Little empirical evidence suggested that independent reading abilities of students enrolled in biology predicted their performance on the Biology I Graduation End-of-Course Assessment (ECA). An archival study was conducted at one Indiana urban public high school in Indianapolis, Indiana, by examining existing educational assessment data to test whether a relationship between reading proficiency and student performance on the Biology I ECA existed. The Pearson product-moment correlation coefficient was r = 0.712 (P < 0.01). A strong positive relationship between Biology I ECA and Lexile reading scores accounted for 50.7% of the variance. The results suggested that any measure to increase reading levels would increase standardized biology assessment scores.
Since 1900, biology has occupied a fundamental place in most high schools at the beginning sequence of science courses (Rosenthal & Bybee, 1988). High school biology was introduced in the United States in the late 19th century and evolved as an introductory and comprehensive curriculum, replacing single courses such as botany, physiology, and zoology. High schools in Milwaukee, Wisconsin, were the first to offer a course in biology (Rosenthal, 1990). Historically, the early high school biology courses were similar to, and influenced by, college versions. In the 20th century, biology as taught in secondary schools was adapted to teaching students how to relate the “science of life” to the experience of everyday living and society, not as preparation for science careers (Mayer, 1986). Advancements and trends changed; however, the basic principles of biology education remained to cultivate scientifically literate generations of responsible, informed, and creative citizens of the future (Moore, 1993).
Over the past three decades, essential biology concepts were taught in systems designed to develop scientific literacy and to remain relevant (National Research Council, 1990; Moore, 1993). Student progress was gauged by various evaluation measures, in the belief that standardized testing raised student achievement (Mayer, 1986; Leonard et al., 2001; Polikoff, 2012). In Indiana, participation in the Biology I Graduation End-of-Course Assessment (ECA) fulfilled the requirement of the federal No Child Left Behind Act of 2001 (NCLB) by assessing student achievement in an entry-level high school science course (Indiana Department of Education, 2011). The NCLB, a standards-based reform model for U.S. school districts, aligned teacher instruction with standards and assessments; hence, in theory, student learning improved (Polikoff, 2012). Since 2002, the NCLB has required public schools to make Adequate Yearly Progress, an accountability requirement, for both the overall student population and any demographic group within the school. A state high school exit examination administered by computer or by paper and pencil was used to assess what a student was expected to demonstrate by the end of the course according to the Indiana Academic Standards (Indiana Academic Standards for Science, 2010). Biology I ECA reexamination was not offered in Indiana, and students were not required to pass to graduate (Indiana Department of Education, 2011). Allowable testing accommodations were made for English Language Learners and students with Individualized Education Programs; however, grade-level reading proficiency could not be assumed for every student. Was there a relationship between reading proficiency and student performance on the Biology I ECA?
Little evidence suggested that independent reading abilities of students enrolled in biology predicted their performance on the Biology I ECA. No previous studies had reported whether the level of reading proficiency was a predictor of student performance on the Biology I ECA pass score. Early identification of high school students who were at relatively high risk of not passing the Biology I ECA might have had the potential to improve teacher focus and student learning. Nevertheless, the Common Core State Standards, adopted by the Indiana State Board of Education (2010), indicated that reading skills were an essential part of science literacy (National Governors Association Center for Best Practices, 2010). Until the Common Core State Standards created a metric of text complexity, a Lexile score was an acceptable guide to reference a student’s individual reading level (Lexile Framework for Reading, MetaMetrics, Durham, NC; http://www.lexile.com/). Lexile was one metric for determining reading proficiency on the basis of actual assessment. The reader and text were placed on a developmental scale measuring reader ability and text complexity with comprehension (Lennon & Burdick, 2004; Nelson et al., 2012).
The present study sought to fill a gap by examining existing educational assessment data as to whether a relationship between reading proficiency and student performance on the Biology I ECA existed. The primary objective was to compare the scores of students in one Indiana high school who had taken the Biology I ECA to their individual Lexile scores. It was expected that there was a direct relationship between reading scores and Biology I ECA pass scores. The secondary objective was to investigate the reading level of the Biology I ECA that was currently administered in Indiana. It was expected that the study would advance the understanding of reading levels in relation to biology assessment scores. The paucity of information required a look at the historical elements to provide the background for the Biology I ECA examination.
Biology as a High School Course
From 1900 to today, biology has been taught in the first year of high school in the United States. The explosion of information that occurred in biology over the past hundred years was never anticipated (Sheppard & Robbins, 2007). In the 2006 report “High school biology today: What the Committee of Ten did not anticipate,” Vázquez contended that the addition of new topics (e.g., DNA replication, biotechnology, human immunodeficiency virus, and cancer) to high school biology, along with decreased instructional time, provided students, at best, a “superficial” level of knowledge. In an earlier review, Mayer (1986) found that education in science had never kept pace with the achievements of science.
Given that nearly 80% of high school students took biology in ninth or tenth grade, it was a reasonable course to examine in trying to understand our educational failures (National Research Council, 1990). To this end, the introduction of the general biology course in the United States and its history of use in state high schools has been well documented (Sheppard & Robbins, 2006).
Government’s Role in Education
The most recent version of the Elementary and Secondary Education Act, the NCLB, increased standards-based reform to drive school curriculum and instruction and linked it directly to federal funding to influence compliance (Jorgensen & Hoffmann, 2003). Built upon previous reforms, the NCLB established minimum requirements for a single, statewide accountability system applied to all public schools. The NCLB mandated test-driven accountability using standards as the instructional targets for teaching. Thus, schools were rewarded for raising test scores (Polikoff, 2012). To be eligible for federal funds under Title I of the NCLB, states were required to adopt content standards in reading, mathematics, and science (Polikoff, 2012). Students were assessed in science at least once in grades 3 through 5, grades 6 through 9, and grades 10 through 12 (Marx & Harris, 2006). Results from these tests demonstrated that the overall student population, designated subgroups, and each school district and school were meeting the state’s requirement for Adequate Yearly Progress.
Literacy & Biology
Adequate reading ability is an essential skill needed to perform well on science achievement tests such as the ECA. A study conducted by Gomez and Gomez (2007) on “reading to learn,” with a focus on science, reported that many students entered ninth grade reading at a fourth-grade level. The complexity of texts students were able to read was way below what was required to achieve science literacy. Nationwide, too many high school students were unprepared for high school reading and the years beyond (Gomez & Gomez, 2007).
The correlational study design was used to explore whether the individual Lexile reading scores of students shared variance with their Biology I ECA pass scores. The mechanism was electronic archival data comprising educational standardized test scores. Data included Biology I ECA test scores, Lexile reading scores, and biology class descriptions coded as honors or regular. The Pearson bivariate correlation coefficient was chosen as the test statistic to quantify the extent to which paired scores occupied the same or opposite positions within their own distributions (Holcomb, 2010). A scatterplot was created for Biology I ECA scores and Lexile reading scores, and Pearson’s bivariate correlation was performed to test for linearity.
The Sample & Instrument
The study was conducted in Biology I at an urban public high school located in Indianapolis, Indiana, in the fall of 2012. The study sample consisted of individuals between 14 and 20 years of age in grades 9 through 12 who were eligible and participated in the 2012 Biology I ECA. There were no sample limitations regarding gender, race, or ethnicity. Demographic characteristics (gender, race, and ethnicity, special education, and socioeconomic status as indicated by free and reduced lunch) were collected and encoded as categorical data. All individuals were identified by a unique study number, and data were statistically analyzed in the aggregate. After all de-identified variables and data were entered using the SPSS 20 statistical software package, an analysis of descriptive statistics and frequencies was performed on the sample group of participants (N = 513) as shown in Table 1. One participant was dropped for an invalid performance indicator. A large random sample was used to reduce effects of outliers (Holcomb, 2010).
|.||Percentage .||Frequencies .|
|American Indian/Alaskan Native||0.0||0|
|Individualized Education Program? No||90.6||465|
|Individualized Education Program? Yes||9.4||48|
|.||Percentage .||Frequencies .|
|American Indian/Alaskan Native||0.0||0|
|Individualized Education Program? No||90.6||465|
|Individualized Education Program? Yes||9.4||48|
Notes: N = 513. One participant in the group was dropped for an invalid Biology I ECA Indiana Performance Index.
The spring 2011–2012 Indiana Biology I ECA was computer administered to the sample group in May 2012. The one-session, online exam was provided by ISTEP+ Indiana Statewide Testing for Educational Progress vendor, Questar Assessment, Inc., for a completion time of 70 minutes (Indiana Department of Education, 2010). Prior to assessment, students had a 5-minute instruction for practice and operation, and a 5-minute practice test with two questions. The 55-minute assessment included 46 scored multiple-choice and constructed-response questions based on Indiana’s Academic Standards for Science–Biology I adopted in 2000 by the Indiana State Board of Education and divided into the following sections or strands: molecules and cells, developmental and organismal biology, genetics, evolution and historical perspective, and ecology (Indiana Department of Education, 2010).
All multiple-choice questions were machine-scored, and each constructed-response question was scored according to its own rubric; students received full or partial credit. Students were expected to supply (rather than select) an appropriate response to demonstrate an ability to communicate an understanding of science (Indiana Department of Education, 2010). All students tested were scored a proficiency rating between 200 and 800 and received a completed Individual Student Report with an overall Indiana Performance Index (IPI) level reported in terms of Pass+ (high-achievement), Pass (demonstrated proficiency), or Did Not Pass (required remedial assistance). An Invalid (I) was reported for students whose test was incomplete and considered invalid by a school administrator. The passing performance scores were reported as follows: Pass 509 and Pass+ 646 (State of Indiana, 2007; Indiana Department of Education, 2011).
The Lexile Framework was one of the six metrics reported in a study to be reliable and to correlate with how students performed on tests with text (Council of Chief State School Officer, 2012). The Lexile Framework for Reading was developed after more than 20 years of research by a privately owned company and research organization, MetaMetrics. The organization’s research was initially funded with grants from the National Institute of Child Health and Human Development, part of the U.S. Department of Health and Human Services, National Institutes of Health (Lennon & Burdick, 2004). Lexile measures were used at the school level in all 50 states. Each year, more than 30 million Lexile measures were reported from reading assessments of over half of the students in the United States. The Lexile produced scores in terms of what students read at various levels of development (Stenner et al., 1988). Measures were conveyed by a numeric value followed by an “L” (Lexile) to represent a reader’s ability with approximate 75% comprehension. Lexile scores ranged from below 200L for beginning readers and text to above 1700L for advanced readers and text. For instance, a measure of 1100L for ninth grade was a reading range of 1000L to 1150L. If the attempted material was above 1150L, the task was too great to construe meaning from text (Lennon & Burdick, 2004; Nelson et al., 2012).
Lexile reading scores were recorded for the sample group in September 2012 by Scholastic Reading Inventory (SRI) computer-adaptive testing. SRI was a research-based, computer-adaptive reading assessment for grades K–12 that measured participants’ reading comprehension and reported levels using the Lexile Framework for Reading. Participants selected passages of reading for approximately 20–50 minutes or more, depending on their reading level and the adaptive nature of the test as it adjusted for Lexile. In contrast to other “snapshot-style” assessments, a number of predetermined response questions on the SRI test did not exist. Since the assessment was truly adaptive, a participant had no required completion time or number of questions to take the test. Because SRI’s standard error of measure was SD = 50, any score below 100L was reported as a beginning reader (Scholastic Reading Inventory, 2006).
The policies and procedures followed were in accordance with the ethical standards and approval of the Marian University Institutional Review Board (IRB), Indianapolis, Indiana (2012).
Only 512 of the 513 individuals who qualified for the 2012 Biology I ECA had complete recorded scores. The mean of Biology I ECA scores was M = 509.29 (SD = 114.767). Only 446 of the 512 individuals with Biology I ECA scores had recorded Lexile reading scores. The mean of Lexile scores was M = 1140.90 (SD = 216.255; see Table 2).
A sufficient number of individuals (n = 446) with recorded 2012 Biology I ECA scores and individual Lexile reading scores was analyzed. The range of all Biology I ECA scores was 200 to 800. The range of individual Lexile reading scores was 77L to 1786L.
The 446 pairs of scores were graphed in a scatterplot to check for linearity and outliers. A positive association between Biology I ECA and Lexile reading scores was depicted (Figure 1). A strong positive relationship between the variables was indicated. The Pearson product-moment correlation coefficient was r = 0.712 (P < 0.01), accounting for 50.7% of the variance in Biology I ECA scores. Approximately 68% of the scores in the sample fell within one standard deviation of the mean. The distribution of scores had a slight negative skew; however, the skew was not significant. As expected, a relationship existed between individual reading scores and Biology I ECA scores.
The passing-scale scores were Pass 509 and Pass+ 646 (State of Indiana, 2007). The mean Biology I ECA score M = 593.47 (SD = 63.327) and the mean Lexile reading score M = 1242.75 (SD = 162.506) were the results in the analyzed Pass and Pass+ Indicator. The mean Biology I ECA score M = 414.64 (SD = 80.836) and the mean Lexile reading score M = 1010.99 (SD = 206.733) were the results in the Did Not Pass. As indicated, a very positive slope existed between individual reading scores and Biology I ECA scores. Thus, as one variable increased, the other variable increased. Using only reading scores to predict biology scores, one could account for 50.7% of variation.
Although a large random sample was used to reduce outliers, reporting may have been limited. Research limitations may have included some eligible individuals with no recorded Lexile reading score as well as individuals that did not participate in the 2012 Biology I ECA; as a result, no comparisons were made. Likewise, aspects that threatened internal or external validity that an investigation could not offset were the 2012 Biology I ECA based on former 2000 Indiana Academic Standards and the current Indiana Biology Academic Standards adopted and taught since 2010 (Indiana Academic Standards for Science, 2010; Indiana Department of Education, 2010).
The secondary objective was to obtain the Lexile reading level of the 2012 Biology I ECA that was administered to the sample group. Multiple-choice and constructed-response questions were unable to be analyzed because they lacked predictability in length and punctuation, influencing Lexile accuracy (Lennon & Burdick, 2004). Furthermore, study results neither included nor accounted for other chance events that may have negatively influenced study conditions.
Discussion & Implications
The results indicate that Lexile reading scores had a strong relationship to Biology I ECA scores. The significance of the study was to advance the understanding of reading levels in relation to biology assessment scores. For instance, as reported in Figure 1, the higher the Lexile reading score, the better the Biology I ECA score.
Immediate extension of this work may support having additional studies done by adding analysis of variance to measure group differences as well as explain information that might have important practical consequences. Adding more variables would enable specific interventions to increase reading levels that may improve biology scores. The implication of a reading-level effect is positively correlated with ability and chance to pass the Biology I ECA. The study trends indicate that the level of reading needs to improve to at least 1000 Lexile, or about the seventh-grade level.
Teachers and schools cannot assume that students’ reading abilities will be adequate by the time they enroll in high school biology. Students need to see reading as an active daily practice for learning in biology and other content areas. The results support the conclusion that any measure to increase reading levels and enable critical thinking would increase standardized biology assessment scores.