Examining an Effective Encoding and Decoding Prevention ...

Learning Disability Quarterly36(3) 161 –177© Hammill Institute on Disabilities 2012Reprints and permissions: sagepub.com/journalsPermissions.navDOI: 10.1177/0731948712450017ldq.sagepub.com

Approximately, 80% to 85% of students with learning dis-abilities (LD) have been described as having reading dis-abilities (Shaywitz, Morris, & Shaywitz, 2008). Intervention studies designed to prevent reading disabilities have accu-mulated over recent years because of the increased ability for researchers to predict those students who will develop these disabilities (e.g., Mathes et al., 2005). Typically, explicit and systematic intervention is given to those stu-dents either randomly or quasi selected who score below the 25th percentile on screeners in areas of phonological pro-cessing skills, letter name/sounds knowledge, vocabulary, decoding, and spelling abilities. These early intervention studies may help determine if at-risk students are just a result of poor prereading skills and/or low economic or minority status and those who may truly be developing reading disabilities (Fletcher, Lyon, Fuchs, & Barnes, 2007; Fletcher & Vaughn, 2009), and thus, may reduce the amount of students requiring special education services (Scanlon, Vellutino, Small, Fanuele, & Sweeney, 2005).

Reading performance has been demonstrated to be highly related to spelling ability, with correlations ranging from .68 to .93 (Foorman et al., 2006; Morris, Bloodgood, & Perney, 2003). One reason for this relationship is reading and spelling’s shared role of phonological and orthographi-cal processing ability (Adams, 1990; Ehri, 1997). Spelling requires the capacity to process language, segment pho-nemes (i.e., breaking words into individual sounds), and

match each phoneme to its respective letter or letters (i.e., graphemes). Likewise, reading requires the ability to pro-cess language by matching grapheme–phoneme pairs into individual parts of speech to pronounce words.

The process of decoding (i.e., reading words) requires the processing of written symbols into speech, whereas the practice of encoding (i.e., spelling or the ability to build words) involves transposing speech into writing (Moats, 2010; Weiser & Mathes, 2011). Decoding tasks, for pur-poses of this study, include independent, partner, small group, and whole class activities of orally or silently read-ing letters, blending and reading single words and isolated pseudowords (i.e., decodable, nonsense words), lists of words and pseudowords, and connected text (i.e., reading real words in phrases, sentences, paragraphs, stories, and books).

Encoding instruction, for purposes of this study, includes learning to add prefixes and suffixes, spelling rules, word patterns, and syllable types, and how to distinguish between

450017 LDQ36310.1177/0731948712450017Learning Disorder QuarterlyWeiser

Learning Disability Quarterly

1Southern Methodist University, Dallas, TX, USA

Corresponding Author:Beverly L. Weiser, Institute of Evidenced-Based Education and Department of Teaching and Learning, Annette Caldwell Simmons School of Education and Human Development, Southern Methodist University, P.O. Box 750381, Dallas, TX 75275-0381, USA. Email: [email protected]

Ameliorating Reading Disabilities Early: Examining an Effective Encoding and Decoding Prevention Instruction Model

Beverly L. Weiser, PhD1

Abstract

The purpose of this study was to determine whether integrating encoding instruction with reading instruction provides stronger gains for students who struggle with reading than instruction that includes little or no encoding. An instructional design model was investigated to best fit the data of 175 first-grade readers at risk for reading disabilities. Using cross-classified hierarchical linear modeling, variance in students’ posttest scores could adequately be explained by students’ initial encoding and decoding abilities, classroom and intervention encoding instruction time, and the number of supplemental integrated encoding and decoding intervention lessons received. Results indicated that integrating encoding and decoding instruction in first-grade classrooms, as well as supplemental intervention programs, may be the missing link to decreasing and possibly preventing future reading failure for students previously at risk for reading disabilities.

Keywords

instructional strategies, language arts, qualitative methods, quantitative methods, reading

Article

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162 Learning Disability Quarterly 36(3)

words that can be spelled by these strategies and when to memorize irregularly spelled words. In addition, encoding activities include alphabetic knowledge tasks (i.e., using manipulatives—for example, counters, tiles, Elkonin boxes [Elkonin, 1973], plastic letters, letter cards—to represent phonemes in spoken words to increase phonological, pho-nemic, and linguistic awareness, learning to print phoneme–grapheme correspondences and blends), manipulating sounds using letters (i.e., substituting initial or final consonants to make new words), writing or orally spelling words from dictation, and activities that allow for the practice of writing unknown spellings by using previously taught phoneme–grapheme relationships (Henry, 2009; Moats, 2010).

Recent experimental studies have used systematic spell-ing programs to increase struggling spellers develop stron-ger encoding skills (Christensen & Bowey, 2005; Graham, Harris, & Chorzempa, 2002; Roberts & Meiring, 2006). These studies included systematic and explicit encoding instruction that afforded guided encoding practice with the alphabetic principle, grapheme–phoneme correspondences, and manipulations of letters within words. For example, Christensen and Bowey (2005) investigated whether first graders who were given explicit instruction and activities to learn and write phoneme–grapheme correspondences and words made up from these taught correspondences would in fact make stronger gains in phonemic awareness, alphabetic understanding, decoding and spelling compared with other similar students receiving alternate onset–rime or implicit phonics instruction. While all three groups received seventy 20-min small group lessons from trained research assis-tants, the group receiving explicit phoneme–grapheme ses-sions outperformed the contrast groups on spelling measures of untaught words using various vowel combinations, F(2, 102) = 19.11, p < .001, d = 1.4.

Graham et al. (2002) found similar results with 25 sec-ond graders who received forty-eight 20-min supplemen-tal explicit encoding instructions, delivered by graduate research assistants (GRAs), that concentrated on spelling pattern skills, letter-sound correspondences, word sorting, word building (encoding), word studying, and word dicta-tion activities. Students made more significant gains on tests of spelling predictable words, F(1, 27) = 16.25, p = .00, d = 0.91, and unpredictable words, F(1, 27) = 4.26, p = .05, d = 0.63, when compared with a contrast group receiving math intervention. Roberts and Meiring (2006) also com-pared students in two classrooms receiving one of two types of spelling instruction (i.e., explicit phonics instruction embedded in encoding activities that emphasized phono-logical processing, whereas spelling or implicit phonics embedded in literature with no encoding instruction) from their classroom teacher daily for 20 min between September and February of the school year. Students in the class receiving encoding instruction and activities outperformed the other class in spelling untrained regular real words and

pseudowords at the end of the intervention (d = 1.10, p < .05, and d = 1.11, p < .05, respectively), and those students were again significantly higher than the other class at a follow-up assessment in May (d = 0.51, p < .05, and d = 0.81, p < .05, respectively).

In summary, these spelling intervention studies support the synergy between encoding and decoding instruction. Each of these studies used spelling programs that, as expected, had Cohen’s d effect sizes1 for spelling ranging from 0.51 to 1.10, but as an added bonus, those students who received explicit encoding instruction also experienced positive transfer effects to reading words, fluency, and comprehension, with Cohen’s d effect sizes ranging from 0.45 to 2.15, although these students did not receive any supplemental reading intervention.1

Experimental Studies Supporting the Synergistic Relationship of Encoding and Decoding

Recent experimental research has given evidence to sup-port the synergy of integrated encoding and decoding instruction to improve the performance of students at risk for reading disabilities (Blachman et al., 2004; Denton, Fletcher, Anthony, & Francis, 2006; Mathes et al., 2005). Although these studies did not specifically say their inter-ventions consisted of integrating encoding and decoding instruction, careful examination of their interventions revealed that they in fact were teaching encoding and decoding simultaneously (see Weiser & Mathes, 2011).

Blachman et al. (2004) compared 69 second and third graders with poor word reading skills. Students in the treat-ment group (n = 37) received one hundred twenty-six 50-min lessons from certified reading specialists that included the following steps: (a) the introduction and review of letter-sound correspondences; (b) manipulating and building words using sound boards, tiles, and letter cards; (c) fluency exercises of reading the words that were built on flashcards and in connected text; and (d) the writing of practiced sounds and words in dictation activities. These students made greater significant gains than a control group (n = 32) receiving their school’s typical resource instruction by certified special education teachers, with Cohen’s d effect sizes ranging from 0.21 to 0.89 in areas of decod-ing real and pseudowords, comprehension, spelling, and fluency.1

Denton and colleagues (2006) also compared 27 stu-dents with severe reading disabilities in Grades 2 to 3 who had not made adequate progress during the first grade. Students in the treatment group (n = 16) received intensive Tier III decoding and encoding instruction (i.e., either one-on-one or one teacher with two students) that focused on the nature of the English phoneme–grapheme system. Students



Weiser 163

were taught to blend, segment, and manipulate phonemes within spoken words, connect phonemes to graphemes, blend phoneme–grapheme correspondences to form words, and segment and spell words based on their alphabetic knowledge and syllable type. These students outperformed a contrast group (n = 11) that continued to receive their typi-cal special education instruction, with Cohen’s d effect sizes ranging from 0.29 to 1.77 in areas of decoding real and pseudowords, comprehension, spelling, fluency, and writing.1

Mathes et al. (2005) investigated different types of instruction to determine which would best benefit 252 at-risk first-grade students struggling with reading difficulties. Treatment Group I students (n = 78) received enhanced classroom support (i.e., continuous monitoring assessments and professional development on how to use this data for instructional planning). In addition, this treatment group participated in daily scripted 40-min small group lessons (i.e., three students per reading teacher) for 9 months that focused on (a) manipulating and writing letters to help stu-dents understand the relationship of the alphabetic princi-ple, (b) word reading in isolation and in decodable text that systematically included previously learned phoneme–grapheme correspondences, and (c) encoding words from these correspondences. Treatment Group II students (n = 83) received the same classroom support, but reading teachers were able to design their own 40-min lesson to include word work, activities focused on phoneme–grapheme pairs, and guided decoding and writing instruction that focused on the alphabetic principle. Students in a Group III (n = 91) just received the enhanced classroom support and instruc-tion within their classrooms using the school’s existing cur-riculum. Although there were no differences between Groups I and II in decoding, spelling, and comprehension, both groups outperformed Group III students in word read-ing accuracy, nonsense word reading, spelling, and fluency with Cohen’s d ranging from 0.24 to 0.63.1

In summary, these supplemental encoding and decoding interventions included giving at-risk students, with or with-out LD, the opportunity to practice writing previously taught phoneme–grapheme correspondences, blending and segmenting words with these correspondences, encoding words with these correspondences, and practice reading connected text with words using these correspondences. Statistically, significant Cohen’s d effect sizes favoring the treatment groups over the comparison groups not receiving encoding instruction, ranged from 0.26 to 1.77 for measures of decoding, spelling, comprehension, and fluency.

The Present InvestigationThis study investigated the synergistic relationship of simultaneously integrating encoding and decoding instruc-tion to enhance the reading and spelling performances of

first graders at risk for reading disabilities. Although there is empirical intervention research to support connection-ists’ theories that readers must be able to use the phono-logical and orthographic processors to be able to read and understand text (see Weiser & Mathes, 2011), the synergy between encoding and decoding instruction in the early grades has never been examined. Modeling and investigat-ing this relationship could elucidate how integrated encod-ing and decoding instruction (vs. treating decoding and encoding as separate constructs with different curriculums) may assist with ameliorating or at least lessoning the risk of future reading failure.

To explore integrating encoding and decoding instruc-tion to support first graders struggling with reading difficul-ties, an instructional design model was investigated (see Figure 1). This design incorporates using encoding and decoding instruction together to help struggling students gain growth in reading and spelling ability during core classroom reading time, as well as during supplemental small group reading intervention. Initial student status (e.g., pretest score) and intervention teacher variables (e.g., years or certification as a reading specialist) will also be investi-gated. It is hypothesized that students who receive a greater amount of integrated decoding and encoding instruction and practice as a part of their core instruction and supple-mental small group instruction will make the strongest gains in reading and spelling. Investigating this model will also allow careful examination of the following research questions:

Question 1: What are the individual and combined contributions of time allocated to encoding and decoding instruction during core classroom time and supplemental reading intervention on the read-ing outcomes of first graders at risk for reading disabilities?

Question 2: Accounting for both the initial status of these students and their intervention teachers, as well as the time allocated to encoding and decod-ing instruction, can the synergistic relationship between integrated encoding and decoding instruc-tion time be accurately modeled for the possible amelioration of future reading failure?

MethodParticipants

Schools. A total of 22 schools participated in the present study. In all, 19 of these schools were located in a large urban public school district in North Texas, serving diverse student populations in terms of ethnicity (i.e., 17% African American, 53% Hispanic, 21% Caucasian, 8% Asian, 1% Native American/Other) and socioeconomic status (SES).




The remaining 3 schools were Catholic Diocese schools located in a major urban city in North Texas, again with diverse students (i.e., 14% African American, 52% His-panic, 20% Caucasian, 3% Asian, and 11% Multiracial). Each school in the study had between 3 and 15 students in our study (M = 7.95, SD = 3.85).

Classroom teachers. A total of 56 classroom teachers pro-vided core reading instruction to the participating students. Teachers had a range of 1 to 8 participating students in their classrooms (M = 3.12, SD = 1.92). All 56 classroom teach-ers had elementary education degrees. Specific demograph-ics on the classroom teachers were not collected, but we do know that the majority of the teachers were female and Caucasian, and many held English as a Second Language (ESL) certifications.

Intervention teachers. A total of 26 reading specialists from 22 schools provided supplemental reading instruction. In all, 4 of the public schools had 2 reading specialists on their campuses to serve the needs of the school; both teach-ers participated during the year of interest. Intervention

teachers had between 3 and 12 participating students (M = 6.73, SD = 2.74) and taught these students in groups of 3 or 4. All intervention teachers held degrees in elemen-tary education. In total, 18 teachers additionally held ESL certifications and 3 held certifications in special education. Demographics on teachers’ experience were as follows: The mean time as a professional educator was 25.54 years (SD = 10.37), the mean time at their current school was 12.15 years (SD = 7.77), and the mean time as a reading specialist at their current school was 2.19 years (SD = 0.69).

Students. Students were part of the 2007–2008 cohort of a 5-year federally funded grant (i.e., Scaling-Up Effective Interventions for Preventing Reading Difficulties, Institute of Educational Services Grant R305W03257). To identify struggling first-grade readers, classroom teachers nomi-nated students who showed risk for reading difficulties dur-ing the first 2 weeks of the school year. All recommended students were screened to identify possible participants. Although nearly 300 students qualified for the study, fund-ing was available for only 194 students. These students

Student Characteris�cs

Synergy

Classroom Teacher Instruc�ona Supplemental Interven�on Teacher Instruc�on

Ini�al Phonemic,

Reading, and Spelling Ability

+Demographic

Factors

Growth in Reading and

Spelling

Encoding Instruc�on Time Alloca�on

Decoding Instruc�on Time Alloca�on

Encoding Instruc�on Time Alloca�on

Decoding Instruc�on Time Alloca�on

Other Possible Variablesb

Encoding Instruc�on

Decoding Instruc�on

Integrated Encoding and Decoding

Instruc�on

+Greater Growth

in Reading and

Spelling

Growth PerformanceInstruc�on Time

Figure 1. An instructional design of how encoding and decoding time variables may moderate the impact of student’s initial reading and spelling outcomes.aDemographics on classroom teachers were not gathered during this study as the main focus was on the intervention. However, observational data were collected on the engagement of the participating students during their core instruction. This data also gave a picture of how time was spent in the classrooms during the reading/language arts block.bThese variables consisted of intervention teachers’ years as a professional educator, years as a reading specialist, type of reading certification (if held), as well as fidelity of implementation variables such as adhering to the script, pacing, and number of lessons taught compared with the number of lessons expected.



Weiser 165

were randomly chosen to be part of the data collection, although this did not prevent any of the other students from receiving supplemental reading intervention. In all, 19 stu-dents moved during the study, leaving 175 students to be included in this research. Demographics on these students indicated that 108 (62%) were male, 117 (67%) received free or reduced lunch, 30 (17%) received ESL services, and 10 (6%) received special education services for speech. In total, 35 (20%) students were African American, 15 (9%) students were of Asian descent, 42 (24%) students were Caucasian, and 83 (47%) students were Hispanic.

Description of Instructional ContextClassroom teachers agreed to provide at least 90 min of daily reading and language arts instruction as part of their school’s regularly scheduled core reading block using their district’s adopted reading curriculums. In addition, inter-vention teachers were asked to deliver 30 to 40 min of daily supplemental small group instruction (i.e., 3–5 students) using Proactive Early Interventions in Reading (PEIR; Mathes & Torgesen, 2005). The PEIR curriculum has been used successfully to provide effective small group instruc-tion for reading-delayed students (i.e., students with LD and/or at risk for reading disabilities) in addition to their core reading classroom instruction (Denton et al., 2006; Mathes et al., 2005; Wanzek & Vaughn, 2007).

PEIR is a supplemental intervention curriculum that simultaneously provides encoding and decoding instruction to help students understand the alphabetic principle and develop in all forms of literacy. The underlying instructional design of PEIR arranges the instructional environment to reduce errors by the teacher implementing the intervention as well as by the students receiving the instruction. Teachers follow scripted, systematic 30- to 35-min lesson plans built from a carefully laid out scope and sequence. Level 1’s 120 lessons are constructed so that each of these strands is inter-woven into daily lessons comprising 7 to 10 short interrelated activities that focus on building alphabetic decoding skills and having students reading fully decodable connected text by Lesson 7. Importantly, daily scripted lessons include mul-tiple opportunities for students to play word games to pro-mote phonemic awareness, manipulate and practice writing new and previously taught grapheme–phoneme correspon-dences, decode words using previously taught correspon-dences, independently encode words with these relationships, and read connected text, again using words with these grapheme–phoneme correspondences. An analysis of these lessons determined that teachers should spend approximately 36% of their time teaching letter-sound correspondences by having students manipulate printed letters, practice sound/letter(s) dictation, whole word spelling dictation, and other encoding activities. Remaining time was for decoding prac-tice, fluency improvement, comprehension, and review.2

Intervention teacher implementation. Each participating reading specialist received a total of four 6-hr days of pro-fessional development to learn how to teach PEIR by two reading coaches hired for the study. Two of these days were held prior to the intervention start date, and two other follow-up days were held during the school year. During these mandatory workshops, teachers had multiple opportu-nities for practicing the scripted lessons, developing visual and auditory cues, giving appropriate error corrections, learning appropriate pacing, and asking questions. Interven-tion teachers also received feedback from their three obser-vations (described below) and had monthly visits from the coaches to ask questions about implementation, go over stu-dents’ data, and collaborate on how to improve instruction and student performance.

MeasuresScreener/Placement Test

A first-grade passage from the Continuous Monitoring of Early Reading Skills (CMERS) Oral Reading Fluency (ORF) tests was given as a screener to select participants. These passages were developed as part of an Internet-delivered program, Istation’s Indicators of Progress–Early Reading (ISIP-ER; Mathes, Torgesen, & Heron, 2008) and had recently been subjected to substantial field testing to determine equivalence of difficulty (see Mathes et al., 2005). Alternate form reliability for the first-grade passages is reported as .95. ORF was measured as words read cor-rectly per minute (WRCM) on a 1-min timed end-of-first-grade-level passages. Those students reading six or fewer words qualified to be in the pool of possible participants. Students were then given an untimed placement test, including identifying unalphabetized letter names and letter sounds and a short comprehension assessment, where stu-dents were to read increasingly longer paragraphs and then asked questions at the end of each paragraph. Due to the severity of the reading difficulties of these students (e.g., mean ORF was 0.2 WRCM; comprehension mean = 0), all students were placed to begin at Level 1, Lesson 1.

Pretest/Posttest MeasuresDecoding measures. Two decoding subtests of the

Woodcock–Johnson III (WJIII; Woodcock, McGrew, & Mather, 2001) were administered: Letter Word Identifica-tion and Word Attack. The Letter Word Identification sub-test is an untimed measure of a student’s ability to read progressively harder phonetically and nonphonetically spelled words. The Word Attack subtest is an untimed test of a stu-dent’s ability to read progressively harder pseudowords that are all phonetically decodable. Testing is discontinued once a student misses six items. The median reliability coefficients’




alpha for all age groups for the standard battery of the WJIII ranges from .81 to .94. Each subtest has two forms (Forms A and B) that are of equivalent difficulty, and the average alternate reliability coefficients all exceed .90.

Encoding measure. The Spelling subtest of the WJIII was administered to measure students’ encoding ability. Stu-dents were first dictated letters to write, followed by pro-gressively harder dictated words. Once a student had six consecutive errors, the testing was stopped. Reliability for the Spelling subtest of the WJIII is .90.

ORF. CMERS ORF tests were administered to determine words read correctly during a 1-min timed test (see the sec-tion “Screener/Placement Test” for a description). For the pretest battery, students read another 1-min timed passage and words read correctly during that time became the stu-dents’ score. Although it was intended for students to read two CMERS passages, GRAs were told to discontinue if the student read less than six words correctly (which was the case for all 194 students). For the posttest battery, however, students read two, 1-min timed passages. Words read cor-rectly were counted for each of these passages and then the two scores were then averaged to attain the students’ post-test score.

Comprehension measure. The Passage Comprehension subtest of the WJIII was administered to measure students’ ability of performing comprehension cloze activities. Stu-dents read short passages and try to determine a correct or adequate response to a missing word in one of the sentences. Once a student misses six consecutive items, the testing is stopped. Passage Comprehension reliability is .88.

Progress monitoring. Progress monitoring oral fluency assessments, as well as daily group mastery checks, are built into the PEIR curriculum, allowing intervention teach-ers to keep records on their students and to inform instruc-tion for the following session.2

Data CollectionStudent Progress Monitoring, Attendance, Instruction Time, and Lessons Received

Intervention teachers kept records of the previously men-tioned oral fluency assessments and mastery checks for each student on printed forms that were collected by GRAs twice a month. The information on these forms also included student attendance, taught lesson numbers, oral fluency graphs, and daily length of instruction time.

AssessmentsFive GRAs were trained to give the screener, pre-, and posttest assessments by the reading coaches and the author. All GRAs participated in testing administration workshops and completed practice administrations successfully prior

to testing. Tester interrater reliability checks were con-ducted prior to screening, pre testing, and post testing on all subtests to assure high reliability of testing and scoring. Reliability had to be above .90 prior to administering assessments; actual reliability ranged from .91 to .97 (M = 0.95, SD = 0.04). All assessment teleforms were checked for accuracy by another GRA before scanning into a computer.

Direct Observation MeasuresClassroom teacher observations. Three classroom

reading/language arts block observations (i.e., October, January, and March) were completed by the author (trainer) and two other GRAs. Interscorer reliability between observ-ers ranged from .92 to .99 (M = 0.96, SD = 0.05). Although the main purpose of these observations was to determine the level of academic engagement of participating stu-dents, information was also gathered on the nature of the instruction being provided, that is, materials used, type of instruction (i.e., whole group, small group, independent practice, etc.), and time spent on various activities (i.e., minutes on phonemic awareness, decoding, spelling, etc.). Observers used a tool previously used in studies to mea-sure how time is spent in the classroom during the reading/language arts block (see Mathes et al., 2005; see also Weiser, 2010). For purposes of this study, only classroom activities involving decoding (i.e., minutes spent blending words and any reading done independently, with a partner, with a group) and encoding (i.e., minutes used for writing or manipulation tasks involving alphabetic knowledge, word study, word building, and spelling) were included, coded, and analyzed. Coding and calculations were checked by another GRA, making sure to have 100% accuracy.

Intervention Implementation ObservationsIntervention teachers’ groups were also observed 3 times across the academic year by two hired reading coaches, using an observation tool previously used to measure fidel-ity (Denton et al., 2006; Mathes et al., 2005). Interscorer reliability was .92. A 4-point rating scale (i.e., 0 = expected, but did not occur; 1 = poor; 2 = average; 3 = excellent; and NA = not applicable) was used to evaluate the fidelity of implementation of each activity during a lesson across three categories: (a) instructional consistency of observed lesson with respect to expected lesson, (b) appropriate activity pacing, and (c) implementation of prescribed procedures including following the script, scaffolding instruction, and error correction techniques. After the observations, the reading coaches would meet with the intervention teacher to discuss areas of strength and weaknesses, going over previously discussed mastery check forms, and answering questions.



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Data Analytic StrategyStatistical ProceduresThe statistical package R (R Development Core Team, 2007) was used to investigate an instruction model that included integrated encoding and decoding instruction from classroom reading teachers and supplemental inter-vention teachers (see Figure 1). Statistical procedures will now be outlined in terms of the questions they intend to answer.

To identify the instruction time variables that explained the most variance in students’ ending decoding and encod-ing performances, multiple linear regression models and commonality coefficient analyses were consulted. According to Nimon, Lewis, Kane, and Haynes (2008), commonality coefficient data analysis provides an alternative method to multiple regression for determining the partitioning vari-ance (i.e., unique or shared variance) accounted for by each of the respective predictor variables (i.e., student initial characteristics, classroom and intervention teachers’ encod-ing and decoding instruction time, and implementation variables such as pacing and accuracy) on various depen-dent variables (i.e., posttest scores).

A cross-classified hierarchical linear modeling (HLM)3 approach was used to represent the data of the participat-ing students and teachers in this study to investigate whether the synergistic relationship between encoding and decoding instruction and student outcomes can be accurately modeled. This multilevel cross-classified instruc-tional model accounted for the initial status (i.e., pretest scores and demographic factors) and final performances (i.e., posttest scores) of the participating students and included core classroom and intervention reading teacher variables (i.e., encoding time, decoding time, amount of lessons, etc.). Students included in this present study were nested at a second, cross-classified level that included the combined teaching of classroom and intervention teach-ers for the following reasons. First of all, students within an intervention teacher group may have come from differ-ent classroom teachers. Second, some students from the same classroom may have been in different intervention groups or even have different intervention teachers, therefore not allowing for a typical three-level nesting model.

The cross-classified instructional model used here attempted to measure growth at the individual student level (Level 1) by incorporating variables from the classroom teachers and intervention teachers (Level 2). Specifically, the following random intercept model was analyzed:

Yi(jk)

= γ00

+ γ10X

i(jk) + γ

01W

(jk) + υ

0j +v

0k + e

i(jk).

This model can be broken down and explained in the following two levels:

Level 1 model: Yi(jk)

= β0(jk)

+ β1(jk)X

i(jk) + e

i(jk),

where Y is the dependent variable, i is the student, j is the classroom reading teacher, and k is the supplemental inter-vention reading teacher. (jk) is the combined effect of classroom and intervention teachers. X equals the student’s initial status, and X

i(jk) are the vectors of student level

covariates. β0(jk)

is the expected value for the dependent variable when X

i(jk) equals zero, and e

i(jk) represents the

residual error of the model.

Level 2 model: β0(jk)

= γ00

+γ01W

(jk) + υ

0j + v

0k.

β1(jk)

= γ10

.

At this second level, γ01

represented the cross-classified fixed effects of both teachers around the intercept, W

(jk) are

the vectors of both teacher covariates, υ0j

equals the vari-ance from the classroom teacher, and v

0k is the variance

from the intervention teacher. Fixed effects from the core classroom teachers and the reading intervention teachers were added at this level.

Exploratory multilevel analyses were conducted to determine the best model fit in predicting the reading and spelling performances of first graders struggling with read-ing difficulties. To make the best predictor model, the Akaike information criteria (AIC; Akaike, 1987) and the Bayesian information criteria (BIC; Schwarz, 1978) mea-sures were consulted to find the goodness of fit for each statistical model. According to Cohen, Cohen, West, and Aiken (2003), lower AICs and BICs mean that a model is considered to be more likely to be the true model.

ResultsMeasuring Growth

To discuss a cross-classified multilevel model of instruc-tion for first-grade struggling readers, it was first necessary to (a) examine the contribution of students’ initial charac-teristics and intervention teacher demographics (i.e., years as a professional educator, years as a reading specialist, certification status); (b) verify that on average, significant academic growth occurred over the academic year; and (c) investigate intervention teachers’ fidelity scores to deter-mine whether any of these variables contributed to post-test achievement. Linear regression exploratory models revealed pretest scores (in all measured constructs) as sig-nificant predictor variables of dependent posttest scores, with variance explained ranging from 4.45% to 19.77%. Other student initial characteristics (i.e., ethnicity, gender, SES, attention-deficit hyperactivity disorder [ADHD], and services received) explained less than 1% of the variance in students’ posttest scores, leaving only students’ pretest




scores to be included as Level 1 initial status predictor vari-ables. Initial intervention teacher demographics (e.g., years as a reading specialist and years of being a professional educator) also only explained less that 1% of the variance in students’ posttest scores.

Second, it was important to verify whether participating students, all of whom received core reading classroom instruction and PEIR supplemental instruction, made on average statistically significant gains in the given depen-dent measures across the academic year. Paired-sample t tests were performed to measure if there was statistically significant growth in areas of encoding, decoding, compre-hension, phonemic awareness, and fluency. Results indi-cated that the participating students did make statistically significant gains from pre- to post test of these constructs, with Cohen’s d effect sizes ranging from 0.80 to 3.43 (M = 1.95, SD = 0.80).

Data from the intervention teacher fidelity observations were then consulted for any other possible instructional fac-tors that may have been predictive of student final posttest performance. Although two variables (i.e., adhering to the script’s instruction, error corrections, and scaffolding tech-niques, and adjusting pacing to reflect the students’ strengths and weaknesses) did not produce statistically significant differences in students’ posttest scores, the quantity of lessons (i.e., lessons taught compared with the expected

number) actually completed was highly variable. Although it was the intent that teachers complete one lesson per day, some teachers often were not able to complete a whole les-son and would have to continue the following day. Lessons actually taught ranged from 54 to 120, and due to this span of instructional intervention time, the number of lessons received was added as a factor in the following statistical analyses.

Next, to investigate Question 1 (i.e., What are the indi-vidual and combined contributions of time allocated to encoding and decoding instruction during core classroom time and supplemental reading intervention on the reading outcomes of first graders at risk for reading disabilities?) it was necessary to determine which instructional variables explained differences of individual students’ posttest scores. Descriptive statistics were computed for classroom and intervention time spent on encoding and decoding instruc-tion (see Table 1 for results).

Analyzing the time variables to examine the variance among participating student outcomes, there were discrep-ancies between what was expected versus what was actu-ally observed. For example, classroom teachers were asked to spend 90 or more minutes for their reading and language block, but the average observed range was 26 to 107 min (M = 68.67, SD = 18.82). Intervention teachers were requested to provide 30 to 40 min of instruction, but actual

Table 1. Descriptive Statistics of Classroom and Intervention Encoding and Decoding Time.

VariableNumber of students

Mean amount of time

Percentage of time (%) SD

Minimum time

Minimum percentage of time (%)

Maximum time

Maximum percentage of time (%) SE

CT reading-language arts block total time per day

163a 68.67 18.82 26.00 107.00 1.47

CT encoding time in minutes per day

163 12.82 10.38 0.00 34.00 0.81

CT encoding percentage of total block time

163 18 0.15 0 51 0.01

CT decoding time in minutes per day

163 16.01 10.13 0.00 46.00 0.79

CT decoding percentage of total block time

163 23 0.14 0 78 0.01

IT small group instruction total time per day

175 36.71 4.43 17.00 46.67 0.33

IT encoding time in minutes per day

175 8.18 1.99 3.33 11.33 0.15

IT encoding percentage of total time per day

175 23 0.06 8 34 0.00

IT decoding time in minutes per day

175 18.72 4.43 8.33 28.33 1.00

IT decoding percentage of total time per day

175 51 0.01 27 76 1.24

Note: CT = classroom teacher; IT = intervention teacher. All numbers are the average amount of time per day taken over all observations.aThree CTs (involving 12 students) did not consent to be observed during the study.



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observed times ranged from 17 to 47 min (M = 36.71, SD = 4.43). Time spent on encoding and decoding activities from both teachers was also highly variable (e.g., classroom teachers spent between 0 and 34 min for encoding instruc-tion and between 0 and 46 min on decoding instruction). It was noted that these time variables needed further investigation.

Linear regression models were then run to determine the R2 variance explained by classroom encoding and decoding activity time and intervention encoding and decoding time. Commonality coefficient analyses revealed how these time variables contributed uniquely or collectively with other variables. These findings are presented in Table 2.

For measures of encoding, decoding, fluency, and pas-sage comprehension posttest assessments, classroom and intervention encoding time explained a much larger per-centage of the defined R2 variance in posttest performance than time spent of decoding. For these measures, classroom encoding time explained an average of 35.65% of the R2 variance of student posttest scores (SD = 7.92, SE = 3.54)

and supplemental intervention encoding time accounted for an average of 40.53% of the R2 variance (SD = 10.91, SE = 4.88), meaning that total encoding instruction time students on average received explained about 76% of the variance in posttest scores.

The results presented above for Question 1 were further investigated with the results from Question 2: Accounting for both the initial status of these students and their inter-vention teachers, as well as the time allocated to encoding and decoding instruction, can the synergistic relationship between integrated encoding and decoding instruction time be accurately modeled for the possible amelioration of future reading failure?

Several HLM “model building” procedures were com-puted for each of the dependent variables, including three types of model fit examinations using (a) encoding covari-ates (i.e., initial pretest encoding scores, classroom and intervention encoding time, and amount of supplemental encoding and decoding intervention lessons that students received) and (b) decoding covariates (i.e., initial pretest

Table 2. Regression Effects Using Encoding and Decoding Variables to Predict Posttest Performance.

Dependent variable Predictor (x) R2 B estimate p Unique Common Total R2 (%)

Word Identification Intercept .117 448.172 .000 Class encoding time 0.449 .001 0.067 −0.010 0.057 49.19 Class decoding time 0.071 .611 0.002 −0.002 0.000 0.00 Intervention encoding time −0.795 .001 0.058 −0.021 0.038 32.36 Intervention decoding time 0.100 .351 0.005 −0.005 0.000 8.58Word Attack Intercept .079 485.829 .000 Class encoding time 0.253 .014 0.036 −0.010 0.026 32.53 Class decoding time −0.034 .755 0.001 0.004 0.005 5.70 Intervention encoding time −0.522 .007 0.043 −0.001 0.042 53.16 Intervention decoding time 0.021 .902 0.000 0.002 0.003 3.29Passage

ComprehensionIntercept .088 459.394 .000

Class encoding time 0.234 .021 0.031 −0.004 0.027 30.68 Class decoding time 0.106 .327 0.006 −0.004 0.002 2.27 Intervention encoding time −0.515 .002 0.060 −0.027 0.033 37.50 Intervention decoding time 0.117 .162 0.011 −0.009 0.002 2.50Spelling Intercept .069 460.889 .000 Class encoding time 0.225 .023 0.039 −0.006 0.025 35.94 Class decoding time −0.102 .329 0.006 −0.003 0.008 11.59 Intervention encoding time −0.410 .029 0.029 −0.008 0.021 29.86 Intervention decoding time 0.115 .156 0.012 −0.009 0.003 3.77Oral Reading

Fluency (ORF)Intercept .092 49.370 .000

Class encoding time 0.414 .014 0.036 −0.008 0.028 29.89 Class decoding time 0.035 .846 0.000 0.001 0.001 1.09 Intervention encoding time −1.039 .001 0.063 −0.016 0.047 50.76 Intervention decoding time 0.146 .290 0.007 −0.007 0.000 0.00

Note: Dependent variable = posttest score; Word Identification, Word Attack, Passage Comprehension, and Spelling subtests = Woodcock–Johnson III (WJIII; Woodcock, McGrew, & Mather, 2001); ORF = Continuous Monitoring of Early Reading Skills (CMERS; Passages taken from ISIP-ER; Mathes, Torgesen, & Heron, 2008); Unique = x’s unique effect; Common = Σx’s common effects; Total = unique + common; R² (%) = total/R².




decoding scores, classroom and intervention decoding time, and the amount of intervention lessons that students received). Posttest scores were grand mean centered to allow for the cross-classification of both teachers. To evalu-ate the significance of the models, the lowest AIC and BIC values were identified to find the models that most truly fit the data for each dependent variable. Table 3 presents the model fit estimates for three of the posttest scores.3

The null model for each dependent variable was repre-sented by the same statistical formula:

Yi(jk)

= γ00

+ υ0j

+v0k

+ ei(jk)

,

where Y is the dependent variable (i.e., students’ posttest score), i is the student, j is the classroom reading teacher, and k is the supplemental intervention reading teacher. (jk) is the combined effect of both teachers at the student level. γ

00 represents the grand intercept fixed effects estimate for

all participating students, υ0j

represents the variance from the classroom teacher around the intercept

, v

0k represents

the variance from the intervention teacher, and ei(jk)

is the error represented by the students in their cross-classified nesting structure. This null model represented the average predicted posttest score for each of the 175 participating students, and the AIC and BIC values of this null model were compared with each of the subsequent models.

Model 1 for each of the dependent variables was then computed using the following formula:

Posttest Scorei(jk)

= γ00

+ γ10

× Initial Status

i(jk) + υ

0j +v

0k + e

i(jk).

In this model, students’ initial encoding or decoding pre-test score can be entered as a Level 1 variable to see whether the model becomes a better predictor estimate of posttest outcome.

Model 2 then adds covariates for the classroom and the intervention teachers’ time spent on encoding and/or decod-ing instruction using the following formula:

Posttest Scorei(jk)

= γ00

+ γ10

× Initial Status

i(jk) + γ

01j × Classroom Encoding

or/and Decoding Time(jk)

+ γ02k

× Intervention Encoding or/and Decoding Time

(jk)+ υ

0j +v

0k + e

i(jk).

Here, γ01j

represents the effects of the classroom teachers’ instruction time around the intercept, and γ

02k represents the

effects of the intervention teachers’ instruction time around the intercept.

Previously mentioned findings established that students’ posttest scores were heavily influenced by the amount of supplemental encoding and decoding lessons received. This second level covariate (i.e., γ

03k) will now be included in

Model 3 as shown by the following formula:

Posttest Scorei(jk)

= γ00

+ γ10

× Initial Status

i(jk) + γ

01j × Classroom Encoding

or/and Decoding Timej + γ

02k × Intervention Encoding

or/and Decoding Timek + γ

03k × Number of Intervention

Lessons Receivedk+ υ

0j + v

0k + e

i(jk).

Referring to the first two parts of Table 3 (i.e., model fit estimates for ORF using encoding variables and model fit estimates for ORF using decoding variables), model fit estimates of performance in fluency were better defined in Model 3 because the AIC and BIC values were consider-ably lower from the null model. After careful examination of the model fit estimates for all the other dependent vari-ables (see Table 3), Model 3 continues to be the best fit model when using encoding or/and decoding variables to predict ending performance in encoding, decoding, fluency, and comprehension assessments.4

In summary, statistical growth measurement procedures, linear regression modeling, commonality coefficients, and HLM were consulted to find the best cross-classified instructional model to best fit the data of the participating 175 students. Students’ posttest scores can be statistically predicted by adding the most critical encoding and decod-ing variables to a model: students’ initial encoding or/and decoding pretest score, classroom and intervention encod-ing or/and decoding time, and the number of encoding and decoding supplemental intervention lessons they received during the span of the study.

DiscussionThe purpose of this study was to investigate the possible synergy of using effective integrated encoding and decod-ing instruction simultaneously to enhance the reading and spelling performances of first graders at risk for reading disabilities. An instructional design model was created that incorporated using encoding and decoding instruction to help first-grade students at risk for not gaining growth in reading, spelling, fluency, and comprehension. It was hypothesized that students who received a greater amount of integrated decoding and encoding instruction as a part of their core instruction and supplemental small group instruc-tion would make the strongest gains in reading and spelling growth. In addition, it was hypothesized that modeling and investigating this relationship could elucidate how inte-grated encoding and decoding instruction could assist with ameliorating or at least with preventing reading failure.

These hypotheses were addressed by performing statisti-cal procedures with the data of 175 participating students who were to receive core classroom reading instruction and daily supplemental small group reading intervention instruction. As reported in the “Results” section, growth from pre test to post test for these students was statistically significant on all posttest assessments, and results from



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Table 3. Model Fit Estimates Using Encoding and Decoding Variables for Oral Reading Fluency, Letter Word Identification, and Comprehension.

Model fit estimates for ORF using encoding variables

M0: Null model

M1: + Student

beginning encoding level

M2: + Classroom

encoding time

M3: + Ending

encoding/decoding intervention lesson

Estimate SE Estimate SE Estimate SE Estimate SE

Fixed effects Intercept, γ

0038.81 2.207 38.7981 2.112 34.411 3.027 0.636 9.394

Starting encoding level, γ10

0.641 0.122 0.625 0.121 0.532 0.183 Classroom encoding time, γ01j

0.331 0.172 0.338 0.159 Intervention end lesson, γ

02k0.38 0.102

Random effects σ2

ei(jk) 437.536 382.960 378.071 359.510 σ2

u0j 29.566 <0.001 <0.001 <0.001 σ2

v0k 32.769 48.281 41.057 22.190 Fit AIC 1,480 1,457 1,455 1,442 BIC 1,492 1,472 1,474 1,467 χ² 1,472 1,447 1,443 1,426

Model fit estimates for ORF using decoding variables

M0: Null model

M1: + Student

beginning decoding level

M2: + Classroom

decoding time

M3: + Ending




0038.81 2.207 38.651 1.913 39.872 3.194 5.832 9.668

Starting decoding level, γ10

0.546 0.104 0.512 0.104 0.479 0.102 Classroom decoding time, γ

01j−0.079 0.163 −0.027 0.157

Intervention end lesson, γ02k

0.375 0.101Random effects σ2

ei(jk) 437.536 395.791 396.941 368.900 σ2

u0j 29.566 <0.001 <0.001 <0.001 σ2

v0k 32.769 27.626 25.170 19.367 Fit AIC 1,480 1,457 1,458 1,447 BIC 1,492 1,472 1,477 1,469 χ² 1,472 1,447 1,446 1,433

Model fit estimates for Letter Word Identification using encoding variables

M0: Null model

M1: + Student


M2: + Classroom

encoding time

M3: + Ending




000.712 1.894 0.499 1.668 −4.5006 2.304 −31.576 7.6


0.532 0.095 0.522 0.093 0.457 0.091 Classroom encoding time, γ

01j0.386 0.136 0.37 0.126



ei(jk) 245.943 215.798 213.521 202.037 σ2

u0j 47.135 24.084 17.68 3.171 σ2

v0k 21.82 19.69 12.451 19.611

(continued)





M0: Null model

M1: + Student


M2: + Classroom

encoding time

M3: + Ending



Fit AIC 1,399 1,468 1,367 1,356 BIC 1,411 1,484 1,388 1,377 χ² 1,391 1,458 1,362 1,342

Model fit estimates for Letter Word Identification using decoding variables

M0: Null model

M1: + Student


M2: + Classroom

decoding time

M3: + Ending




000.712 1.894 0.396 1.565 0.931 2.698 −31.16 8.451



01j−0.034 0.14 0.007 0.124



ei(jk)245.943 220.662 220.642 201.822

σ2u0j

47.135 20.201 20.361 2.7.02 σ2

v0k21.82 13.435 13.14 34.705

Fit AIC 1,399 1,371 1,373 1,361 BIC 1,411 1,387 1,392 1,383 χ² 1,391 1,361 1,361 1,347

Model fit estimates for passage comprehension using encoding variables

M0: Null model

M1: + Student


M2: + Classroom

encoding time

M3: + Ending




000.446 1.275 0.261 1.103 −2.286 1.652 −13.306 5.587


0.438 0.072 0.432 0.071 0.405 0.072 Classroom encoding time, γ

01j0.199 0.101 0.197 0.101



ei(jk)157.174 131.670 131.500 127.705

σ2u0j

25.376 16.945 12.807 10.981 σ2

v0k1.246 <0.001 <0.001 <0.001

Fit AIC 1,316 1,285 1,283 1,281 BIC 1,328 1,300 1,302 1,303 χ² 1,308 1,275 1,271 1,267

(continued)

Table 3. (continued)

Model fit estimates for Letter Word Identification using encoding variables



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M0: Null model

M1: + Student


M2: + Classroom

encoding time

M3: + Ending



Model fit estimates for passage comprehension using decoding variables

M0: Null model

M1: + Student


M2: + Classroom

decoding time

M3: + Ending




000.446 1.275 0.197 1.073 −0.975 1.996 −14.069 6.195



01j0.019 0.106 0.053 0.104



ei(jk)157.174 133.620 133.570 127.090

σ2u0j

25.376 13.603 13.638 9.569 σ2

v0k1.246 <0.001 <0.001 <0.001

Fit AIC 1,316 1,284 1,286 1,284 BIC 1,328 1,300 1,305 1,305

χ² 1,308 1,274 1,274 1,270

Note: ORF = Oral Reading Fluency; AIC = Akaike information criteria; BIC = Bayesian information criteria.

Table 3. (continued)

multiple regression analyses revealed that students’ initial pretest scores were very predictive of later performance. Commonality coefficients and exploratory linear regression models indicated strong support for incorporating more encoding instruction in the classroom and the importance of daily supplemental integrated encoding and decoding inter-vention instruction for first graders struggling with reading disabilities.

After carefully examining and investigating this instruc-tional design model and student outcome data in areas of encoding, decoding, fluency, and comprehension, it became necessary to explain the variability in posttest scores. Although on average, students did make statistically sig-nificant gains in all dependent measures, there was a sub-stantial range in posttest achievement between participating students. After consulting the regression effects of using separate and combined encoding and decoding instruction time on posttest assessments, encoding instruction time explained significantly more of the multiple R2 variances in students’ scores on all reading and spelling posttest assess-ments. Using observational data, we knew that as the major-ity of teachers spent adequate time with decoding instruction and practice, the variability in word reading, passage com-prehension, spelling, and fluency was adequately explained by (a) the encoding instruction each student received from their core classroom reading teacher and (b) the number of integrated encoding and decoding lessons they received from their supplemental reading intervention teacher. It is important to note here that we are not saying that only encoding instruction made the difference in posttest scores.

Instead, we are discussing how teaching decoding instruc-tion integrated with more encoding instruction may help reduce the number of students at risk for reading LD. In addition, the supplemental small group reading intervention lessons that students were receiving included an average of 74% of instructional time spent on integrated encoding and decoding activities. The number of these integrated inter-vention lessons received was also strongly predicative of posttest achievement in all construct assessments, and thus gives evidence to support using encoding and decoding instruction simultaneously to enhance reading and spelling performance.

This research indicates that the synergistic relationship between encoding and decoding instruction can be accu-rately modeled to minimize the number of early readers who may be prone to have reading and spelling disabilities. The best fit models for investigating ending performance using encoding or/and decoding instruction included the following critical variables: students’ initial encoding or/and decoding ability, core classroom and supplemental encoding and decoding instruction time, and the number of integrated encoding and decoding lessons that students received.

To best illustrate how integrated encoding and decoding instructional models can be used to explain the possible amelioration of first-grade reading disabilities, simulations using data from Model 3 of Table 3 were conducted. For example, Table 4 illustrates hypothetical examples to pre-dict the differences that educators can contribute to students reading the recommended end-of-first-grade benchmark of 40 WRCM on ORF (Good & Kaminski, 2002).

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Table 4. Using the Data to Show the Importance of Using Classroom Encoding Instruction With Supplemental Encoding and Decoding Lessons.

Intercept: Initial starting estimate

Initial status: Encoding (WJIII spelling pretest mean centered

score) 0 × 0.532

Average classroom encoding time spent

per day × 0.338

Number of supplemental encoding/decoding intervention

lessons received × 0.380

Student’s predicted amount of words read correctly at post test

0.636 0 5 min per day = 1.88 60 lessons = 22.80 24.3160.636 0 5 min per day = 1.88 120 lessons = 45.60 48.1160.636 0 30 min per day = 10.08 60 lessons = 22.80 33.5160.636 0 30 min per day = 10.08 120 lessons = 45.60 57.116

Note: WJIII = Woodcock–Johnson III.

All participating students read on average about one word per minute at pre test (range = 0–6 words, with the majority of students reading 0 words per minute). The first two columns of Table 4 are representative of the starting intercept for all students (i.e., 0.634) and the students’ average initial encoding ability. This initial mean centered score of 0 (which according to the model was multiplied by 0.532) was similar across all students as the majority of students were only able to write a few letters and no words at pre test. The third column was something that varied between students. As students received a range of 0 to 34 min per day of classroom encoding instruction, it is possible that students receiving 30 min per day were at an advan-tage over students who only received an average of only 5 min. In addition, integrated encoding and decoding sup-plemental lessons received by participating students ranged from 54 to 120 lessons. Referring to the fourth column, the amount of these intervention lessons that a student received was critical in predicting whether these previ-ously at-risk students would reach the previously recom-mended benchmark.

LimitationsAlthough the proposed instructional design model may look promising to enhance the performance of first graders struggling with reading difficulties, it may not be represen-tative of all students struggling with reading difficulties, as classroom settings and reading curriculums vary between schools, and some schools may not offer supplemental reading intervention instruction. Careful consideration was taken, however, to define the characteristics of the students, the classroom teachers, the intervention teachers, the mea-sures and coding forms used, the intervention, and the sta-tistical procedures used to be beneficial in generalizing to other populations similar to the first-grade students in the present investigation.

Another limiting factor in this study was that there were only three observations of each instructional setting. All teachers were allowed to pick from a list of possible dates so that they would know that they were being observed and

hopefully had time to prepare for their lessons. We may not have observed the same results if we had come on another day unannounced.

Finally, this study examined using encoding and decod-ing instruction simultaneously to enhance the reading abili-ties of a relatively small sample of first graders at risk for reading disabilities. Therefore, the results and the models may not generalize to other students, especially those in other grades. Although parsimonious models help educa-tors and researchers better understand what reading and spelling subskills need to be strengthened, these statistical models reflect the characteristics of the students and the teachers participating in this study.

There is also a basic methodological limitation in the design of this research study as the children were not ran-domly allocated to an experimental or contrast group. Although the growth of these students’ achievement in the posttest assessments represented statistically significant differences from pretest scores and substantial Cohen’s d effect sizes to show the effectiveness of the intervention, caution should be used when interpreting the results as there was not a contrast group to compare growth on the outcome variables. It could be that students in other intervention groups or classrooms where the intervention was not pres-ent would have the same rate of growth. Although it was not the purpose of this present study to compare instructional settings, this particular study intended to investigate how classroom and intervention encoding and decoding instruc-tion were related to growth for this particular group of at-risk first graders. Another limitation of this research study that may reduce the generalizability of these findings is that the intervention teachers, while being employees of the participating schools, were highly qualified reading spe-cialists who received quality training and support to teach PEIR, a research based, scripted encoding and decoding program, with excellent fidelity. It is not clear whether sim-ilar results would be achieved with less knowledgeable teachers who were not getting support to implement the les-sons (and perhaps had more fidelity variability) or whether the same results could be seen with using other supplemen-tal reading intervention curriculums.



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Future Research

Although future educational research will focus on how educators, principals, school boards, and administrators can contribute to better teacher instruction and improved stu-dent performance, it is important that high-quality experi-mental research continue to investigate the best teaching methods that have proven to help students with reading difficulties. It is also necessary for statistical data analyses to be run on effective programs to see the areas that explain the most variance in students’ scores. Identifying key miss-ing links in teacher knowledge and student performance is critical to improving teacher instruction and student achievement.

As with most experimental and statistical studies, more longitudinal research is needed to confirm the findings pre-sented here and to see whether integrating encoding instruc-tion into reading curriculums during the beginning grades helps students continue to experience literacy success in the following grades. More studies should be initiated to deter-mine how much supplemental intervention is needed to pre-pare at-risk students or students with LD to be on track for later comprehension, the end goal of reading. Additional experimental research is also needed to investigate whether beginning encoding instruction in kindergarten and first-grade core reading curriculums may minimize the number of students experiencing reading disabilities and thus lessen the number of students referred to special education. Most importantly, this research supports further investigation with older students who already have reading LD.

An impactful by-product of this research study was using a system for observing both reading classroom and inter-vention teachers in first-grade elementary schools. This study showed that it is possible to collect meaningful obser-vational data of teachers’ use of encoding and decoding instruction time to support the reading and spelling abilities of their students. This study was able to use this observa-tional data to play a very important role in a quantitative HLM analysis of the relationship between encoding and decoding instruction and student reading and spelling growth. Future experimental studies should consider gath-ering more frequent documented information about what is actually occurring in today’s classrooms; this critical infor-mation appears necessary when analyzing and interpreting test scores so appropriate recommendations in improving student academic growth are truly justified.

Educational ImplicationsThe results of this preventive encoding and decoding instructional model have significant implications not only for improving educational practices but also in lessening the amounts of students inadequately described as having reading LD. First, this study confirms the findings from

previous studies that suggest early elementary school teachers and intervention teachers can effectively contrib-ute to increasing the reading and spelling performances of students struggling with reading difficulties, and perhaps, reduce the number of students referred for special educa-tion services. Due to the reciprocal relationship of encoding and decoding instruction and its combined predictive strength in these students’ reading and spelling skills, inte-grating encoding instruction in these first-grade core class-rooms’ and supplemental reading intervention curriculums may be critical to helping students at risk for reading LD. Those students who were provided with opportunities to practice phoneme–grapheme combinations with direct classroom teacher encoding instruction and guided decod-ing and encoding supplemental intervention made the greatest gains in encoding, decoding, comprehension, and fluency.

Furthermore, the research presented here confirms the need to improve the typical spelling instruction our students are receiving. Although some of the classroom teachers were using encoding instruction and guided practice, field notes from the observational data collected in this study confirm that students in other classrooms were receiving spelling instruction that mostly just included copying words multiple times to memorize them for a weekly spelling test. Any other time allotted to spelling practice in these class-rooms was usually spent with students working indepen-dently in workbooks copying already printed words. Findings from this research suggest the need to encourage teachers to implement direct, explicit encoding instruction and guided encoding practice. Allowing multiple opportu-nities to practice manipulating previously taught phoneme–grapheme combinations is also likely to give students tools for acquiring the alphabetic principle and developing fully specified orthographic representations of words, both of which are necessary to learning to read, spell, and write for all students of varying abilities (Adams, 1990; Conrad, 2008; Treiman, 1998).

Another educational implication for the findings of the present study is suggesting that principals should allow their reading teachers to be able to work with students as often as possible. Surprisingly, participating students received a range of between 54 and 120 intervention les-sons. By examining the attendance records that were filled out by the intervention teachers, it was apparent that many of them were often called on to be substitutes for classroom teachers, to monitor testing or recess, or other administra-tive jobs, which unfortunately kept them away from work-ing daily with their students. Intervention time was also a factor as teachers were not always able to keep their stu-dents for 30 to 40 min of requested time. Investigating the teachers’ logs, there were several reasons for this, including classroom teachers not releasing their students on time, school functions, announcements, schedule changes, and




other school duties asked of them. Principals need to exam-ine the results from studies such as this one to understand how important their reading teachers’ instructional time is for enhancing the performance of students at risk for read-ing disabilities and for lessoning the number of students requiring special education services.

ConclusionIn summary, this study reinforces the need for research-based quality education for all students, especially those at risk for reading disabilities. The research adds empirical evidence to previous research that affirms the value of direct and explicit instruction that incorporates integrated encoding and decoding instruction and guided practice for first-grade students experiencing difficulties with reading. Although it appears that the classroom teachers in this study were spending ample time on decoding instruction, many were giving little or no explicit encoding instruction and practice for their students. Knowing that on average, first-grade students typically receive very little spelling instruction during their school day (i.e., less than 4%; Foorman et al., 2006; Weiser & Mathes, 2011), it may be critical to investigate incorporating more encoding instruc-tion with all beginning readers and spellers in the hopes to prevent future reading failure. It seems plausible that giving students multiple opportunities to practice linking pro-nounced phonemes to written graphemes (and then com-bining these graphemes into words) will make it more likely that they will begin to develop fully specified ortho-graphic representations of words, which will in turn, facili-tate reading and spelling. The outcomes from the present study indicate that explicit encoding instruction and guided practice may be the missing link for students at risk for experiencing reading difficulties.

Author’s Note

Dr. Weiser was the Council for Learning Disabilities (CLD) 2011 Outstanding Researcher Award recipient.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. Refer to Weiser and Mathes (2011) for a best-evidence synthe-sis including the literature base on this subject, as well as describing the statistical measures used in meta-analyses. Effect sizes reported by the journal articles were not used to ensure

that all effect sizes reported in this synthesis were calibrated using the same formulas. Cohen’s d effect sizes were com-puted by the authors to measure how much the mean of the treatment group(s) exceeded the mean of the contrast group at post test in standard deviation (SD) units and then pooled effect sizes for ranges by using procedures explicated for meta-analysis by Glass, McGaw, and Smith (1981).

2. For more information about Proactive Early Interventions in Reading’s (PEIR) curriculum, please consult the author at [email protected]

3. Refer to Chapter 7 of Hox (2002) for more information on cross-classified hierarchical models.

4. Although all posttest scores revealed very similar results as the tables presented, other posttest results can be requested by contacting the author at [email protected]

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