1. A CORRELATION STUDY OF READING LEVEL AND SUCCESS RATE IN DEVELOPMENTAL MATHEMATICS COURSES
Name: Marcial L. Echenique
Sponsor: Dr. David D. Kumar
Research Symposium VI
Tuesday, April 3, 2007

2. Introduction Why developmental mathematics education?
Overview of the background, purpose, and significance of the study.
Research questions and hypotheses.
Procedure and data analysis.
Results, conclusions, and recommendations.
Why?
Work with students in developmental mathematics course every semester
High failure rate
Study can have a direct effect on improving developmental mathematics courses
Doable study

3. Background of the Study
Developmental education students enrolled in college continues to increase (Bettinger & Long, 2005).
Funding for developmental education continues to decrease (Ingash, 1997; Saxon & Boylan, 2001).
Demographics of “typical” community college student is changing (Bragg, 2001).
Gap between passing high-stakes tests (FCAT, TASP) and academically prepared for college-level coursework (McCabe, 2000; Mills, 1998, Schults, 2000).
More students at CC/Less at 4-year institutions
Less funds available for developmental education (college-prep)
More opposition to developmental education in higher education
Different student demographics: female, minority
Students passing FCAT (10th grade), but not ready for college level course (need arithmetic and Algebra I topics)

4. Purpose of the Study
To investigate the correlation between reading level and mathematics success rate of students enrolled in developmental courses.
To determine is the order in which developmental mathematics and reading course are taken has a significant effect on the success rate in developmental mathematics courses.
*To develop a model for predicting success rate in developmental mathematics courses.

5. Significance of the Study
Critical need to improve success rate in developmental mathematics courses at the community college.
Eliminate repeaters in order to free resources devoted to developmental education to serve more students in need of a college education.
Cope with the increasing number of entering college students not academically prepared to enroll in college-level courses.

6. Issues Affecting Developmental Education – Mathematics
Percentage of freshmen enrolling in developmental mathematics courses increasing higher than reading and writing (Shore & Shore, 2003).
Success rate in developmental mathematics courses is less than 50%. This is lower than reading and writing (Grimes & David, 1999; McCabe, 2000).
Community colleges face a dual mission – open access versus high academic standards (Perin, 2006).
Decrease of developmental education offerings at four-year colleges and universities (Bettinger & Long, 2005).
This is the 3rd time that the number of students enrolling in developmental education has been mentioned. This is a critical problem that community colleges have to cope with now and for the foreseeable future.

7. Language and Mathematics Connection…
NCTM standards emphasize communicating mathematically (NCTM, 2000).
Research shows a connection between language and student performance in content areas such as mathematics (Aiken, 1971, Aiken, 1972, McGregor & Price, 1999; Borasi & Siegel, 2000).
ELLs and LEP students scored significantly lower on mathematics tests (Abedi & Lord, 2001; Brown, 2005).
Most research done at the K-12 level (Husander, 2005; Borasi & Siegel, 2000).

8. Language and Mathematics Connection
Language of mathematics is embedded when children come to school (Monroe, 1996).
The difficulty with students learning mathematics occurs when the mathematical concepts are “segregated from the language” (Monroe, 1996, p. 369). Monroe goes on to say that language and mathematics are best learned together.
McGregor and Price quote research by Secada (1992, p. 639) that says “language proficiency, however it is measured… is related to mathematics achievement and to learning”.

9. Research Questions and Hypotheses…
Hypotheses one through four were tested using Spearman rank correlation, at the .05 significance level. Reading level was measured by CPT reading score and developmental mathematics course success was measured by the final letter grade in the course.
RQ and H #
Description #1
Correlation between reading level and success rate in developmental mathematics course for students who took reading before math. #2
Correlation between reading level and success rate in developmental mathematics course for student who took reading and math at the same time. #3
Correlation between reading level and success rate in developmental mathematics course for students who took reading after math. #4
Correlation between reading level and success rate in developmental mathematics course for student who took reading and math at the same time, but had different reading levels in combination with math levels (High Math – High Reading, High Math – Low Reading, Low Math – High Reading, Low Math – Low Reading).
*High or Low level denotes a different developmental course in each subject are.

10. Sample
The sample for the study consists of all 975 FTIC students who placed into all three developmental courses during the Fall 2003 semester and had a full set of CPT scores.
In the Fall 2003 semester, 538 students actually enrolled in developmental mathematics and 559 enrolled in developmental reading.
All students in the study were enrolled in two consecutive semesters, Fall 2003 and Spring 2004.
Most critical students in danger of not doing well. Place in developmental mathematics, reading, and writing.

11. Coding of Variables – Dependent Variable
DEVMATHC – outcome in developmental mathematics class. Coded based on course letter grade.
Value 1 = A, B, or C denotes success.
Value 0 = D, F, W, X, or AU denotes failure.

12. Coding of Variables – Independent Variable
Name
Description
CPT Math
Placement score on math portion of CPT. Continuous variable ranging from 1 through 71.
CPT Reading
Placement score on reading portion of CPT. Continuous variable ranging from 1 through 82.
CPT Writing
Placement score on writing portion of CPT. Continuous variable ranging from 1 through 82.
Race
Student’s race. Recoded into dichotomous variable RAGE_G with Value = 1 if White, Value = 0 for all others.
Gender
Student’s gender coded as Value = 1 if Male, Value = 0 if Female.
Traditional Student
Traditional student if age when enrolled is less than 24 years old and Nontraditional otherwise. Value = 1 if Traditional, Value = 0 if Nontraditional.
Age
Age of student when enrolled, continuous variable recorded in years.
Limited English Proficiency
Recorded English proficiency. Value = 1 if coded LEP, Value = 0 if not LEP.
Enrollment Status
Full- or part-time. Value = 1 if Full-time, Value = 0 if Part-time.

13. Descriptive Analysis – Course Outcome…
To interpret this page, you have to note what the percentages mean. Here 70.3% of the students that passed were female compared to 29.7% for male.
There were a total of 206 males (140+66) and 332 females ( ). Overall, 47% of females passes (156 out of 332) and 32% of males passes (66 out of 206).
Also, 53% of females (176 out of 332) failed and 68% of males (140 out 206) failed.
Similarly interpretation of the percentages for Race/Ethnicity gives the following:
Within each racial group, of the student that failed or succeeded, the failure and passing rates where similar.
There were a total of 113 Whites, 57% failed and 43% passed.
There were a total of 243 Blacks, 61% failed and 39% passed.
There were a total of 144 Hispanic, 59% failed and 41% passed.

14. Descriptive Analysis – Course Outcome…
There were a total of 203 Part-time students with 59% failing and 41% succeeding.
There were a total of 335 Full-time students with 59% failing and 41% succeeding.
Nontraditional students are the only group that had a higher success rate than failure rate.
There were a total of 84 Nontraditional students with 45% failing and 55% succeeding. Only group with a higher than 50% success rate.
There were a total of 454 Traditional students with 61% failing and 39% succeeding.

15. Descriptive Analysis – Course Outcome…
The failure rate for students in the High developmental mathematics course is higher than for the students in the Low developmental mathematics course.
The success rate for students in the Low developmental mathematics course is higher than for the students in the High developmental mathematics courses.
These two facts would seem to indicate that the cutoff score for placing in students in the developmental mathematics courses should be reevaluated.
CPT Math
There were 250 students enrolled in a Low class with 54% failing and 46% succeeding.
There were 288 students enrolled in a High class with 63% failing and 37% succeeding. You would expect more students in the High class to pass.
CPT Reading
There were 92 students enrolled in a Low class with 65% failing and 35% succeeding.
There were 446 students enrolled in a High class with 57% failing and 43% succeeding. High reading has a higher success rate than High math.

16. Descriptive Analysis – Course Outcome
Note that the cutoff score for placement based on the CPT math score is lower than the mean CPT math score. More student are being place in the High math level course.
The failure rate for students in the High developmental mathematics course is higher than for the students in the Low developmental mathematics course.
The success rate for students in the Low developmental mathematics course is higher than for the students in the High developmental mathematics courses.
These two facts would seem to indicate that the cutoff score for placing in students in the developmental mathematics courses should be re-evaluated.
The students needing developmental mathematics courses, 46% are placing in the Low and 54% are placing in the High developmental mathematics course.
However, the failure rate in the High is 63% and in the Low is 54% at the same time the success rate in the Low is 46% and in the High is 37%.

17. Correlations Yielding Significant Results…
Hypothesis one tested the relationship between reading level and success rate in developmental mathematics courses for students who completed the developmental reading course before completing the developmental mathematics course. Based on the significance of the correlation (rs = .233, p = .011, n = 120), the null hypothesis was rejected.
Hypothesis two looked at the relationship between reading level and success rate in developmental mathematics courses for students who completed the developmental reading course and the developmental mathematics course at the same time. The correlation is significant (rs = .143, p = .015, n = 288) and the null hypothesis was rejected.

18. Correlations Yielding Significant Results
Hypothesis four (Low Math – High Reading) examined the relationship between reading level and success rate in developmental mathematics courses for students who took the developmental reading and developmental mathematics course at the same time but had a Low CPT Math score and a High CPT Reading score. The correlation between the two variables (rs = .212, p = .035, n = 99) is significant. The null hypothesis was rejected.

19. Correlations Not Yielding Significant Results…
Hypothesis three tested the relationship between reading level and success rate in developmental mathematics courses for students who completed the developmental reading course after completing the developmental mathematics course. The null hypothesis was not rejected based on the correlation results (rs = .087, p = .542, n = 51).
Hypothesis four (High Math – High Reading) examined the relationship between reading level and success rate in developmental mathematics courses for students who took the developmental reading and developmental mathematics course at the same time but had a High CPT Math score and a High CPT Reading scores. The relationship was not significant (rs = .144, p = .095, n = 136) and the null hypothesis was rejected.

20. Correlations Not Yielding Significant Results
Hypothesis four (High Math – Low Reading) looked at the relationship between reading level and success rate in developmental mathematics courses for students who took the developmental reading and developmental mathematics course at the same time but had a High CPT Math score and a Low CPT Reading scores. The relationship was not significant (rs = .239, p = .325, n = 19) The null hypothesis was rejected.
Hypothesis four (Low Math – Low Reading) examined the relationship between reading level and success rate in developmental mathematics courses for students who took the developmental reading and developmental mathematics course at the same time but had a Low CPT Math score and a Low CPT Reading scores. The relationship was not significant (rs = .165, p = .351, n = 34) and the null hypothesis was rejected.

21. Discussion of Results – Correlations…
Based on the results of Hypothesis #1, #4 (Low Math – High Reading), and #2; there is a positive, but weak correlation between reading level and success rate in developmental mathematics class outcome.
In these three hypotheses, developmental reading is taken before or at the same time as developmental math. The order in which reading and mathematics courses are taking seem to be correlated with the success rate in the developmental mathematics course.
In Hypothesis #3, where reading is taken after mathematics there is no correlation (rs = .087, p = .542, n = 51) between reading level and success rate.

22. Discussion of Results – Correlations
Two cases in Hypothesis #4 (High Math – High Reading, High Math – Low Reading), are not statistically significant. This is counterintuitive since you would expect a High CPT math score to be statistically significantly correlated with success rate in a math class.
These two cases and the results in Table 6, seem to indicate that the math CPT cutoff score is not accurately placing student in the correct mathematics course
Of the 141 students who barely placed into the High mathematics class who had a CPT math score ranging from 33 to 43, 75.2% (n = 106) failed. This failure percentage is higher than the overall failure rate in developmental mathematics class which is 58.7%.

23. Conclusions
Keeping in perspective the limitation of the study, these are the conclusions:
The order in which the developmental mathematics and reading courses are taking does make a difference in the success rate in developmental mathematics course. Reading and math are positively correlated. A higher CPT reading score is positively correlated with success rate in the developmental math course, but only for students who take reading before or at the same time as math.
The math CPT cutoff score is not properly placing students in the correct course. Large percentage of student who barely placed in the High math course are failing at a much higher percentage than the overall student students in the study.
*The gender and the traditional college student status, age of the student, have the greatest impact in the developmental mathematics course.
Limitation to point out:
Looked only at student who placed based on their CPT scores so not all student who placed into all three developmental subjects through possibly another method were included in the study.
Demographic of students at BCC are different than at other community colleges.
Cutoff scores on placement tests, CPT in particular, are different at other community colleges.
Students that only placed in developmental mathematics and reading are excluded from the study.
Students who chose not to take developmental mathematics and reading in consecutive semester are excluded from the study.

24. Further Research Recommendations
There is a need to investigate the CPT math cutoff score used to place student in the developmental mathematics course.
There is a need to investigate the effects of LEP students in developmental mathematics courses.
There is a need to include all student who place into all three developmental mathematics course not just the ones placed solely on their CPT scores.
*There is a need to include more than the nine variables used in the mode in predicting success rate in developmental mathematics courses.
*There is a need to develop a more balance model, number of students who failed and succeeded, to avoid skewing the results of the model.
**There is a need to develop a model (SEM) to determine why the success rate is low.

25. Implications of the Study
Based on the finding of the study, success rate in developmental mathematics courses needs improvement.
Policy should be reviewed to determine the order students should take the developmental mathematics and reading course.
The cutoff score used to place students in the developmental mathematics course should be revised.
More cooperation with the local high schools should be considered to determine why large number of student are entering college underprepared for college-level courses.
Teacher preparation programs have to better prepare teacher to teach mathematics at the K-5 level.
It is too late to wait until high school to remedy the deficiencies in mathematics.
It has to start during the K-5 school years.

26. Summary…
Students will continue to attend community college for financial and academic considerations, among other reasons.
The number of students enrolled in community colleges continues to increase, in particularly, the number of underprepared students.
Financial support is not keeping up with the demand for developmental education.
Success rate for underprepared students is less than 50%, with mathematics having the lowest success rate.

27. Summary
Community colleges have played, and will continue to play, a major role in the American education system. The multitude of courses and the various times these courses are offered allows traditional and nontraditional student to fit the dream of higher education into their schedule. However, remedial education courses offered at community colleges keeps open the option that students who need remediation in mathematics, reading and writing, will someday be able to achieve this dream. Improving the success rate in remedial education will make this dream accessible to a larger number of students.

28. Thank you…