Milan, Italy, 26th-27th April, 2019 23

Effect of Remedial Math-Taking in College on Degree Attainment

significant predictor, however, the odds ratio was 1.04, indicating virtually no difference between graduates and non-graduates.

The main predictor variable in this study was remedial math-taking in college. Results indicated the remedial math-taking was a significant predictor and negatively correlated with degree attainment in all four groups of students. Students were 1.4 times more likely to graduate if theydid not take remedial math in college.

Limitations in this study related to remedial mathtaking include the timing of the remedial math-taking and whether it was mandatory. It is unknown when the student took the remedial course and if the course was optional. Additionally, a student could have taken more than one remedial math course and no distinction was made as to whether they passed all the remedial math courses in which they enrolled. Furthermore, the level, format, and credit hours of the remedial course(s) is unknown.

The literature indicates that students at 4-year institutions are less likely to be first-generation and lower income, and more likely to have higher high school academic preparation. The results of this study are consistent with the literature in this respect. More recent studiesindicate that remedial math-taking may have a positive effect on degree completion. Our results do not necessarily support this conclusion. In this study, 19.7% (N = 786) of the sample had taken remedial math. Of those students, 59% (N = 462) graduated. It may be that students who took remedial math were lower academic performers in general, accounting for their lower completion rates. However, remedial math-taking is purported to aid students in graduating, thus it should be positively correlated with degree attainment and students who have taken it should have a completion rate similar to that of non-remedial students (78% completion) yet, we did not find that in this study.

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Proceedings of ISER 198th International Conference, Milan, Italy, 26th-27th April, 2019 24

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