What we will do - TextProjecttextproject.org/assets/library/handouts/Mesmer-Hiebert-CunninghamL... · Schneider, 1985; Mesmer, 2005) Mesmer (2001, 2005) Readers use a letter/ sound

LRA, 2010 Mesmer, Hiebert, & Cunningham

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Literacy Research Association/NRC‐ Dec. 1, 2010 Heidi Anne Mesmer Elfrieda Hiebert James W. Cunningham

Source: Core Standards.org

  National Research Agenda for Beginning Reading Materials and Text

$ in Publishing

$ in Text R and D

Text Project Bringing the right texts to beginning readers (textproject.org)

Our own longstanding interest

What we will do:

•  Identify overlooked variables • Mine the literature • Synergy

New Knowledge

Constructs

Vocabulary

Cohesion


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  Take extreme views   Conduct a traditional literature review   Perpetuate a racehorse mentality

  decodable vs. meaningful   leveled vs. Lexiled   controlled vs. authentic

  Vocabulary and Word Recognition   Syntax and Referential Cohesion   Construct Validity

Word recognition Vocabulary • Orthographic/phonological structure

•Morphological structure; number of syllables, letters

•Frequency •Frequency •Familiarity •Familiarity •Concreteness-Abstractness [in some contexts: picture-text match]

•Concreteness- Abstractness

•Conceptual Complexity

Animal moms take care of their babies. This mother robin feeds her babies. The baby birds eat worms. A mother kangaroo carries her baby. The baby kangaroo rides in her pouch.


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Text Analyzer: Beginning Books (TABB)™ © Elfrieda H. Hiebert, 2010

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Text Analyzer: Beginning Books (TABB)™ © Elfrieda H. Hiebert, 2010

  1) Kinds of Words: (a) Concreteness/ Familiarity

  Know: concreteness makes a difference in word learning

From T.K. Landauer, K. Kireyev, & C. Panaccione (20090. A new yardstick and tool for personalized vocabulary building. Proceedings of the NAACL HLT Workshop on Innovative Use of NLP for Building Educational Applications, pp. 27‐33. Boulder, CO.

  1) Kinds of Words: (a) Concreteness/ Familiarity

  Know: concreteness makes a difference in word learning

  Don’t know: Degree to which concreteness/familiarity is a factor in the beginning reading of children who learn primarily in the school setting. Particularly: When concreteness/familiarity is overshadowed by an emphasis on minimally different words in extreme decodables.

Sis had a pin. Tad hid it in a rag bag. Did it jab Sis? Did Sis yip? Did Tad fib?


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  2) Type‐Token Ratio (TTR)   Know: Beginners need to have high levels of success to persevere & learn (Brophy et al., 1979)

  Don’t Know: Optimal levels of TTR at different phases in learning to read

Murray, M., Munger, K., & Hiebert, E.H. (2010). A comparison of the beginning reading task in leveled and decodable texts. Paper submitted for presentation at the annual meeting of Society for the Scientific Study of Reading.

  2) Type‐Token Ratio (TTR)   Know: Beginners need to have high levels of success to persevere & learn (Brophy et al., 1979)

  Don’t Know: Optimal levels of TTR at different phases in learning to read

  Probably Know: TTR of .15+ is too high for students in first of three early reading phases

  3) Repetition   Know: Repetition makes a difference in word learning

Murray, M., Munger, K., & Hiebert, E.H. (2010). A comparison of beginning reading task in leveled and decodable texts. Paper submitted for presentation at the annual meeting of Society for the Scientific Study of Reading.

Murray, M., Munger, K., & Hiebert, E.H. (2010). A comparison of beginning reading task in leveled and decodable texts. Paper submitted for presentation at the annual meeting of Society for the Scientific Study of Reading.


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  3) Repetition   Know: Repetition makes a difference in word learning   Don’t know: How much repetition is needed of words with different features as a function of proficiency

  Probably know: Less than 5 repetitions of a word (especially if the word is not highly concrete and has a highly prolific rime) are not enough with students in phase 1 of three phases of learning to read

  Word recognition is an aspect of vocabulary knowledge. Words always have orthographic‐phonological‐morphological features. They also vary considerably in the frequency with which they are used in oral and written language and in the degree to which they are familiar and represent concrete entities in children’s worlds.

  In attempting to find the “winner” of the best word characteristic for teaching children to read, researchers have disregarded three critical variables:

  The overall distribution of words that are highly frequent, decodable, and familiar/concrete in a beginning reading diet.

  The ratio of unique words to total words in a beginning reading diet.

  The repetition of words (with particular features and at particular points in students’ reading acquisition)

  Local or linear text structure  Within‐sentence/within‐paragraph or sentence‐to‐sentence/paragraph‐to‐paragraph

  Ability to parse sentence syntax is an essential if lower‐level component of the text comprehension process (e.g., Graesser, Swamer, Baggett, & Sell, 1996)

  There is near‐universal inclusion of a measure of sentence length in readability formulas (Klare, 1984), including those designed especially for use with primary‐grade text (e.g., Spache, 1953)

  Is sentence length an adequate indicator of syntactic demands in primary‐grade text?


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  Shortening sentences in a text often fails to make it easier and can make it harder (Davison & Kantor, 1982; Pearson, 1974)

  Average sentence length can contribute to overestimating the difficulty of primary‐grade reading materials (Bormuth, 1985; Carver, 1985)

  Average words per T‐unit is a statistically significant predictor of books’ Reading Recovery level, but average words per sentence is not (Cunningham, Spadorcia, Erickson, Koppenhaver, Sturm, & Yoder, 2005)

  Yet, many books are leveled for K‐3 readers using systems that rely on average sentence length such as the Lexile Framework

  Cohesive text is easier for readers in general to comprehend (Gernsbacher, 1990)

  Comprehenders make use of referential cohesion to construct their macrostructure memory model for a text (Kintsch, 1998; Van Dijk & Kintsch, 1978)

  Phonics‐Firsters generally say yes  Whole‐Languagers generally say don’t worry about it   Neither‐Ones generally say no, texts should get gradually harder simultaneously along multiple dimensions and we call for research to teach us how they can do it best

  Phonics‐Firsters generally say no  Whole‐Languagers generally say don’t worry about it   Neither‐Ones generally say yes, and we call for research to teach us how best to take it into consideration


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  Other than the tradition of using average sentence length to predict a text’s syntactic complexity—a tradition we have good reason to question,

  there is very little research to help us select or author text for K‐3 readers that would place appropriate syntactic and cohesion demands on them

  Fortunately, there are a number of researchable questions, the answers to which could advance our knowledge of how the syntax and referential cohesion of primary‐grade text can facilitate reading development

  Degree to which measurements in a study are actually capturing the constructs that they purport to measure

  Study results rest on construct validity   Can have predictive validity but no construct validity   Can have face validity but no construct validity

 Multiple measures to represent constructs   Can be supported but not proven.

Why is it important?

Measures

Constructs

Generalizations/Applications

Theory=Puzzle

Measure = Raw material for puzzle pieces

•  Theory‐driven research

•  Well specified constructs

•  Well‐matched measures to constructs

Constructs = Puzzle pieces


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  Lack of agreement on theory driving text   Language, content, format? (Brooks, 1996; Fountas & Pinnell,

2006; 2002; 1999; Hart‐Hewins & Wells, 1999; Miller, 2000; Peterson, 2001; Syymusiak & Sibberson, 2001; Weaver, 2001)

  Three‐cueing systems?   Theory very complex

  Language (sentence complexity, vocabulary, cohesion, familiarity , structure, predictability)

  Content (story line, familiarity, genre)

  Theory is too simple   One piece puzzle.   Letter/sound supports only

  The constructs break down because they are measured in contrasting ways   Regularity is measured in 5‐10 different ways.

  Text features are operating that are not part of the theory   Decodable text‐ repetition, pacing   Leveled text‐# words/book

  Two definitions:   Words matched to theoretical lessons articulated in teachers’

manuals (e.g. Foorman, Francis, Davidson, Harm, & Griffin, 2004; Stein, Johnson, & Gutlohn, 1999)

  Words matched to actual lessons students received (Mesmer, 2001; 2005; 2010; Jenkins, Peyton, Sanders, & Vadasy, 2004). T

  Small groups of students in short term interventions (14 sessions) had more graphically‐similar error when reading highly decodable text (Mesmer, 2001; 2005).‐Opportunity?

  One‐on‐one tutoring with phonics instruction in long term intervention no effects of text decodability (Jenkins et al. 2005).

  Text analysis:Foorman et al. (2004)   between 15‐69% of words are actually decodable at the time

presented to students


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Study Measure Finding

Juel & Roper‐Schneider (1985)

Three‐Tiered Rating Transfer (seat, bag, yell) Association (law, that) Irregular (come, pear)

Preprimer levels Decodable M= 1.2 Contr0l M=1.8

Mesmer (2005) Hiebert & Menon 8‐level system‐vowel complexity

Highly decodable text: ‐More cvc ‐Fewer silent e words ‐Fewer vowel digraphs; ‐‐‐‐Fewer diphthongs Also ‐more repetitions per word (4.4 vs. 3.1); ‐fewer syllables per word (.1. vs. 1.3).

Study Measure Finding

Compton et al. (2004) Menon & Hiebert Decodable words (all 8 levels) Non‐decodable ‐ multisyllabic ‐nondecodable monosyllabic words Also percentage of high frequency words ‐frequency, rather than decodability to predicted accuracy whereas ‐both high frequency and decodable words predicted fluency

  The different operationalizations of LTTM and regularity impact the interpretations of findings and their potential applicability.

Study Globally‐State Generalization Unexamined Construct

Unpacking the Construct ‐Reinterpretation

Compton et al. (2005) Decodability supports fluency

Single‐syllable, high frequency words enhances reading rate.

Foorman et al. (2004) Some basals offer close to 70% decodability

If teachers follow teachers’ manuals exactly and children learned everything they are taught, basals would range from 15‐70% decodable (M =42%, across six basals)

Juel & Roper/Schneider, 1985

Decodability at the preprimer level supports better word recognition in the first two‐thirds of first grade

Word regularity at the preprimer level along with word repetition and number of syllables supports word recognition

Compton et al. (2004) Juel & Roper/Schneider (1985) Mesmer (2005)

The active ingredients inDluencing word recognition in highly decodable text are regularity and LTTM.

Active ingredients in highly decodable texts that inDluence word recognition include word frequency (Compton, et al. 2005; Hoffman et al., 2001) and word repetition (Juel & Roper/Schneider, 1985; Mesmer, 2005)

Mesmer (2001, 2005)

Readers use a letter/sound strategy more in a decodable text.

At initial stages of reading, a tightly controlled text provides more opportunities to decode but we don’t know if other formats, with the same number of opportunities would result in similar levels of decoding.


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Study Measures Findings Cunningham et al (2005) Texts have three levels of support operating: Discourse‐level Sentence‐level Word‐level

Discourse-Level -Number of morphemes in the book -Number of running words (tokens) in the book -Number of sentences in the book –Number of T-units in the book -Number of unique words (types) in the book Sentence Level -Morphemes/ sentence -Morphemes/t-unit -Words /sentence -Words/t-unit

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Study Measures Findings Cunningham et al (2005) Texts have three levels of support operating: Discourse‐level Sentence‐level Word‐level

Word Level ‐Mean U of the words ‐Morphemes per 100 words – ‐Percentage of high‐frequency words ‐Percentage of high‐frequency words ‐Percentage of onset‐rime decodable words(3 lists ) ‐Type‐token ratio U of the word ending the mostfrequent75 % of the words

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Hatcher Texts support beginning readers in using: Phonological (visual) cues Semantic cues Syntactic cues r (2000)

Phonological & Semantic # of words with 6 or more letters Syntactic ‐Number of words in the longest sentence ‐Syntactic features ??? ‐Number of words in the book ‐Number of pages

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  Ask, “Does the emperor have clothes? Is there construct validity here?”   Face validity ≠ construct validity

  Saying something matches a theory or construct does not make it so.

  Predictive validity ≠ construct validity   Shoe size predicts reading level

  Studying text is complex   Manipulating one text variable shifts others

  Complex =entangled

  It is incomplete to design texts with attention only at the word level (i.e. decodable) or only at the sentence‐level or only at the text level (i.e. leveled).

Decoding

Understand sentences

Connecting sentences

Understanding the Whole

Elaborating


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  Clearly articulated theories should drive text research   Cunningham et al. (2005)   Hiebert & Fisher (2005) –Critical word factor

  Publishers must request that the actual texts used in studies be made available.

Documents

What we will do - TextProjecttextproject.org/assets/library/handouts/Mesmer-Hiebert-CunninghamL... · Schneider, 1985; Mesmer, 2005) Mesmer (2001, 2005) Readers use a letter/ sound