The effects of environment on children's executive function: a study of three private schools.
Keywords: executive function, elementary education, curriculum, school environment
Executive function refers to a collection of thinking processes involved in guiding, managing, and directing cognitive functions and behavioral or emotional responses (Giola, Isquith, Guy, & Kenworthy, 2000). Many of the processes involved in planning, monitoring, and evaluating during problem solving require executive functioning. Executive function skills may contribute to academic success and are particularly important when people are faced with novel situations or ill-structured problems. Regulation of the lower order cognitive functions, such as recalling factual knowledge, requires the integration of different facets of executive function (Garon, Bryson, & Smith, 2008).
Executive function is often attributed to one of three processes: working memory, shift, and inhibition (Garon et al., 2008). Working memory usually refers to a set of processes dedicated to controlled attention, such that activated information remains active, even after being exposed to interference (Engel de Abreu, Conway, & Gathercole, 2010). Working memory functions to maintain simultaneous storage and processing of information, to coordinate or integrate other areas of processing, and also may serve as a supervisory process for executive function (Sub, Oberauer, Wittman, Wilhelm, & Schulze, 2002). Adults generally exhibit more advanced executive function skills and working memory functions than young children, perhaps due in part to the rapid development of working memory during middle to late childhood, which then leads to greater integration between different parts of the memory in adults (Engel de Abreu et al., 2010).
Shift is one of the executive function processes that may fall under the control of working memory. Scales measuring the construct known as shift utilize tasks that require the participant to intentionally change between tasks (Giola et al., 2000). Cognitive flexibility is often referred to as "shift" (Garon et al., 2008). The construct has been linked to functional magnetic resonance imaging (fMRI) evidence showing activation of the inferior frontal cortex and motor system during certain tasks (Aron, 2008). Scales measuring shift may measure the ability to initiate action, as this indicates greater cognitive flexibility (Giola et al., 2000), but behavioral evidence of shift also may involve measuring the difference between the time required to complete a nonshift task and the time required to complete a task that requires shift (Aron, 2008). The difference between these two tasks is often referred to as the "switch cost," or the time lost due to the shift, and may give an accurate measurement of this executive function process.
Inhibit, in terms of executive function, refers to the ability to control impulses, whether emotional or behavioral (Giola et al., 2000). Like shift, the construct of inhibit has been linked to activation of neural circuits in the inferior frontal cortex and the motor area through an fMRI taken during activities requiring inhibition of emotional and behavioral responses (Aron, 2008). To measure inhibition without an fMRI, individuals must show a withheld or restrained behavioral response (Garon et al., 2008).
Executive function skills contribute to success in school by supporting the underlying cognitive processes required for learning. For example, the working memory holds information active during task completion, which contributes to speed of processing, to drawing connections between different activities, and to the use of higher order processing skills (Garon et al., 2008). Students with a stronger working memory may have an advantage in classroom activities, because they may be less likely to need as much instruction or scaffolding to complete complex tasks. These students are generally more organized and are able to identify the key concepts of most instruction (Giola et al., 2000). Shift also contributes to success in the classroom by allowing cognitive flexibility and transitions from one activity to another (Giola et al., 2000). Students who can execute a shift are able to find alternative solutions to problems, think creatively, and leave a task unfinished until later. Inhibition contributes to classroom success through the control mechanism. When exercising inhibition, students are able to control inappropriate responses, whether the responses are related to behavior, emotions, or academics.
The development of executive function occurs in tandem with cognitive functions. Each executive function process follows a separate trajectory and may develop at different speeds, depending on the supporting level of cognitive development (Garon et al., 2008). For example, a student may develop the phonological awareness necessary to decode a word, which will, in turn, require development of a more advanced working memory (Denckla, 2008). Much of the variance seen in the development of executive function may be tied to original differences in focused attention measured during infancy (Garon et al., 2008). From previous research, it would appear that the strands of executive function follow separate trajectories but are linked through attention. With age and educational opportunities, attention span increases and the frontal lobes develop more fully, which is also correlated with an increase in executive function skills (Garon et al., 2008).
The development of executive function skills responds positively to instruction and practice in the classroom but also may develop in conjunction with the cognitive skills gained through additional schooling (Best, Miller, & Jones, 2009). As cognitive skills become more advanced, it is possible that students with stronger executive function skills may have an advantage in the classroom, but the relationship between executive function skill development and cognitive development has not been demonstrated conclusively. It has been hypothesized that a general level of executive function is required for domain-specific factors, such as experiences in the classroom, to increase development in specific types of executive function (Sabbagh, Xu, Carlson, Moses, & Lee, 2006). A general level of executive function development, as a precursor to further development of specific areas in executive function, seems to parallel other research indicating that the level of focused attention in infancy relates to the level of executive function found in later childhood.
Recent research indicates that educational experiences may play a role in the development of executive function skills (Dreher & Oerter, 1987; McCrea, Mueller, & Parrila, 1999; Morrison, Smith, & Dow-Ehrensberger, 1995). For example, McCrea et al. (1999) found that additional schooling improved performance on tasks requiring planning and shift, or cognitive flexibility. According to Best et al. (2009), the majority of research on executive function has been conducted with preschool children, but to examine the effects of schooling on executive function development, these studies need to be conducted with older students in a variety of education settings. Although the predominant structure of the classroom may foster the development of executive function skills that then support more efficient development of cognitive skills, the current research base is inconclusive, and the identification of specific contextual factors related to executive function development will require studies examining these variables specifically (Best et al.).
Because related research suggests that the education environment may influence the development of executive function, the purpose of this study was to examine the executive function of 4th- to 6th-grade students, as rated by parents and teachers in three distinctively different private school environments: a Montessori school, a classical school, and a Catholic school. To our knowledge, no empirical evidence exists showing that executive function is enhanced by a specific school environment. Therefore, this study provides an initial examination of this possible connection.
We selected these schools not only because of their distinctly different school environments, but also because the demographics of their student populations were similar. We chose private schools because one of the researchers had an established relationship with the schools' administrators, which led to easy access to the participants. We also anticipated lower participant attrition in private schools, compared to public schools.
Montessori education is based on the extensive work of Maria Montessori, an Italian physician who lived during the first half of the 20th century. A Montessori classroom is organized in content areas emphasizing hands-on learning materials, with each area having a primary purpose and way to use the material. Teachers give formal lessons to the students before they are allowed to work with the materials, and students work individually, or in small groups, following a work plan designed specifically for them. Montessori classrooms are traditionally multiage, with three-year age groupings (Lillard, 2007).
Classical education, derived from the practices of the ancient Greeks and Romans, focuses on the study of grammar, literature, logic, and rhetoric taught through the Trivium, which is Latin for "three ways." Grammar school students learn factual information using such techniques as recitation, singing, and clapping. Logic students, typically of middle school age, are encouraged to strengthen and expand their knowledge through reasoning and critical thinking. In the rhetoric stage, high school students focus on integrating previous knowledge and developing persuasive skills through writing and speech (Perrin, 2004).
American Catholic education has existed since 1606, when the first school opened in St. Augustine, Florida (National Catholic Educational Association, 2010). With a long tradition of focusing on social justice issues, Catholic education seeks to address students' moral, spiritual, and academic needs. Faith and learning are integrated within a traditional educational curriculum in order to strengthen the students' "union with Christ" and the Catholic church (U.S. Conference of Catholic Bishops, 2005).
We examined two research questions in this study. First, whether there were any significant differences in the executive functions of children among the three school environments, while statistically controlling for age and number of years in their respective school environments. Previous research on parent-teacher dyads has indicated that parent ratings tend to be lower than teacher ratings on cognitive and social-emotional scales (Brown et al., 2006; Gioia et al., 2000; Keogh, Bernheimer, Gallimore, & Weisner, 1998). Therefore, our second research question examined whether there were any significant differences between parent and teacher ratings across the three school environments.
A total of 224 participants volunteered to complete the current study conducted in the southwestern region of the United States, with 112 of the participants being parents of a child and the other 112 participants being the primary classroom teachers of the same child. Parents were first asked to rate the executive function of their child; only upon completion by parents were teachers then asked to rate the children's executive function. The average age of the children (as reported by the parents) was 10.53, with a standard deviation of 1.03. The average number of years that the children had spent in their current school environment, as reported by parents, was 4.49 years, with a standard deviation of 2.21. Approximately 53.6% (n = 60) of the children were reported as female, whereas 46.4% (n = 52) of the children were reported as male. With regard to ethnicity, approximately 2.5% (n = 3) of the children were reported by parents as being Asian American, whereas 78% (n = 92) of the children were White, 18.6% (n = 22) of the children were Hispanic, and 1.7% (n = 2) were Native American; no children in this study were reported as being African American. Please note that these percentages do not add up to 100%, as parents were permitted to select more than one ethnicity. Approximately 11.8% (n = 13) of the children were reported by parents as having a special learning need (e.g., attention-deficit/hyperactivity disorder [ADHD], learning disability, or conduct disorder). Approximately 28% (n = 33) of the children attended a secular, Montessori school, 35.7% (n = 40) of the children attended a classical school with a nondenominational Christian emphasis, and 34.8% (n = 39) of the children attended a traditional Catholic school.
To measure executive function in children, we employed the Behavior Rating Inventory of Executive Function (BRIEF), which assesses the executive function behaviors of school-age children in home and school environments (Giola et al., 2000). The teacher and parent rating forms of the BRIEF consist of 86 items, with a 3-point response format with values ranging from never, sometimes, and often. The BRIEF parent and teacher rating forms achieve acceptable levels of reliability, with internal consistencies of scores ranging from [alpha] = .80 to [alpha] = .98 for the data obtained in previous data collections (Giola et al., 2000). The eight subscales of the BRIEF, though not disaggregated for our analyses, also appeared to achieve acceptable levels of reliability, with internal consistencies ranging from [alpha] = .85 to [alpha] = .96 for the data obtained from previous research (Giola et al., 2000). The eight subscales are Inhibition, Initiation, Organization of Materials, Shift, Working Memory, Monitor, Emotional Control, and Planning/Organization. With regard to the validity of the parent and teacher rating forms, exploratory factor analyses appear to reveal a consistent factor structure in the BRIEF on the subscale level for both forms (Giola et al., 2000). In this study, data from the teacher and parent rating forms achieved an internal consistency of [alpha] = .85 and [alpha] = .96, respectively, for the teacher and parent rating forms. Parent rating scores on the BRIEF revealed a mean of 113.42 and a standard deviation of 24.33. Teacher rating scores on the BRIEF revealed a mean of 106.58 and a standard deviation of 27.78. In this study, the correlation between teacher and parent rating scores was r = .47, p < .05, which was in the range found in previous research with r values between .30 and .50 (Giola et al., 2000).
Each of the three schools was contacted and asked to participate in this study. All three schools agreed and were compensated for recruitment efforts, with a nominal sum paid per teacher and parent participants who volunteered to complete the study. As a result, this research was supported by an internal grant from the university with which the authors are affiliated. This research protocol was approved by the university's Institutional Review Board committee for the protection of human subjects. Once data were collected, all analyses were performed in SPSS (v. 16.0). Approximately 6.5% of the values were missing; thus, missing values were handled via a pairwise method of deletion.
To answer our first research question, two one-way analyses of covariance (ANCOVA) were performed to examine for statistically significant differences in executive function, as rated by teachers and parents according to school environment, while statistically controlling for the age of the child and how long the child had spent in his or her current school setting. The assumption of the homogeneity of variances was not violated in view of Levene's F test for the equality of variances for teacher and parent ratings, respectively, F(2, 97) = 1.37, p = .26 and F(2, 91) = .652, p = .52. The assumption of the homogeneity of slopes requisite for conducting an ANCOVA was not violated either for teacher and parent ratings, respectively, F(3, 96) = .188, p = .904 and F(3, 90) = .830, p = .48. Cohen's f was calculated as the measure of effect size, with values of. 10, .30, and .50 or larger indicating small-, medium-, or large effect sizes, respectively (Cohen, 1988). Post hoc analyses were performed as needed upon conducting omnibus ANCOVAs. Values of Cohen's d were calculated as the measure of effect size in evaluating post hoc contrasts. Values of .20, .50, and .80 or larger indicate small-, medium-, or large-effect sizes, respectively, for Cohen's d (Cohen, 1988). To answer our second research question, a paired or dependent samples t test was performed to detect for significant differences between parent and teacher ratings across the three school environments. Subsequent t-tests were performed upon finding significant results across the three learning environments. A Bonferroni correction to the level of significance (<) (where < was divided by the number of comparisons) was applied in conducting multiple comparisons to reduce the likelihood of committing a Type I error. Cohen' s d was again calculated as the measure of effect size.
In examining for differences in parent ratings of executive function, the results did not indicate statistically significant differences in parent ratings according to school environment, F(2, 89) = .081, p = .92. Post hoc power analyses indicated an acceptable level of power, 1 - [beta] = .93 based upon the sample size and analysis performed. In examining for differences in teacher ratings of executive function, results indicated statistically significant differences in teacher ratings according to school environment, F(2, 95) = 9.69, p < .05, f = .45. This value of Cohen's f indicates a medium- to large effect size. Thus, it appears that teacher ratings do appear to differ according to learning environment. Specifically, our results indicated no statistically significant difference between students in their executive function, as rated by teachers in Montessori and the Catholic school environments. However, results indicated that students in the Montessori (M = 105.70, SD = 25.99) and Catholic (M = 103.62 SD = 24.70) school environments appeared to have better executive function, rated by teachers, compared to the students in the classical school environment (M = 133.01, SD = 25.35), with corresponding d values of -1.06 and -5.16, respectively. These values of Cohen's d indicate large effect sizes. Table 1 provides the descriptive statistics for teacher and parent ratings according to school.
In examining whether any significant differences existed between parent and teacher ratings across the three learning environments, our results indicate that parents appear to rate the executive function of their students as significantly better than that of teachers across the three school environments, t(86) = 3.53, p < .05, d = .32. Subsequent post hoc comparisons within each of the three school environments indicate the same trend of parents rating the executive function of their students as significantly better than that of teachers, with values of Cohen's d of .23 for the Montessori school environment, .46 for classical school environment, and .30 for the Catholic school environment. These values of Cohen' s d indicate a small- to medium-effect size. These results are contrary to previous research indicating that teachers appeared to rate their students as significantly better than that of parents on executive function as measured by the BRIEF (Gioia et al., 2000), as well as on a mental health scale (Brown et al., 2006) and a developmental delays scale (Keogh et al., 1998). The results from this study are discussed later in depth.
The results from this study indicate that parents from Montessori, classical, and Catholic schools do not rate their children's executive function skills differently, but the ratings assigned by teachers do appear to differ according to school environment. The teachers from the Montessori and Catholic schools assigned ratings indicative of better executive function skills when compared with the ratings from teachers in the classical school. Parents tended to assign higher ratings of executive function skills when compared with teacher ratings on the same child. This finding contradicts previous research indicating that parents, as compared to teachers, tend to rate their children lower on a variety of skills (Brown et al., 2006; Gioia et al., 2000; Keogh et al., 1998).
However, it appears that there may be empirical difficulty in determining the effects of schooling on the development of executive function skills. One potential reason for this difficulty pivots on the issue of study design. To examine a causal relationship between school environment and the development of executive function, an experimental design must be employed, which would involve manipulating the independent variable of school environment (Kirk, 1995). As parents selected the private school environment for their children in this study, the manipulation of the independent variable would be quite difficult to arrange. Thus, any study examining the effect of school environment on the development of executive function would have to be quasi-experimental at best, as the independent variable would be observed, rather than manipulated (Kirk, 1995). We recognize this as a limitation of our initial study and recommend that a study of this nature be conducted to gather empirical evidence, from which strategies, curricula, and teacher practices could effectively enhance children's executive functioning within an education environment.
Another potential reason for the trouble in determining the effects of school environment on the development of executive function lies in the difficulties related to measuring executive function as the outcome variable of interest. Rating scales, like the BRIEF (Giola et al., 2000), are often used to rate overt behaviors that may be evidence of a psychological construct. Measuring a psychological construct relies on making inferences from behavioral evidence.
Complications in the measurement of executive function are compounded by measuring a construct and using multiple sources to rate the construct. Evaluating psychological constructs in children usually requires the completion of rating scales by family members and teachers, as well as by other professionals, to provide a more complete picture. Because family members may observe children in less-structured environments, the concordance between these ratings may demonstrate lower inter-rater agreement than desired. Brown et al. (2006) found that parent rating scales failed to detect 52% of children in a sample that were rated as seriously emotionally disturbed by teacher rating scales. Likewise, the concordance between mother and teacher ratings in a study on behavior problems for children with developmental delays found that teachers tended to assign more positive ratings to children, whereas mothers tended to rate children's externalizing behaviors as more problematic (Keogh et al., 1998). Similar studies highlight the need to interpret ratings according to additional factors, such as parental education level and the child's education setting.
Many factors may contribute to the development of executive function skills, or to the set of processes used for guiding and managing lower order cognitive functions, behaviors, and emotional responses. This study examined the executive function skills of children from three different school environments, as previous research has indicated that the education settings may enhance the development of executive function skills (Best et al., 2009; Dreher & Oerter, 1987; McCrea et al., 1999; Morrison et al., 1995). In addition, many education publications now promote practical strategies, aimed at parents (e.g., Dawson & Guare, 2009) and teachers (e.g., Dawson & Guare, 2010; Kaufman, 2010), for improving children's executive functioning in the home and school environments.
Our results indicated different ratings in executive function skills by school environment, as well as by the source of the rating, with parents tending to rate the executive function skills of children higher than the teachers did. Future research should consider introducing additional covariates, such as family background, the parents' education level, the school's instructional effectiveness, and the quality of the curriculum in analyses. This study may be considered limited with respect to these variables, which should be further examined more in-depth, using qualitative research methods.
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Baylor University, Waco, Texas
Texas Tech University, Lubbock, Texas
Tracey Sulak, Natalie Jones, and Mary Walter
Baylor University, Waco, Texas
Submitted February 16, 2011; accepted August 1, 2011.
Address correspondence to Dr. Janet Bagby, Department of Educational Psychology, Baylor University, One Bear Place #97301, Waco, TX 76798. E-mail: email@example.com
TABLE 1 Descriptive Statistics by Learning Environment School M SD Teachers Montessori 105.70 25.99 Classical 133.01 25.35 Traditional 103.62 24.70 Parents Montessori 99.67 26.26 Classical 121.20 25.95 Traditional 96.07 25.08
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|Title Annotation:||a Montessori school, a classical school, and a Catholic school|
|Author:||Bagby, Janet; Barnard-Brak, Lucy; Sulak, Tracey; Jones, Natalie; Walter, Mary|
|Publication:||Journal of Research in Childhood Education|
|Date:||Oct 1, 2012|
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