Toward a Positive Explanation of Student Differences in Reading Growth


by Curt M. Adams & Anna H. Palmer - 2017

Background: Education has much in common with professions that are using positive psychology and positive organizational scholarship to transform practice, yet the science behind peak human and group functioning has been slow to displace deficit-based framing of reform policies and improvement strategies in education.

Purpose of the Study: This study used self-determination theory to identify a general type of instructional environment that has positive consequences for learning outcomes. We hypothesized that a self-regulatory climate is related to school-level differences in student reading growth and that student perceptions of autonomy-supportive instruction are related to student differences in reading growth.

Setting: Data were collected during the 20132014 school year from a city school system, located in a metropolitan area of about 900,000 residents, that serves approximately 42,000 students in 88 school sites. During the 20132014 school year, 80% of the students qualified for free or reduced-priced lunch (FRL); 26% were Black, 27% White, 30% Hispanic, 6% Native American, 9% multiracial, and 1% Asian. For this study, data come from students and teachers at all 51 elementary schools in the school system that have a 5th grade.

Data Analysis: Hypotheses were tested using a three-level linear growth analysis in HLM 7.0. The first step was to estimate the average reading growth for fifth-grade students using an unconditional growth model. The second step was to test a controlled-effects growth model, with FRL and racial/ethnic minority status included as student controls and FRL rate and percentage of White students enrolled in the school as school-level controls; self-regulatory climate was entered as a school-level predictor in this model. The final step was to add student-perceived autonomy-supportive instruction as a student-level explanatory variable.

Findings: Results showed that students in schools with self-regulatory climates achieved a higher reading growth rate than other students. Similar results were found with autonomy-supportive instruction: Students who experienced classroom instruction as autonomy-supportive had higher average reading growth than other students.

Conclusions: The aim of positive education is to develop a body of scientific evidence capable of explaining sources of exceptional teaching and learning. Self-regulatory climate and autonomy-supportive instruction appear to be two positive school conditions that enable students to flourish.



Positive psychology and positive organizational scholarship have established a body of theory and evidence about personality traits, psychological states, mindsets, and organizational conditions that underlie adaptive and effective human performance (Cameron & Spreitzer, 2012; Peterson, 2006; Seligman, 2002). Concepts such as optimism, grit, hope, flow, happiness, and resilience, to name a few, have changed how researchers explain differences in individual and group behavior. For instance, a person who pursues her goals with zeal, persistence, and resilience is likely to have a degree of grit (Duckworth, Quinn, & Seligman, 2009; Eskreis-Winkler, Duckworth, Shulman, & Beal, 2014; Robertson-Kraft & Duckworth, 2014; Von Culin, Tsukayama, & Duckworth, 2014). Studying a positive trait or state such as grit, in contrast to diagnosing what is wrong with individuals, explores the side of life that boosts wellness, well-being, and performance (Duckworth & Gross, 2014).


Knowledge about positive traits and states has fueled fundamental changes in how work processes are organized, diseases and disorders treated, innovation and creativity nurtured, and performance trainings conducted (Caza & Cameron, 2009; Steck, Abrams, & Phelps, 2004). Interventions derived from positive psychology are credited with enhancing organizational productivity (Rothbard & Patil, 2012), treating mental disorders (Sin & Lyubomirsky, 2009; Slade, 2010), supporting recovery from addiction and traumatic events (Resnick & Rosenheck, 2006), and even improving health-related biological processes (Steptoe, Wardle, & Marmot, 2005). Education has much in common with professions that use positive psychology and positive organizational scholarship to transform practice, yet the science behind peak human and group functioning has been slow to displace the deficit-based framing of reform policies and improvement strategies (Hoy & Tarter, 2011).


Attention to the positive side of school life complements what is known about stressors and tension in the educational system (Seligman, Steen, Park, & Peterson, 2005). Problems such as teacher instability, low achievement, and opportunity gaps will not get better without the ability to explain why and how some school systems create stimulating places to teach and learn, and others do not. Certainly, a case can be made for effective teachers, an organized and rigorous curriculum, good programs, and quality leadership, but we cannot establish a culture in which school members continuously get better simply by identifying characteristics of effective schools or adopting programs that work (Bryk, Gomez, Grunow, & LeMahieu, 2015). Educators need to understand how processes and practices bring out the best in students.

This study uses self-determination theory to identify a general type of instructional environment that has positive consequences for learning outcomes. Focusing specifically on reading growth during an academic year, the purpose of the study was to determine whether support for student psychological needs is related to student differences in reading growth. A focus on reading growth, not just test-score differences at one testing occasion, draws attention to school conditions that can boost reading improvement over time. Additionally, examining repeated achievement measures controls for the possibility that any estimated reading differences at the end of the year may result from unmeasured differences in the prior reading abilities of students.

FROM POSITIVE PSYCHOLOGY TO POSITIVE EDUCATION

Positive education applies the principles of positive psychology to educational research, policy, and practice (Seligman, Ernst, Gillham, Reivich, & Linkins, 2009). Positive psychology is defined as the study and development of personality traits, psychological states, healthy behaviors, and organizational conditions that fuel peak functioning (Gable & Haidt, 2005; Seligman & Csikszentmihalyi, 2000; Sheldon & King, 2001). In calling for psychology to address generative conditions, Seligman (2002) argued that “psychology is not just the study of disease, weakness, and damage; it also is the study of strength and virtue” (p.4). A focus on the positive side of human behavior and group performance does not deny or take away from efforts to understand distress and dysfunction; instead, it builds complementary knowledge about the traits, states, and conditions that enable individuals to thrive (Gable & Haidt, 2005).

Interest in the positive aspect of human experience is not new or particularly novel. William James reflected on and wrote about healthy mindedness in the early 20th century (Gable & Haidt, 2005), and humanistic psychologists such as Maslow, McGregor, and Herzberg advanced influential theories about essential human needs that remain relevant today (Seligman & Csikszentmihalyi, 2000). So what makes positive psychology different? It uses the framework of science to develop and empirically test theoretical explanations of wellness, well-being, and high-functioning individuals, groups, and organizations (Seligman et al., 2005). Positive psychology has established the empirical evidence to match the conceptual and theoretical accounts of healthy growth and development that early humanistic psychologists were not able to mount (Pajares, 2001).

Science is by no means the only way of knowing (Kerlinger, 1986; Peirce, 1940), but scientific inquiry has started to map the productive side of the human experience that had previously been left unexplained (Gable & Haidt, 2005). Positive education extends research and practice about optimal individual, group, and organizational functioning to schools and school systems. We define positive education as the study and development of individual and organizational features that drive peak performance and promote overall student, school, and school-system health.

Concepts, theories, and practices that fit within a positive-education framework are not necessarily new (Benard & Slade, 2009; Pajares, 2001). Character traits such as self-regulation, efficacy, and resilience have a long and deep history in education research and practice, and newer concepts populating positive psychology—such as grit, optimism, and growth mindsets—can be found in the educational vernacular as well. Improvement interventions are even starting to take a positive bent. Social-emotional learning, for example, has emerged as a legitimate, and even essential, framework to guide school processes and practices (Durlak, Domitrovich, Weissberg, & Gullotta, 2015). There is growing evidence from programs such as the PENN Resiliency Program, the Strath Haven Psychological Curriculum, and the Collaborative for Academic, Social, and Emotional Learning (CASTLE) that social-emotional learning can support improved well-being and performance in children and adolescents (Seligman et al., 2009).

Knowledge of the positive side of school life is not derived merely from empirical evidence linking programs, behaviors, or mindsets to student outcomes. Positive education requires theoretical and empirical explanations of how processes and strategies directed toward the affective dimension of schools contribute to differences in students’ holistic development. Evidence capable of explaining how and why positive conditions and states ignite productive behavior and desired outcomes is also needed. Toward this end, we situate our explanation of differences in reading growth during an academic year within self-determination theory.

SELF-DETERMINATION THEORY AND POSITIVE STUDENT GROWTH

Self-determination theory (SDT) has emerged through extensive empirical study of the social and psychological determinants of motivation, well-being, personality, interest, and developmental processes (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015; Niemiec & Ryan, 2009; Reeve & Jang, 2006; Ryan & Deci, 2000). Fundamental to SDT is the assumption that individuals are naturally oriented toward growth-generating experiences. Applied to students, this means that curiosity, interest in learning, self-development, and goal-directed behavior are natural tendencies of children and adolescents (Reeve, Jang, Carrell, Jeon, & Barch, 2004). That students may exhibit boredom in school, lose interest in learning, and disengaged from academic tasks should not be mistaken as a flaw in the basic premise of SDT. Instead, beliefs and behaviors detrimental to deep learning reflect the dialectic that individuals experience with their social surroundings (Niemiec & Ryan, 2009).

Mindsets and behavior contrary to optimal functioning are as much a result of the social environment as is our natural propensity toward self-actualization and fulfillment (Deci & Ryan, 2000; Niemiec & Ryan, 2009; Ryan & Deci, 2000). The dialectic, or contrasting responses between the self and the social world, plays out daily in classrooms and schools across the country. Children, at different times and by varying degrees, experience school routines and regularities either as energizing their interest and motivation to excel in learning activities or, conversely, as thwarting their natural drive and determination to flourish (Reeve & Halusic, 2009). The function of social stimuli for student motivation and performance raises two important questions for educators: What type of experiences can ignite growth producing motivation in students? What type of experiences may undermine students’ natural tendency to seek challenging learning tasks? These questions are central to great teaching, quality learning, and effective schooling.

As advanced by SDT, the answer to the above questions can be found in the motivational effects of basic psychological needs. Need, as defined by Ryan and Deci (2002), is an internal, biological force that supplies the energy to sustain purposeful and goal-directed action.  Much as calories sustain the body’s performance during physical activity, psychological needs supply the emotional and cognitive energy to fuel autonomous motivation for an activity (Reeve & Halusic, 2009; Reeve, Ryan, Deci, & Jang, 2008). Extensive empirical evidence has identified autonomy, competence, and relatedness as three psychological needs inherent in all individuals (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015; Ryan & Deci, 2000, 2002). For students, autonomy is a psychological state characterized by perceived agency and internal control over learning goals and outcomes; competence is experienced as a belief that students can meet the challenges of schoolwork and perform at high academic levels (Niemiec & Ryan, 2009); and relatedness reflects feelings of security, belonging, and attachment to educators and the school (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015; Ryan & Deci, 2000).

The inner motivation behind students’ growth tendencies comes, in part, from a school environment that reinforces autonomy, competence, and relatedness (Reeve & Jang, 2006). Students who experience classroom and school contexts as supporting their psychological needs exhibit greater enjoyment, satisfaction, interest, motivation, and fulfillment in academic tasks (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015; Reeve, 2002; Reeve & Halusic, 2009). Support for psychological needs is substantively different from the needs themselves. As defined previously, needs are basic biological and psychological qualities of individuals. Need support is the social dimension of motivation; it represents aspects of school and classroom life that can draw out or suppress innate tendencies toward growth (Adams, Ware, Miskell, & Forsyth, 2016; Assor, Kaplan, & Roth, 2002; Jang, Reeve, & Deci, 2010; Soenens & Vansteenkiste, 2005). Students who experience school and classroom structures as affording autonomy support, competence support, and relational support have better attitudes toward school, persist in challenging tasks, find interest in learning, and achieve at higher levels (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015; Reeve & Halusic, 2009; Reeve & Jang, 2006; Ryan & Deci, 2002).

Consistent with a positive-education framework, need support characterizes a general disposition of schools that is oriented toward high-quality individual and group functioning (Adams, Ware, Miskell, & Forsyth, 2016). Reinforcement of autonomy, competence, and relatedness in students enhances learning by drawing out the natural desire and determination of children and adolescents to put forth their best effort. Reading growth is an ideal process to overlay the need-support element of SDT. Becoming a proficient reader is a cognitively demanding task that requires sustained engagement and commitment to vocabulary acquisition, word structure, phonemic awareness, and reading comprehension. Differential motivation, to some extent, explains why certain students experience rapid reading growth whereas others lag behind their peers (Guthrie & Wigfield, 2000). Children who experience joy and pleasure in reading are driven by their intrinsic desire to spend more time doing it; consequently, they are likely to become confident and engaged readers (Fredricks, Blumenfeld, & Paris, 2004). Students who do not experience reading enjoyment at a young age are not necessarily less motivated. For these students, the source of their motivation comes from an environment experienced as autonomy-supportive, competence-supportive, and relatedness-supportive (Reeve, 2002; Reeve & Halusic, 2009; Ryan & Deci, 2002, 2002).

As explained by SDT, schools and teachers that align routines, materials, expectations, instructional strategies, and interactions with students’ inner motivations can make students see reading as an expression of their self. This, in turn, fuels the desire to learn and grow, even if inherent joy is not obtained from the activity. Controlling routines, in contrast to need-supporting environments, constrain student potential by obstructing development of their inner resources (Reeve, 2002; Reeve & Jang, 2006). The motivation effects of students’ active engagement with their environment establish the rationale and hypotheses for the empirical part of the study.

RATIONALE AND HYPOTHESES

As postulated by SDT, support and reinforcement of autonomy, competence, and relatedness is a universal resource for high-functioning individuals and groups (Ryan & Deci, 2000, 2002).  Schools may indeed differ in their design, instructional approach, configurations, and resources, but they do not differ in terms of needing to energize student psychological needs if they are to maximize learning opportunities and outcomes (Reeve, 2002). Need and need support are universal human experiences that vary based on the degree to which one’s social environment supports or thwarts autonomy, competence, and relatedness (Ryan & Deci, 2000, 2002). Need support can manifest as a property of the school or as a characteristic of classroom instruction (Adams, Ware, Miskell, & Forsyth, 2016).

At the school level, need support is signaled by a self-regulatory climate (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015). Self-regulatory climate is not an individual state, nor is it the aggregation of individual beliefs; it is a social feature of schools, built through trustworthy, cooperative, and academically focused teacher-student interactions (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015). Faculty trust in students, student trust in teachers, and student-perceived academic emphasis come together to form a self-regulatory climate (see Figure 1). The presence of only one trust form, or having academic emphasis without trust, does not constitute a self-regulatory climate: All three conditions are interrelated and necessary to form a climate that enhances the internal capacity of students (Adams, Ware, Miskell, & Forsyth, 2016). For instance, faculty trust emerges when teachers experience students as being interested in academic tasks and engaged in learning activities. Student trust becomes normative when students experience teachers as genuinely committed to their learning and well-being. Academic emphasis depends on high teacher and student press for academic achievement.

Figure 1. The interdependent properties of self-regulatory climate (SRC)

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Student Trust in Teachers; Faculty Trust in Students; Student Perceived Academic Emphasis

A self-regulatory climate leverages students’ inner capacity for growth through trustworthy interactions directed toward achieving high academic expectations. Empirically, self-regulatory climate is associated with higher school performance (Adams, Ware, Miskell, & Forsyth, 2016), stronger student-regulated learning, and better math achievement (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015). Positive performance persists even after controlling for school differences in prior achievement and student composition. Additionally, Adams and Palmer (2015a) found that free or reduced-price lunch (FRL) students who attended schools with higher self-regulatory climates had better average math achievement than non-FRL students in schools where need support was lacking. Empirical and theoretical evidence present a solid case that self-regulatory climate explains differences in student reading growth during an academic year. For this reason, we advance the following hypothesis:

H1: Self-regulatory climate is related to school-level differences in student reading growth.

The classroom environment is another social context that has a distinct and powerful influence on student motivational resources (Reeve & Halusic, 2009). If students experience classroom interactions as aligned with their psychological needs, then engagement and learning are more likely to be driven by internal mechanisms (Reeve & Jang, 2006). Internal drive declines as external instruments, such as inducements and close supervision, situate the locus of control outside students’ inner capacity and desires (Assor et al., 2002). Strong empirical evidence advances a general set of instructional practices associated with need-supporting teachers, including teachers using informational language, explaining the value of and rationale for academic tasks, accepting and respecting student opinions and negative expressions, introducing optimally challenging learning activities, and providing regular and open performance feedback (Niemiec & Ryan, 2009; Reeve, 2002; Reeve & Jang, 2006).

The motivational and performance effects of need-supporting instructional strategies are well established. Students who experience classrooms as reinforcing their psychological needs have higher interest in academic tasks (Niemiec & Ryan, 2009), greater persistence (Kusurkar, Cate, Vos, Westers, & Croiset, 2012), more engagement in uninteresting activities (Reeve, 2002), and higher achievement (Fortier, Valerand, & Guay, 1995; Niemiec & Ryan, 2009; Vallerand, Fortier, & Guay, 1997). Classrooms experienced as controlling erode interest and motivation (Reeve, 2002; Tsai, Kunter, Ludtke, Trautwein, & Ryan, 2008), reduce creativity and expression (Koestner, Ryan, Bernieri, & Holt, 1984), enhance anxiety, and reduce learning (Ryan & Weinstein, 2009).

Evidence specific to instructional strategies conducive to reading growth corroborates the general performance effects found in need-supporting classrooms. In their experimental work, Guthrie and colleagues have found that students exposed to concept-oriented reading instruction (CORI), a framework derived from internal motivational processes that align with principles of SDT, show more engagement in reading and better reading comprehension compared with students exposed to traditional instructional strategies (Guthrie et al., 2004; Guthrie et al., 2006; Wigfield et al., 2008). Furthermore, reading strategies linked to increased student motivation and engagement—such as setting content goals, affording choices, choosing interesting text, cooperative learning, listening to student concerns, and emphasizing mastery (Guthrie et al., 2006)—are common to need-supporting classrooms. Given the combined evidence, we advance the following additional hypothesis:

H2: Student perceptions of autonomy-supportive instruction are related to student differences in reading growth.

METHOD

The empirical part of the research used a longitudinal design to model the reading growth of fifth-grade students across three testing occasions during the academic year. Data were collected during the 2013–2014 school year as part of a larger project on the study of school capacity in a city school system, located in a metropolitan area of about 900,000 residents, serving approximately 42,000 students in 88 school sites. During the 2013–2014 school year, 80% of the students qualified for free or reduced-price lunch (FRL); 26% were Black, 27% White, 30% Hispanic, 6% Native American, 9% multiracial, and 1% Asian. For this study, data came from students and teachers in 51 elementary schools, comprising all elementary schools in the school system that have a 5th grade.

In 2011–2012, the school system aligned reading instruction across grades and schools through the development of a balanced literacy instructional model, the Metametrics Lexile Framework, and the use of the Scholastic Reading Inventory (SRI). Balanced literacy provides a structured, yet open, instructional model that includes daily time for reading aloud, small-group guided reading at students’ Lexile level, independent reading, word study, and writing. The Lexile framework and SRI scores identify students’ zone of proximal reading development and are used to link students with appropriate text and to monitor reading growth over time.

SAMPLE

Half of the fifth-grade students in the 51 elementary schools were randomly assigned to complete a student survey measuring their perceptions of the learning environment in school. Trained school liaisons administered student surveys during the school day.  Student participation was voluntary. Of the 1,302 students who were assigned a survey, we received usable responses from 1,122, for a response rate of 86%. We matched students’ SRI reading scores to the 1,122 students who returned usable surveys. A total of 428 teachers were randomly assigned a survey as well; we received 320 usable teacher returns, for a response rate of 75%.

MEASURES

Reading Achievement and Growth

SRI scores from the fall, winter, and spring testing periods were used as a measure of reading achievement and reading growth. SRI is an adaptive, diagnostic assessment of reading skills that measures reading growth from kindergarten through high school. The SRI Reading Comprehension Assessment was used for fifth-grade students; this assessment measures reading comprehension through reading passages that ask students to recall details in the text, draw inferences and conclusions about what was read, and make generalizations from the content (Scholastic, 2014). Tests are adapted to the reading level of the student. Rasch item response modeling is used to convert raw test scores into Lexile scores. Considerable validity and reliability evidence documents the appropriate use of the SRI assessment for diagnosing reading comprehension and growth over time (Scholastic, 2014). Lexile bands for fifth-grade students include 0–615 as below basic, 620–825 as basic, 830–1110 as proficient, and above 1115 as advanced. An internal predictive validity test conducted by the district found a strong positive relationship between SRI scores and student performance on the state reading test (r = 0.76, p < 0.01).

Self-Regulatory Climate

Self-regulatory climate is a school environment that supports and reinforces the psychological needs of autonomy, competence, and relatedness in students (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015). Autonomy support is observable in faculty trust in students. Competence support is observable in student perceptions that the school collectively presses toward high academic expectations. Relational support exists as student trust in teachers. Existing evidence demonstrates that these conditions share variance around the latent self-regulatory climate construct (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015, 2015b).

Five items from the Omnibus Trust Scale were used for the autonomy-supportive dimension of self-regulatory climate (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015). These items had a six-point Likert response set, ranging from strongly disagree to strongly agree. Factor loadings ranged from 0.77–0.86, with strong inter-item consistency as represented by a Cronbach alpha of 0.97. Sample items included “Teachers in this school trust their students,” “Teachers in this school believe students are competent learners,” and “Students in this school can be counted on to do their work.”

Five items from the Student Trust in Teachers Scale (Adams & Palmer, 2011) were used to capture the relational dimension of self-regulatory climate. Student trust items had a four-point Likert response set, ranging from strongly disagree to strongly agree. These items had strong factor loading (0.69–0.75) and good item consistency, with a Cronbach alpha of 0.91. Sample items included “Teachers are always ready to help at this school,” “Teachers at this school really listen to students,” and “Teachers at this school are honest with me.” Five items from the Academic Emphasis Scale (Goddard, Sweetland, & Hoy, 2000) were used to capture competence support as perceived by students. Student-perceived academic emphasis items have a four-point Likert response set, ranging from strongly disagree to strongly agree. Factor loadings ranged from 0.67 to 0.73, with a Cronbach alpha of 0.83. Sample items included “This school has high expectations for student achievement,” “Teachers in this school encourage student to keep trying even when the work is challenging,” and “Teachers in this school think it is important that all students do well in their classes.”

Schools were classified as either reaching a threshold required for students to experience a self-regulatory climate or falling below the threshold. Schools achieving the threshold had to have an average item-response rate for student trust in teachers and student-perceived academic press above 3.0 on a 4.0 Likert scale. Additionally, schools had to have an average item-response rate for faculty trust in students at or above 4.0 on a 6.0 scale. These standards (3.0 and 4.0) were established because the values reflect shared positive perceptions. Schools had to meet or exceed all three standards to be classified as having a self-regulatory climate.

Autonomy-Supportive Instruction

Autonomy-supportive instruction represents teaching practices centered on using noncontrolling language, providing rationale for learning tasks, and acknowledging students’ negative feelings (Assor et al., 2002). Eight items were adapted from the Autonomy-Enhancement Scale; items captured the facets of noncontrolling language, choice, rationale, and respect for student opinions. The four-point Likert response set ranged from strongly disagree to strongly agree. Sample items included “Teachers encourage students to work in their own way,” Teachers talk about the connection between what is studied in school and what happens in real life.” “Teachers listen to the opinions and ideas of students.” “Teachers show students how to solve problems themselves.” Evidence from an exploratory factor analysis with principal axes rotation supports strong structural validity, with factor loadings ranging from .68–.74 and good internal item consistency with a Cronbach alpha of .90.

Student and School Characteristics

<TXT>We controlled for differences in student backgrounds through dummy coding. Students who qualified for the free and reduced-price lunch (FRL) program were coded as 1 and non-FRL students coded as 0. White students were coded as 1 and non-White as 0. We also measured school poverty with the percentage of students in a school who qualified for the FRL program and the percentage of students in a school identified as White.

ANALYSIS

The first step in the analysis was to establish empirical support for specifying self-regulatory climate as a school property and autonomy support as a perception of students. To do this, we estimated two types of intraclass correlation coefficients, ICC (1) and ICC (2). ICC(1) estimates the amount of variance attributable to school differences and is calculated from the variance components of an unconditional multilevel model. ICC (2) estimates the within-group agreement among respondents with the mean square between and within estimates from a one-way ANOVA (Glisson & James, 2002). The observable dimensions of self-regulatory climate had significant and large between-school variance. Additionally, within-group agreement exceeded the 0.60 standard used to justify data aggregation (Cohen, Doveh, & Eick, 2001). Between-school variance for student-perceived autonomy support was small, and the within-group agreement fell well below the 0.60 standard.


Table 1. Descriptive Student and School Data

Student variables

Mean

SD

Min

Max

SRI score

731.13

247.45

0

1391

FRL

0.81

0.40

0

1.00

White

0.27

0.45

0

1.00

Black

0.22

0.51

0

1.00

Hispanic

0.34

0.47

0

1.00

Native American

0.07

0.25

0

1.00

Multiracial

0.08

0.28

0

1.00

Autonomy-supportive instruction

3.46

0.53

1.00

4.00

School variables

Mean

SD

Min

Max

FRL rate

86.64

22.71

17.00

100

Percentage White

37.96

20.11

6.00

79.00

Self-regulatory climate

0.40

0.49

0

1.00

Note: N = 3,324 test scores nested within 1,122 students nested within 51 elementary schools. Self-regulatory climate was converted into a categorical variable based on schools meeting the following criteria: high levels of student trust in teachers, student-perceived academic emphasis, and faculty trust in students. The mean value indicates that 40% of schools in the sample achieved this status.

Hypotheses were tested using a three-level linear growth analysis in HLM 7.0. Linear-growth models assume a constant rate of change over time, as opposed to polynomial-growth models that estimate increases and decreases in growth rates (Nese, Lai, & Anderson, 2013). The first step in the model-building process was to estimate the average reading growth for fifth-grade students with an unconditional-growth model. In this model, test occasions (beginning, middle, and end of the year) were entered as an uncentered time variable in level one. The uncentered time variable means that the intercept (π0ij) is interpreted as the average SRI score at the beginning of the school year. Growth (π0ij) reflects the average SRI change across the three testing occasions. Both average SRI at occasion one and SRI growth were allowed to vary randomly across students and schools.

Level One

ZSRItij = π0ij + π1ij (Timetij) + etij

Level Two

π0ij = β00j + r0ij

π1ij = β10j + r1ij

Level Three

β00j = γ000 + u00j

β10j = γ100 + u00j

The second step was to test a controlled-effects growth model with FRL and racial/ethnic minority included as student controls and FRL rate and percentage of White students enrolled in the school as school-level controls. FRL qualification was coded as 1 and non-FRL as 0. Similarly, White students were coded as 1 and non-White students as 0. Student variables were grand-mean centered in order to control for possible multicollinearity with school level predictors. School level variables were also grand-mean centered and standardized to a mean of 0 and standard deviation of 1.

At the student level, differences in average reading achievement at time one (π0i) are explained as a function of the average school SRI at time one (β00j), and the effects of FRL status (β01j), racial/ethnic minority (β02j), and random school variance (r0ij). Likewise, differences in the average SRI growth were explained as a function of school average z-score for SRI growth (β10j) and the effects of FRL status (β11j), racial/ethnic minority (β12j), and random school variance (r1ij). School-level differences in reading achievement at time one (β00j) and SRI growth (β10j) were explained as a function of the grand mean (γ000 and γ100) and the effects of FRL rate (γ001 and γ101), percentage White (γ002 and γ102), self-regulatory climate (γ003 and γ103), and random variance (u00j and u00j).

Level One

ZSRItij = π0ij + π1ij (Timetij) + etij

Level Two

π0ij = β00j + β01j(ZFRLij) +β02j (ZCaucasianij)+r0ij

π1ij = β10j + β11j(ZFRLij)+β12j(ZCauscianij)+r1ij

Level Three

β00j = γ000 + γ001(ZFRLj)+ γ002(Zpercent Caucasianij)+ γ003(ZSRCj)+ u00j

β10j = γ100 + γ101(ZFRLj)+ γ102(Zpercent Caucasianij)+ γ103(ZSRCj)+ u10j

β01j = γ010

β02j = γ020

β11j = γ110

β12j = γ120

The final step was to add student-perceived autonomy-supportive instruction as a student-level explanatory variable. Autonomy support was grand-mean centered in order to control for potential multicollinearity with school predictors, and its variance was fixed at the school level. Adding autonomy support in the final step allowed for an assessment of any moderating effect of self-regulatory climate on SRI growth. We also ran the model with the time variable reversed so that the final testing occasion would be set as the intercept. This model allowed for an estimate of reading differences at the end of fifth grade. Equations for the final model follow:

Level One

ZSRItij = π0ij + π1ij (Timetij) + etij

Level Two

π0ij = β00j + β01j(ZFRLij) +β02j (ZCaucasianij)+β03j (ZASij)+r0ij

π1ij = β10j + β11j(ZFRLij)+β12j(ZCauscianij)+β13j(ZASij)+r1ij

Level Three

β00j = γ000 + γ001(ZFRLj)+ γ002(Zpercent Caucasianij)+ γ003(ZSRCj)+ u00j

β10j = γ100 + γ101(ZFRLj)+ γ102(Zpercent Caucasianij)+ γ103(ZSRCj)+ u10j

β01j = γ010

β02j = γ020

β03j = γ030

β11j = γ110

β12j = γ120

β13j = γ130

RESULTS

Descriptive statistics describe students and schools in the sample (see Table 1). The ethnic distribution was 27% White, 22% Black, 34% Hispanic, 7% Native American, and 8% multiracial. The average SRI score for all three testing occasions was 731, with a standard deviation of 247. This places the average SRI score for the three testing occasions at the basic performance level. Students had an average autonomy-support score of 3.46, with a standard deviation of 0.53. Schools had an average FRL rate of 87% and an average White representation of 27%. Of the schools, 40% met the criteria for high self-regulatory climate, and the other 60% did not. All scaled variables were converted into z-scores with a mean of 0 and standard deviation of 1 for the analysis. This procedure placed all variables on a common scale, allowing for estimates to be reported in standard deviation units. In addition, converting SRI to z-scores for the analysis provided for a normative comparison of the reading growth rate.


Table 1. Descriptive Student and School Data

Student variables

Mean

SD

Min

Max

SRI score

731.13

247.45

0

1391

FRL

0.81

0.40

0

1.00

White

0.27

0.45

0

1.00

Black

0.22

0.51

0

1.00

Hispanic

0.34

0.47

0

1.00

Native American

0.07

0.25

0

1.00

Multiracial

0.08

0.28

0

1.00

Autonomy-supportive instruction

3.46

0.53

1.00

4.00

School variables

Mean

SD

Min

Max

FRL rate

86.64

22.71

17.00

100

Percentage White

37.96

20.11

6.00

79.00

Self-regulatory climate

0.40

0.49

0

1.00

Note: N = 3,324 test scores nested within 1,122 students nested within 51 elementary schools. Self-regulatory climate was converted into a categorical variable based on schools meeting the following criteria: high levels of student trust in teachers, student-perceived academic emphasis, and faculty trust in students. The mean value indicates that 40% of schools in the sample achieved this status.

Table 2 reports the variance decomposition from the unconditional-growth model. Variance components reveal statistically significant differences in reading growth attributed to schools and students. Specifically, approximately 16% of the variance in reading growth existed at the school level, and about 50% was attributed to student differences. Reading growth that was statistically different from 0 allowed us to model variance as being a function of student and school characteristics.

Table 2. Variance Components from the Unconditional Growth Model

Random effect

Variance components

% variance

df

��2

Reading growth (student-level)

0.02

0.50

1059

3020*

  Level one residual

0.03

   

  Reading growth intercept (school-level)

0.01

0.16

49

99*

Reliability of regression coefficients

    

First testing occasion

0.96

   

Growth rate

0.75

   

*p < 0.01.

Table 3 presents evidence to test both hypotheses. Model one includes results of the controlled-effects growth model with self-regulatory climate at the school level. Autonomy support was not included as a student-level variable in this model. We report SRI differences at the first testing occasion and differences in SRI growth. It is important to examine SRI differences at the beginning of the year to ensure that any changes in growth are not simply a function of initial differences at the first testing occasion. Results indicate that gaps in SRI at the first testing occasion were largely related to student characteristics more than to the school features included in the model. At time one, FRL students averaged approximately 0.43 standard deviations lower than the average non-FRL student, and White students averaged approximately 0.27 standard deviations higher than the average student of other ethnicity. At the school level, the only statistically significant relationship was between SRI and the FRL rate of the school (γ001  =  −0.04). The difference in initial SRI scores between self-regulatory climate schools and non-self-regulatory climate schools did not meet the threshold for statistical significance.

Table 3. Estimates from the Controlled-Effects Growth Models with SRI Scores as the Outcome Variable

Fixed effects at first testing occasion

Model 1

Model 2

Model 3

   FRL status

   Racial/ethnic minority status

   Autonomy support

School intercept

   FRL rate

   Percentage White

   School SRC


Fixed effects for reading growth

   FRL status

   Racial/ethnic minority status

   Autonomy support

School intercept

   FRL rate

   Percentage White

   School SRC

−0.43 (.07)**

0.27 (.06)**

-----

0.08 (.03)**

−0.01 (.00)**

0.00 (.00)

0.10 (.06)


1.1 (.02)

1.2 (.01)*

-----

0.01 (.00)

0.00 (.00)

0.00 (.00)

0.04 (.01)**

−0.43 (.07)**

0.28 (.06)**

0.08 (.05)

0.08 (.03)*

−0.01 (.00)**

0.00 (.00)

0.09 (.07)


0.01 (.02)

0.01 (.01)

0.04 (.01)**

0.01 (.00)

0.00 (.00)

0.00 (.00)

0.03 (.01)*

−0.38 (.07)**

0.35 (.06)**

0.13 (.05)**

0.13 (.04) **

−0.007 (.00)

0.00 (.00)

0.15 (.08)*


−0.01 (.02)

−0.01 (.01)

−0.04 (.01)**

−0.01 (.00)

−0.00 (.00)

−0.00 (.00)

−0.03 (.01)*

Random effects for reading growth

Model 1

Model 2

Model 3

School intercept

Deviance (−2 log likelihood)

Δ deviance

Explained student-level variance in reading growth (%)

Explained school-level variance in reading growth (%)

0.0018

3666

85**

7

82

0.0015

3555

196**

12

86

0.0015

3555

196**

12

86

Note: SRI scores were standardized to a mean of 0 and a standard deviation of 1. Models were tested in HLM 7.0 using full maximum likelihood. Model one is the controlled effects growth model without autonomy-supportive instruction. Model two includes autonomy-supportive instruction. For model three, the intercept was set to the third testing period. Thus, estimates represent SRI differences at the end of fifth grade. The negative signs for growth are consist with higher average SRI scores by the end of the year.

**p < .01. p < .05*.


Continuing with estimates in model one, a different pattern emerged with SRI growth. At the student level, White students averaged slightly higher reading growth than students of other ethnicities (β12j = 0.02, p < 0.05), but there was no statistically significant difference between FRL and non-FRL students. At the school level, the only statistically significant relationship was with self-regulatory climate: The average student in a school with a self-regulatory climate (γ103 = 0.04, p < 0.05) had a slightly higher reading growth rate than students in other schools.  FRL rate and percentage White were not related to SRI growth. Overall, model one explained approximately 82% of the between-school variance in reading growth. Additionally, the statistically significant reduction in deviance statistic from the unconditional model supported strong model fit.

The relationship between self-regulatory climate and growth rate is illustrated in Figures 2 and 3. Figure 2 compares SRI scores across the three testing periods between students in schools classified as having a self-regulatory climate and those not achieving this standard. As the graph shows, students in self-regulatory climate schools had a higher average performance at time period one; a higher growth rate, as indicated by a steeper slope of the line; and higher average achievement at time period three. In contrast, students in non-self-regulatory climate schools had lower average scores at time one, an average growth rate consistent with the sample average, and lower achievement at the end of the year.

Figure 2. Model equation graph of the difference in average student reading growth between self-regulatory climate and non-self-regulatory climate schools

[39_21917.htm_g/00004.jpg]

Note: Reading scores were standardized to a mean of 0 and standard deviation of 1.

Figure 3 delves deeper into the relationship between self-regulatory climate and SRI scores by disaggregating reading growth by FRL and non-FRL students. Non-FRL students in self-regulatory climate schools had the highest average SRI scores at the first and last testing occasions. Comparatively, non-FRL students in non-self-regulatory climate schools had lower initial achievement, a lower growth rate, and lower achievement at the end of the year than their non-FRL peers. The highest growth rate was for FRL students in self-regulatory climate schools. These students had lower initial scores than non-FRL students, but they nearly closed the gap with non-FRL students in schools that were not classified as having a self-regulatory climate. The lowest test score averages were for FRL students in non-self-regulatory climate schools. These students had a slight growth relative to the average, but their ending scores remained considerably lower than other students.

Figure 3. Model equation graph of the difference in average student reading growth between FRL and non-FRL students in self-regulatory climate and non-self-regulatory climate schools









[39_21917.htm_g/00006.jpg]

Note: Reading scores were standardized to a mean of 0 and standard deviation of 1.

Evidence to test our second hypothesis, the relationship between autonomy support and reading growth, is found in model two. In model two, we added student-perceived autonomy-supportive instruction as a student variable to the controlled effects model (see Table 3). Similarly to the model one results, we found statistically significant differences in initial SRI scores attributed to FRL (β01j = −.43, p < 0.01) and racial/ethnic minority status (β02j = 0.28, p < 0.01) status. There was not a statistically significant relationship between autonomy-supportive instruction and SRI scores at the first testing occasion. However, we did find a relationship between autonomy support and SRI growth (β13j = .04, p < .01). Additionally, the significant effect of FRL status on reading growth in the first model washed out with the inclusion of autonomy support in model two. There was a small attenuation of the self-regulatory climate effect at the school level, but the relationship remained statistically significant. It is important to also point out that the addition of autonomy support explained an additional 4% of the school-level variance in reading growth. Based on the estimated deviance, model two was a better fitting model and explained more school-level variance in reading scores.

Figures 4 and 5 illustrate the relationship between autonomy-supportive instruction and student reading growth. Relative to students at the bottom quartile of perceived autonomy support, top quartile students had better reading scores at the first testing occasion, a higher rate of growth, and much stronger scores at the end of the year (Figure 4). Figure 5 shows the combined effects of autonomy support and self-regulatory climate on reading growth. Notice that students in the top quartile of autonomy support and in a self-regulatory climate school had the highest SRI scores and the strongest rate of reading growth. Reading growth was relatively stagnant without both autonomy support and being in a school with a self-regulatory climate. Growth actually declined for students in the bottom quartile of autonomy support and not in a self-regulatory climate school.

Figure 4. Model equation graph of the relationship between autonomy-supportive instruction and reading growth. Lines represent scores at the top and bottom quartiles of the distribution

[39_21917.htm_g/00008.jpg]


Note: Reading scores were standardized to a mean of 0 and standard deviation of 1.

Figure 5. Model equation graph of the relationship between autonomy-supportive instruction and reading growth in self-regulatory climate and non-self-regulatory climate schools

[39_21917.htm_g/00010.jpg]

Note: Reading scores were standardized to a mean of 0 and standard deviation of 1.

In model three, we set the intercept to the last testing period to estimate achievement differences at the end of fifth grade, as opposed to the first testing occasion. This allowed us to account for student- and school-level variance in SRI scores at the last testing occasion. Our interest in test score differences at the third occasion was to determine whether score differences at the end of the year were larger than initial differences. We found that at the third testing occasion there was a larger and statistically significant relationship (increase from 0.08 to 0.13) between SRI scores and autonomy-supportive instruction than at time one. Further, the average SRI difference attributed to self-regulatory climate increased from 0.09 standard deviations to 0.15 standard deviations at the third testing occasion.

In summary, results support the hypothesized effects of self-regulatory climate and autonomy-supportive instruction on reading growth. Although the effects were small, both self-regulatory climate and autonomy-supportive instruction were related to reading growth. By the end of fifth grade, students in schools with a self-regulatory climate who perceived instruction to be autonomy-supportive had significantly higher SRI scores.

DISCUSSION

Positive education, like positive psychology, aims to build a body of explanatory knowledge about processes and practices that are capable of maximizing the innate talent and abilities of teachers and students (Seligman et al., 2009; Pajares, 2001). To further this aim, we turned to self-determination theory to identify a general type of school and teaching environment that has positive consequences for student reading growth. Based on empirical evidence of the role of psychological needs and need support for peak human functioning (Ryan & Deci, 2000, 2002), we hypothesized that a self-regulatory climate and autonomy-supportive instruction would be uniquely related to school and student differences in reading growth during the academic year. Results of the empirical investigation supported our hypotheses: Self-regulatory climate and autonomy-supportive instruction were related to student and school differences in reading growth.

Results showed that students in self-regulatory climate schools achieved a higher reading growth rate than other students. The difference of 0.02 standard deviations relative to the average growth rate for the sample may seem small, but this amount compounds over time, as evidenced by the estimated achievement differences at testing occasions one and three. Students in a self-regulatory climate school scored 0.10 standard deviations better than peers at the beginning of the year. Average growth for these students would have resulted in a 0.10 standard deviation differential by the end of the year, but students in self-regulatory climate schools actually scored 0.15 standard deviations better. Not only was reading achievement higher in self-regulatory climate schools at the beginning of the year, but gains during the year were stronger, resulting in a statistically significant achievement difference by the end of fifth grade.

Similar results were found with autonomy-supportive instruction. Students who experienced classroom instruction as autonomy-supportive had higher average reading growth than other students. The estimated effect of autonomy-supportive instruction increased from 0.08 at the first testing occasion to 0.13 at the third testing occasion. Although the general findings confirm the importance of self-regulatory climate and autonomy-supportive instruction, the strongest effect on reading achievement and reading growth likely comes when both conditions define the teaching and learning environment. As illustrated in figure 5, lower student perception of autonomy support attenuates the self-regulatory climate effect. Similarly, effects of autonomy support diminished in schools not classified as having a self-regulatory climate. When combined, a self-regulatory climate and autonomy-supportive teaching represented an environment in which students had higher reading achievement and reading growth. It would seem from our findings that the combined effect of both conditions outweighs the unique effect of each condition.

Evidence from self-determination theory supports a synchronous relationship between self-regulatory climate and autonomy-supportive instruction. Several studies have found that controlling teaching practices are more common in schools with controlling administrative processes, whereas supportive teaching environments contribute to need-supporting instruction (Pelletier, Seguin-Levesque, & Legault, 2002; Pelletier & Sharp, 2009; Pelletier & Vallerand, 1996; Taylor & Ntoumanis, 2007). Deci, Spiegel, Ryan, Koestner, and Kauffman (1982) found that teachers who experienced hierarchical pressure to help students improve problem solving used more controlling language and directives than teachers without the external pressure. Taylor and Ntoumanis (2007) found that teachers reported using more controlling structures and practices in the classroom when administrators coordinated instructional practices with rigid curricula, teaching evaluations, and expectations. Pelletier et al. (2002) found teachers to be autonomy-supportive toward students when they experienced the teaching context as enabling their growth. Keep in mind that a self-regulatory climate is favorable to autonomy-supportive instruction, but it does not guarantee that students will experience the classroom environment aligning with their psychological needs.

Unaddressed at this point is an explanation for the effects of self-regulatory climate and autonomy support. For this, we return to self-determination theory. As suggested by the theory, a social environment supportive of student psychological needs has an energizing effect on students’ autonomous motivation and self-directed action (Reeve, 2002; Ryan & Deci, 2000, 2002). With respect to literacy development, schools where trust and academic emphasis define the climate provide a setting where students are more likely to identify with and internalize the importance of reading (Adams, Ware, Miskell, & Forsyth, 2016). The same is true for the classroom environment: Students are more inclined to work to their potential when teachers structure learning in ways that align with student psychological needs (Reeve, 2002). In essence, the social environment can either enable or inhibit more engaged student work, leading to better student outcomes. Trust, academic emphasis, and autonomy-supportive teaching enable better performance, whereas controlling practices tend to undermine it (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015).

We should point out that normative conditions in schools do not make students better readers per se; instead, as Guthrie et al. have learned (Guthrie et al., 2006; Guthrie et al., 2004; Wigfield et al., 2008) instructional strategies and routines ignite the desire and determination in students to become proficient in reading and other cognitive skills. This distinction is critical; it situates the locus of growth within students themselves, not with factors external to students. The function of external factors, such as trusting relationships or autonomy-supportive teaching, is to stimulate the interest, curiosity, and desire in students so they will be driven to learn (Adams, Ware, Miskell, & Forsyth, 2016). Need-supporting schools and classrooms work by engendering beliefs and behavior that drive students to excel academically and personally (Reeve & Jang, 2006).

From the perspective of positive education, self-regulatory climate and autonomy-supportive instruction reflect a healthy and generative side of school life that, if developed, provides essential support for student learning and growth. For educators and policymakers, evidence capable of explaining why and how schools and teachers attain higher achievement and better growth unlocks a different set of improvement ideas and strategies from those that derive from problem-based views of poor performance. To illustrate, our findings suggest that a viable resource to improve reading in urban schools reflects processes and practices that support student psychological needs of autonomy, competence, and relatedness. Need support does not require additional revenue, new technology, punitive sanctions, or a better evaluation system (Adams, Forsyth, Dollarhide, Miskell, & Ware, 2015), but it does require purposeful action to create a learning environment in which trusting relationships, high academic expectations, and autonomy-supportive instruction are the norm.

Because this study was limited to correlational evidence, we cannot rule out the possibility that rival hypotheses associated with unmeasured student and school characteristics may have confounded the results. Experimental research, replication studies with different samples, and additional statistical controls for correlational designs can address internal validity concerns. Testing the relationships with a larger and more diverse sample of schools will be important as well. Different school contexts may change the nature of the relationships we found in this study. Even with these limitations, the evidence points to the potential of self-regulatory climate and autonomy-supportive instruction to operate as positive, social resources for student learning.

CONCLUSION

Problems and failures experienced by schools may make for sensational headlines and good political fodder, but paying attention only to symptoms of dysfunction obscures the positive and healthy side of school life. For every case in which schools struggle to deliver high-quality learning, there are many examples of innovative and creative teachers who are harvesting the imagination, curiosity, and ingenuity of students (Benninga, Berkowitz, Kuehn, & Smith, 2006).  We have as much to learn from what is right in schools as we do from evidence of failure and dysfunction. As positive psychology has discovered, mapping the functional side of the human experience changes how we think about performance. For instance, it is now accepted that exceptional performance has more to do with cultivating certain traits, mindsets, and behaviors than with being lucky enough to have good genes (Coyle, 2009).

In a similar way, positive education can change how we think about and seek to improve quality schooling. For instance, using self-determination theory we can infer that high-performing schools are not based on zip code so much as the ability to cultivate the inner strengths and resources of teachers and students. The aim of positive education is to develop a body of scientific evidence capable of explaining sources of exceptional teaching and learning (Seligman et al., 2009). Self-regulatory climate and autonomy-supportive instruction appear to be two positive school conditions that enable students to flourish.

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Cite This Article as: Teachers College Record Volume 119 Number 8, 2017, p. 1-30
https://www.tcrecord.org ID Number: 21917, Date Accessed: 10/23/2021 7:12:52 PM

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