The goal for this study was to explore the English language development of different groups of English learners and determine whether differences exist in their English Language Proficiency (ELP) growth. The study was a secondary data analysis of existing data taken from one of the largest school districts in the U.S., known for serving a large EL population in Florida. Data on students’ English language development and demographic information was requested and obtained from the school district.
The data requested was for students ELs that attended district schools during the 2014 – 2015 school year. During that particular school year, the school district served a total of 356,902 students from Kindergarten to 12th grade. The demographic breakup of the district during that year was 7.47% White, 22.43% Black, 68.32% Hispanic, and 1.75% of other ethnic groups. The other ethnic groups included learners who are identified as American Indian, Asian and
Multiracial. During the same school year, there were 69,349 students served in the district’s English for Speakers of Other Languages (ESOL) programs. Tables 4 and 5 provide the district’s total demographic membership information and English Learner totals for grades Pre-Kinder to 12th Grade.
Table 4
District PK-12 Demographics for 2014-2015
Ethnicity / Race Student Membership
White 26,649
Black 80,066
Hispanic 243,850
Other (Includes American Indian, Asian and Multiracial categories) 6,337
Total 356,902
(Florida Department of Education, 2015)
Table 5
District English Learners Currently in ESOL Programs - Demographics for 2014 – 2015
Ethnicity / Race Student Membership
White 1,747
Black 5,432
Hispanic 61,400
Asian 635
Other 135
(Includes Native Hawaiian, Pacific Islander, American Indian and Multiracial categories)
Total 69,349
(Florida Department of Education, 2015)
Participants
The data provided by the school district included information on 6,940 ELs, who attended 5th grade during the 2014 – 2015 school year. The retrospective data included English proficiency scores from the years 2008 – 2015. This group of students, and grade level, were
selected because placement decisions for exceptional student education programs tend to occur while students are in the elementary grades (Matthews & Kirsch, 2011; Samson & Lesaux, 2009).
The group of participants selected were in school while the district participated in the implementation of the Comprehensive English Language Learning Assessment (CELLA) for English language proficiency. The CELLA was first administered in the district during the 2005 – 2006 as part of a pilot study. The 2006 – 2007 school year was the first year that the CELLA was fully implemented throughout the district. In 2010, some of the scores were revised and the online CELLA was implemented for students entering the school district.
Data Collection
The data for this study was provided by the school district from their existing database. Information on demographics, academics and English language proficiency scores were
requested. Table 6 shows the data that was provided by the school district for each student participating in the study.
Table 6
Data Provide by the School District
Data Category Variables
Identifiable Information Study ID, Date of Birth
Demographics Gender, Ethnicity, Home Language, Free/Reduced Lunch Status (SES), Exceptionality, Entry into United States Academic Information Grade Levels (for each year of data)
Placement date into Gifted program (if applicable) ESOL /English Proficiency Comprehensive English Language Learning Assessment
(CELLA), Total scale scores and sub scores (Listening/ Speaking*, Reading, and Writing) for each school year the
Measures
Table 7
CELLA Levels and Test Administration
CELLA Levels Grades Functional Levels (Reading/Writing)
Administration Level A K – 2 Initial literacy skills One-to-One Section
administered individually for grades K-2
Listening, Reading, and Writing sections may be administered in small groups (individual administration is recommended for Kindergarten) Level B 3 – 5 Application of literacy skills
towards development of new knowledge
Listening, Reading, and Writing, sections are administered in group settings
Speaking section is individually administered Level C 6 – 8 Advanced applications of
literacy skills towards development of new knowledge
Listening, Reading, and Writing, sections are administered in group settings
Speaking section is individually administered Level D 9 – 12 Literacy skills necessary for
success in higher education or the workforce
Listening, Reading, and Writing, sections are administered in group settings
Speaking section is individually administered (Rebarber et al., 2007, p. 71)
The Comprehensive English Language Learning Assessment (CELLA) is an English language proficiency test used to measure the growth of ELs in mastering the English skills
needed to succeed in school (Florida Department of Education, 2006). The CELLA tests four domains: listening, speaking, reading and writing. The CELLA provides sub-scores for listening/ speaking (combined as one score), reading, and writing. A composite Total Scale Score is also provided. English Learners often demonstrate a wide range of English proficiency levels and a disparity often exist between grade level and functional level (Rebarber et al., 2007). To address this issue, the CELLA’s reading and writing subtests are organized into four levels: A, B, C, and D.
The CELLA is administered individually or in small or large groups depending on a student’s grade level and/or the level of the test being administered. Though the test come in four levels and each level corresponds to specific grades. students may be tested using their grade level and any lower levels if needed. The CELLA is vertically scaled, which allows for comparison of performance on one level of the test to performance on another level by domain (Rebarber et al., 2007).
The CELLA was developed by a partnership of the non-profit organization
Accountability Works (AW) and the Educational Testing Service (ETS) (Rebarber et al., 2007). This partnership consulted ESL standards and teachers to align the test content to expectations of language acquisition and using rigorous test development procedures to ensure the validity of the test. Items were evaluated in terms of bias and factor analysis was used to analyze the four domains of the test for factor structure. High correlations among the four domains were observed.
The reliability of the CELLA was estimated using Cronbach’s alpha for listening and reading sections. Internal consistency ranged from .76 to .95 (Porter & Vega, 2007). The
test blueprints. Expert review of the CELLA was conducted to ensure that all items matched the proficiency benchmarks.
Data Preparation and Analysis
Once the data was received from the district, it was organized and analyzed using the IBM SPSS 24.0 software. The variables for ethnicity, home language, socio-economic status and exceptionalities were recoded, and dummy variables created. For ethnicity, Multicultural and Native Americans were recoded into a variable labeled “other” due to their low numbers. There were over 70 home languages represented in the sample. However, most of the sample had identified Spanish (88%) or Hatian-Creole (8%) as their home language. The other four percent were recoded into the “Other-Language” category. For socioeconomic status, students receiving free and reduced lunch were recoded as one group. Finally, the exceptionalities category was separated into three dummy variables: gifted, ESE, and no exceptionality.
The original data set provided English Proficiency scores from 2008 to 2015. This included scores for students that repeated grades. Data for students who repeated a grade level were excluded from the final data set as other factors that were not being examined may have contributed to their scores. Also, due to score revisions and the implementation of the online CELLA in 2010, scores before 2010 were also removed. This left a final data set that included CELLA scores from grades 1 to 5.
To explore the English proficiency growth trajectories of ELs, growth curve modeling was used. Data from the CELLA was organized into waves based on testing dates and grade levels. According to Singer and Willett (2003), three or more waves of data are an important feature of change and the data set provided 5 waves of data with grade levels used as the time metric. Growth curve modeling allows for missing data, however for the sake of obtaining the
most accurate growth curves, cases with only one data point or less were excluded. Missing data was dealt with by using maximum likelihood estimates as it is assumed that data is missing at random.
Descriptive analysis.
Using IBM SPSS 24.0 descriptive statistical analyses including means, standard
deviations were calculated for each wave of CELLA scores. Means and standard deviations for the CELLA were calculated for sub-groups of ELs determined by gender, home language, exceptionalities, age of entry to the U.S. and socio-economic status. Covariance and correlation matrices for the variables were produced using the same software.
Growth Curve Modeling.
The purpose of the study was to explore the English proficiency growth trajectories of ELs identified as gifted and their non-identified peers. To achieve this purpose, latent growth curve model (LGM) analysis was conducted for CELLA scores using Mplus Version 8.0
software. CELLA data is longitudinal in nature, and though students may start at different levels and reach proficiency at different times, LGM develops individual growth curves that will allow us to estimate growth trajectories for ELP. Latent growth curves include an intercept which represents the initial level of the outcome measure (CELLA scores), and a slope which
represents the rate of change. Both the intercept and slope are latent factors which will indicate a pattern of growth. This pattern may be linear or quadratic.
To begin, an unconditional model was used to examine the growth of ELP and determine the mean slopes and intercepts and variability around the means. This model allows for linear growth. An unconditional quadratic model was developed to allow for non-linear growth. Time
predictors to develop conditional models. All models were tested for model fit using fit indices including chi-square, comparative-fit indices (CFI), and root mean square error of approximation (RMSEA). Linear and quadratic unconditional models and conditional models were created for each of the CELLA sub-scores: listening/speaking, reading, and writing skills.
Ethical Considerations and Diversity Issues
This study is a secondary analysis of data previously-collected by the district, which presents minimal risk to the participants. There is no need for direct contact with participants and therefore presents no direct risk. An extra effort was made to request data with no direct identifiable information to further protect participants from any harm.
Though no direct contact with the population was required, efforts were made to consider the diversity of the sample during analysis. Special attention was given to home language, ethnicity, and exceptionalities during the analysis and reporting phases.
CHAPTER 4: RESULTS