Development,Validation,and Evaluation of Literacy 3D: A Package Supporting Tier 1 Preschool Literacy Instruction Implementation and Intervention

Abstract

Increasingly, prekindergarten programs with literacy outcome goals are seeking to implement evidence-based practices to improve results. Such efforts require instructional intervention strategies to engage children as well as strategies to support teacher implementation. Reported is the iterative development of Literacy 3D, an enhanced support system for Tier 1 literacy instruction that combines evidence-based strategies for teacher implementation with instructional intervention strategies. A waitlist randomized control trial (W-RCT) design was used over two years. In Year 1, classroom clusters were randomized to two groups, one Literacy 3D and the other a waitlist BAU comparison. In Year 2, the waitlist group received Literacy 3D. First year results indicated that Literacy 3D was promising with regard to improving teachers’ use of Literacy 3D practices as well as some intermediate teacher outcomes. Improvements were made and re-tested with the waitlist group in Year 2. Results produced better outcomes in teacher, child, and early literacy outcomes. Implications are discussed.

Keywords

behavior classroom at risk for developmental delays/disabilities early education programs evidence-based practices literacy intervention strategies phonological awareness

Literacy 3D is an enhanced language and preliteracy strategy combining implementation and intervention components, which is designed to address the generally low level of instructional support commonly reported in early education (Greenwood et al., 2013; Justice, Hamre, & Pianta, 2008; Neuman & Dwyer, 2009) and inclusive early childhood special education classrooms (Guo, Sawyer, Justice, & Kaderavek, 2013). Literacy 3D development was guided by an ecobehavioral theory of change, positing that momentary interactions between the teacher and children in the context of literacy content/activities and teacher’s behaviors set the occasion for children’s engagement in literacy behaviors (Greenwood, Abbott, & Tapia, 2003). Compounding of these experiences over time leads to improvements in children’s formative and summative literacy outcomes (Watson, Gable, & Greenwood, 2010).

Accordingly, raising the level of preschool instructional support to children requires evidence-based practices/interventions and procedures that support teacher classroom implementation. Procedures in Literacy 3D that support classroom implementation include (a) didactic professional development (PD) and (b) practice-based teacher coaching (PBTC). Procedures for children’s instructional support include (a) evidence-based literacy instructional strategies compatible with existing curriculum and (b) data-based decision making.

Rationale for PD That Achieves Implementation With Fidelity

Teachers need knowledge of preliteracy skills and instruction. Sheridan, Edwards, Marvin, and Knoche (2009) reported that the combination of didactic training workshops and teacher coaching led teachers from knowledge acquisition to classroom implementation. Convergence of findings indicates that classroom implementation at fidelity is not readily achieved by training alone but also requires coaching support, both live and/or electronic (Fixsen, Naoom, Blase, Friedman, & Wallace, 2005; Hindman & Wasik, 2012).

PBTC is a cyclical process inclusive of (a) planning goals and action steps, (b) engaging in focused observation, and (c) reflecting on and sharing feedback about observed teaching practices. Each component in the cycle is designed to inform the actions taken by the teacher while a coach provides lessons in implementing data-based decision making. The cyclical nature of PBTC emphasizes that the expectations, understandings, and desired outcomes of coaching are regularly reviewed and updated (Hemmeter, Snyder, Kinder, & Artman, 2011; Snyder, Hemmeter, & Fox, 2015). Literacy 3D was inspired by earlier PBTC models (Snyder, 2012) and past work (Abbott, Atwater, Lee, & Edwards, 2011; Abbott, Knoche, Beecher, Petersen, & Payette, 2012).

Abbott, Beecher, Petersen, Greenwood, and Atwater (2015) reported using a Tune-Up Checklist (TUC) as a means of planning the activities to be implemented in a coaching cycle. The TUC was used to determine what needed to be accomplished across six topics (content of instruction, opportunities to learn, grouping for instruction, explicitness of instruction, strategy selection, and language challenge considerations).

Rationale for the Selection of Early Literacy Practices (Top 10)

Teachers need preliteracy teaching strategies for structuring and increasing the frequency and duration of the literacy-focused instructional interactions they have with children. Young children with Individualized Education Programs (IEPs), particularly those with language, speech, and cognitive impairments, struggle with learning vocabulary and phonological skills but benefit from instructional support (Carta & Driscoll, 2013).

Literature review identified 10 instructional practices (see Table 1) that increase children’s opportunities to practice critical language and literacy skills (Aram & Biram, 2004; McGinty, Breit-Smith, Justice, Kaderavek, & Fan, 2012). The strategies can also be applied to different content targets of instruction (e.g., vocab, letter sounds, etc.). The strategies included in Literacy 3D also support scaffolding of new concepts, provide multiple trials, and increase child talk and responding (see Table 1). For example, the Transition Password Game is 2 min in length and includes asking a question and getting a response from every child. The Interactive Writing activities are a 7-min lesson. Development work with teachers confirmed that the strategies were feasible and could be implemented often during the day, teaching target skills with fidelity and increasing dosage.

Table 1.

Ten Evidence-Based Instructional Interaction Strategies Used in Literacy 3D.

Strategy	Teacher focus/child engagement linkage to literacy impacts	Citation	Effects
1. I do it, You do it, We do it!	Oral language, alphabet knowledge, phonological awareness	Connor, Morrison, and Slominski (2006) Howes et al. (2008) McGinty, Justice, Piasta, Kaderavek, and Fan (2012)	Correlational d = 0.28 Longitudinal analysis: Teacher instructional support and scaffolding; d = 0.05–0.07 RCT explicit print referencing; β = .22; p = .02
2. Peer talk	Oral language	Bowyer-Crane et al. (2008)	RCT of oral language vs PA; d = 0.15–0.28 for voc and comp
3. Pocket intervention card (replaced conversation routines in Year 1)	Alphabet knowledge, phonological awareness	Connor et al. (2006; explicit teaching)	Correlational; d = 0.28 Poster
4. Interactive writing (replaced language experience Year 1)	Oral language, alphabet knowledge, phonological awareness	Aram and Biram (2004) Craig (2006)	RCT; η²_p = .17–.35 Pre- post-comparison group; d = 1.63 for word attack
5. Transition password game:	Alphabet knowledge, phonological awareness, vocabulary	Connor et al. (2006; explicit teaching)	Correlational; d = 0.28
6. Choral reading	Oral language, alphabet knowledge	Justice and Ezell (2002) Piasta, Justice, McGinty, and Kaderavek (2012) Mol, Bus, and de Jong (2009) Wasik and Bond (2001)	RCT-pre/post; η² = .54 RCT; Mean d = 0.21 Meta-analysis; r = .25 RCT; ANOVA F(l, 120) = 128.43, p < .001
7. Identify, describe, examine, ask, say (IDEAS)	Vocabulary/oral language, alphabet knowledge	Marulis and Neuman (2010) McGinty et al. (2012)	Meta-analysis explicit vocab instr; Mean d = 0.88 RCT; explicit print referencing β = .22; p = .02
8. Children sign in	Alphabet knowledge, phonological awareness	Aram and Biram (2004)	RCT; $η_{p}^{2}$ = .17–.35
9. Learning quests	Oral language, alphabet knowledge, phonological awareness	McGinty et al. (2012)	RCT explicit print referencing; β = .22; p = .02
10. Vocabulary word bank games	Oral language, alphabet knowledge, phonological awareness	Marulis and Neuman (2010)	Meta-analysis explicit vocab teaching Mean d = 0.88

Note. PA = phonological awareness; RCT = randomized control trial.

Rationale for Data-Based Decision Making and Performance Feedback

Teachers need feedback on progress and maintaining change goals. Feedback is helpful when teachers are (a) learning to implement instruction as planned (Is it being done?), (b) planning what skills to be taught given students’ needs (What should I be teaching?), and (c) selecting new practices to use (How will I teach it?). PBTC combined with data-based feedback has been effective in increasing preschool teachers’ use of praise (Hemmeter et al., 2011), increasing emergent literacy instruction (McCollum, Hemmeter, & Hsieh, 2013), intentional teaching (Snyder, 2012), individualizing instruction (Akers et al., 2014), and implementing pyramid model practices (Fox, Hemmeter, Snyder, Binder, & Clarke, 2011). Combining PD and PBTC with data-based feedback and decision making in addition to specific instructional strategies adds another evidence-based practice for reaching strategy implementation (Akers et al., 2014). Coach interpretations of observational data leading to action plans have been highly valued by classroom teachers (Roehrig, Duggar, Moats, Glover, & Mincey, 2008). Sources of data used in Literacy 3D are fidelity checklists (one for each strategy), observations of behavior, and formative measures of literacy skills.

Fidelity checklists are useful for marking progress toward full implementation and for feedback and retraining (Snyder, Wixson, Talapatra, & Roach, 2008; Wolery, 1994). In Literacy 3D, implementation fidelity checklists are completed by the coach for each strategy selected. The first is a fine-tuned observation by the coach, followed by additional occasions for observations as needed to document reaching fidelity at a benchmark of 85%.

Direct observations are useful in documenting that children are receiving more teacher literacy focus (TLF) and that children are becoming more engaged in literacy behaviors (children’s literacy engagement [CLE]). Intentional language and literacy experiences as part of classroom instruction are known to influence children’s growth in readiness skills (Powell & Diamond, 2011) and thus are an important target for teacher coaching because they are proximal indicators of the successful impact of the practices implemented. Formative measures of children’s growth in preliteracy skills are another useful source of information used in Literacy 3D to guide coaching and instructional decision making. For example, data on vocabulary, comprehension, letter names, and phonological awareness are known to aid literacy decision making (Kaminski, Abbott, Bravo-Aguayo, Latimer, & Good, 2014; Kaminski, Cummings, Powell-Smith, & Good, 2008).

Based on these explicit connections to both implementation and instructional supports, we hypothesized that Literacy 3D will support teachers’ fidelity of implementation of literacy-focused instructional practices in the classroom and promote impacts on TLF, CLE, and formative literacy outcomes. Thus, the purpose of this research was to evaluate Literacy 3D in these terms and to iteratively refine it as indicated by the evidence. The investigation addressed several gaps in the literature, including the low level of instructional support for core Tier 1 literacy instruction, improving teachers’ preliteracy knowledge and skills, and understanding how coaching and teacher support leads to improvement of practice and child outcomes (Diamond, Justice, Siegler, & Snyder, 2013; Neuman & Carta, 2011). The following questions were addressed:

Research Question 1: Was Literacy 3D well implemented in Years 1 and 2?

Research Question 2: Did the Literacy 3D group realize significantly greater impacts versus the Waitlist BAU group in (a) TLF and (b) CLE in Year 1?

Research Question 3: Did the Waitlist group receiving Literacy 3D in Year 2 realize significantly greater impacts in Year 2 compared with their Year 1 (a) TLF, (b) CLE, and (c) preliteracy skills?

Research Question 4: Were differential impacts observed for children with IEPs in Year 2?

Research Question 5: Was Literacy 3D socially valid?

General Method

Design

The design was a 2-year waitlist randomized control trial (W-RCT; Shadish, Cook, & Campbell, 2002). In this design, preschool teacher/children clusters in one school district’s preschool programs in its elementary schools and early childhood center were randomized to either receive Literacy 3D or BAU Waitlist in Year 1. The waitlist group served as the counterfactual comparison (BAU) in Year 1. A year later, the Waitlist BAU group received the Literacy 3D intervention. Children in both studies were intact age cohorts and no Year 1 children repeated participation in Year 2.

Year I: Initial Test of Literacy 3D

Participants and Settings

Over two years, participants included 20 teachers and 297 children served in half-day Pre-K programs in a suburban, Midwestern school district (see Table 2). In Year 1, there were 20 teachers and 206 children. Twenty preschool classrooms (teacher/children clusters) were randomized in Year 1 to receive either Literacy 3D (n = 10 teachers/109 children) or “business as usual” (BAU; n = 10 teachers/97 children) in a waitlist comparison group. In Year 2, seven of the original Waitlist teachers returned and participated serving a new cohort of 91 children.

Table 2.

Children’s and Teachers’ Demographics.

Children	Year 1						Year 2
	Literacy 3D		Waitlist BAU				Waitlist-Literacy3D
	n	%	n	%	χ²	p	n	%
Gender
Female	46	44.7	43	47.3	0.131	.718	32	37.2
Male	57	55.3	48	52.7			54	62.8
Preferred language
English	102	100.0	89	97.8		.221	79	94.0
Other	0	0.0	2	2.2			7	6.0
Special needs
IEP	38	36.9	29	31.9	0.540	.463	39	45.3
No IEP	65	63.1	62	68.1			47	54.7
Total children	108		91				81
Parent education
No degree past HS	21	21.2	18	20.0	0.042	.837	18	22.0
College degree	78	78.8	72	80.0			64	78.0
Total parents	99		90				82
Lead teachers
4-year degree	2	20.0	1	10.0		1.000	1	14.0
Graduate degree	8	80.0	9	90.0			6	86.0
Other teacher staff
No degree past HS	14	60.9	9	42.9	1.428	.232	9	45.0
AA degree or higher	9	39.1	12	57.1			11	55.0
Lead teachers	10	15.3	10	16.1	−0.203	.841	7	17.7
Other teaching staff	23	6.9	21	6.5	0.263	.794	20	5.6

Note. Total N = 20 teachers over 2 years, 206 children in Year 1, and 91 in Year 2. Missing demographic data on the children/parents accounted for the difference reflected in the table. BAU = business as usual; IEP = Individualized Education Program; HS = high school; AA = associate of arts.

The District provided a “reverse inclusion” program wherein children without IEPs were integrated with those with IEPs. Parents of children without IEPs paid tuition and Part B funds paid for children with IEPs. The average class size in this district was 11 (8–14). A majority of children were White (81%), 40% had IEPs, and 27% of children qualified for free or reduced lunch. Only 10 children (3%) were English language learners (ELL). Children were served in 16 elementary schools (17 classrooms) and at two early childhood centers (four classrooms). Literacy program goals were stated in the districts’ outcomes statement; otherwise, no formal curriculum was required.

All consented children participated in the intervention conditions and an initial preliteracy screen at pretest, but only subsamples participated in additional measurement activities to reduce testing burden and conserve resources. To identify literacy skill levels, all children with informed consent were screened with the Get Ready to Read (GRTR) early literacy assessment (Whitehurst & Lonigan, 2001a; see Table 3). In each classroom, a representative subsample of six target children was drawn randomly from each preliteracy GRTR quartile as reported by the authors. This resulted in n = 60 children per experimental group, (N = 120, see below) who received additional assessments going forward. Only this targeted sample was assessed of all participating. A smaller subsample was randomly drawn: three of these six target children per classroom (one each from the three strata) received observations using the Code for Interactive Recording of Children’s Learning Environments (CIRCLE; Atwater et al., 2012). This resulted in n = 30 children per group, N = 60 children in all to be observed. There were no significant pretest differences in the two randomized groups at pretest (Literacy 3D vs. BAU) in child demographics, teacher characteristics, IEP status, or early literacy skills (see Tables 2 and 3).

Table 3.

Children’s Pretest Literacy Skills.

Measure	Year 1								Year 2
	Literacy 3D group			Waitlist BAU group					Waitlist-Literacy3D
	n	M	SD	n	M	SD	t	p	n	M	SD
GRTR screen
Raw score	109	15.4	5.22	97	15.7	5.50	−.473	.64	82	14.9	5.05
SS score	108	97.3	15.27	97	99.2	16.22	−.875	.38	82	97.1	14.47
PELI
AK	60	17.4	9.18	60	15.8	10.0	.952	.34	54	16.2	8.91
COMP	60	14.0	5.04	60	14.9	3.74	−1.216	.23	54	12.1	5.27
PA	60	5.4	5.38	60	6.6	5.66	−1.185	.24	54	5.6	5.51
VOC	60	17.9	4.50	60	17.0	5.15	.951	.34	54	18.7	6.99
COMPOSITE	60	165.7	56.11	60	168.5	57.31	−.278	.78	54	159.0	63.71

Note. BAU = business as usual; GRTR = get ready to read; SS = standard score; PELI = preschool early literacy indicator; AK = alphabet knowledge; Comp = comprehension; VOC = vocabulary; PA = phonological awareness.

Coaches

The three coaches were project research staff. Two coaches had previous experience from an Early Reading First project, using coaching to improve literacy practices in Head Start classrooms. Another coach was a post-doctoral researcher with a PhD in special education and several years of classroom teaching experience. All coaches received orientation and training from the lead researchers in four, 2-hour sessions. They met twice a month for planning and problem solving. In Year 2, one coach left and was replaced by another who had 21 years of experience in early childhood research projects, including Early Reading First. Caseloads were three to four teacher/classrooms per coach.

Experimental Conditions: Literacy 3D

As previously described, Literacy 3D is a multi-component intervention package focused on promoting teacher implementation, leading to increasing teacher–child interactions focused on preliteracy skills. Literacy 3D was implemented from September through May each year.

PD

To establish classroom implementation, teachers were provided the background, logic, and practices of Literacy 3D in six 2-hour PD sessions (total of 12 hours) facilitated by research staff (see Table 4). The content provided was the basis for subsequent coaching and classroom implementation linked to each of three coaching cycles (see Figure 1).

Table 4.

Workshop Topics Covered in Six 2-Hr Workshops and Three Coaching Cycles.

Cycle	Workshop	Topic
1	1	Provided an overview of goals, activities, and timeline; child and classroom measurements, the Tune-Up Checklist procedures, fidelity check procedures; and the oral language and phonological skills needed by children. The paraprofessional participation was included in Study 2 only.
1	2	Established links between oral language skills to be taught and the Top 10 strategies, self-reflection, data-based decision making, and upward momentum, understanding the data/reports, and planning for the first Tune-Up Checklist.
2	3	Established the link between Literacy 3D and child literacy engagement leading to comprehension, developing comprehension, differentiating based on student needs, more understanding of the new data/reports, and planning for Tune-Up Checklist 2.
3	4	Literacy 3D intensity and duration of literacy instruction was covered along with more on differentiation; benefits of small group instruction, and embedding instruction in centers; word bank games and language experience approach for centers; strategies that work, teacher videos and sharing, and planning for Tune-Up Checklist 3.
3	5	More on Literacy 3D intensity and increasing duration of instruction; interactive writing; phonological awareness; transition password game and pocket intervention card.
3	6	Covered end-of-year data sharing, individual teacher data reviews, and certificates of completion.

Figure 1.

Literacy 3D coaching cycle (3 cycles per year).

Practice-based coaching

The dosage of coaching planned for each teacher included 19 to 20 hours of classroom visits, tune-up activities, fidelity checking, email communications regarding planning dosage, and feedback. Additional non-coach staff time was needed for CIRCLE and The Preschool Early Literacy Indicator (PELI; Kaminski et al., 2014) used for teacher decision making (10 hours). On a weekly basis, the coaching required about 1 hr per teacher. Coaching was guided by the TUC (Abbott et al., 2015; Abbott, Knoche, et al., 2012), with one completed and updated in each of three cycles.

Within a cycle, the coach proceeded as follows. The coach spent a complete half day observing the teacher and completing an evaluation of instructional quality. The coach and teacher met to review information on the PELI children’s preliteracy skills and TLF. This review led to selection of the skills to be taught (e.g., letter knowledge, oral language). Teachers chose from 10 research-based strategies known to be drivers of change in teacher and child interactions (see Table 1). Each strategy was readily implementable during the school day and manualized to include core elements that maximized impact on students’ literacy engagement. Each strategy had a matched fidelity checklist that documented the extent that teachers were carrying out the strategies as intended (Petersen, Beecher, & Abbott, 2013). The coach assisted the teacher with implementation, observed, and provided feedback. Additional observations and feedback were provided as needed to reach fidelity. If persistent low fidelity called for modifications, additional plans were made and carried out. Coaches met with teachers and used email to provide suggestions, feedback, and progress reports.

Following classroom observation and child progress monitoring, the coaching cycle was repeated two more times during the year. Each cycle began with workshops and the selection of new practices that were layered on the previous practices. It was intended that teachers would learn to implement five to six of the Top 10 strategies over the year. Teachers received feedback on all strategies implemented in each cycle after every coach observation, as well as impact feedback at each PD session based on their own classroom’s TLF and child data.

The counterfactual

BAU literacy instruction served as the counterfactual. In all classrooms, children participated in literacy instruction during circle, centers, and storybook reading time. Literacy activities included alphabet knowledge, phonics, and vocabulary.

Measurement

Assessment and observation team

Child literacy assessments and CIRCLE observations were conducted by seven research staff members who were trained to accuracy and reliability standards by staff coordinators with established expertise. Prior to data collection, each assessor was required to meet a set of standard test administration steps (100%) and also reach an inter-observer agreement standard of 85% on CIRCLE coding. Coaches did not conduct any child assessments or CIRCLE observations; however, due to a staff reduction in Year 2, two coaches assisted with measurement in BAU classrooms.

Child and family characteristics (fall)

Sociodemographic characteristics of the children and their families were reported by parents using a 25-item parent survey. For the child, details of date of birth, age, gender, race/ethnicity, language preference, and disability status (IEP) were collected in the fall. For the family, marital status, education, and family income were collected.

Teacher preparation and experience (fall)

Teachers reported their training and teaching experience using a 25-item survey developed by the research team. They also reported instructional practices for teaching language and early literacy, including curricula used.

Fidelity of intervention

Coaches’ fidelity with coaching procedures was evaluated with a checklist completed by the teachers. Teachers were asked to indicate steps in the coaching process when completed and the date each occurred. Teachers’ fidelity was rated by coaches as part of the feedback cycle using strategy-specific checklists previously described. Teachers’ implementation was rated as 0 = not present, 1 = partially implemented, or 2 = fully implemented for each step by the coach. The percentage was calculated by summing all the points earned and dividing by total possible times 100. Teachers were expected to reach a score of 85% for each strategy. Dosage was indicated by the number of strategies scheduled for implementation in Year 1. In Year 2, teachers reported their actual implementation weekly by emailed survey.

Instructional quality indicator

The Quality of Literacy Implementation (Abbott, Petersen, Payette, & Beecher, 2012) tapped alignment with evidence-based literacy practices. Following observation, teachers and their assistants were scored on 21 checklist items covering preparedness, intentionality, and strategies used for literacy instruction. The coach rated each item as 0 = does not do, 1 = does on limited basis, or 2 = fully implements. A percentage was calculated by adding the ratings and dividing by 42 (2 possible points for 21 items). Inter-observer agreement (IOA) ranged from 93% to 96% across research staff in both years. In Year 1, administration to the Literacy 3D teachers was for one occasion in fall and spring; for the BAU group, one administration occurred at mid-year only. In Year 2, both the teacher groups were assessed one occasion each in fall/spring with reliability above 80%.

Literacy experiences

TLF and CLE were observed directly using the CIRCLE (Atwater et al., 2012). TLF is an indicator of the percentage of time a teacher is talking about vocabulary, phonological awareness, story comprehension, alphabet, and reading, whereas CLE is an indicator of the percentage occurrence of a target child’s reading, writing, verbalization, manipulation, and attention behaviors.

Based on 15s momentary time-sampling, CIRCLE measures the percentage occurrence of events from the perspective of a target child: (a) classroom context (e.g., structure, content), (b) teachers’ behavior toward the focus child (e.g., literacy focus, involvement), and (c) child behavior (e.g., engagement) as previously described. Inter-observer agreement is monitored across all observation occasions. Overall, IOA averaged 96.5% across observations (range = 88%–100%). For specific measures used in Year 1, average agreement was 96.6% for TLF (range = 75%–100%) and 92.3% for CLE (range = 70%–100%). To save resources in both years, the BAU groups were observed on only three occasions, fall, mid-year, and spring. The Literacy 3D group was observed on five occasions in Year 1, and on four occasions in Year 2.

Child literacy screening

In fall, all consented children received the GRTR, a widely used 25-item screener that taps phonological awareness, concepts of print, and alphabet knowledge (Whitehurst & Lonigan, 2001a, 2001b). Alpha coefficient reliability of .78, and split-half reliability of .80 are reported with validity ranging from .58 to .69 (Phillips, Lonigan, & Wyatt, 2009). Raw and standard scores are produced.

Literacy progress monitoring in Year 2

The PELI (Kaminski et al., 2014) is a formative progress monitoring measure of preliteracy skills as previously described. The PELI is a storybook-embedded assessment of essential preliteracy and oral language skills (ages 3–5) and is sensitive to growth. In its alternate forms, reliability estimates range from .89 to .92 in preliminary studies, with inter-rater reliability correlations in the high 90s (Kaminski et al., 2014). Concurrent validity of PELI with the Clinical Evaluation of Language Fundamentals (CELF-5; Wiig, Secord, & Semel, 2004) was .54 and .71 with Recalling Sentences.

Participant satisfaction

At the end of the school year, teachers reported their satisfaction with the intervention components (e.g., coaching, PD, data reflection, etc.). They were also asked to state which strategies they would continue to carry out and explain how their teaching benefited or not from the Literacy 3D intervention.

Statistical Analysis

Descriptive statistics, Pearson r, t tests, and graphical displays were used to examine the participant demographics, behavioral, and literacy outcomes. We used linear growth curve analysis (GCA) to examine group differences in change over time (Raudenbush & Bryk, 2002). GCA has several advantages such as estimation of individual slopes (growth) and intercepts in a series of repeated occasions of measurement of the same participants, its tolerance of missing data (planned and random) among repeated measures (as was the case in Year 1) and flexibility in testing differences between groups at any single occasion using centering.

TLF was modeled at the teacher level, while CLE was modeled at the child-level. The first step was to determine the appropriate Level 1 model. Because a linear increase in TLF and CLE was followed by a decline beginning in January (Occasion 3), a two-piece linear model with time centered at Occasion 3 was used (Raudenbush & Bryk, 2002). This allowed a Level 2 test of the effects of experimental groups’ mean intercepts at this point in time, as well as between the slopes up to and down from Occasion 3. For this purpose, the Level-2 effects of experimental group were coded where 0 = waitlist BAU and 1 = Literacy 3D, respectively. For each outcome of interest, treatment differences were tested Year 1.

To address the longitudinal growth research question in waitlist teachers in Years 1 and 2, a within-teachers only analysis was conducted using all seven measurement occasions from both years and conditions (see Figure 2). We tested the main effect differences between BAU versus Literacy 3D by adding Year as effect of treatment where Year 1 = BAU and Year 2 = Literacy 3D in the model. To estimate slopes between years, we centered the intercept at the fourth occasion in Year 2 calculating a slope for each of the pieces: before (all BAU occasions) versus after (all Literacy 3D occasions).

Figure 2.

Waitlist-Literacy 3D children’s exposure to teacher literacy focus, growth in children’s literacy engagement, and in PELI Literacy scores.

To address the same question for CLE, the data were graphed by year, and as a condition of the study, we used a visual comparison of CLE across the two groups of children. Because children of the waitlist teachers were different between years and conditions (BAU vs. Literacy 3D) statistical comparison between child groups was not possible. However, it was possible to fit a two-piece growth model fit to children’s growth in CLE within Year 2 only comparing the BAU baseline to the Literacy 3D intervention. For this model, we centered the intercept at the fifth data point (see Figure 2) and compared slope before and after this point. We examined the Year 2 PELI literacy data using a linear growth curve modeling. We examined the effect of IEP status on differences in children’s growth as a fixed effect (Level 2) where children were coded as no IEP = 0 and IEP = 1.

Results and Discussion (Year 1)

Was Literacy 3D Well Implemented in Year 1?

Coaching was well implemented because measures indicated that coaching met or exceeded the standard of 85% for all coaches. All 10 Literacy 3D teachers participated and none dropped out. The program was in place for 26 weeks, from October to May. Most teachers targeted vocabulary, phonological awareness, and comprehension (in descending order). Teachers implemented the strategies with high fidelity in coaching Cycles 1 and 3 at 89.6 (SD = 6.9) and 93.9 (SD = 6.9), respectively, where the fidelity benchmark was set at 85%. However, for Cycle 2, the average fidelity of implementation dipped to 81.9 (SD = 21.0). The instructional quality mean score improved over time within the Literacy 3D intervention group, increasing from 66% (SD = 12) in fall and increasing to 86% (SD = 10) in spring, t(9) = 5.478, p < .001. This improvement covaried with the uptake in evidence-based literacy practices in this group.

Did the Literacy 3D Group Realize Significantly Greater Impacts Compared With the Waitlist BAU Group in Year 1?

The BAU group started with a TLF M = 31.4% and declined linearly to 28.1% on the last occasion. Literacy 3D teachers started below the waitlist BAU group at 26.2%, but increased their TLF to a mean of 42.2% by mid-year, a 16% gain, F(1, 18) = 7.387, p = .014, d = 1.28. Unfortunately, even though Literacy 3D teachers were at fidelity they missed implementing planned sessions in spring (dosage), resulting in TLF declining to a year-end mean of 30.1% compared with the BAU group at 28.1%.

Child-level CLE in the Literacy 3D group followed a similar pattern with the Literacy 3D gaining faster than BAU, but then declining thereafter. None of the CLE group effects were statistically significant, however. The correlation between TLF and CLE was strong (r = .79) for the classroom and child levels of analysis.

Was Literacy 3D Socially Valid in Year 1?

Teachers’ satisfaction reports using Literacy 3D were high. For example, 82% of teachers reported the usefulness of the TUC and fidelity checks, coaches’ verbal and written feedback, and data reports. Furthermore, 92% of teachers felt in control of the decision-making process and choice of strategies. Most concerns were related to the lack of time to plan, implement, and meet with coaches. Teachers reported satisfaction in the good-to-excellent range on all questions asked about the PD provided.

Discussion

The initial test of Literacy 3D indicated that it was feasible to implement based on the coach and teacher fidelity measures, and on teachers’ schedules of strategy implementation. TLF and CLE increased in fall to a mid-year peak. These gains were followed by declines in spring. Follow-up discussions with teachers indicated that they had less time to devote to Literacy 3D during spring due to increased administrative demands (e.g., screening students for the next year, and in IEP and parent meetings). Thus, the frequency of planned implementation (dosage) had decreased throughout the year and the dosage reporting system used based on teacher scheduling was not effective. Because most teachers had not opted to involve their classroom assistants in Literacy 3D, they were not empowered to fill in when teachers were occupied. Procedures to address these challenges were developed and tested in Year 2.

New procedures were added to Literacy 3D to prevent missed sessions in Year 2. We established a weekly email survey to improve coordination overall. This allowed teachers to consider their non-teaching obligations and plan ways to ensure strategy implementation took place, in addition to reporting their actual implementation each week. We presented data to the district demonstrating that paraprofessionals should be added to the PD sessions and they agreed. We also responded to feedback that two of the 10 strategies were comparatively more difficult to implement. Conversation Routines was replaced with the Pocket Intervention Card and modifications were made to the Language Experience Approach making it easier to implement as Interactive Writing (see Table 1). We worked with teachers to effectively increase TLF by using small groups and center activities. We strengthened teachers’ understanding and use of differentiated instruction by adapting strategies based on student skills.

Results (Year 2)

Was Literacy 3D Well Implemented in Year 2?

Coaching fidelity in Year 2 again met or exceeded the standard of 85% in all 3 coaching cycles. Waitlist teachers also implemented the Literacy 3D strategies with high fidelity over all three coaching cycles at 89.3 (SD = 7.0), 90.1 (SD = 4.2), and 93.1 (SD = 5.1), respectively. The frequency and duration of strategy use per week increased linearly over each coaching cycle. The weekly mean strategy usage was reported to be 3.7, 5.3, and 10.5 times, respectively, for Cycles 1, 2, and 3. Values for weekly duration of strategy use were 16.2, 38.7, and 63.1 min per cycle. Instructional quality scores again increased significantly for the Literacy 3D group as in Year 2, gaining 11 percentage points from 73% (SD = 13) to 84% (SD = 10).

What Was the Longitudinal Impact of Literacy 3D on TLF?

The two-year unfitted data for the waitlist group are shown in Figure 2 with a BAU baseline in Year 1 and start of Year 2, interrupted with the onset of Literacy 3D in Year 2. Use of Literacy 3D by waitlist group in Year 2 produced a significant level change in TLF compared with their earlier BAU performance (see Figure 2, upper panel). Waitlist teachers with BAU (M = 29.3%, SE = 3.9) provided students an average of 7.3% more TLF using Literacy 3D, M = 36.6%, SE = 3.1, t(47) = 2.736, p = .024. Slope estimates in both conditions of the waitlist group were essentially flat.

Similar results were noted for CLE (see Figure 2, middle panel). Visual analysis indicated that the children of the waitlist teachers in Year 1 were highly similar in mean CLE (ranging from only 30% to 33% per occasion in BAU). The onset of Literacy 3D produced a level change in CLE and a sustained upward trend over time through the end of the school year. The mean intercept at the four occasion was 33.8%, increasing linearly with Literacy 3D to 44.8% by the seventh occasion, a gain of 11% points on average. The slope before the sixth occasion was 4.2% per occasion (SE = 2.00), t(42) = 2.095, p = .042, compared with only 0.29% (p = ns) after then.

Did Literacy 3D Increase Child Preliteracy (PELI) Outcomes in Year 2 and Were Their Differences by IEP Groups?

The children of waitlist teachers using Literacy 3D made significant linear growth in the PELI Composite (see Figure 2, lower panel). The slope was 14.7 per occasion, SE = 1.00, t(73) = 14.79, p = .001. Children with IEPs versus no IEP had significantly lower composite scores at the first occasion, 116.3 versus 205.5, SE = 11.89, t(72) = −7.439, p = .001, and again at the last occasion, 190.6 versus 254.8, SE = 10.39, t(72) = −6.175, p = .001. However, the rate of growth for the IEP group was significantly higher than the no IEP group 18.6 versus 12.3 per occasion, SE = 2.05, t(72) = 3.060, p = .003, indicating that they were closing the gap in early literacy skills.

Two of the PELI subscales, Vocabulary and Comprehension, also had significant slopes favoring the children with IEPs over those not having IEPs. The IEP slope for Vocabulary was 1.62 compared with only .80 for children without IEPs, SE = 0.32, t(72) = 2.582, p = .012. Similarly, for Comprehension the IEP slope was 1.35 versus .93 for children without IEPs, SE = 0.21, t(72) = 2.017, p = .047.

Was Literacy 3D Socially Valid?

Teachers’ satisfaction reports using Literacy 3D were high, replicating Year 1. Teachers were comfortable having coaches in their classrooms (88%). Furthermore, 90% of teachers felt in control of the decision-making process and choice of strategies used. Teachers reported the usefulness of the TUC (90%) and fidelity checks, coaches’ verbal and written feedback, and data reports.

General Discussion

The purpose of this research was to iteratively develop and refine Literacy 3D based on evidence of implementation and teacher and child impacts. Year 1 was promising, but not sufficient to sustain implementation in competition with increased demands on teacher time. Involving teaching assistants and making other improvements in Year 2 proved much more successful. Studies in both years contributed to our knowledge of combining PD, PBTC, the selected strategies, and data-based decision making, as well as the importance of dosage. In addition to better classroom implementation and dosage in Year 2 because of the involvement of paraprofessionals, impacts on TLF, CLE, and PELI were observed. It was interesting to note that IEP status was not a significant moderator of CLE in comparisons between groups, suggesting perhaps that teachers were doing a good job of engaging students in the Literacy 3D strategies.

Overall, the findings added new knowledge regarding the process of change beyond just fidelity and dosage feedback by including intermediate teacher and child behavior changes leading to growth in Year 2 PELI. The data on coaching intensity also added information on the coaching effort needed where data are often not reported (Isner et al., 2011).

The work adds to what we know about iterative development of PBTC interventions in preschool settings using the W-RCT. In the classic sense, the waitlist experimental design offers opportunities for demonstrating effects compared with a counterfactual, followed by a replication opportunity in a second year for the waitlist group. In this study of the new Literacy 3D intervention, Year 1 proved to be promising (implementation fidelity, TLF, CLE, and social validity), but lessons were learned in terms of missed sessions and thus the need to manage these effectively in Year 2. TLF, CLE, and PELI results in Year 2 were much improved over Year 1, and overall Literacy 3D was incrementally improved. Use of the waitlist design in the context of an intervention development goal is less often seen in the literature and thus the potential benefit of using it demonstrated here.

These design features proved useful in a development effort because they enabled several experimental tests of effects (replications) with multiple participants within and between years, using multiple measures, and retesting of Literacy 3D improvements based on lessons learned. As a result, Literacy 3D was demonstrated feasible to implement with fidelity, and it produced pilot data impacts useful in planning future research.

Limitations and Future Research

As an intervention development study, the design was purposely small N and under-powered. However, the number of measures in the design and their frequent occasions did improve power as well as provide the opportunity to examine details of the intervention’s impacts in relation to theory and implementation in a real school setting. Because the design was under-powered, effects of classroom/child clusters within schools and centers were not considered statistically. Also, randomizing classrooms within schools and centers risked contamination of the independent variable. However, BAU teachers in schools did not have access to PD, PBTC, the Top 10 Strategies, and data for decision making, rendering it unlikely they could pick up the procedures on their own. In addition, instructional quality and fidelity measures of BAU practices ruled out the presence of Literacy 3D. Future well-powered, multilevel efficacy research with Literacy 3D is needed for greater confidence in the generality of Literacy 3D effects. Finally, the coaching and data collection functions in the project were carried out by research staff; thus, demonstration that Literacy 3D can be fully implemented by preschool personnel is needed.

The design did not provide a counterfactual comparison in Year 2 and that provided some limitations. One was the issue of comparing two different student cohorts across years of the study and the second was lack of a comparison group for growth in the PELI preliteracy measure. Thus, these findings were descriptive and need additional research to demonstrate their causal relationship to Literacy 3D.

Implications for Practice

In its present form, Literacy 3D provides a systematic approach to combining PD and PBTC. An obvious advantage of Literacy 3D was the ability to improve the duration of literacy instruction experienced by all children, including those with IEPs, with short, easily-implemented strategies used regularly over time. Literacy 3D was compatible with the curricula used including existing vocabulary, comprehension, and phonological skill development targets and teachers made choices with their coaches. Thus, Literacy 3D is also useful in strengthening the effectiveness of Tier 1 in a Multi-Tiered System of Supports. Thus, Literacy 3D as tested in Year 2 appears feasible and likely to produce the literacy impacts of interest.

Footnotes

Acknowledgements

We thank the Literacy 3D Project Staff who supported the implementation of the project: Jenne Bryant, Sunday Dove, April Fleming, Joan Fogel, Susan Higgins, Edie Larson, Kathy Mosher, Christine Muehe, Carla Payette, Bernadine Roberts, Jeanie Schiefelbusch, and Diana Skill. A special mention is owed to Shye Reynolds who developed the software for collection of the CIRCLE observation data using unobtrusive tablet computing devices. A debt of gratitude is owed to the children, families, teachers, and administrators of the two participating school districts.

Authors’ Note

The opinions presented in this article are solely those of the authors, and no official endorsement from the Office of Special Education Programs, U. S. Department of Education should be inferred.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Abbott is a coauthor of a measure used in the research known as the Preschool Early Literacy Indicator (PELI). This intellectual property is being marketed by the Dynamic Measurement Group (DMG), Eugene, Oregon.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funds were provided by a grant to the University of Kansas (H327A110052) by the Office of Special Education Programs, U. S. Department of Education, for a Phase 2 Steppingstones of Technology Innovation Effectiveness Project; Charles Greenwood and Mary Abbott, principal investigators.

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