Remixing Resilience: A Critical Examination of Urban Middle School Learning Environments among Resilient African American Learners

Abstract

This study examines a sample of African American students attending urban middle schools in a Southern city, and considers their perceptions of learning environments within mathematics classrooms. This study concluded that variables like Academic Self-Concept, Mathematics Anxiety, Satisfaction, Involvement, and Academic Aspiration varied significantly among higher and lower performing students. These variables are informed by the classic resilience literature on learning environment that tends to be less culturally affirming. In an effort to move resilience theory away from racial ideologies, we reconceptualize resilience as a cultural trait common among African American learners that should not be conceptualized dichotomously nor hierarchically

Keywords

African American learners resilience urban middle schools culturally affirming

I was not a great student growing up. I was considered a troubled youth by many, “disadvantaged” some would say. I was a slow reader, speech delayed, and a behavioral challenge for my teachers, until I met Mrs. Simms in the fifth grade. Her classroom was like magic. Before Mrs. Simms, I was just another young Black boy from an improvised single parent family. I was literally from the “wrong side of the tracks.” But within months of meeting Mrs. Simms, I earned a coveted position on the Academic Olympics Team for my grade school. My mom always told me that I was smart, but I saw my grades, and they were still the lowest among my five brothers and older sister. Despite this, Mrs. Simms convinced me that I could be anything—a doctor, a lawyer, an astronaut. In her classroom, I was remixed from “at risk” to “at promise.”

American education persists as two systems, separate and disparately unequal (Ladson-Billings, 2006). Consequently, U.S. educational progress has become stagnate with no measurable growth in Reading, Math or Science on the Program in International Student Assessment (PISA) since 2000 (Kastberg et al., 2016). Among African American learners, the consequences have meant a persistent and pervasive test score gap on the National Assessment of Education Progress (NAEP) across every grade level, subject area and socioeconomic status, and these disparities have changed in no measurable way over the past three decades (Hansen et al., 2018; Lewis et al., 2008). The flat trend lines among the nation’s school children has garnered increased attention as calls for global competitiveness (Darling-Hammond, 2010; Ladson-Billings, 2011) prompt a renewed discussion about how to address the needs of African American learners and eliminate the much-debated “achievement gap” (Merolla & Jackson, 2019; Wells et al., 2012). Although education systems maintain the gap, every single student can make significant yearly academic progress. Thus, while for me the catalyst was the environmental force created by a determined teacher and a classroom culture of care (Wandix-White, 2020), there is an inherent spark of resilience that exists within every African American student. Finding that spark and creating the right environment to kindle a flame may ignite a slow burn of the systems that perpetuate the gap.

Significance of Study

Academic resilience is an area of research that has important implications for the present movement toward strength-based inquiries capable of redressing long-standing educational debts and disparities is resilience. These frameworks examine students who succeed in education and life despite exposure to adverse conditions (James & Lewis, 2014; Morales & Trotman, 2011; Williams & Bryan, 2013). Henderson and Milstein (1996) define resilience as: “the capacity to spring back, rebound, successfully adapt in the face of adversity, and develop social, academic, and vocational competence despite exposure to severe stress or simply to the stress that is inherent in today’s world” (p. 7). Morales and Trotman (2011) discuss academic resilience as “the process and outcome of students who, despite coming from statistically ‘at-risk’ backgrounds, do succeed academically” (p. 1). Both definitions juxtapose stressful environments with resilient overcomers. In addition to everyday stressors, African American students must overcome roadblocks in the form of longstanding systemic educational inequities, and they must also persist educationally despite daily exposure to microaggressions in classrooms and the effects of systemic racism (APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Darvin, 2018; James & Lewis, 2014; National Urban League, 2007). Sadly, these realities are rarely factored into resilience research, and far more research is needed on how African Americans persist despite these social realities.

Several research syntheses have concluded that the learning environment, or the socio-psychological interactions, relationships, perceptions and reactions that students have within classrooms significantly affect students’ cognitive, affective and academic outcomes (Durlak et al., 2011; Fraser, 1998, 1990; Haertel et al., 1981). Additionally, the learning environment framework emphasizes the child-mediating or student-cognition paradigm, which maintains that how students perceive and react to their learning environment significantly impacts student development and outcomes (Tuitt et al., 2016; Waxman & Huang, 1996; Winne & Marx, 1977; Wittrock, 1986). Additionally, studies have shown that for Black and brown students especially, learning environment significantly impacts behavior (Monroe, 2006), willingness to take on academic challenges (Hwang et al., 2014), engagement (McCormick et al., 2013), and even cardio metabolic health (Levine et al., 2019). Consequently, teachers are tasked with establishing learning environments that promote measurable student growth and development; and given reports of the Black/White “achievement gap” and the deficit view of students of colors, teachers are also charged with creating environments that contribute to the educational resilience and success of students of color.

Purpose of Study

Thus, the purpose of the present study is to examine factors in the identification of African American students as resilient or non-resilient, consider how learning environments impact such labeling, and add to the limited body of scholarly research that acknowledges the transforming of learning environments as a path forward for schools, communities, and educators committed to helping Black youth activate their resilience. In typical resilience studies, students are categorized as resilient or non-resilient based on test score outcomes, grade point average (GPA), and behavioral outcomes (APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Masten et al., 1990; Waxman et al., 2004). However, for this study, we consider all African American learners resilient, given that they must cope with pervasive racism, and resurgent White supremacy in US society and schools (Curry, 2018; French, 2018). Moreover, the current generation of African Americans enrolled in P-12 urban schools are confronted with a type of ever-present racism through social media, cop cameras, and increasingly aggressive forms of White nationalism masked as a legitimate political, economic, religious, and educational doctrine. Nevertheless, present generations of African American learners boast the highest test scores, graduation rates, college attendance and completion rates, and economic health compared to past generations of Black Americans (Whiting et al., 2013). For example, an Economic Policy Institute (EPI) study found that while in 1968, only 54% of Black Americans graduated high school, today 92% obtain a high school diploma (Jones et al., 2018). The EPI study also found that college graduation rates for African Americans have doubled over the last five decades; and according to a Stanford Center for Education Policy Analysis (2013) report, Black students today are “roughly three years ahead of their parents’ generation in math skills” and “in reading, they are roughly two to three years ahead of their parents” (para. 6). Within this context, the traditional approach within resilience research that labels some African American students as resilient and others as non-resilient is antiquated.

For this study, we compare differences in how students perceive their learning environment among two groups of resilient students, those who test well (evident resilient) and those whose test scores fall in lower ranges (emergent resilient). Our sample of African American middle school students attended urban middle schools, and we examined their perceptions within mathematics classrooms. Moreover, we focus on the learning environment because, while past research too often considered factors outside educator’s direct control (APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Spencer, 1995), educators can exert the most influence in their respective classrooms. More recent studies have provided seminal theoretical shifts in resilience research by incorporating the cultural, social, and historical realities of African Americans (Brown & Tylka, 2011; Chesmore et al., 2016; Spencer et al., 2006). Still, their work has not transitioned into educational research circles at a rate that acknowledges the critical contributions of the Phenomenological, Variant of Ecological Systems Theory (P-VEST) approach (Spencer, 1995) and the American Psychological Association, Task Force on Resilience and Strength in Black Children and Adolescent’s (2008) Portrait of Resilient, Optimally Functioning African American Youth framework. Thus, prior to presenting findings, we review research on the importance of learning environments to promoting resilience, while considering the importance of culturally sensitive frameworks like P-VEST and the APA’s framework. We conclude by interrogating how the dichotomous and hierarchical nature of resilience ties this concept to racist ideologies that resist the infusion of cultural sensitivity, and limits its usefulness without a radical remixing.

Literature Review

Operationalizing Resilience in Education Research

Resilience research now spans five decades but remains highly contested; and educational resilience research requires reconceptualization or remixing to truly divorce the future of this line of inquiry from its deficit orientations. Supportively, APA, Task Force on Resilience and Strength in Black Children and Adolescents (2008) asserted that resilience research, in general, is compromised due to “. . .confusion in terms of the conceptualization, operationalization, and measurement of resilience” (p. 22). The construct, for example, has been widely used in professions like developmental psychopathology (Garmezy, 1991; Masten, 1994; Masten et al., 1990; Rutter, 1987, 1990) to explore how children and adolescents overcome family violence (Straus, 1983); stress and family dysfunction (Rutter, 1979); as well as poverty and drug abuse (Garmezy & Masten, 1991; Werner & Smith, 1977, 1992). While understanding these social ills is critical, educational researchers have largely failed to question the assumptions and applicability of these pathology driven frameworks to supporting the maturation of children in schools and classrooms. Allen (2017) notes that teachers “regularly draw upon dominant pathologizing discourses when thinking about, interacting with, and talking about their black male students” (p. 270), and that the normalization of such discourses that present a deficit view of Black make students are often internalized, causing Black males to “downplay the structural barriers in their schooling and contribute to the school’s deficit positioning by blaming themselves for their school failure” (p. 271). Unlike psychopathologists who start with the assumption of a deficit, educators cannot or should not initiate their practice with a pathological frame of reference.

Wang et al. (1994) define “educational resilience” as “the heightened likelihood of success in school and other life accomplishments despite environmental adversities brought about by early traits, conditions, and experiences” (p. 46). While this line of research seems progressive on the surface, researchers, including Waxman and Huang (1996), Howard et al. (1999), Brown et al. (2001), Waxman et al. (2004) and James and Lewis (2009) have provided consistent critique of the limitations in educational resilience research. Traditional educational resilience studies are critiqued for not questioning negative assumptions about marginalized groups, and labeling African American children, their culture and communities as pathological. In this light, communities of color are “risk” filled environments rather than nurturing spaces. This stereotype driven approach ignores established research detailing the inherent strengths within Black children, and their culture, communities, and families (Hill, 1972; James et al., 2018; Ladson-Billings, 2006; Perry et al., 2003). Furthermore, more recent resilience frameworks like the Framework of Understanding the Plight of Black Males (Bonner, 2014), the Fragile and Robust Mathematical Identity Framework (McGee, 2015), counter-storytelling as an analytical framework (Solórzano & Yosso, 2002), and the Community and Youth Resiliency conceptual framework (Brennan, 2008) are just a few that attempt to avoid deficit models, focusing instead on the inherent strength of African American learners. It is critical that educational researchers, particularly those researching diverse youth, differentiate between which resilience frameworks are appropriate and applicable to classrooms and school settings, and which are not.

Culturally Affirming Resilience Frameworks

The movement to remix resilience research into a more culturally affirming model, one that recognizes the unique positionality of African American youth, was greatly aided by Spencer (1995), Spencer et al. (2006) and APA, Task Force on Resilience and Strength in Black Children and Adolescents (2008). Spencer’s (1995) groundbreaking synthesis of the Phenomenological, Variant of Ecological Systems Theory (P-VEST) model for normative human development accounts for how individuals develop resilience within varying social, gender, racial, and economic ecologies. In short, P-VEST considers the positionality of individuals within society as raced and gendered beings. Spencer et al. (2006) conclude the following:

A multifaceted, context-linked, and systems-oriented human development perspective is essential for a maximized understanding of resilience and vulnerability; in fact, a carefully nuanced approach is particularly needed when considering broad ethnic enclaves and, more generally, all humans’ normative pursuit of stage-specific life course competencies. The perils that youth face, along with the successful strategies they employ in coping with these risks, must be understood both in relation to their maturational and identity formation statuses and as linked to the larger social, cultural, and historical contexts of development. (p. 627)

Furthermore, P-VEST was arguably the first resilience framework to consider the racial and gender contexts of the US, and how resilience is nested within these complex historical and cultural realities.

Moreover, the American Psychological Association, Task Force on Resilience and Strength in Black Children and Adolescents (2008) report provided a comprehensive review of the challenges, shortcomings, and potential of resilience research. This APA task force sought to correct for cultural insensitivity in resilience research by proposing a reasonably comprehensive framework for researching and supporting the development of resilience among African American youth. The contributing authors noted, “We contend that understanding resilience among African American youth requires acknowledging their experiences in the United States and appreciating the continuing legacy of oppression and discrimination that affects their daily lives” (p. 24). APA, Task Force on Resilience and Strength in Black Children and Adolescents (2008) advanced resilience research by proposing that African American youth must learn to thrive, not just cope with risk. More specifically, African American youth who are developing along a healthy trajectory will be critically minded (critique of social inequalities), actively engaged (positively impacts others in varying settings), emotionally flexible (fluid adjustment to the socio-emotional and cognitive demands of various settings), and communal oriented (focus on interdependence, collective well-being, and social responsibility) (p. 26). While these two frameworks should be required considerations for resilience research on African Americans, they are not calibrated or appropriate for P-12 educational research as they focus mostly on matters beyond the control of schools and teachers. The present study acknowledges the strengths of these culturally responsive models, while also examining factors within the classroom learning environment.

Promoting Resilience in Learning Environments

This line of research is both appropriate and applicable in schools, and many of the factors explored in learning environment research are under the direct influence of educators. Reviews of this research have found that students’ perceptions of their classroom learning environment significantly contribute to gains in students’ cognitive, affective, and behavioral outcomes (Boon et al., 2019; Fraser et al., 1987). Other reviews have found that classroom environment measures can be appropriately used as criterion or outcome variables in a wide range of research (Fraser & Wubbles, 1995). Waxman and Huang (1996) found that several aspects of the classroom learning environment can be distinguished between minority students who test well (evident resilient) and those whose test scores fall in lower ranges (emergent resilient). In a classic resilience study, Lee et al. (1991) analyzed National Assessment for Education (NAPE) data from the eighth-grade sample to examine the family background, school characteristics, and academic related behaviors of high achieving African-American students and lower achieving students. The results from their study indicate that the characteristics of the schools students attend, as well as the individual actions of students in those schools, are significant contributors to their academic performance.

Furthermore, according to Allen (2006) learning environments that promote educational success for African Americans are characterized by positive student-teacher relationships; ridicule free classroom environments; praise and positive criticism; high academic expectations for all students; opportunities for meaningful participation; and use of various instructional strategies. Moreover, the conceptualization of learning environment can be expanded to include positive peer relationships and activities, which have been shown to encourage student resilience. For instance, Reis et al. (2005) concluded that high-achieving students were not only characterized by a strong belief in self, but they also tended to receive more support from like-minded peers, and engage in a wide variety of after-school activities. The authors note that all high achieving students in this study were actively involved in numerous activities, including sports, part-time jobs, clubs, band, and service groups.

Within this context, the present study compares how African American middle school students in Math classes perceive their learning environments in urban classrooms. We consider variations in perceptions of learning environments for African Americans who are high performing (measured by standardized tests outcomes) compared to African American students whose test scores fall in lower ranges. Also, other essential background characteristics such as grade level, gender, academic aspirations, attendance record, and students’ time allocation are examined as past research has linked these factors with students’ academic achievement (Allen, 2006; Reis et al., 2005; Waxman & Eash, 1983).

Methods

Participants

The present study was conducted in the five middle schools of a culturally diverse school district located in a major metropolitan city in the Mid-South region of the United States. The River Heights Independent School District (pseudonym) was selected because of the relatively large enrollment of Black students, and because the district reported persistent test score gaps between White, Asian and African American learners. Our dataset consisted of a randomly selected stratified sample of 180 high-performing (evident resilient) and 180 lower performing (emergent resilient) African American middle school students. Students identified as “gifted or talented” or “special education” were excluded from the population in order to reduce the likelihood of outliers, given that these categories represent two extremes in students’ experiences.

Students were classified as “emergent resilient” if they scored at or above the 75th percentile on the standardized mathematics achievement test during the prior academic year, and reported receiving mostly “A’s” or “B’s” on mathematics assignments during the present academic year. Conversely, students were classified as “emergent resilient” if they scored at or below the 25th percentile on the standardized mathematics achievement test from the prior academic year, and reported receiving mostly “C’s,” “D’s,” or “F’s” on mathematics assignments during the present school year. A stratified sampling technique was used in order to obtain an equal number of students by gender (i.e., males and females) and grade (i.e., sixth, seventh, and eighth grade) within each student group (i.e., evident resilient and emergent resilient).

Reliability and Validity of Instruments

Several standardized instruments were adapted and incorporated for use in the present study: (a) the Multidimensional Motivational Instrument (MMI) (Uguroglu et al., 1981; Uguroglu & Walberg, 1986), (b) the Classroom Environment Scale (CES) (Fraser, 1982b), (c) the Instructional Learning Environment Questionnaire (ILEQ) (Knight & Waxman, 1989, 1990), (d) Motivation Strategies Learning Questionnaire (MSLQ) (Pintrich et al., 1993). All instruments have been found to be reliable and valid in a number of studies examining student learning environment and motivation in culturally-diverse settings (Chipangura & Aldridge, 2016; Fraser, 2014; Padrón et al., 1999; Waxman & Huang, 1996).

The MMI is a questionnaire that measures the motivation constructs of Achievement Motivation, Academic Self-Concept, and Social Self-Concept. The instrument has been found to have test-retest reliability, and construct and predictive validity. For the present study, only Achievement Motivation and Academic Self-Concept scales were used. The CES is a questionnaire that has been widely used in a variety of educational settings to measure students’ perceptions of their relationships with students and teachers as well as the organizational structure of the classroom. The content and concurrent validity of the CES have been established through correlational studies and classroom observations (Deieso & Fraser, 2019; Fisher & Fraser, 1983; Moos, 1979). Adequate internal consistency reliability coefficients were also obtained in previous studies (Fisher & Fraser, 1983; Fraser, 1982a, 2014; Moos, 1979). For the present study, only the Involvement and Affiliation scales were used. The ILEQ measures students’ perceptions of seven aspects of instructional learning environments. It has been found to have adequate internal consistency reliability coefficients and test-retest reliability (Waxman & Huang, 1996). For the present study, only the Satisfaction and Parent Involvement scales section of the ILEQ were used. In addition, Mathematics Anxiety, Ego-Oriented, Task Oriented, and Aspiration Scales were also used. The Mathematics Anxiety scale taken from the MSLQ measures the extent which students feel stressful, uncomfortable, and helpless in taking mathematics tests. Lastly, Academic Aspirations measures the extent to which a student is sure he/she will graduate from high school or how far in school he/she will get. Each scale from the instruments includes three to four items, except for the Aspiration scale that has two items.

Description of Scales

The following provides descriptions of key constructs measured by selected scales with a sample question related to each construct. The scales include measures for the following:

Achievement Motivation—the extent to which students feel the intrinsic desire to succeed and earn “good” grades in mathematics (e.g., When I have a hard problem in mathematics, I usually keep trying to solve it);

Academic Self-Concept—the extent to which students exhibit pride in their classwork and expect to do well in mathematics (e.g., I am proud of my work in mathematics);

Involvement—the extent to which students participate actively and attentively in their mathematics class (e.g., In my mathematics class, I really pay attention to what the teacher is saying);

Affiliation—the extent to which students know, help, and are friendly toward each other in their mathematics class (e.g., I know other students in my mathematics class really well);

Satisfaction—the extent of students’ enjoyment of their mathematics class and schoolwork in mathematics (e.g., I enjoy the schoolwork in my mathematics class);

Parent Involvement—the extent to which parents are interested and involved in what their children are doing in mathematics (e.g., My parents often ask me about what I learned in mathematics);

Mathematics Anxiety—the extent to which students are apprehensive or distressed about taking mathematics tests (e.g., I have an uneasy, upset feeling when I take a mathematics test);

Ego-Oriented—the extent to which students are preoccupied with doing better than others do (e.g., I feel most successful if I do the work better than others do);

Task-Oriented—the extent to which students view success as gaining knowledge or performing to one’s best (e.g., I feel most successful if I get a new idea about how things work); and

Academic Aspirations—students’ intentions to finish high school and continue their education beyond high school (e.g., How far in school do you think you will get?).

All of the items were measured on a four-point, Likert-type scale such that responses of “not at all true” corresponds to the value of “1”; “not very true” was assigned the value of “2”; “sort of true” corresponds to the “3”, and “very true” was assigned the value of “4”. Students’ responses to each item within the same scale were added and averaged. Consequently, a mean value of “4” indicates the student responded favorably to the scale, whereas a mean value of “1” indicates that the student responded unfavorably to the scale. All of the items on these instruments were modified to a “personal form” in the present study, which elicits an individual students’ responses to his/her role in their mathematics class, rather than a student’s perception of the class as a whole (Fraser, 1990, 1998).

In order to ensure adequate reliability and validity of the ten scales used in this study, internal consistency (Cronbach’s alpha) reliability and discriminant validity (correlations between scales) were conducted. These coefficients were calculated using the individual student as the unit of statistical analysis. The results reported in Table 1 reveal the mean alpha coefficient of these scales was .69 and the individual coefficients ranged from .57 to .84, indicating that the survey instrument has adequate reliability given the relative few number of items per scale. The reliabilities in the present study also are generally higher than those found in similar studies examining students’ learning environments and motivation in culturally diverse settings (Padrón et al., 1999; Waxman & Huang, 1996).

Table 1.

Number of Items, Alpha Reliability, Discriminant Validity, and ANOVA Results (F and Eta2) for All Scales.

Number scale	Alpha of items	Mean r with		ANOVA
Number scale	Alpha of items	Reliability	Other scales	F	Eta2
Achievement motivation	3	.59	.29	1.31	.20
Academic self-concept	3	.57	.30	1.36	.21
Involvement	4	.61	.31	1.76**	.25
Affiliation	4	.57	.17	1.11	.17
Satisfaction	4	.80	.31	0.76	.13
Parent involvement	3	.70	.17	0.99	.16
Mathematics anxiety	4	.74	.16	0.93	.15
Ego-oriented	4	.84	.11	1.03	.16
Task-oriented	4	.75	.27	0.92	.15
Academic aspirations	2	.70	.21	0.86	.14

Note. **p < .01.

In Table 1, the mean correlation between the scales was .26 and the mean correlations for individual scales ranged from .11 to .31, indicating that the survey instrument has good discriminant validity. The univariate analysis of variance (ANOVA) using classroom grade level as the factor indicates only the Student Involvement Scale was significantly affected by students’ grade level. The value of Eta² ranged from .14 to .25 with an average of .17, suggesting about 17% of the variance in students’ motivation and perceptions of learning environment can be explained by class membership in specific grade levels. Several background items selected from the National Educational Longitudinal Study were also included in the final study survey. These items included questions about students’ (a) background characteristics (e.g., mathematics grades), (b) attendance (e.g., number of days missed), and (c) time allocation (e.g., time spent on homework).

Data Collection and Analysis

The scales from the five instruments and the background items from the NELS:88 survey were combined into one survey and were administered concurrently by researchers approximately 2 months before the end of the school year, during students’ regular mathematics class. Students were informed by the researchers that they were not tests, and completed questionnaires would not be seen by their teachers or other school personnel. Chi-Square tests were used to compare the frequencies of responses between evident resilient (high preforming) and emergent resilient (lower performing) students on the items. A three-way multivariate analysis of variance (MANOVA) was used to determine (a) whether there are motivational and perceptional differences by students’ gender, grade level (6th, 7th 8th), and student grouping as evident-resilient or emergent-resilient, and (b) whether there are any interaction effects by gender and/or grade level. As a follow-up procedure, univariate analysis of variance (ANOVA) and post hoc multiple comparison tests were performed to determine which measures of learning environment were significantly different for evident and emergent resilient students, and for gender (female and male). Finally, descriptive discriminant analysis was used to determine the extent to which the two resilient groups differ with respect to their classroom learning environment, instructional learning environment, motivation, and background characteristics.

Results

Background and Behavioral Differences in Resilient Learners

First, while we consider all African American learners resilient, there are statistically significant differences between students more adept at actualizing resilience in schools, and those in need of additional support. Table 2 reports the chi-square test results for several background and individual student variables, and indicates that there are statistically significant differences between evident resilient and emergent resilient student groups on the extent to which students reported being held back a grade in school. For instance, 51.67% of lower performing students (emergent resilient) reported being held back a grade in school, but only 21.11% of evident resilient students reported being held back. Additionally, evident resilient African Americans had stronger track records in past math classes, and reported earning a grade of an “A” 10 times more often (56.67%) when compared to emergent resilient students (9.44%). The most striking difference among the student groups was in the grade they expected to earn in their math class during the present year. One hundred percent of evident resilient students expected to earn a grade of “A” or “B” during the present academic year, while no student categorized as emergent resilient expected to earn a grade of “A” or “B” in math.

Table 2.

Descriptive and Chi-Square Results for the Two Resilient Student Groups.

Indicators	Evident resilient (n = 180) %	Emergent-resilient (n = 180) %	Chi-square
Background characteristics
Have been held back a grade in school			39.40***
Yes	21.11	51.67
No	78.89	48.33
Math grade received last year			142.95***
Mostly A’s (90–100)	56.67	9.44
Mostly B’s (80–89)	36.11	27.78
Mostly C’s (75–79)	5.56	34.44
Mostly D’s (70–74)	0.56	13.89
Mostly below D (below 70)	1.11	14.44
Math grade received this year			360.00***
Mostly A’s (90–100)	54.44	0.0
Mostly B’s (80–89)	45.56	0.0
Mostly C’s (75–79)	0.0	50.56
Mostly D’s (70–74)	0.0	28.89
Mostly below D (below 70)	0.0	20.56
Attendance record
Days of school you missed over the past 4 weeks			11.60*
Perfect attendance	66.67	52.22
Missed 1 or 2 days	20.56	28.9
Missed 3 or 4 days	6.11	15.0
Missed 6 to 10 days	5.00	6.1
Missed more than 10 days	1.67	6.7
How often you cut or skip classes			11.13*
Never or almost never	98.33	90.50
Sometimes, but less than once a week	0.56	6.15
Not everyday, but at least once a week	0.56	1.12
Daily	0.56	2.23
Days of school you were late over the past 4 weeks			16.31**
None	73.33	55.56
1 or 2 days	21.11	28.33
3 or 4 days	3.89	10.56
6 to 10 days	1.11	5.00
More than 10 days	0.56	0.56
Time allocation
Time spent on math homework each week			13.41**
None	8.89	12.78
Less than 1 hour a week	31.67	46.67
1 to 2 hours a week	37.22	27.22
3 or 4 hours a week	11.67	7.78
More than 4 hours	10.56	5.56
Time spent on additional reading			9.74*
None	15.56	21.67
1 hour or less per week	37.22	46.11
2 hours per week	22.78	17.22
3 to 4 hours per week	10.00	7.78
5 hours per week	14.44	7.22
Time spent on TV per weekday			8.26
Don’t watch TV	3.35	2.78
Less than one hour per day	7.26	8.33
1 to 2 hours a day	18.44	15.00
2 to 3 hours a day	18.44	9.44
Over 3 hours a day	52.51	64.44
Time spent on TV on weekends			3.69
Don’t watch TV	3.35	1.67
Less than one hour a day	7.26	7.78
1 to 2 hours a day	14.53	16.67
2 to 3 hours a day	16.76	11.11
Over 3 hours a day	58.10	62.78
Time spent on listening to CD, tapes, radio, etc.			5.73
None	3.33	5.59
1 hour or less per week	25.56	23.46
2 hours	26.67	17.88
3 to 4 hours	15.56	19.55
5 hours or more per week	28.89	33.52

Note. *p < .05. **p < .01. ***p < .001.

Moreover, there were marginal differences that were still statistically significant, and were reported in “days missed school,” “skip classes,” and being tardy to class, with more pro-achievement behaviors among evident resilient learners. Similarly, evident resilient students reported spending more time doing “math homework each week” and “additional reading.” However, there were no significant differences between evident and emergent students in how much time they spent watching television or listening to CDs, tapes, or the radio on weekdays or weekends. In sum, the students were statistically different in student outcomes (Grade Retention & Math Grade Earned), expected outcomes (Expected Math Grade), and to a much lesser degree, attendance and study habits.

Group Differences in Motivation and Perceptions of Learning Environment

A three-way multivariate analysis of variance (MANOVA) was used to determine (a) whether there are motivational and perceptional differences by students’ gender, grade level (6th, 7th 8th), and student grouping as evident-resilient or emergent-resilient, and (b) whether there are any interaction effects by gender and/or grade level. Table 3 reports the three-way MANOVA results, and indicates significant main effects on African American middle-school students’ motivation and perceptions of their learning environment by student gender (female and male) and resilient grouping (evident and emergent). Plainly stated, girls’ motivations and perceptions differed significantly from boys, as did the motivations and perceptions of evident resilient students when compared to emergent resilient students (F(10,331) = 21.86, p = .0001). There was, however, no significant main effect for grade level, which means 6th, 7th, and 8th grade African American students’ motivation and perceptions of learning environment were not significantly different from one other. Lastly, there were no interaction effects when considering (a) resilience group by gender, (b) resilience group by grade level, (c) gender by grade level, or (d) resilience group by gender and grade level.

Table 3.

MANOVA Results of Resilience Group, Gender, and Grade-Level Effects on Students’ Motivation and Learning Environment.

Variable	Wilks’ lambda	df	F	p
Group (resilience)	.602	10, 331	21.86	.000
Gender	.932	10, 331	2.42	.009
Grade	.950	20, 662	0.86	.634
Group × Gender	.973	10, 331	0.91	.524
Group × Grade	.932	20, 662	1.19	.254
Gender × Grade	.913	20, 662	1.53	.065
Group × Gender × Grade	.935	20, 662	1.13	.316

Note. Significance of bold values is P value the last column.

Group Difference in Dimensions of Learning Environment in Math Classrooms

Given the results of the three-way multivariate analysis of variance, the researchers conducted a univariate analysis of variance (ANOVA) and post hoc multiple comparison tests to determine which measures of learning environment were significantly different for evident and emergent resilient students, and gender (female and male). Table 4 reports the follow-up ANOVA results, and indicates that evident resilient (high performing) students had significantly higher perceptions of their learning environment when compared to emergent resilient learners. Most significantly, evident resilient students perceived their math classes more positively in Involvement (active/attentive in math class), Satisfaction (enjoyment of math), Academic Self-Concept (expect to do well in math), Achievement Motivation (desire to succeed in math), and Academic Aspiration (intent to attend college) than emergent resilient students. They also had greater Ego-Oriented (competitive) and Task-Oriented (performing one’s best) views of success but lower mathematics anxiety (distressed with math) than emergent resilient students. There were no significant differences between the two groups of students on the Affiliation (peer relations) or Parent Involvement scales. In other words, students reported virtually equal levels of parental involvement with math, and both groups reported favorable relationships with their peers in their Math classes. Moreover, the standard deviations for emergent resilient students were generally higher than those for evident resilient students, suggesting there was greater variation among lower performing students’ responses.

Table 4.

Descriptive and Univariate Analysis of Variance Results of Students’ Motivation and Learning Environment by Resilience Groups.

Scale	Evident resilient (n = 180)		Emergent resilient (n = 180)		F
Scale	M	SD	M	SD	F
Involvement	3.14	.62	2.68	.56	50.70***
Affiliation	3.22	.54	3.10	.60	2.31
Satisfaction	3.22	.69	2.60	.77	57.83***
Parent involvement	2.93	.86	2.98	.80	0.00
Academic self-concept	3.41	.57	2.72	.67	84.85***
Achievement motivation	3.24	.60	3.07	.66	5.41**
Mathematics anxiety	1.85	.75	2.64	.83	90.09***
Ego-oriented success	3.31	.80	3.16	.77	4.25*
Task-oriented success	3.34	.67	3.16	.72	6.30*
Academic aspirations	3.69	.44	3.23	.72	45.61***

Note. A score of 4 indicates that the student responded “Very true” to all of the items on the scale. A score of 1 indicates that the student responded “Not at all true” to all of the items on the scale.

p < .05. **p < .01. ***p < .001.

Additionally, Table 5 reports the follow-up ANOVA results by gender that showed in which measures of learning environment male students differed significantly from female students (F (10,331) = 2.42, p = .0085, reported in Table 3). Overall, there were only two significant differences between male and female students on all scales of learning environment, but female students generally had more positive perceptions of learning environment than male students. The two significant differences were that female students perceived greater mathematics anxiety than male students, but male students were more ego-oriented (competitive) than female students in math classes.

Table 5.

Motivation and Classroom Learning Environment Differences by Student Gender.

Variable	Male students (n = 157)		Female students (n = 203)		F
Variable	M	SD	M	SD	F
Involvement	2.88	.66	2.93	.62	0.02
Affiliation	3.08	.66	3.22	.64	2.57
Satisfaction	2.92	.75	2.91	.85	1.32
Parent involvement	3.03	.77	2.89	.87	2.68
Academic self-concept	3.04	.73	3.08	.70	0.71
Achievement motivation	3.14	.70	3.17	.58	0.00
Mathematics anxiety	2.20	.88	2.27	.89	4.32*
Ego oriented	3.32	.70	3.18	.85	4.34*
Task oriented	3.23	.73	3.26	.68	0.00
Academic aspiration	3.40	.61	3.51	.65	0.53

Note. *p < .05. ***p < .001.

Descriptive Model for Variance in Resilient Students

Lastly, a discriminant function analysis was performed to determine the extent to which differences in classroom learning environment, instructional learning environment, motivation, and background characteristics were distinctive for emergent resilient students compared to evident resilient students. Descriptive discriminant analysis was used instead of predictive discriminant analysis because the purpose of the analysis was to describe the MANOVA results that were presented in prior sections (Huberty & Barton, 1989). This direct entry model examines the independent contribution of each of the scales in determining resilience group membership. Overall, according to Table 5 the model produced a Wilks Lambda (df [19, 329]) of .548, which was statistically significant at the p < .0001 level. The discriminant function had a canonical correlation of .65, indicating a moderately strong relationship between the student resilience groups and the discriminant function (learning environment, instructional environment, motivation and background variables). The squared canonical correlation coefficient for the model was .45, indicating that 45% of the variance between the two groups could be explained by the variables in this model. A classification matrix revealed that overall, 81% of the cases were correctly classified, with 82% of the evident resilient and 80% of the emergent resilient students being correctly classified.

Finally, the discriminant analysis yielded both the standardized canonical coefficients and the canonical structure coefficients, which were reported in Table 6. The standardized discriminant function coefficients describe the independent contribution of a given variable (learning environment, instructional environment, motivation or background variables) on the resilience grouping variable, holding constant the contributions of other discriminating variables (learning environment, instructional environment, motivation and background variables). The variables of Academic Self-Concept [.56], Mathematics Anxiety [−.44], Satisfaction [.29], Not Held Back in School [.36], and Academic Aspiration [.28] were found to have the greatest impact on resilient status of students, after adjusting for all the other variables in the analysis.

Table 6.

Discriminant Analysis Results Between Evident Resilient and Emergent Resilient Students.

Independent variables	Standardized canonical coefficients	Canonical structure coefficients
Academic self-concept	.56	.75
Mathematics anxiety	−.44	−.67
Satisfaction	.29	.57
Academic aspirations	.28	.53
Involvement	.14	.56
Not held back in school	.36	.45
Late for school	−.15	−.31
Time spent on homework	.14	.26
Time spent reading more material	.04	.24
Achievement motivation	−.23	.23
Task-oriented	.02	.21
Cut or skipped class	−.00	−.20
Days missed in school	−.11	−.18
Ego-oriented	.19	.14
Affiliation	−.08	.12
Watch TV weekdays	.01	−.10
Listening to radio	−.07	−.08
Watch TV weekends	−.09	−.05
Parent involvement	−.18	−.02

The canonical structure coefficients for each variable provide an indication of the relative contribution of each variable to the overall discriminant function. It describes how closely a variable and the discriminant function are related. Six of the 19 independent variables included in the discriminant analysis were found to be statistically significant and had structure coefficient values of .20 or greater. The variables of Academic Self-Concept [.75], Mathematics Anxiety [−.67], Satisfaction [.57], Involvement [.56], Academic Aspiration [.53], Not Held Back in School [.45], Late for School [−.31], Time Spent on Homework [.26], Time Spent on Reading Additional Materials [.26], Achievement Motivation [.23], Task-Oriented View of Success [.21], and Cutting or Skipping Class [−.20] are all related to the discriminant function. The six variables that have a structure coefficient value of .40 or greater and have the greatest practical significance for distinguishing between evident and emergent resilient students are Academic Self-Concept [.75], Mathematics Anxiety [−.67], Satisfaction [.57], Involvement [.56], Academic Aspiration [.53], Not Held Back in School [.45]. Overall, this model represents a significant finding and points to the need for further research on learning environment and resilience among African American learners. Yet in the forthcoming discussion section we argue for a radical remixing of the conceptualization of resilience.

Discussion

In the present study, we specifically focused on resilient Black students from an urban school district, and found that students’ perceptions of the quality of their mathematics classrooms as instructional learning environments significantly differed between evident resilient and emergent resilient students. While this study concluded that variables like Academic Self-Concept, Mathematics Anxiety, Satisfaction, Involvement, Academic Aspiration, and Not Held Back in School provided insight into how African American middle schoolers perceived their learning environments, these variables are informed by the classic resilience literature on learning environment (Padrón et al., 1999; Waxman & Huang, 1996). This is a critical consideration because past research related to learning environments produced statistically significant results, yet they are not aligned with the lived realities of African Americans. Thus, this line of inquiry would be advanced by an infusion of culturally responsive frameworks related to resilience (Allen, 2006; APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Carter-Andrews, 2012; James et al., 2018; Joseph et al., 2017; Spencer et al., 2006).

The remixing of resilience synthesizes learning environment research with the present social-cultural realities of African Americans, while emphasizing how race, class and gender intersectionality impacts classroom interactions and student perceptions of their learning experiences. Toward this goal, in an effort to move theorizing about African American resilience forward and away from racial ideologies, we conceptualize that resilience is a cultural trait common among African Americans and should not be conceptualized dichotomously nor hierarchically.

Beyond the Resilience Dichotomy

Resilience research can be advanced through redressing two critical shortcomings in this line of research: the tendency to conceptualize resilience dichotomously and the propensity to view human development as hierarchical progressive stages. The process to remix resilience into a culturally responsive and respectful construct begins with shedding these limiting and dehumanizing approaches. In reference to the dichotomous nature of resilience, this conceptualization has resulted in the long-standing view that some African American students are resilient, while others are not. In fact, a more nuanced understanding of the shared-character displayed by African Americans, historically and presently, renders a dichotomous conceptualization of resilience antiquated and ill-informed at best. Instead, we contend that resilience is a collective cultural trait more common than exclusive among African Americans, particularly among learners in urban schools and communities. Historically, resilience or bouncing back has been a way of life for the vast majority of Black America; we therefore, consider resilience as a developmental range or spectrum with several expressions rather than a dichotomous state (resilient or not resilient). Explicating the nature of this spectrum is beyond the focus of the present research, thus we continue exploring how to undo the dichotomous conceptualization at the heart of resilience research.

Dichotomies such as “resilient or not resilient” reproduce beliefs in social hierarchies like classism, racism and sexism. Supportively, Black feminist theorist Patricia Hill-Collins (1986) asserted that, “Either/or dualistic thinking, or . . ., dichotomous oppositional difference, may be a philosophical lynchpin in systems of race, class, and gender oppression” (p. 520). In this line of reasoning, human reality is reduced to “dichotomies such as black/white, male/female, reason/emotion, fact/opinion, and subject/object gain their meaning only in relation to their difference from their oppositional counterparts” (p. 520). In this light, resilience is only meaningful if there are individuals who lack this important quality. But are we as researchers contributing to the oppression of Black youth by marrying this line of inquiry to a dichotomy of “haves” and “have nots”? Clarity, we contend, lays in Hill-Collins’ (1986) final note on dichotomous oppositional difference. Hill-Collins states, “since such dualities rarely represent different but equal relationships, the inherently unstable relationship is resolved by subordinating one half of each pair to the other” (p. 520). In other words, there is always value added to one end of a dichotomy, and society tends to debase people, qualities or entities categorized at the opposite ill-favored end of the spectrum. While dichotomous variables are valuable statistically, in reality they may reinforce deficit notions about African American learners, particularly those labeled as “not resilient,” “at risk,” or “disadvantaged.” In short, resilience needs remixing to distance this line of research from the limitations of dichotomous thinking.

Beyond Hierarchical Development

A second matter to consider when remixing resilience is that human development has been traditionally conceptualized as a series of hierarchal stages, which limits theorizing about the unique nature of African American social, emotional, and academic development. The African American experience is nested within institutions and systems that actively and passively oppose optimal human development (James & Lewis, 2014). Thus, a responsive model of resilience has to capture the recursive, fluid and adaptive nature of African American persistence (APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008). Historically, human development has been conceptualized within a number of popular hierarchal stage specific models, like Maslow’s Hierarchy of Needs, Bloom’s Taxonomy, and Kohlberg’s Moral Development Stages. Even Black identity development has been conceptualized as the five-staged Nigrescence Model (Cross, 1991). However, researchers have also analyzed human development as a byproduct of interactions within the social-cultural ecologies of society (Bronfenbrenner, 1994), such that individuals respond to environmental realities like racism, ineffective teaching or health concerns with a range of prosocial and maladaptive behaviors (Spencer et al., 2006). Yet, these ecological models remain tied to stage-specific human development theories, which seems to be incongruent with the messiness, uncertainty and instability created by exposure to race, class, and gender oppression.

Imagining that humans evolve along progressive hierarchical stages is only possible if one believes society treats all people equally, and the opportunity structures (education, health care, employment, and housing) of society are uniformly accessible to all. With countless examples of present-day racial inequality in the US, why is human development still being conceptualized hierarchically rather than iteratively? A careful review of 18th century European research and philosophy uncovers how the dubious link between human development and hierarchical logic evolved. Hierarchies require establishing two fixed and opposing extremes, in short, a dichotomy. All dichotomies as discussed in the prior section are conceptualized with a favorable end, and an opposing less ideal extreme. Arguably, the race binary of White/Black is the most common, if not fixed conceptualization in American culture; and it continues to have real and lasting consequences for social groups categorized as “superior” and those deemed “inferior.”

It may shock some to find that the Black/White binary bookends the original racial categorization and ranking systems dating back to the 1758 Systema Natura by Carl Linnaeus, and the dissertation of Johann Blumenbach, Unity of Mankind, published in 1795 (Feagin & Feagin, 2008). During this time, European Colonialism was in full swing and increasingly justified by social scientific theories that suggested human beings should be divided into five to seven distinct groups and ranked along a fixed inferior-superior hierarchy. Race became a part of the collective conscious of humankind as typologies introduced terms like Caucasian and Negroid as the “superior” and “inferior” extremes in the racial hierarchy, respectively. Moreover, Feagin and Feagin (2008) noted that Linnaeus’ and Blumenbach’s models also linked racial hierarchies (Caucasian, Mongoloid, Negroid) to racial temperaments or hierarchal expressions of morality, intellect, emotional temperament and work ethic. These racial temperaments stereotyped the “inferior races” as incapable of intellect, morality, self-discipline, or work ethic; thus, justifying their dehumanization and ultimately ingraining racial social hierarchies into Western civilizations.

Unfortunately, this model of human oppression was particularly useful in the New World as racial hierarchies became an organizing force in the US, determining the right to life, freedom, education, employment, housing, voting and equal protection under the law. Of particular interest here is how racial categorizations and temperaments became intertwined with US educational research, philosophy, policy and practice. This coupling was solidified between 1890-1920 as the US reorganized education in response to the demands of freedom and citizenship bequeathed to enslaved African Americans (Anderson, 1988). Kliebard (2002) summed up the genesis of this coupling noting that, “the new science of sociology provided reformers. . .with an authoritative basis for pursuing an educational policy based on racial and ethnic typologies” (p. 29). By the late 1920’s, US educational philosophy, policy and practice were firmly comingled with American racism. US education’s dubious goal became to redress the social, moral, emotional and mental “deficiencies” inherited by or nurtured among the “lesser” races. For African Americans this meant a labor and vocational intensive curriculum designed to instill all of the qualities that were perceived as lacking within this group like hard work, morality, and thrift (Kliebard, 2002).

Thus, dichotomous thinking (White/Black) and hierarchical conceptions of human development (superior/inferior) were both planted within the soil of US educational philosophy and practice, and today inhibit our ability to imagine that humans do not develop according to the ideas of Maslow, Bloom, Kohlberg and others. The potential for resilience to inform the academic, social, emotional, and cultural development of Black youth is directly tied to reimagining a framework that is neither dichotomously nor hierarchically conceptualized. Also, this reconceptualization must integrate the social-cultural realities of African Americans, while emphasizing how race, class and gender intersectionality creates uniquely situated experiences and developmental possibilities (APA, Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Spencer et al., 2006).

Conclusion

The importance of the learning environment to Black student resilience is a critical finding, because fostering culturally responsive classrooms is a critical micro level input to redressing the structural racism in US society and achieving educational equity in urban schools. While dismantling the policies, laws, and institutional practices that animate structural racism is urgent and necessary, we must also attend to ways of promoting local change in communities and classrooms. Transforming learning environments represents a path forward for schools, communities, and educators committed to helping Black youth activate their resilience. Supportively, Carter-Andrews (2012) concluded that African American learners demonstrate a unique form of resilience that resist both racism and hostile schooling environments by adopting a wide array of resilience strategies to promote their academic success (James et al., 2018). Within this context, it is critical that classrooms support the development of resilience among African Americans, or such learning environments will remain sites of resistance and defiance.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iDs

Marlon C. James

Willie C. Harmon Jr.

Author Biographies

Marlon C. James is an assistant professor in Multicultural Education in the Department of Teaching, Learning, and Culture at Texas A&M University. His research employees critical race theory and methodologies to unearth the persistent nature and negative impacts of racial inequality in urban schools and in higher education.

Diana Wandix White is a clinical assistant professor in the Department of Educator and Leadership Preparation at Texas A&M – San Antonio, and a Faculty-in-Residence with the university’s Institute for School and Community Partnerships. With a specialization in Urban and Multicultural Education, Dr. White’s research investigates intercultural communication at the intersection of equity pedagogy.

Hersh Waxman, director of the Texas A&M Education Research Center and a professor in the Department of Teaching, Learning, and Culture, has been appointed as a co-director of the Texas A&M Center for Mathematics and Science Education. An expert in educational culture, curriculum, and instruction, he has written more than 150 research articles about urban education, classroom-learning environments, and students at risk of failure.

Héctor Rivera is an associate professor of educational psychology in the College of Education and Human Development at Texas A&M. Dr. Rivera’s research and service focuses on developing youth academies, parental engagement programs, and teacher academies and institutes in collaboration with districts in Texas and other states.

Willie C. Harmon Jr. is a doctoral candidate of Curriculum & Instruction with foci in Urban and Multicultural Education in the Department of Teaching, Learning, & Culture at Texas A&M University. His research agenda fills gaps in extant literature related to the educational needs of African American youth in military-connected families and Black male educational development.

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