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Early Education and Development

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What Preschool Classroom Experiences AreAssociated With Whether Children Improve inVisuomotor Integration?

Anthony I. Byers, Claire E. Cameron, Michelle Ko, Jennifer LoCasale-Crouch &David W. Grissmer

To cite this article: Anthony I. Byers, Claire E. Cameron, Michelle Ko, Jennifer LoCasale-Crouch& David W. Grissmer (2016) What Preschool Classroom Experiences Are Associated WithWhether Children Improve in Visuomotor Integration?, Early Education and Development, 27:7,976-1003, DOI: 10.1080/10409289.2016.1175243

To link to this article: http://dx.doi.org/10.1080/10409289.2016.1175243

Published online: 19 May 2016.

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What Preschool Classroom Experiences Are Associated WithWhether Children Improve in Visuomotor Integration?Anthony I. Byersa, Claire E. Cameronb, Michelle Koc, Jennifer LoCasale-Crouchc,and David W. Grissmerc

aCambridge Public Schools; bLearning and Instruction, University at Buffalo, State University of New York; cCenter forAdvanced Study of Teaching & Learning, University of Virginia

ABSTRACTResearch Findings: This study examined the contribution of several class-room experience measures (classroom characteristics, teacher characteris-tics, and teacher–child interactions) to preschoolers’ improvement invisuomotor integration. Children (N = 467) ranged in age from 3 to5 years old and were enrolled in 115 classrooms in 5 U.S. states.Children’s visuomotor integration was measured twice (on average5.2 months apart) using the Beery-Buktenica Developmental Test ofVisual-Motor Integration (visuomotor integration subtest). Hierarchical lin-ear models controlling for background characteristics and inhibitory con-trol showed that children improved more in visuomotor integration whenthey were in classrooms with fewer 3-year-olds, when their teacher had atleast a bachelor’s degree, and when teachers demonstrated high quality intheir interactions. Practice or Policy: Visuomotor integration, and specifi-cally the ability to copy designs with a writing utensil, is a robust indicatorof children’s school readiness and longitudinal achievement. U.S. pre-schoolers gained more on visuomotor integration in classrooms withfewer 3-year-old children that were taught by a college-educated teacherand when such classrooms provided high-quality organizational andinstructional interactions. These results expand the outcomes linked toearly childhood education experiences and emphasize the need for well-prepared early childhood teachers who interact with children effectively.

To inform policy and understand how sociodemographic inequality affects children, scholarsof early education and development seek to establish connections between early childhooddevelopment and preschool experiences (National Institute of Child Health and HumanDevelopment Early Child Care Research Network, 2005; Pianta, Cox, & Snow, 2007).Meanwhile, researchers have also determined that a wide range of skills, including bothpreacademic and cognitive skills, are foundational for young children’s academic learningand school readiness (Duncan et al., 2007). Recent work has revealed the importance ofvisuomotor integration, a skill that combines motor and cognitive processes, in young chil-dren’s school performance (Becker, Miao, Duncan, & McClelland, 2014; Carlson, Rowe, &Curby, 2013). Studies across diverse samples have demonstrated that children who enterkindergarten with strong visuomotor integration, usually assessed by the ability to copydesigns, have better concurrent and later achievement in multiple areas (Cameron et al.,2012; Grissmer, Grimm, Aiyer, Murrah, & Steele, 2010) and earn higher behavioral ratingsfrom their teachers (Kim et al., 2015, 2016). Relatively little is known, however, about class-room experiences that contribute to the emergence of young children’s visuomotor integration.

CONTACT Claire E. Cameron cecamero@buffalo.edu Learning and Instruction, University at Buffalo, State University ofNew York, 572 Baldy Hall, Buffalo, NY 14260.© 2016 Taylor & Francis

EARLY EDUCATION AND DEVELOPMENT2016, VOL. 27, NO. 7, 976–1003http://dx.doi.org/10.1080/10409289.2016.1175243

To our knowledge, the present study is the first to explore associations between a compre-hensive set of early childhood classroom experience indicators and U.S. preschoolers’ improve-ment in visuomotor integration using a common measure of copying designs.

Visuomotor Integration in Early Childhood

Visuomotor integration is usually considered under the umbrella domain of fine motor skills,which are a familiar emphasis among early childhood experts (Bredekamp & Copple, 2009;Brosterman, 1997; Lillard, 2005), special education researchers, and occupational therapists(Aylward & Schmidt, 1986; Tseng & Chow, 2000). Using three large data sets and controllingfor prior achievement, Grissmer et al. (2010) found that typically developing children with goodfine motor skills at age 5 have higher math and reading achievement in fifth grade. Children ofhigher socioeconomic status (SES) have on average better fine motor skills than children living inpoverty (Potter, Mashburn, & Grissmer, 2012). In addition to SES differences, girls and Caucasianor Asian children tend to have advantages in fine motor skills over boys and children fromAfrican American or Hispanic backgrounds (Grissmer & Eiseman, 2008; Potter et al., 2012). Theexpansive longitudinal evidence favoring early fine motor skills among children without disabil-ities (Luo, Jose, Huntsinger, & Pigott, 2007; Roebers & Jäger, 2014; Son & Meisels, 2006)provided a foundation for efforts to identify which aspect of fine motor skills underlies learning,with follow-up research pointing to a specific subskill, namely, visuomotor integration (Grissmeret al., 2010).

Defining Visuomotor Integration

The type of fine motor measure most consistently associated with academic outcomes requires children tocopy designs with a writing utensil (Becker et al., 2014; Cameron et al., 2012; Carlson et al., 2013). Inaddition to requiring precise small muscle movements, a visuomotor integration copying task requireschildren to direct their attention to the task at hand, create a visual representation in memory, plan andreproduce that representation while comparing their efforts to the original stimulus, and inhibit unneces-sary or unhelpful movements with the pencil (Del Giudice et al., 2000; Ogawa, Erato, & Inui, 2010).

Design copying therefore involves motor skills but also requires visuospatial integration (e.g., seeCarlson et al., 2013) because spatial processing is involved when children must integrate theirperceptions with the motor actions required to reproduce a stimulus in two or three dimensions(Korkman, Kirk, & Kemp, 2007; Verdine, Irwin, Golinkoff, & Hirsh-Pasek, 2014). In other words,despite being commonly lumped with fine motor skills, visuomotor integration involves both motorand cognitive processes, including components of executive function (EF) given the attention,planning, and self-control required to carry out an integration task.

Associations Between Visuomotor Integration and School Outcomes

Across studies and samples, children with strengths in visuomotor integration have had greater con-current and later academic achievement (Becker et al., 2014; Carlson et al., 2013; Grissmer et al., 2010).For example, Cameron and colleagues (2012) found that when child demographics and a measure of EFwere controlled, children with stronger copying skills learned more in word reading, comprehension,and phonological awareness over the kindergarten year. In another study of 127 children ages 4 and 5,those with good copying skills scored higher in literacy and mathematics compared to peers with weakvisuomotor integration (Becker et al., 2014). Furthermore, Kim et al. (2015) found that elementaryteachers rated their students as having better classroom behaviors when children had strong visuomotorintegration. Together, perceptual skills and visuomotor integration fully explain why motor and cogni-tive latent constructs are correlated between children ages 5 to 11 (Davis, Pitchford, & Limback, 2011).

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Unique Contributions of Visuomotor Integration

Even though visuomotor integration requires EF (Korkman et al., 2007), the seminal large-scalestudies of fine motor skills and visuomotor integration did not include direct measures of EFcomponents such as inhibitory control (Duncan et al., 2007; Grissmer et al., 2010). Inhibitorycontrol requires children to control their automatic impulses in the service of more adaptiveresponses (Best & Miller, 2010) and is uniquely linked to school outcomes. For example, childrenwho have better inhibitory control measured during the transition to formal schooling attain greatermath and reading achievement later, even after researchers control for their previous academicperformance (McClelland et al., 2007) and general intelligence (Blair & Razza, 2007). Becker et al.(2014) found that visuomotor integration and inhibitory control were correlated at r = .43. Takentogether, empirical studies suggest that visuomotor integration skills are worthy of examination butthat children’s inhibitory control skills should be measured as well.

Early Childhood Learning Contexts and Visuomotor Integration

According to ecological systems perspectives, a single developmental outcome is shaped by anindividual child’s characteristics, aspects of his or her environment(s), and interactions betweenthe child and environment (Blair & Raver, 2015; Bronfenbrenner & Morris, 2006; Cameron, 2012).Empirical work in the early school years aligned with ecological systems theory illuminates howchildren’s skills emerge in particular contexts that support such skills (Carr & Pike, 2012; Curby,Brock, & Hamre, 2013; Hamre & Pianta, 2001; Rimm-Kaufman, Curby, Grimm, Nathanson, &Brock, 2009).

Although many studies indicate that high-quality preschool experiences bolster children’s earlyacademic and behavioral skills at school entry and beyond (Cameron & Morrison, 2011;Campbell & Ramey, 1995; Connor, Son, Hindman, & Morrison, 2005; Justice, Kaderavek,Xitao, Sofka, & Hunt, 2009; Sarama, Lange, Clements, & Wolfe, 2012; Schweinhart & Weikart,1997), little is known about what classroom experiences are associated with the emergence ofvisuomotor integration. Twin studies and large-scale correlational work have established sub-stantial early environmental contributors to young children’s fine motor skills, of which visuo-motor integration is a part (Grissmer & Eiseman, 2008; Potter et al., 2012). For example, Potteret al. (2012) found that family and parenting variables, such as parent reports of children’sinvolvement in educational activities, explained 30% of the SES gap in 5-year-olds’ fine motorskills measured as a composite. A natural expansion of this line of work seeks to describe thecontribution of preschool classroom variables to 3- to 5-year-olds’ improvement over time in akey fine motor skill: visuomotor integration.

Several large-scale studies, such as the National Center for Early Development and Learning’s(NCEDL) Multi-State Study of Pre-Kindergarten and the Study of State-Wide Early EducationPrograms, have collected extensive data on classroom and teacher variables. Combined, these twostudies collected data from more than 700 prekindergarten classrooms and teachers enrolling morethan 2,000 children from six U.S. states. But to our knowledge, no studies have examined whethervarious classroom experiences are associated with change in children’s visuomotor integration. Thisis important to investigate given the rise in prominence of fine and visuomotor skills in contem-porary school readiness conversations (Cameron, Cottone, Murrah, & Grissmer, 2016; Grissmeret al., 2010; Pagani & Messier, 2012). In the present study, we examine how classroom character-istics, teacher characteristics, and teacher–child interactions relate to children’s improvement invisuomotor integration. The paucity of research connecting preschool experiences to children’svisuomotor integration makes this study largely exploratory. However, when possible, we maketentative hypotheses based on existing research that has examined fine or visuomotor skills in othercontexts (Carr & Pike, 2012) or has connected preschool experiences with academic or cognitiveoutcomes that are related to visuomotor integration.

978 A. I. BYERS ET AL.

Classroom Characteristics

Classroom characteristics are defined as structural features of the learning environment thatremain relatively stable over the school year. These include classroom composition, includingclass size, the age range of students, and sociodemographic composition (LoCasale-Crouchet al., 2007).

Class SizeOf the various classroom characteristics, class size has received the most sustained attention,with most stakeholders endorsing the intuitive belief that smaller classes facilitate learning(Ahn & Brewer, 2009). Several large studies that randomly assigned students to larger (>20) orsmaller (<16) class sizes at school entry found positive effects of small class sizes on academicgains in early elementary school for both minority and nonminority students (Grissmer, 1999;Nye, Hedges, & Konstantopoulos, 2001; Shin & Raudenbush, 2011). Similar work on preschoolclass size is largely correlational and yields somewhat more ambiguous results, however.Although a smaller class size seems to be particularly important for toddlers (Phillips,Mekos, Scarr, McCartney, & Abbott-Shim, 2000), studies in preschool have found eithersmall or no associations between class size on the one hand and preschoolers’ academic skillsand observed measures of classroom quality on the other (Mashburn et al., 2008; NationalInstitute of Child Health and Human Development Early Child Care Research Network, 2002;Pianta et al., 2005). Previous work in preschool is also limited by a lack of variation in classsize because most states mandate relatively low student-to-teacher ratios before elementaryschool (Barnett, Carolan, Fitzgerald, & Squires, 2011). These requirements are based on theestablished importance of low student-to-teacher ratios for very young children (Bredekamp &Copple, 2009), which characterized the classrooms in the present study as well.

Age RangeIn the United States, where this study was conducted, early childhood (henceforth preschool) class-rooms often serve 3- to 5-year-olds in mixed-age groups; this practice is endorsed by the NationalAssociation for the Education of Young Children. In an experimental study, Bailey, Burchinal, andMcWilliam (1993) randomly assigned preschoolers to either a mixed-age or a single-age classroom andfound that mixed-age classrooms helped relatively younger children gain on a range of schoolreadiness skills from ages 2 to 4 but that the reverse was true after 4 years of age. Further complicatingthe story, later studies using large and diverse samples showed either null effects or negative effects ofbeing in a mixed-age classroom for preschoolers of all ages (Bell, Greenfield, & Bulotsky-Shearer, 2013;Moller, Forbes-Jones, & Hightower, 2008). Relevant to the current inquiry, Moller et al. (2008) foundthat compared to children enrolled in a same-age class, those enrolled in a mixed-age class made fewergains in motor skills as reported by their teachers. This negative association was most pronounced forolder children, which was similar to findings by Winsler et al. (2002) in relation to social behaviorsamong 4- and 3-year-olds in mixed-age settings. Thus, more research is needed, especially for directlyassessed visuomotor integration as an outcome.

Sociodemographic CompositionSociodemographic features include classroom average SES and the number of boys versus girls.In the NCEDL study, classrooms with large numbers of disadvantaged children provided fewerobserved learning opportunities and also spent relatively more time in teacher-directed ordidactic settings (Early et al., 2010; LoCasale-Crouch et al., 2007). Note that this associationdoes not mean that classrooms in high-poverty communities never provide high-quality learningexperiences, but it does suggest that children from disadvantaged contexts are more likely thanmiddle-SES children to experience lower quality preschool experiences. The pattern for gendercomposition is less clear: Despite evidence suggesting that in elementary school, boys experience

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more behavioral and learning challenges than girls (Beaman, Wheldall, & Kemp, 2006; Ready,LoGerfo, Burkam, & Lee, 2005), Early et al. (2010) reported no differences in how preschoolchildren spent their time based on classroom gender composition.

Teacher Characteristics

Similar to classroom characteristics, teacher characteristics are relatively static variables measured atthe teacher level. Factors thought to influence children’s learning include credentials, years ofexperience, self-efficacy beliefs, and beliefs about children.

Teacher CredentialsThe research on teacher credentials is mixed, sometimes even using similar data: Early et al.’s (2006)analysis of NCEDL data suggested benefits to children’s mathematics when teachers had a bachelor’sdegree, but Mashburn et al.’s (2008) analysis using both NCEDL plus Study of State-Wide EarlyEducation Programs (SWEEP) data indicated no association. A meta-analysis of 32 studies showed asmall (effect size = 0.16) contribution of early childhood teachers having a bachelor’s degree tochildren’s outcomes (Kelley & Camilli, 2007), whereas Early et al.’s (2007) analysis of seven largedata sets showed null results or both positive and negative contributions to children’s mathematicsskills depending on the data. Despite these inconsistencies in child outcomes, holding a college degreeis positively connected to how adults behave with children (Bueno, Darling-Hammond, & Gonzales,2010). For example, Carr and Pike (2012) found that mothers with a bachelor’s degree interacted moreeffectively with their children during a joint design copying task. Given these mixed findings, researchwith visuomotor integration as an outcome in relation to teacher credentials is needed.

Teacher ExperienceLike the research on teacher credentials, results connecting teacher experience to child outcomes vary bystudy. Nevertheless, there is evidence that more experienced teachers focus more on literacy instruction(Phillips, Gormley, & Lowenstein, 2009) and have more responsive and stimulating interactions withchildren (Pianta et al., 2005; Stipek, Feiler, Daniels, & Milburn, 1995). Teacher experience has also beenpositively associated with longitudinal improvement in a range of school readiness skills amongchildren attending Head Start (McWayne, Hahs-Vaughn, Cheung, & Wright, 2012).

Self-Efficacy BeliefsIn studies of preschool, teacher self-efficacy is positively associated with both higher quality literacyinstruction (Justice, Mashburn, Hamre, & Pianta, 2008) and children’s literacy skill gains (Guo,Piasta, Justice, & Kaderavek, 2009). At the same time, a meta-analytic review of more than a decadeof teacher efficacy research tempered these positive results by showing overall null or inconsistentassociations among teacher efficacy and student outcomes (Klassen, Tze, Betts, & Gordon, 2011).

Beliefs About ChildrenTeachers who endorse children’s choice and autonomy in the classroom are described ashaving child-centered (or developmentally appropriate) beliefs (see Bredekamp & Copple,2009). Child-centered beliefs contrast with teacher-directed beliefs, in which the teachertakes a more didactic approach to decision making and activity selection (Decker &Rimm-Kaufman, 2008). A handful of studies in preschool have established a direct linkbetween teachers holding child-centered beliefs, their provision of higher quality instruction,and improvements in children’s academic skills (Peisner-Feinberg et al., 2001; Pianta et al.,2005).

980 A. I. BYERS ET AL.

Teacher–Child Interactions

Although the evidence on classroom and teacher characteristics suggests small, inconsistent, or nullcontributions to child outcomes, studies using observational data have helped expand the schoolreadiness conversation to how children and teachers interact when they share the same space(Mashburn et al., 2008; Pianta et al., 2007). We considered three theoretical domains of interactions:those that support children’s social and emotional development; interactions to organize classroomtime and activities; and interactions that directly target students’ academic skills, such as criticalthinking and content knowledge; and interactions to organize classroom time and activities.

Social and Emotional Learning SupportTeachers support children’s social and emotional learning by promoting warm and caring associa-tions with and among children, showing sensitivity to individual needs, and valuing child-initiatedlearning (Domitrovich, Cortes, & Greenberg, 2007). Observed teacher emotional support is asso-ciated with early childhood students’ enhanced socioemotional skills and behavior (Merritt, Wanless,Rimm-Kaufman, Cameron, & Peugh, 2012; Morris, Millenky, Raver, & Jones, 2014) and appears tobe especially important for children from low-income backgrounds (Hamre & Pianta, 2005).

Support for Language and Cognitive DevelopmentTeacher interactions that explicitly target children’s cognitive development include facilitating higher orderthinking, giving high-quality feedback, and providing rich linguistic experiences (LoCasale-Crouch et al.,2007). High levels of these instructionally supportive interactions are a strong predictor of preschoolers’reading gains (Howes et al., 2008; Wasik, Bond, & Hindman, 2006) and, to a lesser extent, gains inmathematics (Howes et al., 2008). Compared with social and emotional support, instructionally supportiveinteractions are more consistently linked to children’s cognitive and academic skills (Hamre, Hatfield,Pianta, & Jamil, 2013; Howes et al., 2008; Mashburn et al., 2008).

Classroom Organization and ManagementOrganization and management includes teacher behaviors that facilitate a smoothly running class-room and that maximize student attention, productivity, and engagement in learning opportunities(Brophy, 1979; Emmer & Stough, 2001). In an observational study of 41 classrooms, more classroomtime spent organizing for upcoming activities was associated with 3- and 4-year-olds makingachievement and EF gains (Cameron & Morrison, 2011). The association of organization andmanagement with visuomotor integration is worth studying, given the large amount of time devotedto centers and activities that require fine motor skills in early childhood settings (Marr, Cermak,Cohn, & Henderson, 2003).

The Present Study

Despite the emerging importance of visuomotor integration for school readiness and longitudinalsuccess, no studies to date have examined how classroom experiences contribute to children’svisuomotor integration skills. This study had two broad research aims.

First, we examined child-level predictors of initial visuomotor integration, including age, gender,race/ethnicity, and SES. Based on previous nationally representative work (Grissmer et al., 2010;Potter et al., 2012), we expected older children, girls, children from Caucasian and Asian racial/ethnic backgrounds, and those from advantaged homes to earn higher initial scores on visuomotorintegration.

Second, we explored associations between a diverse set of early childhood classroom predictorsand children’s improvement in visuomotor integration over approximately 5 months in the pre-school year. We divided multiple classroom experience measures into three broad categories: class-room characteristics, teacher characteristics, and observed teacher–child interactions. Prior work

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suggested that children in mixed-age classrooms might make fewer gains (e.g., Bell et al., 2013;Moller et al., 2008), but beyond that we made few specific hypotheses about classroom and teachercharacteristics. Based on the cognitive underpinnings of visuomotor integration and evidence thatcognitive skills are linked to organizational and instructional teacher–child interactions (Cameron &Morrison, 2011; Mashburn et al., 2008), we expected that preschoolers who were exposed to higherquality teacher–child interactions would improve relatively more in visuomotor integration.

Method

This correlational study included children who were enrolled in the follow-up year (postintervention phaseor Phase 3) of a multisite randomized controlled trial (RCT) of a teacher professional development supportprogram (Hamre et al., 2012). Teachers were recruited to participate in the RCT in group meetings thatresearchers followed up individually by phone and e-mail. In one of three conditions to which teacherswere randomly assigned, the professional development involved teachers participating in a 14-week onlinecourse, and/or receiving year-long individualized coaching on the quality of interactions that teachers hadwith their class as a whole, in the general areas of socioemotional relationships, organization and manage-ment, and instructional quality. The RCT tried to develop teachers’ skills in interacting effectively withchildren overall rather than focusing on improving a particular cognitive skill. Furthermore, the visuomo-tor integration measure was added in the postintervention study phase and was not a planned childoutcome; thus, we had no expectation that the RCT would affect children’s visuomotor integration skills.Figure 1 shows a timeline of study phases with intervention group descriptions and when measures thatwere relevant to the present study were collected.

Teachers were eligible to participate if they were English-speaking lead teachers in a publiclyfunded preschool classroom in which the majority of children both were eligible for kindergarten thefollowing year and did not have an individualized education plan at the beginning of the school year.These criteria were derived from the larger study and focus of the research sponsor, which targetedchildren without disabilities in the year before kindergarten entry.

Phase I Phase II Phase III

FALL SPRING FALL SPRING FALL SPRING

Intervention

Condition

Control group Recruit/randomize BAU BAU BAU

Treatment group 1 Recruit/randomize

14-week PD

course

Treatment group 2 Recruit/randomize

Year-long

Coaching

Treatment group 3 Recruit/randomize

14-week PD

course

Year-long

Coaching

Classroom

Measures

XXClass size X

XXAge composition X

XXGender composition X

CLASS observations X X

Teacher

Measures

Education X X a X

Experience X X a X

XXSelf-efficacy X

Beliefs about children X a

Child/Family

Measures

Family demographics X

Inhibitory control X

Visuo-motor

integration X X

Figure 1. Timeline of the larger invention study and present study measures. The time points for measures used in the presentstudy are shown in bold (X). BAU = business as usual; PD = professional development; CLASS = Classroom Assessment ScoringSystem; a = measures were only collected for newly recruited teachers.

982 A. I. BYERS ET AL.

Participants

After centers and teachers were recruited and then randomized into intervention study conditions(see Figure 1), more than 1,000 children from participating classrooms were recruited into the studyto be considered for follow-up testing. On the first day of child assessments, trained data collectorswho were blind to teachers’ treatment condition determined positive consent (parent consented andsigned document) and eligibility for children in each classroom. In addition to being enrolled in thestudy teacher’s classroom and having no individualized education plan at study entry, eligiblechildren were randomly selected from consented children in the classroom and had to have relevantchild, family, and teacher measures available. Data collectors were trained to use a precreated list ofrandomly generated numbers (using a Web-based tool that generated different sequences for everyclassroom) to select consented children for study participation. Giving priority to 4-year-oldchildren, data collectors then randomly selected children, two boys and two girls when possible;they assessed as many of these children as possible during that visit. If a child on the list was absent,the data collector used the precreated list to select a different child.

TeachersTeachers in this study (N = 115) ranged in age from 22 to 62 years (M = 40.55, SD = 10.55), werealmost all women (96%), and identified as predominantly African American (51%) or Caucasian(40%). They reported a mean household income of $56,000 (SD = $27,948); an income-to-needs ratiowas then calculated by dividing the reported family income by the poverty threshold from the U.S.census for that year for a family of the reported size. Only 3% of teachers were at an income-to-needsratio that placed them below the federal poverty threshold.

ChildrenFrom the larger pool of consented children (N = 1,083), four children from each classroom wererandomly selected for the larger study (N = 895). Though 478 children were eventually selected toreceive the visuomotor integration measure, children in the present study (N = 467) met theeligibility criteria described previously. Child participants were attending preschools in five demo-graphically different areas of the United States and were enrolled in 115 different classroomsspanning public, nonprofit, and for-profit sectors.

All children were assessed by trained, bilingual data collectors when they entered preschool andbefore the school year ended. Age at first assessment for this study ranged from 3 years to 5 years,1 month (M = 4.33 years, SD = 5.52 months), at Time 1 (T1) and from 3 years, 5 months, to 6 years,9 months (M = 4.8 years, SD = 5.52 months), at Time 2 (T2). The present study children werepredominantly African American (43%) and Hispanic (32%), with smaller numbers of Caucasians(14%), multiracial children (5%), and Asians/Asian Americans (3%). Moreover, 18% of childrenspoke a language other than English at home, usually Spanish, although half of those also spokeEnglish. Finally, 62% of children came from families with an income-to-needs ratio calculated at 1.0,indicating that their household income was at or below the official U.S. census poverty threshold forthe year of data collection (McLoyd, 1998). This percentage was much higher than the concurrentnational poverty rate of 14.3% (DeNavas-Walt, Proctor, & Smith, 2010).

Given the 895 children in the larger study, t-test comparisons of this study’s sample of 478 to the other417 children who were not administered the Beery-Buktenica Developmental Test of Visual-MotorIntegration (short form; VMI) showed similar demographics: annual family income, t(771) = 0.93,p = .35; gender, χ2(1, N = 895) = 0.35, p = .56; maternal education, t(852) = –0.87, p = .38. Nonetheless,children in this study were slightly older than the non-VMI group (4.33 vs. 4.01), t(893) = −5.48, p < .001.In addition, in a comparison of 605 unselected but consented children and the 478 children withvisuomotor integration information, there were no significant differences across mother’s years ofeducation, annual family income, or child age: mother’s years of education, t(1038) = 0.73, p = .89;annual family income, t(965) = 1.39, p = .12; child age, t(1059.337) = 1.42, p = .16. Thus, this sample

EARLY EDUCATION AND DEVELOPMENT 983

appeared to be representative of the larger groups of children who either consented and were notincluded in the larger study or were included but did not receive the VMI.

Procedures

Data sources for this study included teacher and family questionnaires, classroom observations, andchild direct assessments conducted as part of a larger battery, reported in more detail elsewhere(Hamre et al., 2012).

Measures of Children’s Classroom ExperiencesThe present study used teacher data collected from multiple years of larger RCT participation(intervention phase and postintervention phase) and classroom and child data from a single year(postintervention phase only). Several questionnaires were administered only in the interventionphase, meaning that teachers completed them up to 1 year before child data were collected for thepresent study. These early questionnaires measured teacher characteristics that were unlikely tochange between study phases, such as personal demographics.

Classroom characteristics. Teachers provided information on the number of students in theirpostintervention phase classrooms by gender and age, which was then used to create variablesrepresenting class size, age composition, and gender composition. Income-to-needs data werecollected from families and aggregated to create classroom SES composition.

Teacher characteristics. Teachers also provided information about their education, experience,self-efficacy, and beliefs about children. They reported professional experiences at the beginning ofthe larger study and updated their information again before the postintervention phase (seeFigure 1). Items dealt with years of education, highest level of education attained (from less thanan associate’s degree to a master’s degree or higher), certification to teach preschool and/orkindergarten, and years of teaching experience both in prekindergarten and in other grades.

Teachers reported on their self-efficacy in the classroom on the short form of the Teachers’ Senseof Efficacy Scale (Tschannen-Moran & Hoy, 2001). The 12 items on this 1-to-9 scale represent threemoderately correlated factors: Efficacy for Student Engagement (e.g., “How much can you do to helpyour students value learning?”), Efficacy for Instructional Strategies (e.g., “How well can youimplement alternative strategies in your classroom?”), and Efficacy for Classroom Management(e.g., “How much can you do to control disruptive behavior in the classroom?”). Tschannen-Moran and Hoy (2001) administered the scale to 410 preservice and in-service teachers withreliability for each factor ranging from .87 to .90; reliability was α = .92 in this study. A meanscore, including the three factors, was used in analyses.

Teachers reported their beliefs about raising children and children’s learning using 16 relevantitems from the Parental Modernity Scale (Schaefer & Edgerton, 1985). Each item on the 5-point scalerequires teachers to indicate the extent to which they agree with a statement about children’slearning from strongly disagree to strongly agree. Lower scores indicate more child-centered (lessauthoritarian) attitudes, and higher scores indicate less child-centered (more authoritarian) attitudes.Examples of statements include “The major goal of education is to put basic information into theminds of children” (reversed), “Children’s learning results mainly from being presented basicinformation again and again” (reversed), and “The most important thing to teach children isabsolute obedience to parents” (reversed). The scale is reliable, with a test–retest reliability of .84in the original study; α = .77 in this study.

Teacher–child interactions. Classroom quality was assessed in the postintervention phase using theClassroom Assessment Scoring System (CLASS; Pianta, La Paro, & Hamre, 2008). The CLASSassesses teacher–child interaction quality along 10 dimensions that fall under three theoretical

984 A. I. BYERS ET AL.

factors: Emotional Support (positive climate, negative climate [reverse coded], teacher sensitivity,and regard for student perspectives dimensions), Classroom Organization (behavior management,productivity, and instructional learning formats dimensions), and Instructional Support (conceptdevelopment, quality of feedback, and language modeling dimensions). These factors have been usedextensively in prior research (Cameron Ponitz, Rimm-Kaufman, Grimm, & Curby, 2009; Curbyet al., 2013; Hamre, Mashburn, Pianta, & Downer, 2007; Mashburn et al., 2008).

Data collectors visited classrooms twice during the postintervention phase year to perform liveobservations, which took between 2.5 and 4 hr per visit. To become reliable, data collectors weretrained using master-coded videos at a week-long workshop. Before they could administer theCLASS in the field, data collectors were required to be within 1 point on the 7-point scale of themaster code for 80% of the 10 dimensions and to conduct a live observation in the field along with amaster coder. Reliabilities ranged from 88% to 91% during training. During data collection,observers were required to submit coded videos to a master coder to check for drift from the mastercodes and were required to maintain their reliability status (above 80% reliability) while datacollection was ongoing. In addition, both the data collector and a trained CLASS observer conducted20% of the observations. Both coders met after the observation to reconcile any differences incoding.

Child MeasuresTwo child measures were used from a single individualized administration session lasting30–45 min: the main outcome of visuomotor integration at two time points (T1 and T2) andinhibitory control as a control variable at T1 only. T1 occurred between late October and earlyJanuary; T2 occurred between mid-March and early June. Assessments were separated by an averageof 157 days (SD = 35.9, range = 84–273) or 5.2 months.

Primary cognitive outcome: visuomotor integration. Children’s visuomotor integration wasassessed using the 21-item VMI (Beery & Beery, 1997). The VMI was designed to assesshow well a child can coordinate his or her visual and motor abilities. The visuomotorintegration subtest required children to use a pencil to copy a series of increasingly complexfigures, with a maximum of 21 figures for the short form. Children began by copying simplevertical and horizontal lines and progressed to copying more spatially complex figures withoverlapping shapes and lines. Items were scored according to established criteria and giveneither a 0 or 1. The sum of correct responses (raw score) was used in all analyses; internalconsistency for our sample was strong at α = .92.

Control variable: inhibitory control. We included inhibitory control in all analyses because it isinvolved in visuomotor integration (Korkman et al., 2007). Inhibitory control was assessed using thePencil Tap Test (Diamond & Taylor, 1996; Smith-Donald, Raver, Hayes, & Richardson, 2007), anestablished measure with good concurrent and construct validity. Children were instructed to taptheir pencil once when the experimenter tapped hers twice and to tap twice when the experimentertapped once. Raw scores ranged from 0 to 16 and were converted to the percentage of total correctresponses for analyses.

Results

This study of children’s visuomotor integration at two time points examined child-leveldemographic and cognitive predictors and three categories of classroom-level predictors. Toenhance interpretation of the results, we first examined descriptive statistics including missingdata along with bivariate correlations. Approximately 50 children were not tested in the VMIat either T1 or T2. A two-sample t test indicated that children who were missing VMI scores ateither time point had lower income-to-needs ratios than those with complete data,

EARLY EDUCATION AND DEVELOPMENT 985

t (408) = −3.83, p < .001. Therefore, all analyses used full information maximum likelihood inMplus 7.1 (Muthén & Muthén, 1998–2010) to estimate parameters based on all available data.This approach allowed for an analysis of data from all children and not just a socioeconomi-cally biased subset of children who had both VMI scores. We then examined multilevel modelsto address Research Questions 1 and 2 (RQ1 and RQ2).

Descriptive Statistics and Correlations

Sample descriptive statistics, shown in Table 1, revealed that study children were typically developingand from a range of sociodemographic and SES backgrounds, with a large number of families livingin poverty. Correlations between child variables are shown in Table 2, and correlations between childvariables and classroom experiences are in Appendixes A–C.

Visuomotor Integration and Inhibitory ControlNationwide, average VMI scores for 3- to 5-year-olds range from 7 to 15, depending on the child’sexact age (Beery & Beery, 2006). Similar to national averages, mean scores for the VMI in the currentsample were 10.33 (SD = 2.44) at T1 and 12.08 (SD = 2.42) at T2, with a range from 3 to 19 at both

Table 1. Descriptive Statistics for All Variables (Total n = 467).

Variable n M (%) SD Range

Child demographic variablesChild age in years (T1) 429 4.33 0.46 3–5.16Family income-to-needs ratio 410 1.04 1.02 0.05–4.99Male 233 (49.5) 0.50 0–1Black 204 (43.3) 0.50 0–1White 68 (14.4) 0.35 0–1Hispanic 149 (31.6) 0.47 0–1Asian 12 (2.5) 0.16 0–1Multiracial 22 (4.7) 0.21 0–1Child cognitive variablesT1 VMI 426 10.33 2.44 3–19T2 VMI 421 12.08 2.42 3–19T1 Pencil Tap 427 0.42 0.31 0–1T2 Pencil Tap 421 0.60 0.34 0–1Classroom characteristicsClass size 113 16.72 2.41 7–20Percent 3-year-olds 113 (18.23) 21.64 0–100Average income-to-needs ratio 113 1.04 0.78 0.10–4.34Percent girls in child’s class 113 (47.46) 10.69 21.43–83.33Percent limited English proficiency 113 (17.96) 20.33 0–78.95Percent children with IEP 111 (8.53) 9.77 0–42.86Teacher characteristicsHighest level of educationLess than an associate’s degree 116 (3.4) 0.18 0–1Associate’s degree 116 (23.3) 0.42 0–1Bachelor’s degree 116 (49.1) 0.50 0–1Master’s degree or higher 116 (21.6) 0.41 0–1Has child development credential 116 (23.3) 0.42 0–1Certified to teach K or pre-K 116 (67.2) 0.47 0–1Years of K or pre-K experience 112 12.41 8.37 0–40Years of teaching experience 112 14.16 8.96 0–47Self-efficacy 98 7.90 0.87 2.55–9Beliefs about children 99 2.46 0.55 1.25–3.75Observed teacher–child interactionsCLASS Classroom Organization 115 5.38 0.71 3.50–6.75CLASS Emotional Support 115 5.53 0.76 3.34–7.0CLASS Instructional Support 115 2.43 0.70 1.22–4.46

Note. T1 = Time 1; VMI = Beery-Buktenica Developmental Test of Visual-Motor Integration; T2 = Time 2; IEP = individualizededucation plan; K = kindergarten; pre-K = prekindergarten; CLASS = Classroom Assessment Scoring System.

986 A. I. BYERS ET AL.

Table2.

Correlations

ofCh

ildDem

ograph

icVariables

With

Visuom

otor

IntegrationandInhibitory

Control.

Variable

12

34

56

78

910

1112

Child

cogn

itive

variables

1.FallVM

I—

2.Sprin

gVM

I.70

**—

3.FallPencilTap

.45

**.45

**—

4.Sprin

gPencilTap

.47

**.50

**.58

**—

Child

demog

raph

icvariables

5.Ch

ildagein

mon

ths(fall)

.52

**.42

**.32

**.32

**—

6.Family

income-to-needs

ratio

.07

.15

**.24

**.17

**−.05

—7.

Male

.00

−.03

−.03

−.07

.03

.06

—8.

Black

−.18

**−.16

**−.04

−.11

*−.11

*−.16

**−.09

*—

9.White

.02

.06

.12

*.14

*−.04

.36

**.11

*−.36

**—

10.H

ispanic

.15

**.12

*−.13

**−.02

.22

**−.18

**.02

−.60

**−.28

**—

11.A

sian

.11

*.10

*.10

*.11

*−.04

.04

.00

−.14

*−.07

−.11

*—

12.M

ultiracial

−.02

.01

.08

.04

−.09

.11

*−.06

−.19

**−.09

*−.15

**−.04

—

Note.VM

I=Beery-Bu

ktenicaDevelop

mentalT

estof

Visual-M

otor

Integration.

*p<.05.

**p<.01.

EARLY EDUCATION AND DEVELOPMENT 987

time points. Average scores for the Pencil Tap inhibitory control measure at T1 were also similar tolevels found in previous work with preschoolers (Smith-Donald et al., 2007), with mean percentcorrect scores in this sample in this sample of 42% (SD = 31%) at T1 and 60% (SD = 34%) at T2.

Classroom CompositionPreschool classrooms enrolled about 17 children (precise M = 16.72, SD = 2.41) and varied widely incomposition. On average, classrooms enrolled 18.2% 3-year-olds (SD = 22%, range = 0%–100%) and47.5% girls (SD = 10.69%, range = 21.43%–83.33%). The mean income-to-needs ratio was about 1—just over the poverty threshold. This measure also had considerable variability (SD = 0.78) and range(0.10–4.34), indicating that classrooms varied in the populations they served. Compared to a familyat the poverty threshold (income-to-needs ratio of 1, income of $21,756), a family reporting anincome-to-needs ratio of 1.78 is over the poverty threshold (income of $38,726) but still makes lessthan the U.S. median income of $52,762 (DeNavas-Walt et al., 2010). Thus, this highly variablesample nonetheless included many families living in disadvantaged socioeconomic conditions.

Teacher CharacteristicsTeachers were relatively well educated, with more than 70% possessing either a terminal bachelor’sor master’s degree. Moreover, nearly 70% were certified to teach preschool or kindergarten and hadon average 12.141 (SD = 8.37) years of teaching experience in the early grades. Teachers reportedhigh self-efficacy (7.9 on a 9-point scale) on the Teachers’ Sense of Efficacy Scale, similar to previousresearch using this scale (Tschannen-Moran & Hoy, 2001).

Teacher–Child InteractionsTeachers received medium to high-medium CLASS scores for Emotional Support (M = 5.53,SD = 0.76) and Classroom Organization (M = 5.38, SD = 0.71). The average Instructional Supportscore was the lowest domain score (M = 2.43, SD = 0.70) and was considered between low andlow-medium quality. These scores are consistent with other studies that have used the CLASS inearly childhood settings (Mashburn et al., 2008).

Multilevel Models for RQ1 and RQ2

We next examined associations between child and classroom predictors and visuomotor integrationusing multilevel modeling, which accounts for the nested nature of data. We used Mplus 7.1(Muthén & Muthén, 1998–2010) to specify a total of nine models (Models 1a and 1b, Models2a–2g) with variance estimated at two levels in each model. We grand mean centered all continuousvariables. Coefficients should be interpreted as an increase in initial (T1) VMI or improvement (T2)VMI when all other continuous variables were held constant at their means and with dichotomousvariables at their reference groups (i.e., female Caucasian children of average age).

RQ1: Child Characteristics Associated With Initial Visuomotor IntegrationFor RQ1, Models 1a and 1b examined child predictors of T1 VMI. The between-classroom variancewas divided by the total variance to obtain an intraclass correlation of .15. In other words, even atT1, 15% of the variance in VMI scores was at the classroom level; this is consistent with other workand justifies the use of multilevel modeling (Cameron & Morrison, 2011).

Models for RQ11a. Fully unconditional model for T1 VMI. The fully unconditional model examined the amount ofvariation in T1 VMI scores that lay within versus between preschool classrooms; this produced anintraclass correlation to assess whether the nested nature of the data suggested a need for multilevelmodeling. The equation for this model was represented as follows:

988 A. I. BYERS ET AL.

Level1 : Yij ¼ β0j þ rij (1)

Level 2 : β0j ¼ γ00 þ u0j: (2)

In Level 1, the equation represents the predicted T1 VMI score (Y) for a child (i) in a classroom (j) asa function of his or her classroom average (β0j) and the associated error term (rij). In Level 2, theequation represents the classroom average VMI score (β0j) as a function of the mean T1 VMI scoresfor all classrooms (γ00) and a random effect for each classroom (u0j).1b. Child-level predictors of T1 VMI. This model examined associations between VMI at T1 and age,gender, ethnicity, poverty, and inhibitory control.

Level : Yi ¼ β0j þ β01::::nj ðchild characteristics; inhibitory controlÞþri (3)

Level 2 : β0j ¼ γ00 þ u0j: (4)

The equation for this first level of model represents T1 VMI (Y) for a child (i) as a function of childcharacteristics (β01 . . . n) and the associated error term (ri).

Results for RQ1. Table 3 presents unstandardized coefficients and standard errors, which indicatethe extent to which child characteristics were associated with T1 VMI scores. Asian children had asignificant advantage of 1.37 additional points (0.54 SD) on the VMI compared to Caucasianchildren, who were the reference group; Hispanic ethnicity was trending in a positive direction. Inaddition, scoring 1 SD higher on Pencil Tap (0.31) was associated with scoring 0.75 points (0.30 SD)more than the mean on the VMI. Finally, children who were 1 month older scored 0.16 points(0.07 SD) higher than average-age children on the VMI.

RQ2: Predictors of Improvement in Visuomotor IntegrationAppendix D presents unstandardized coefficients and standard errors for a series of models (Models2a–2f) that indicated the extent to which classroom, teacher, and teacher–child interaction variableswere associated with children’s T2 VMI scores when T1 VMI and Pencil Tap plus demographicvariables were controlled.

Table 3. Contributions of Child Characteristics, Including Inhibitory Control, to Children’s Time 1 Visuomotor Integration.

Model 1a Model 1b

B SE B SE

Intercept 10.31 *** .14 10.24 * .24Child characteristicsFall Pencil Tap 2.42 *** .34Child age in months (fall) 0.18 *** .02Family income-to-needs ratio 0.06 .10Male −0.10 .17Black −0.26 .26Hispanic 0.53 † .30Asian 1.37 * .68Multiracial 0.03 .46Residual variance: within classrooms 5.04 3.47Residual variance: between classrooms 0.90 0.20

†p < .1. *p < .05. ***p < .001.

EARLY EDUCATION AND DEVELOPMENT 989

Models for RQ22a. Fully unconditional model for T2 VMI. This model examined the amount of variation in T2 VMIscores that lay within and between preschool classrooms in the fall. The equation for this model wasrepresented as follows:

Level 1 : Yij ¼ β0j þ rij (5)

Level 2 : β0j ¼ γ00 þ u0j: (6)

2b. Child-level predictors of T2 VMI. This model examined associations between T2 VMI andpreschool classroom quality predictors while controlling for T1 VMI and inhibitory control andfor individual child characteristics. This two-level model required two equations:

Table 4. Contributions of Child Characteristics and Classroom Experiences to Time 2 Visuomotor Integration.

Model 2a Model 2b Model 2g

B SE B SE B SE

Intercept 12.06 *** .13 11.96 *** .23 11.75 *** .39Child characteristicsFall VMI 0.59 *** .04 0.60 *** .04Fall Pencil Tap 1.04 ** .36 1.12 *** .31Child age in months (fall) 0.04 † .02 0.01 .02Family income-to-needs ratio 0.23 * .10 0.09 .10Male 0.06 .19 0.11 .19Black 0.04 .25 0.00 .26Hispanic 0.23 .27 0.05 .28Asian 0.26 .51 0.17 .47Multiracial −0.04 .36 0.06 .33Classroom characteristicsClass size −0.07 † .04Percent 3-year-olds −0.01 ** .00Average income-to-needs ratioPercent girls in child’s classPercent limited English proficiencyPercent children with IEPHead Start −0.29 .22Teacher characteristicsAssociate’s degreeBachelor’s degree 0.36 * .17Master’s degree or higher 0.25 .26Has child development credentialCertified to teach K or pre-K 0.04 .15Years of K or pre-K experienceYears of teaching experienceSelf-efficacyBeliefs about childrenCourse 0.05 .15Consult 0.25 .15Observed Teacher–Child InteractionsClassroom Organization 0.25 ** .09Emotional Support 0.20 * .10Instructional Support 0.20 .14Residual variance: within classrooms 5.39 2.86 2.65Residual variance: between classrooms 0.49 0.00 0.01

Note. Classroom Assessment Scoring System domains are modeled one at a time for Model 2g. VMI = Beery-BuktenicaDevelopmental Test of Visual-Motor Integration; IEP = individualized education plan; K = kindergarten; pre-K = prekindergarten.

†p < .1. *p < .05. **p < .01. ***p < .001.

990 A. I. BYERS ET AL.

Level 1 : Yij ¼ β0jþ β01 fall VMIð Þþβ02::::n ðchild char:; inhibitory controlÞ þ rij (7)

Level 2 : β0j ¼ γ00 þ u0j: (8)

2c–2e. Classroom-quality predictors of T2 VMI. This model examined associations between T2VMI and three categories of preschool classroom quality predictors while controlling for T1 VMI,T1 inhibitory control, and individual child characteristics. This two-level model required twoequations:

Level 1 : Yij ¼ β0j þ β01 ðfall VMIÞ þ β02::::n child characteristics; pencil tapð Þþrij (9)

Level 2 : β0j ¼ γ00 þ γ01�n preschool classroom quality predictorsð Þþu0j: (10)

In Level 1, T2 VMI score (Y) for a child (i) who is in a classroom (j) is a function of the mean T2VMI score for children in this class (β0j) after T1 VMI (β01) and child characteristics (β02 . . . n) andthe error term associated with this mean (rij) are adjusted. In Level 2, the adjusted mean T2 VMIscore for children in each classroom (β0j) is a function of the grand mean T2 VMI score (γ00),preschool classroom quality predictors (γ01-γn), and the error term associated with this estimatedmean (u0j).

2f. Classroom interaction quality predictors entered separately. This model included child-levelcharacteristics but added the three domains of observed teacher–child interactions one at a time toLevel 2. This is because the three domains were highly correlated.

2g. Final model of all significant predictors of T2 VMI. This model included all child-levelcharacteristics plus variables that were statistically significant or trending at p < .10 in previousmodels. Note that this is the model that we interpret in the next section.

Results for RQ2. Table 4 presents Models 2a (fully unconditional model for T2 VMI), 2b (finalchild-level model for T2 VMI), and 2g (final model including child- and classroom-level variables).The coefficients and effect sizes described next are from the final model, Model 2g, which includedall child-level predictors plus classroom-level predictors that reached statistical significance of p < .10in the incremental models shown in Appendix D. We calculated a type of effect size by multiplyingthe unstandardized coefficient for a predictor by that predictor’s standard deviation, then dividing bythe standard deviation of T2 VMI (2.42). Also, given that the average gain from T1 to T2 was 12.08 –10.32, or 1.76 points, and there were on average 5.2 months (20.8 weeks) between T1 and T2, wecalculated that children gained on average 1.76/20.8, or 0.08 points per week. Thus, we couldassociate the standard deviation gain with an amount of time, interpreted as the number of weeksahead for children with a 1 SD advantage on the coefficient of interest. Though these estimates arenot precise given multilevel components, they enable a more practical interpretation of the coeffi-cients (Hill, Bloom, Black, & Lipsey, 2008).Child-level predictors of improvement. A 1-point increase in T1 VMI was associated with a 0.60-point increase in T2 VMI, equivalent to 0.60 SD or 7.14 weeks. A 1-point increase in T1 PencilTap was associated with a 1.12-point increase in T2 VMI, equivalent to 0.14 SD or 1.69 weeks.Classroom characteristic predictors of improvement. Of the classroom characteristics, only thepercentage of 3-year-olds in a classroom was associated with whether children made additionalT1 to T2 improvement on the VMI. The unstandardized coefficient of −0.01 can be interpretedas follows: Given that the average class size was about 17, with 18% 3-year-olds (three children), a1% increase in 3-year-olds—associated with 0.01 points lower on T2 VMI—equals about 0.18 more3-year-olds. Or, a 5% increase in 3-year-olds would be approximately equal to having one additional3-year-old and one fewer 4-year-old in the class. Interpreted like this, and when we hold the overallclass size constant at the mean of 17 children, an increase of 22% more 3-year-olds (the equivalent

EARLY EDUCATION AND DEVELOPMENT 991

to 1 SD for the 3-year-old variable, or about four children) in a classroom would mean a 0.22-pointor 0.09 SD decrease in T2 VMI scores. This was associated with being 1.06 weeks behind.Teacher characteristic predictors of improvement. Children of teachers who had earned a bachelor’sdegree scored an additional 0.50 points ahead (0.07 SD, 0.88 weeks) in T2 VMI.Teacher–child interaction predictors of improvement. With regard to teacher–child interactions, thefinal model had CLASS domains entered separately given the high correlations (greater thanr = .60) between each pair of CLASS domains. Children in classrooms with higher scores on allthree domains (Emotional Support, Classroom Organization, and Instructional Support)improved more in VMI from T1 to T2 when all other variables were controlled. The magnitudewas also similar for each domain. Specifically, an increase of 1 point in Classroom Organizationwas associated with a 0.25-point advantage (0.07 SD, 0.87 weeks) on T2 VMI, a 1-point increasein Emotional Support was associated with a 0.20-point advantage (0.06 SD, 0.73 weeks) in T2VMI, and a 1-point increase in Instructional Support was associated with a 0.20-point advantage(0.07 SD, 0.88 weeks) on T2 VMI.

Summary of Results

Taken together, results for RQ1 show that older children, Asian children, and those with strongerinhibitory control entered preschool with better visuomotor integration. Results for RQ2 show thatpreschoolers with better inhibitory control and visuomotor integration at the beginning of the yearimproved more in visuomotor integration by the end of the year. Cognitive and demographicvariables at TI explained about 46.9% of the child-level variance in T2 visuomotor integration andvirtually all of the classroom-level variance. Results describe a fairly limited array of preschoolclassroom quality indicators associated with children’s improvement in visuomotor integrationover about a 5-month period. Children improved more when they were in classrooms with fewer3-year-olds and when their teachers had earned at least a bachelor’s degree. Finally, children inbetter organized classrooms also improved more on visuomotor integration, with a similar patternfor emotional and instructional support. Classroom-level variables explained an additional 3.9% ofthe child-level variance, with the final model explaining 54.7% of the total variance in T2visuomotor integration.

Discussion

The goals of this study were to examine the extent to which classroom experiences contributed toyoung children’s improvement in visuomotor integration over about 5.2 months of early childhoodprogramming. In this sample of 3- to 5-year-olds from the United States, many of whom were atsociodemographic risk (e.g., from an ethnic minority group or from low-income families), relativelyfew child-level and classroom-level variables were associated with visuomotor integration gains. Notethat the patterns that emerged, especially for classroom experiences, are consistent with existingstudies of other cognitive skills and clarify the importance of classroom age composition, teachereducation, and teacher–child interactions.

Child Characteristics Associated With Visuomotor Integration

It is not surprising that older children had better visuomotor integration than their youngerclassmates. Design copying skills and their underlying cognitive components follow a docu-mented developmental progression (Korkman et al., 2007; Verdine et al., 2014). Beyondaccurate perception and decomposition of the design, successfully rendering a design onpaper requires executive skills, including inhibitory control to plan how to begin and decidingin what order to proceed, and then monitoring progress and avoiding incorrect impulses(Ogawa et al., 2010). In this study, children who could inhibit impulses, or who could already

992 A. I. BYERS ET AL.

copy designs well, may have been more ready to engage with common learning materials, likepuzzles, games, and manipulatives, in purposeful ways that furthered their visuomotor inte-gration over time.

Few Contextual Contributors to Longitudinal Improvement in Visuomotor Integration

Of the six classroom composition variables that we tested, only aggregated age mattered: Children inclassrooms with more 3-year-olds improved less on visuomotor integration, even after we controlledfor their own age. This finding is important because empirical inquiries remain ambiguous abouthow mixed-age classrooms relate to learning outcomes for young children. By incorporatingvisuomotor integration, the present study extends the variety of school readiness skills that havebeen examined in relation to classroom age composition and further suggests that the presence ofyounger children is associated with less learning for this outcome. At least one study suggested thatrelatively older children are most disadvantaged when their classrooms include many youngerchildren (Moller et al., 2008). Teachers in classrooms in which there are many 3-year-old childrenmay struggle to differentiate lessons for such a wide age range and thus pitch their instruction orexpectations to the youngest children who need the most support. Or, older children may adopt lesssophisticated approaches to play and learning to accommodate younger children and may be lesslikely to challenge their visuomotor skills. This possibility is supported by research showing that thebehavior of 4-year-olds in mixed-age classrooms resembles the behavior of 3-year-olds in same-ageclassrooms (Winsler et al., 2002).

Associations between teacher characteristics and children’s improvement in visuomotor integrationwere largely in accord with previous research: Teachers’ classroom experience and beliefs aboutthemselves and children’s learning were not associated with visuomotor integration improvement.Teachers’ education level did matter, however. The contributions of teacher education to children’sschool readiness are generally small (Kelley & Camilli, 2007) and were small in this study as well. Thisassociation is still important because few other teacher characteristics were associated with children’sskills. In addition, this study aligns with findings from some other data sets that have indicated positivecontributions of holding a bachelor’s degree to children’s math gains (Early et al., 2006), though formost child outcomes holding a bachelor’s degree does not appear to matter (Kelley & Camilli, 2007).

Although the mechanisms underlying the association between teacher education and schoolreadiness skills like visuomotor integration are difficult to study, previous work has found somebehavioral differences between teachers who have bachelor’s degrees and those who do not. Forexample, preschool teachers with bachelor’s degrees provide more sensitive caregiving, richerlanguage, and more developmentally appropriate instruction (for a review, see Bueno et al., 2010).Just as teachers with more education can provide their students with richer and more diverselanguage experiences, their broad education may also allow them to share a greater range ofvisuomotor integration activities or ways to solve visuomotor problems. Evidence for this possibilitycomes from the parenting literature, which links maternal education with the provision of moresensitive scaffolding and metacognitive strategies during child problem-solving tasks. In one parti-cularly relevant study, Carr and Pike (2012) asked mothers to help their children copy a series ofblock designs—a task with a large visuomotor component. They found that compared to lesseducated mothers, more highly educated mothers provided more contingent feedback. In otherwords, these mothers provided feedback that was appropriate to their child’s level of success on theprevious design, for example, backing off when children were successful and providing increasinglyexplicit help when they were not. Preschool teachers with a bachelor’s degree may provide similarkinds of support, which could in turn explain children’s visuomotor integration learning.

Though this improved interactions explanation has merit, in this study teacher educationcontinued to predict children’s visuomotor improvement, even after we controlled for teachers’observed interactions with children. Note that the positive results for teacher–child interactionsgenerally align with work in this area. For example, existing work connects early childhood teachers’

EARLY EDUCATION AND DEVELOPMENT 993

interactions—to orient children to upcoming activities and to effectively manage behavior—to chil-dren’s cognitive and behavioral gains (Cameron Ponitz et al., 2009). These interactions providechildren with information about where to direct their attention and how to execute classroom tasks,including how to manage physical materials (Cameron & Morrison, 2011; Rimm-Kaufman et al.,2009). In an observational study of 41 classrooms, Cameron and Morrison (2011) noted that much ofthe time spent organizing in preschool involved teachers demonstrating a hands-on arts-and-craftsactivity while children gathered around, watching how to use the materials. In addition, research oninteraction quality in preschool suggests a key role for so-called instructional interactions, which tendto be more related to children’s cognitive gains than other types of interactions (e.g., organizational orsocioemotional interactions; Mashburn et al., 2008).

Limitations

This study has some important limitations. First, the design was correlational, so despite the fact thatwe controlled for children’s initial visuomotor integration, it is not possible to say that the predictorsassociated with improvement caused that improvement. Second, only a single measure of EF wasavailable, which tapped children’s inhibitory control. Using a more well-rounded measure of EFwould have allowed for a more nuanced examination of its association with visuomotor integration.Becker et al. (2014) found that among both 4- and 5-year-olds, visuomotor integration was positivelyand distinctly correlated with inhibitory control, working memory, and a general measure of EF.This is not surprising, given that visuomotor integration is a complex construct that requiresattention, visuospatial processing, and fine motor coordination, among other skills.

Third, the pattern of findings, especially for teacher–child interactions, does not suggest a clearmechanism, though the fact that all three CLASS domains were separately predictive of visuomotorintegration improvement reiterates that interactions are generally important for this outcome, as withother learning outcomes (Cameron Ponitz et al., 2009). Organizational, emotional, and instructionalinteractions were robustly correlated, meaning that teachers with organizational strengths demonstratedhigh-quality instructional and relationship interactions as well. Given that the type of interactions thatwere statistically related to visuomotor integration depended on the model, we refrain from drawingstrong interpretations of which domain of the CLASS is a most important for visuomotor integration.The CLASS measure is therefore limited for these purposes because it does not focus on visuomotorskills, or even on specific academic skills beyond language and critical thinking. A more precise measurethat incorporates teacher behaviors during visuomotor tasks (see Carr & Pike, 2012) may help clarifywhat types of interactions are related to the development of visuomotor integration.

Conclusion

This exploratory study provides an initial picture of the preschool experiences that are associated with anew cognitive skill on the block, namely, children’s development of visuomotor integration. Resultswere simultaneously encouraging and discouraging from a classroom practice perspective. On the onehand, the standard classroom and teacher characteristics that are usually associated with academic skillsalso predicted visuomotor integration, so high-quality classrooms should foster both kinds of skills. Onthe other hand, these predictors were few in number and had relatively small effects compared tochildren’s skills at the beginning of preschool. Moreover, even the CLASS is a fairly broad measure ofthe quality of interactions in the classroom. A clearer understanding of the development of visuomotorintegration in a classroom context likely requires more fine-grained observations of activities thatspecifically target fine and visuomotor skills. The pattern of findings for teacher education andteacher–child interactions—including teachers’ organization of the classroom environment and thequality of instructional interactions—suggests that engagement during visuomotor activities, and howcaregivers interact with children in such activities, is a good place to begin the search.

994 A. I. BYERS ET AL.

Acknowledgments

We express our sincere gratitude to the children, families, and teachers who participated in this research as well as tostudy team members Elise Rubinstein and Wanda Weaver.

Funding

This study was supported by the National Center for Research on Early Childhood Education, funded by the Instituteof Education Sciences (R305A060021). It was also supported by the National Science Foundation (DRL-0815787 andDRL-1252463).

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Appendix A

Table A-1. Correlations of Classroom Characteristics With Children’s Visuomotor Integration and Inhibitory Control (N = 467).

Variable 1 2 3 4 5 6 7 8 9 10

Child cognitivevariables

1. Fall VMI —2. Spring VMI .70 ** —3. Fall Pencil Tap .45 ** .45 ** —4. Spring Pencil Tap .47 ** .50 ** .58 ** —Classroomcharacteristics

5. Class size .15 ** .03 .06 .01 —6. Percent 3-year-olds −.27 ** −.33 ** −.11 * −.16 * −.12 ** —7. Average income-to-needs ratio

.01 .09 .15 * .13 * −.25 * .03 —

8. Percent girls inchild’s class

.04 .03 .03 .03 −.09 .04 −.09 —

9. Percent limitedEnglish proficiency

.06 .12 * −.09 .04 −.03 −.44 ** −.06 −.10 * —

10. Percent childrenwith IEP

−.03 −.03 −.06 −.04 −.08 .07 −.09 −.22 ** −.12 —

Note. VMI = Beery-Buktenica Developmental Test of Visual-Motor Integration; IEP = individualized education plan.*p < .05. **p < .01.

EARLY EDUCATION AND DEVELOPMENT 999

Appen

dix

B

TableB-1.

Correlations

ofTeacherCh

aracteristicsWith

Visuom

otor

IntegrationandInhibitory

Control.

Variable

12

34

56

78

910

1112

Child

cogn

itive

variables

1.FallVM

I—

2.Sprin

gVM

I.70

**—

3.FallPencilTap

.45

**.45

**—

4.Sprin

gPencilTap

.47

**.50

**.58

**—

Teachercharacteristics

5.Less

than

anassociate’sdegree

−.02

−.04

−.04

−.04

—6.

Associate’sdegree

−.04

−.15

**−.03

−.03

−.10

*—

7.Bachelor’sdegree

.01

.06

.00

−.10

*−.17

**−.54

**—

8.Master’s

degree

orhigh

er.03

.11

*.07

.15

**−.09

*−.29

**−.52

**—

9.Has

child

developm

entcredential

−.04

−.12

*−.06

−.16

**.12

*.15

**.10

*−.29

**—

10.C

ertifiedto

teachKor

pre-K

.13

**.18

**.07

.19

**−.07

−.06

.00

.18

**−.21

**—

11.Y

earsof

Kor

pre-Kexperience

−.02

−.04

−.07

−.13

**.20

**.25

**−.12

*−.20

**.34

**−.20

**—

12.Y

earsof

teaching

experience

−.02

−.03

−.05

−.13

**.18

**.16

**−.13

**−.09

.34

**−.20

**.91

**—

13.Self-EfficacyforStud

entEngagement

−.05

−.01

−.03

−.05

.09

−.09

−.03

.08

−.22

**.13

**.07

−.02

14.Self-EfficacyforInstructionalS

trategies

−.02

−.02

.05

.05

.11

*.03

−.09

.08

−.09

.18

**−.02

−.08

15.Self-EfficacyforClassroom

Managem

ent

−.06

.00

−.03

−.01

.18

**−.10

*−.01

.08

−.20

**.20

**.06

−.02

16.B

eliefsabou

tchildren

−.03

−.04

−.03

−.04

.08

−.02

.10

*−.14

**.16

**−.12

*−.11

*−.08

Note.VM

I=Beery-Bu

ktenicaDevelop

mentalT

estof

Visual-M

otor

Integration;

K=kind

ergarten;p

re-K

=prekindergarten.

*p<.05.

**p<.01.

1000 A. I. BYERS ET AL.

Appendix C

Table C-1. Correlations of Teacher–Child Interactions With Children’s Visuomotor Integration and Inhibitory Control.

Variable 1 2 3 4 5 6 7

Child cognitive variables1. Fall visuospatial processing —2. Spring visuospatial processing .70 ** —3. Fall Pencil Tap .45 ** .45 ** —4. Spring Pencil Tap .47 ** .50 ** .58 ** —Observed teacher–child interactions5. CLASS Classroom Organization −.02 .10 * .09 .14 ** —6. CLASS Emotional Support .00 .08 .08 .13 ** .81 ** —7. CLASS Instructional Support .11 * .21 ** .10 * .15 ** .60 ** .61 ** —

Note. CLASS = Classroom Assessment Scoring System.*p < .05. **p < .01.

EARLY EDUCATION AND DEVELOPMENT 1001

Appendix D

Table D-1. Estimates From Two-Level Models Predicting Time 2 VMI Scores.

Model 2a Model 2b Model 2c Model 2d Model 2e Model 2f

B SE B SE B SE B SE B SE B SEIntercept 12.06** .13 11.96 ** .23 12.33 ** .29 11.27 ** .35 11.95** .24 11.91 ** .24Childcharacteristics

Fall VMI 0.59 ** .04 0.60 ** .04 0.60 ** .04 0.59** .04 0.59 ** .04Fall Pencil Tap 1.04 ** .36 1.18 ** .32 1.15 ** .33 0.99* * .34 0.96 ** .34Child age inmonths (fall)

0.04 † .02 0.00 .02 0.02 .02 0.04 † .02 0.04 * .02

Family income-to-needs ratio

0.23 * .10 0.11 .12 0.13 .10 0.17† .10 0.22 * .10

Male 0.06 .19 0.10 .19 0.08 .19 0.09 .19 0.08 .19Black 0.04 .25 −0.05 .27 −0.03 .27 0.06 .26 0.11 .26Hispanic 0.23 .27 −0.02 .28 0.06 .28 0.18 .27 0.29 .27Asian 0.26 .51 0.28 .54 0.09 .50 0.06 .45 0.14 .45Multiracial −0.04 .36 0.03 .35 0.01 .34 0.01 .35 −0.04 .35Classroomcharacteristics

Class size −.07 † .04Percent 3-year-olds

−0.01 ** .01

Averageincome-to-needs ratio

−0.08 .18

Percent girls inchild’s class

0.00 .01

Percent limitedEnglishproficiency

0.00 .00

Percent childrenwith IEP

0.00 .01

Head Start −0.56 ** .22Teachercharacteristics

Associate’sdegree

−0.10 .30

Bachelor’sdegree

0.55 * .26

Master’s degreeor higher

0.56 † .32

Has childdevelopmentcredential

−0.24 .22

Certified toteach K orpre-K

0.32 † .16

Years of K orpre-Kexperience

0.01 .02

Years ofteachingexperience

0.01 .02

Self-efficacy 0.02 .10

(Continued )

1002 A. I. BYERS ET AL.

Table D-1. (Continued).

Model 2a Model 2b Model 2c Model 2d Model 2e Model 2f

Beliefs aboutchildren

0.00 .18

Course 0.09 .16Consult 0.35 * .16Observedteacher–childinteractions

ClassroomOrganization

0.29† .17 0.35 ** .10

EmotionalSupport

−0.21 .16 0.24 * .10

InstructionalSupport

0.40* .16 0.45 ** .13

Residualvariance:withinclassrooms

5.39 2.86 2.70 2.72 2.75 2.78

Residualvariance:betweenclassrooms

0.49 0.00 0.02 0.02 0.03 0.04

Note. Classroom Assessment Scoring System domains are modeled one at a time for Models 2f and 2g. VMI = Beery-BuktenicaDevelopmental Test of Visual-Motor Integration; IEP = individualized education plan; K = kindergarten; pre-K = prekindergarten.

†p < .1. *p < .05. **p < .01.

EARLY EDUCATION AND DEVELOPMENT 1003