Due to the importance of English language proficiency in education, this study looks to examine the relationship between student performance and language. Especially it explores student performance on standardized tests assessing science, the first language the student learned, the language used to teach students in their first and second year of learning science in the U.S., and race. This paper used the National Education Longitudinal Study: 1988/2000 (NELS: 88) database from the National Center for Educational Statistics (NCES, 2002). The main aim of this paper is to sensitize teachers if primary language and language of instruction influences how students perform, it is imperative that teaching be adjusted for students who may not speak English as a primary language in school.
Table of Contents
Background and Introduction
Present Study
NELS and participants’ description
Variable description and descriptive
Model Determination and Plan of Analysis
Regression Analysis
Model comparison
Final Model
Discussion and Conclusion
Research Objectives and Themes
This study aims to examine the relationship between student performance in standardized science tests and various factors, including race, the first language learned, and the language of instruction used in the early years of science education, using data from the NELS:88 database to help educators adjust their teaching methods for students whose primary language is not English.
- Analysis of student performance gaps based on racial background.
- Impact of primary language and language of instruction on academic outcomes.
- Exploratory modeling of language factors versus other control covariates.
- Assessment of standardized test score disparities in science.
Excerpt from the Book
Background and Introduction
The congressional act of “No Child Left Behind” is a state standardized tests in which it mandates states to administer standardized assessments in order to receive federal school funding (Starr, 2014). According to Connor & Vagyas (2013), standardized tests are used throughout school systems in the US as a means of accountability for the academic performances of K-12 students. Standardized tests help us to judge comparative successes and competitiveness across the school in United States.
Recent national tests show significant differences in student achievement (McKinsey & Company, 2009). The student’s standardized test scores and ultimate academic success are directly influenced by racial makeup of a school. So far there are substantial scoring differentials among various population groups in many standardized tests. The students in American schools with predominant populations of Caucasian children have consistently scored higher on standardized tests than those in schools with predominant populations of African American children (Lupinski & Jenkins 2005).
According to McKinsey & Company (2009), rich students generally perform better than poor students, white students generally perform better on tests than black students, and students of similar backgrounds perform dramatically differently across school systems and classroom. Asian American students’ performances are comparable to those of white students.
Summary of Chapters
Background and Introduction: Discusses the role of standardized testing in the US education system and highlights existing achievement gaps across racial and socioeconomic groups.
Present Study: Defines the research objectives focused on exploring the impact of primary language and instructional language on science test performance.
NELS and participants’ description: Provides an overview of the National Education Longitudinal Study (NELS:88) dataset and the demographic composition of the participants studied.
Variable description and descriptive: Outlines the outcome variables, control variables, and dummy coding strategies used for the statistical analysis.
Model Determination and Plan of Analysis: Presents the mathematical formulation of the five regression models used to analyze the data.
Regression Analysis: Explains the process of data recoding, variable selection, and the statistical methods employed to test for normality and collinearity.
Model comparison: Details the incremental block design used to compare models and identify the influence of covariates on science standardized scores.
Final Model: Describes the final regression equation incorporating race, language variables, and their interactions, along with key control factors.
Discussion and Conclusion: Summarizes the study's findings, confirming significant performance gaps and discussing the limited impact of instructional language compared to other variables.
Keywords
Standardized Tests, Science Achievement, NELS:88, Language Proficiency, English Language Learner, Racial Achievement Gap, Student Performance, Regression Analysis, Educational Statistics, Instructional Language, Primary Language, K-12 Education, Academic Performance, Socioeconomic Factors, Exploratory Research.
Frequently Asked Questions
What is the core focus of this research paper?
The paper examines how factors such as race, the first language learned, and the language of instruction influence student performance on standardized science tests using the NELS:88 dataset.
What are the primary themes addressed in the study?
The study covers racial disparities in test scores, the role of English language proficiency, and the effectiveness of different instructional languages in early science education.
What is the main objective or research question of the work?
The main objective is to sensitize teachers to how primary language and language of instruction influence student performance, suggesting that teaching strategies should be adjusted for non-native English speakers.
Which scientific methodology is employed?
The author uses a linear regression analysis with an incremental block design to evaluate five different statistical models based on the NELS:88 base-year data.
What topics are covered in the main section of the paper?
The main section covers the background of standardized testing, descriptive statistics of the participants, model development, regression procedures, and an evaluation of model results.
Which keywords define this research?
Key terms include NELS:88, standardized tests, achievement gaps, language proficiency, and science performance.
How does socioeconomic status interact with the findings?
The author notes that while socioeconomic status was initially considered, it was removed from the final model due to high correlation with race and violations of statistical assumptions.
What were the findings regarding Asian/Pacific Islander students?
The study found that Asian/Pacific Islander students did not show significant differences in test scores compared to White students when controlled for other variables.
Does the language of instruction have a significant impact?
The study found that language of instruction did not show significance in predicting test scores, largely because it is highly correlated with the student's first language.
- Quote paper
- Tshewang Dorji (Author), 2017, Effect of Race. First Language and Instructional Language on Students, Munich, GRIN Verlag, https://www.grin.com/document/359474
