Many educators have expressed the desire to be able to better differentiate books and more accurately target reading for beginning readers. Kindergarten through second grade is a critical window for reading development and guidance on text complexity was needed for several reasons.
For these reasons, we conducted extensive research to identify the text characteristics that most influence the text complexity of early-reading materials. The findings were incorporated into the Lexile Analyzer in order to more precisely measure content used in K–2 classrooms.
How was the Lexile Analyzer enhanced?
The Lexile Analyzer has been enhanced in several ways:
There are four types of early-reading indicators that are now reported from the Lexile Analyzer to help identify important text features that could present more or less of a challenge in K–2 books. The early-reading indicators are derived from the nine-variable model that the Lexile Analyzer uses to measure text complexity and provided for text that is 650L and below.
To help accommodate and accelerate learning for all, research was conducted to examine the unique properties of K–2 texts and how they affect the reading challenge for students. There is a wide variety of types of K–2 texts such as decodable texts, leveled readers and others that often have unique text characteristics.
For example, K–2 texts often have easy-to-decode words that become orthographically increasingly complex as texts become more challenging. Texts also incorporate certain types of systematic repetition and patterning that changes as the texts become more challenging. To help educators, content publishers and researchers better understand what characteristics are making a text more or less complex, the Lexile Analyzer was enhanced to identify the presence of the various features within text and report this information out as early-reading indicators in addition to the overall level of complexity (i.e., Lexile measure).
Early-reading indicators help to identify which text characteristics are contributing to the reading challenge in a book or piece of text. Educators can use the early-reading indicators to help identify texts with features that could better support comprehension for certain types of students. For example, a student struggling with phonics could benefit from reading books that have a very low to low Decoding Indicator. Books with a low Decoding Indicator tend to contain more monosyllabic words and words with simple orthographic and sound-symbol relationships, like “cat” and “top.” Books like these would make it more likely that a child succeeds when reading the book aloud. When teaching students that are English Language Learners (ELLs), one may want to consider looking for books with a low Semantic Indicator to help improve comprehension. These type of books contain more familiar, high-frequent and concrete words and could help ELLs better understand the material.
It is important to read a variety of different types of reading materials. Early-reading indicators also help ensure that students are getting this variety in their reading experiences by reading a repertoire of texts that have different types of challenges. Early-reading indicators are accessible on the book detail pages of “Find a Book” (www.Lexile.com/fab), a tool used to build custom reading lists based on Lexile range and interest. To see additional examples of early-reading indicators for specific books and how they help identify where the challenge is coming from, see:
We conducted a series of studies to identify the characteristics that most influence the text complexity of early-reading materials. First, a corpus of texts was constructed to represent the wide range of K–2 texts including decodable texts, leveled readers and trade and picture books. Next, as in previous Lexile research, data was collected of children reading the texts and from teachers’ judgments to assess the relative difficulty of the texts. In particular, two studies were conducted. One study determined text complexity based on student performance, and the other study determined text complexity based on educator judgments. Finally, analyses was performed to identify the text characteristics that are important for measuring early-reading texts, such as the decodability of the words and the degree of patterning and repetition present in the text.
To learn more about the research, see:
To see summaries of these articles, visit the Summary of Research Findings section in this toolkit. These summaries will be accessible to the general public on www.Lexile.com/beginning-readers starting June 1st, 2017
The Lexile Analyzer is able to recognize characteristics of books used in K–2 classrooms. The characteristics include patterning, repetition, decodable words and short sentences. The characteristics are identified and evaluated to determine how the text should be measured. If the Lexile Analyzer determines that text possesses early-reading text characteristics, the nine-variable model is used to measure the complexity of that text.
Placing readers and text on the same scale is as important in early grades as it is at any point in a student’s academic career. Lexile text measures now provide more precise measurement of the unique features of early-reading texts which is critical for matching students to texts appropriate for their reading ability level.
As a result of our text complexity research, more information (i.e. early-reading indicators) can now identify which text features affect difficulty most for early-reading texts. In some situations, it may help educators to know more about a text than its overall Lexile measure. Knowing the specific text features of early-reading texts allows educators to use those features in a meaningful way to improve student reading levels. Beginning June 1st, Lexile Professional Development (www.Lexile.com) will offer a workshop about K–2 texts and how the enhancements can be used to differentiate instruction.
The reading experience at home should be a positive one. As a parent, an important goal for reading at home should be to help the child build confidence and foster a love of reading. The Lexile Analyzer is now better equipped to measure the text complexity of books and materials used in K–2 classrooms. With more precise Lexile measures, parents can select books that build confidence and improve their child’s reading ability.
The early-reading indicators can be used to see what factors are considered when determining the reading level of a book or piece of text. Unless directed by the child’s teacher, we recommend parents focus more on the Lexile measure of the book and less on the early-reading indicators. The Lexile measure provides information on the overall complexity of the book, which is the best metric to use when trying to find books at a child’s reading level.
Lexile text measures are often used in the design of reading programs and other curriculum materials. Publishers and content and curriculum developers can now receive additional information about which text features are most contributing to the complexity of a text. Publishers and content and curriculum developers can access early-reading indicators in several ways, including:
Creators of reading programs and reading materials can use the early-reading indicators to help guide content development and help ensure the type of reading materials that are produced contain text characteristics that are ideal for their intended audience. For more information on early-reading indicators, please see our early-reading FAQs section.
Although the vast majority of books that have Lexile measures that will not change, a small subset of books will receive an updated Lexile measure. There are two cases where a published Lexile measure would be issued an updated Lexile measure:
We are working with publishers, booksellers and distributors to help transition them to the updated Lexile measures. Starting June 1st, all updated Lexile measures will be available on Lexile.com.
Lexile text measures can range from below 0L to above 1600L. When a text measure is below 0L, a BR (Beginning Reader) code is reported. These Lexile measures are shown as BR followed by a number and L (e.g., BR150L). The Lexile scale is like a thermometer. Numbers below zero indicate the decreasing text difficulty as the number descends from zero. Like negative numbers, the “larger” the number following the BR code, the less challenging the text. For example, a BR100L book is more complex than a BR300L book. Similarly, a student reading at BR100L is more advanced than a student reading at BR300L. Above 0L, measures indicate increasing text complexity as the numbers increase. For example, a 300L book is more challenging than a 100L book.
Since the Lexile Analyzer was first developed in 1986, there have been numerous enhancements to the technology. Below is a timeline of the updates that have been made through the decades. We will continue to strive to advance the science behind measuring text complexity in the years to come.
There are a growing number of assessments that report out Lexile measures for students in the early grade. Today they include:
Many text-leveling systems in the early grades rely on holistic, subjective human judgments that are the result of individuals going through a training program to learn to apply the leveling system. Fountas and Pinnell’s Text Level Gradient is an example of such a framework. It involves trained individuals independently reviewing and assigning a level to the texts (e.g., A, B, C) based on text characteristics such as word familiarity, sentence complexity and format features. Then, through a group consensus process, they assign a single level to each text.
The Lexile studies of early-reading texts also involved expert judgment; however those judgments were validated with empirical student performance data on the same texts and the judgment decisions were anchored to a quantitative developmental scale — the Lexile scale — appropriate for assessing the complexity of texts from kindergarten to college. Additionally, because Lexile text measures are calculated by a machine (i.e., the Lexile Analyzer), they can be a scaleable solution by being quickly and reliably produced for large collections of texts.
Since the methods used to determine the complexity of texts varies across the different leveling systems, the complexity levels assigned to the texts may also vary. As a result, there is not a direct correspondence between a specific Lexile measure for a text and a level from another system (e.g., Fountas and Pinnell’s Guided Reading Levels), although there is typically a high degree of correlation in the order in which books appear in the various systems. In other words, texts receiving a low Lexile measure typically appear in the low levels of other systems.
[1] MetaMetrics Lexile Construct Norms (2017). Durham, NC.