Remedial English:Teacher input student output

Deborah DavisCandidate for Doctorate of Education (Ed.D)Liberty UniversityEDUC 919-390-Winter 2016-LUOProfessional Writing and ResearchMethodology Presentation (1-14-15B)

December 31, 2015

Remedial English: Teacher input student output• Research Design Research Questions• Null Hypotheses• Participants and Setting• Instrumentation• Procedures• Data Analysis• References

Research Design and Variables• This quantitative causal-comparative ex post facto study

attempts to determine the extent of relationship between student scores before and after proscribed coursework provided by teachers with varying levels of education at a small four year university in the foothills of Appalachia– Independent variable is the education level of the teacher.– Dependent variables include the student scores on the placement exam taken at

the end of the course..– Covariant variable includes the category of placement determined by exam prior

to placement within the course.

Research Design Rationale• A causal-comparative research method is appropriate for this

study is one “in which groups are matched on some participant characteristic” (Warner, 2013, p. 1079). While this research will not prove cause and affect, it will identify trends and relationships. There is no manipulation of the data, identification and study of existing data and their relationship is the purpose of this study.  

Research question• Is there a statistically significant difference between the degree of

the instructor (Baccalaureate/Masters) and the exit scores (COMPASS) for three groups of students (High/Medium/Low) based on entrance scores (COMPASS/ACT/SAT) after one semester of remedial English at a small four-year university in the foothills of Appalachia?

Null Hypothesis• There is no significant difference on the successful completion

(Pass/Fail) (criterion variable) of students completing one course in remedial English by ACT Compass ® test scores (covariant) regardless of level of placement (High/Medium/Low) when evaluated against the level of teacher education (Bachelor/Master) (predictor variable).

Participants -• Sample – The sample is a convenience sample from students

enrolled as freshmen at a small four year university in the foothills of Appalachia pseudonomynously named University Alpha (UA). From the entirety of the incoming freshman class of the most recently archived information (N=XXXXX), many are placed (n=xxxxx) in the remedial English section called Basic Writing (ENGL0096).

• The sample size for an Analysis of Covariance (ANOVA) with for three groups requires a sample size of at least 126 for a medium effect size (statistical power .7) (Gall, Gall, & Borg, 2007, p. 145).

Participants - continued• Students are assigned by the results of a placement test

(ACT/SAT/Compass). Students are categorized by placement into group high (H) within 10% of placement to freshman English (n=xxx), group medium (M) between 10-25% below the placement margin of assignment to freshman English (n=xxx), and group low (L) below 25% below the placement margin.

Participants - continued• Each class is limited to 20 students. There may be as many as 28

teachers assigned to the courses (of an incoming class of 1000 students, generally 700-750 students are assigned to remedial English . The teachers vary in experience and education from those who have recently received bachelors degrees to those who have achieved masters degrees or are pursuing doctorates.

• Any material related to student or teacher identification will be locked by password or physical security measures.

Setting• The school is a state funded public university in the foothills of

Appalachia. The lowest tuition in the state, the student population is diverse providing opportunities for people of all ages and economic status. This mid-sized, university is fully accredited as an academically rigorous university.

Instrumentation • A variety of placement tests are used for to assign students to

college coursework. The ACT/SAT/Compass are the approved tests for this school for entrance, and scores below a specific level result in placement in a basic writing course for remedial English. To complete the program, the students are tested using the Compass test program.

• The 2012 version of the ACT Compass Reference Manual indicates an assured validity of 92% and reliability of 88% by the Mante-Haenszel scale of common odds ratio (pt. 3, ch. 1, p. 6).

Instrumentation • The Statistical Package for the Social Sciences (SPSS) 20.2 and

Microsoft Excel were used to calculate descriptive statistics such as correlation matrices and percentages.

Procedures• Data exists in the academic archives regarding student population.

Among that archived data are the records of the students who were placed in remedial English in the school year most recently archived.

• Through archival research under the student id number (preserving anonymity), the student placement scores may be accessed.

• Those test scores will evaluate the student population into groups related to the scores (HML).

Procedures • The archival records record the exit exam (COMPASS) taken by each

student. That score is essentially converted to a pass/fail record and the student then may or may not progress to the freshman composition course.

• The archival records also relate the student records to the professor of each class.

• Separate records at the school allow for relationship of the professor of record and that teacher’s education level (BA, MA).

Procedures • The information will be processed through SPSS using a two-way

between groups analysis of co-variance (ANCOVA). • Reporting will include:

– Outliers: Use a Box and Whisker plot for each group and/or variable. Look for extreme outliers.

– Assumption of Normality: Kolmogorov-Smirnov for (n>50).– Assumption of Linearity: Use a series of scatter plots between the pre-test

variable and post-test variable for each group– Assumption of Bivariate Normal Distribution: Use a series of scatter plots

between the pre-test variable and post-test variable for each group. Look for the classic “cigar shape.”

– Assumption of Homogeneity of Slopes: Look for interactions.– Assumption of Equal Variance: Use Levene's Test of Equality of Error Variance

Procedures • Report will be submitted to participating university

• Thank you letters will be sent to all who assisted

Data Analysis • The following tests and results will be reported (Green & Salkind,

2014, p.227-230) :– Pillai’s Trace– Wilk’s Lambda– Hotelling’s Trace– Roy’s Largest Root– Paired t tests (if needed)– Holm’s sequential Bonferroni procedure will control Type I error as needed.

ReferencesACT. (2012). COMPASS internet version reference manual. Retrieved

from: http://www.act.org/compass/pdf/CompassReferenceManual.pdf

Arendale, D. R. (2011). Then and now: The early years of developmental education. Research & Teaching in Developmental Education, 27(2), 58-76.

Bahr, P. (2012). Deconstructing remediation in community colleges: Exploring associations between course-taking patterns, course outcomes, and attrition from the remedial math and remedial writing sequences. Research in Higher Education, 53(6), 661-693. doi:10.1007/s11162-011-9243-2

Bahr, P. (2013). The aftermath of remedial math: Investigating the low rate of certificate completion among remedial math students. Research in Higher Education, 54(2), 171-200. doi:10.1007/s11162-012-9281-4

Bannier, B. (2006). The impact of the GI bill on developmental education. Learning Assistance Review (TLAR), 11(1).

References (continued)Gall, M., & Gall, J. (2007). Educational research: An introduction (8th

ed.). Boston, MA: Pearson/Allyn & Bacon.Glessner, K. (2015). Only the best need apply? Journal of College

Admission(226), 30-33. Green, S., & Salkind, N. (2014). Using SPSS for Windows and

Macintosh analyzing and understanding data (7th ed.). Boston, MA: Pearson.

Harris, S., Lowery-Moore, H., & Farrow, V. (2008). Extending transfer of learning theory to transformative learning theory: A model for promoting teacher leadership. Theory Into Practice, 47(4), 318-326. doi:10.1080/00405840802329318

Hendrickson, K. A. (2012). Student resistance to schooling: Disconnections with education in rural Appalachia. High School Journal, 95(4), 37-49.

Kidron, Y., & Lindsay, J. (2014). Stated briefly: What does the research say about increased learning time and student outcomes? REL 2015-061 (ED547261). Retrieved from Alexandria, VA:

McCormick, J., Hafner, A. L., & Germain, M. S. (2013). From high school to college: Teachers and students assess the impact of an expository reading and writing course on college readiness. Journal of Educational Research & Practice, 3(1), 30-49. doi:10.5590/JERAP.2013.03.1.03

Moghaddam, A. N., & Araghi, S. M. (2013). Brain-based aspects of cognitive learning approaches in second language learning. English Language Teaching, 6(5), 55-61.

Moss, B. G., Kelcey, B., & Showers, N. (2014). Does classroom composition matter? College classrooms as moderators of developmental education effectiveness. Community College Review, 42(3), 201-220.

References (continued)

Plank, S. B., & Jordan, W. J. (2001). Effects of information, guidance, and actions on postsecondary destinations: A study of talent loss. American Educational Research Journal, 38(4), 947-979.

Sana, F., & Fenesi, B. (2013). Grade 12 versus grade 13: Benefits of an extra year of high school. Journal Of Educational Research, 106(5), 384-392. doi:10.1080/00220671.2012.736433

Stanley, M. (2003). College education and the midcentury GI bills. The Quarterly Journal of Economics, 118(2), 671-708.

Warner, R. M. (2012). Applied statistics: From bivariate through multivariate techniques (2ed). Thousand Oaks, CA: Sage Publications.

Vygotsky, L. S. (1978). Mind in Society: the Development of Higher Psychological Processes. Cambridge, MA: Harvard Univeristy Press.

References (continued)