Stress,Sleep,and Performance on Standardized Tests: Understudied Pathways to the Achievement Gap

The Stress Disparity Model

Here we provide a brief overview of a theoretical model of how stress exposure and resulting biological stress responses might affect academic achievement as measured by standardized tests. We call this model the stress disparity model. Figure 1 displays the pathways of this model; Figure 2 focuses on the outcomes measured as an achievement gap. We present evidence for this model throughout the paper.

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Figure 1.

Stress disparity pathways. Pathways of the stress disparity model lead from demographic factors to perceived achievement gaps. The outcomes component of the stress disparity model (Pathways I and J in this figure) are further elaborated in Figure 2.

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Figure 2.

Stress disparity outcomes. The observed achievement gap is composed of input/effort gains, stress-related learning inhibitors, and test-day stress deficits. Figure displays three hypothetical students with the following characteristics: (a) moderate inputs/effort (from students, families, teachers, and schools) and high stress during the learning period (solid black line), (b) moderate inputs/effort and low stress during the learning period (dashed black line), and (c) high inputs/effort and low stress during the learning period (dashed gray line). Each inverse U-shaped curve represents potential performance on the standardized test on the testing day, with optimal performance occurring with moderate stress. Learning determines students’ potential curve on testing day, and their response to the test determines where they fall on the curve on the day of the test. Together, stress-related learning inhibitors and the test-day stress deficits form the stress gap due to differences in stress exposure during learning and the stress response during the test, and stress-related learning inhibitors and input/effort gains form the knowledge gap due to differences in learning.

We begin by noting that low-SES and racial-/ethnic-minority children are more likely to be exposed to stressful life events relative to higher-income or White students (Pathway A; Hatch & Dohrenwend, 2007; Repetti, Taylor, & Seeman, 2002). Exposure to stressors affects the HPA axis, one of the body’s key biological stress systems (Pathway B; Adam, 2012; Kudielka, Buske-Kirschbaum, Hellhammer, & Kirschbaum, 2004). HPA axis activation can be advantageous by assisting individuals in meeting the demands of daily life (Del Giudice, Ellis, & Shirtcliff, 2011), but long-term stress exposure can lead to changes in the HPA axis that can be maladaptive in some contexts (McEwen & Gianaros, 2010). HPA axis changes can affect students’ ability to learn new material and their ability to respond to an acute cognitive challenge, such as a standardized test (Pathways E and F; Schwabe, Joëls, Roozendaal, Wolf, & Oitzl, 2012). Similarly, exposure to stressors can affect sleep (Pathway C; Sadeh, Raviv, & Gruber, 2000), which subsequently affects students’ ongoing learning (Pathway G; Walker & Stickgold, 2006) and ability to function in the face of a cognitive challenge, such as an academic test (Pathway H; Gillen-O’Neel, Huynh, & Fuligni, 2013). The HPA axis and sleep systems can also affect one another (Pathway D; Zeiders, Doane, & Adam, 2011).

Bringing this literature together, we theorize that the effects of stress biology on students’ learning before a test and their ability to bring optimal levels of arousal and cognitive readiness to the testing environment affect their performance on standardized tests (Pathways I and J, respectively). In Pathway K, we note that the transition to adolescence brings increases in stress exposure (Romeo, 2010), as well as changes to the HPA axis and sleep systems, which may make the impact of stress exposure on academic performance particularly important during adolescence (Gunnar, Wewerka, Frenn, Long, & Griggs, 2010; Jenni & Carskadon, 2012).

We provide a second figure to argue that the achievement gap, as measured by standardized tests, reflects a gap in actual knowledge and a gap in cognitive performance during test taking, both of which are affected by home, neighborhood, and other stressors. We explore these pathways in the review that follows.

Demographic Factors and Life Stress (Pathway A)

Low-SES and racial-/ethnic-minority children (particularly, Black children) in the United States are more likely than higher-SES or White children to experience negative life events, such as a very violent incident; harsh or inconsistent parenting; or parental divorce, death, or alcohol abuse (Glasscock, Andersen, Labriola, Rasmussen, & Hansen, 2013; Hatch & Dohrenwend, 2007; Repetti et al., 2002). Rising inequality (Duncan & Murnane, 2011; Heathcote, Perri, & Violante, 2010) may lead to increasingly divergent childhoods in low- and high-income families. Stressful events may also affect the way children interpret the world, with low-SES children more likely to interpret ambiguous events as hostile (Chen & Matthews, 2001).

Likewise, some racial-ethnic minorities face more stressors than White youth, on average. For instance, Black children are more likely to witness domestic violence than White children (Roberts, Gilman, Breslau, Breslau, & Koenen, 2011). Some of these stressors are income-related (Myers, 2008). Other stressors are specific to the experience of race-ethnicity, such as exposure to stereotype threat (i.e., the fear of confirming negative stereotypes concerning one’s social group) and perceived discrimination (Adam et al., 2015; Clark, Anderson, Clark, & Williams, 1999; Levy, Heissel, Richeson, & Adam, 2016; Steele & Aronson, 1995). Past experiences with discrimination can increase vigilance for potential future racism (Bennett, Merritt, Edwards, & Sollers, 2004; Chen & Matthews, 2001; Myers, 2008).

Life Stress and the HPA Axis (Pathway B)

The biological stress response includes multiple systems, but we focus here on the HPA axis and its primary hormonal product, cortisol. Cortisol levels show a strong circadian rhythm across the day, known as the diurnal cortisol rhythm, with highest cortisol levels occurring shortly after waking and lowest levels occurring about 30 minutes after sleep begins (Adam, Hawkley, Kudielka, & Cacioppo, 2006). Researchers often measure the waking cortisol level and the daily cortisol slope, which is the rate at which cortisol levels drop from wake to bedtime. They also measure the area under the diurnal cortisol curve (AUC), which is the estimated total cortisol exposure across the day from wake to bedtime. A sharp increase in cortisol in the 30 to 40 minutes after waking is called the cortisol awakening response, or CAR.

Real or perceived stressors can increase cortisol above typical diurnal levels (called acute cortisol reactivity; Adam, 2012; Miller, Chen, & Zhou, 2007; Sapolsky, Romero, & Munck, 2000). For routine stressors, cortisol levels return to the typical diurnal pattern approximately an hour after the stressor has passed (Adam, 2012; Miller et al., 2007). The cortisol stress response is typically adaptive rather than problematic: The HPA axis mobilizes psychological and physiological responses when presented with a stressor (Del Giudice et al., 2011; Shirtcliff, Peres, Dismukes, Lee, & Phan, 2014).

Normally, the CAR is helpful in promoting the transition from sleep to waking, and it is thought to provide an energetic boost to help individuals meet the expected demands of the upcoming day (Adam et al., 2006; Clow, Hucklebridge, Stalder, Evans, & Thorn, 2010; Doane & Adam, 2010; Fries, Dettenborn, & Kirschbaum, 2009; Vrshek-Schallhorn et al., 2012). Frequently experiencing large CARs may indicate the individual faces excessive perceived demands—or insufficient perceived resources to meet those demands (Adam et al., 2010; Vrshek-Schallhorn et al., 2012).

Chronic stress system activation can have substantial, long-lasting repercussions on HPA axis functioning. The concept of allostatic load suggests that the body undergoes “wear and tear” as it experiences repeated biological stress responses during stressful events (Juster, McEwen, & Lupien, 2010; McEwen & Stellar, 1993). Prolonged periods of hyperactivity of the HPA axis may be followed by hypocortisolism, wherein the AUC is lower, the cortisol slope is flattened, the CAR is muted, and the system no longer sufficiently activates in response to perceived stressors (Fries et al., 2009; Fries, Hesse, Hellhammer, & Hellhammer, 2005; Gunnar & Vazquez, 2001). This pattern is often accompanied by fatigue (Fries et al., 2005, 2009). In particular, low waking cortisol and nonexistent or very small CARs may not provide the individual with sufficient energetic mobilization (Adam et al., 2006). Exposure to international orphanages, serious financial strain, and physical abuse have been associated with hypocortisolism in children (Badanes, Watamura, & Hankin, 2011; Cicchetti & Rogosch, 2001; Koss, Mliner, Donzella, & Gunnar, 2016).

Changes to the stress response system are not necessarily maladaptive. The adaptive calibration model argues that humans take in environmental inputs and calibrate their stress response system to match their surroundings (Del Giudice et al., 2011; Shirtcliff et al., 2014). High or rising cortisol is expected in cases when individuals are engaged with their environment, are in situations that are personally relevant, or are facing a difficult (but manageable) challenge; low or diminishing cortisol is expected in cases when an individual is disengaged from the environment or a challenge is impossible or no longer novel (Del Giudice et al., 2011; Dickerson & Kemeny, 2004; Shirtcliff et al., 2012, 2014). The particular diurnal pattern likely depends on what is adaptive in the primary socialization environment. For instance, whereas physically abused children display symptoms of shutting out the world through hypocortisolism, children who experience sexual abuse tend to have a hypervigilant pattern that maintain high levels of cortisol throughout the day (Cicchetti & Rogosch, 2001). As we argue in more detail below, changes that are adaptive for the child’s primary context may not serve children well in school settings.

HPA Axis, Learning, and Cognition (Pathways E and F)

The level of cortisol that students bring into the classroom is a combination of their diurnal pattern and their acute stress responses, which may have implications for learning new material and mentally retrieving material during tests.

Stress, Sleep, and Academic Performance (Pathways C, G, and H)

Sleep is an important influence on cognitive functioning. Sleep can be measured in everyday settings both for quantity (e.g., asking participants, “How many hours did you sleep last night?”) and quality (e.g., “How rested do you feel?). It can also be objectively measured using actigraphy/accelerometry tools like sleep watches for quantity (e.g., how many hours are spent sleeping) and quality (e.g., sleep efficiency, a measure of the percentage of time from the first start of sleep at night to the final end of sleep in the morning that is spent in waking bouts). Children and teenagers in the United States sleep less than recommended (Adam, Snell, & Pendry, 2007; National Sleep Foundation, 2004, 2006).

HPA Axis Activity and Sleep (Pathway D)

The effects of perceived stressors on the HPA axis and on sleep are not independent but, rather, interact with one another in ways that may accentuate effects on academic outcomes. The HPA axis helps regulate the sleep-wake cycle, and HPA axis alterations associated with acute or chronic stress exposure can change sleep timing and architecture (Clow et al., 2010; Van Reeth et al., 2000). Higher afternoon cortisol levels in children have been associated with worse subjectively and objectively measured sleep quality as well as shorter objectively measured sleep duration (El-Sheikh, Buckhalt, Keller, & Granger, 2008). Among adolescents, more hours of objectively measured sleep and a later waking time predicted a higher waking cortisol level and steeper cortisol slope over the day, and higher waking cortisol and steeper cortisol slope subsequently predicted more sleep the next evening (Zeiders et al., 2011). A 24-hour period of sleep deprivation increased stress hormones, including cortisol, in adult men (Joo et al., 2012). In children, shorter sleep duration was associated with a higher and longer-lasting CAR and elevated cortisol levels throughout the day (Räikkönen et al., 2010). Relative to those with average or high sleep efficiency, those with lower sleep efficiency demonstrated a larger cortisol increase after exposure to the TSST for Children (Räikkönen et al., 2010). In other words, those with poor sleep quality had a larger cortisol reaction to a stressful situation.

Cortisol also plays a role in the memory consolidation process that occurs during sleep. Artificially elevating cortisol during sleep impaired consolidation of newly learned words, and inactivation of the GRs may be an essential part of the memory consolidation process during sleep (Plihal & Born, 1999).

Overall, we argue that stress-related changes in the HPA axis and sleep can interact with one another to influence and exacerbate each other’s impacts on cognitive performance during learning and test taking.

Gender Differences and Developmental Changes in Stress and Sleep (Pathway K)

Although we do not focus extensively on developmental changes in this paper, we note that the transition from childhood to adolescence is associated with changes in many of the variables in the model. As children move into adolescence, they are exposed to multiple novel stressors, including stress associated with puberty and additional social and academic demands (Arnett, 1999; Compas, Orosan, & Grant, 1993; Romeo, 2010). Adolescents have higher cortisol levels and flatter slopes relative to younger children, and HPA axis reactivity also increases with the pubertal transition (Gunnar et al., 2010; Romeo, 2010; Shirtcliff et al., 2012; Stroud et al., 2009). At the same time, adolescents experience delays in their circadian rhythm that mean they are biologically inclined to stay up later than younger children and adults (Carskadon, Acebo, & Jenni, 2004; Crowley, Acebo, & Carskadon, 2007; Jenni & Carskadon, 2012; Laberge et al., 2001; Sadeh, Dahl, Shahar, & Rosenblat-Stein, 2009). Changes in social and activity demands and in how strictly parents enforce bedtimes may also be a factor (Adam et al., 2007). Academic schedules are rarely synchronized with adolescents’ naturally “owl-like” sleep patterns (American Academy of Pediatrics, 2014; Kirby, Maggi, & D’Angiulli, 2011), leading to decreases in sleep quantity (Crowley et al., 2007; Jenni & Carskadon, 2012). Increases in stress exposure, increases in biological stress reactivity, and changes in sleep resulting in increased sleep deprivation imply that the impact of stress exposure on academic performance may be particularly important during adolescence.

There are also differences in stress responses by gender that we do not thoroughly explore in this paper (Del Giudice et al., 2011; Sauro et al., 2003; Stroud, Salovey, & Epel, 2002; Weekes et al., 2006). In particular, males may be more responsive to achievement-related stressors, whereas females may be more responsive to social rejection (Stroud et al., 2002). This may mean that our model is particularly salient for adolescent males.

Stress Disparity Outcomes (Pathways I and J)

Throughout this paper, we have argued that differences in life stress affect HPA axis functioning and sleep patterns, which in turn lead to changes to learning and test-taking performance. This section introduces the outcomes component of our model, which explains how differences in knowledge and testing performance can create achievement gaps. The primary contribution of the outcomes component of the overall model is to make clear that the observed achievement gap reflects the cumulative effects of stress exposure on both learning and cognitive readiness for the test-taking environment as well as the differences in input and ability that tests are meant to measure.

We call differences in what groups of students know in the days leading up to the test a knowledge gap. The knowledge gap accumulates across days and years of schooling and is broken into two parts: (a) input and effort gains attributed to the work of students, families, and schools and (b) stress-related learning inhibitors attributed to attention and memory problems induced by stress-related HPA axis and sleep dysregulation. The combination of input/ability gains and stress-related learning inhibitors can lead to a gap in the knowledge base that groups of students bring to the testing situation, depicted as different curves in Figure 2.

We argue that in addition to differences in knowledge, student performance on a standardized test is also affected by differences in stress/arousal during testing, or what we call a test-day stress deficit. This response, depicted as specific points on the curve in Figure 2, represents how a student performs on the test day, although the availability of an appropriate acute biological stress response may be influenced by longer-term stress exposure. The deficit is the difference between a student’s peak potential performance that day (the top of the inverse-U curve) and where they actually perform. Moderate intrinsic stress, to the extent that it leads to students being alert and focused on the test, improves performance in the model, as it results in performing at the peak of the curve. We argue that it is the students with either no reaction or a very large reaction to the test who experience this deficit. The test-day stress deficit can create a divergence between what students know and how much they are judged to know by a given standardized test. The stress gap is the combination of the test-day stress deficit and stress-related learning inhibitors.

Researchers currently do not have data that can distinguish between input and effort gains, stress-related learning inhibitors, and test-day stress deficits, but it seems unlikely that stress gaps and demonstrated ability are independently distributed. Low-achieving students may be more likely to be burdened both by true input/effort gaps and stress-induced achievement gaps, for an overall higher observed achievement gap. For instance, the gap between Point 2 and Point 5 in Model 2 includes differences in input/effort gains, stress-related learning inhibitors, and test-day stress deficits.

The model presented in this section is a simplification of the complicated, simultaneous processes that occur in humans. Individuals’ stress response to a given event (such as a test) depends on their interpretation of the situation, their past experiences, their biological systems, their mood states, and their trait personalities (Adam et al., 2006; Doane & Adam, 2010). Some students are more susceptible to perceiving events as stressful, relative to others, and some students may have greater biological responses to the same perceived stressor (Belsky et al., 2007; Del Giudice et al., 2011; Malarkey et al., 1995; Shirtcliff et al., 2014). A more nuanced model could show some students with flatter stress-cognition curves or shift curves laterally to have peak performance at different points of stress. The key takeaway, however, is that in standardized testing, what appears as a difference in achievement may actually be a combination of a true knowledge gap and a stress gap.

Suggested Future Research

Although evidence supports components of the proposed pathways, the overall model remains theoretical. No research has comprehensively examined all of these processes together. The magnitudes of stress-induced achievement gaps remain unclear, and future research should attempt to quantify the existence and degree of stress-related impairments to learning and test-taking as well as racial-ethnic and SES differences in these processes. Any research on this model will be complicated by the fact that input/effort gains, stress-related learning inhibitors, and test-day stress deficits may be correlated with each other, so any component of the model will need to carefully consider other potential factors.

We suggest that researchers begin by addressing small components of the model. For instance, Sharkey (2010) demonstrates that homicides affect test-day performance in a way that cannot be associated with learning, but it is unclear how this stressor “gets under the skin.” Recent data from our research team shows that local violent crime increased bedtime the following night and increased the CAR the following morning (Heissel et al., in press). Future research could combine these analyses, examining an acute stressful event, potential mechanisms (i.e., cortisol and sleep), and cognitive outcomes in the same study. Given the higher prevalence of violent crime in low-income and high-minority areas, this is a pathway by which demographic factors can lead to the perceived achievement gap (that is, Pathway A→Pathway C→Pathway H→Pathway J).

We note that much of the present research is difficult to fully compare and integrate, given the many different measures in sleep (subjective vs. objective, quantity vs. quality) and cortisol (waking levels, CAR, slope, AUC, or specific time points throughout the day). Future research should utilize multiple methods of measurement for both cortisol and sleep over a multiday period; such a study could identify when and why different measures of sleep and cortisol are associated with acute stressors, chronic stressors, and performance on academic tests.

Further complicating such studies, different measures of the diurnal pattern may change on different time scales (Adam, 2012; Doane, Chen, Sladek, Van Lenten, & Granger, 2015; Ross, Murphy, Adam, Chen, & Miller, 2014; Wang et al., 2014). These differences by measure are important for researchers considering using cortisol as a measure of acute or chronic stress to test components of our proposed model. For instance, the CAR may be a better measure of acute versus chronic stress. ¹ Researchers should carefully consider the timing relative to the stressor and the time of day as they design studies.

Future research should also explore ways to reduce student stress and modify how students respond to stress. Improvements in neighborhood and family climates, such as reduced exposure to violence and financial stress, could improve student performance. Preliminary research indicates “mindfulness” approaches in K–12 education may provide psychological, social, and cognitive benefits (Meiklejohn et al., 2012). Programs could try to promote positive coping resources, such as positive parenting, high family or community support, or strong racial-ethnic identities (Ai et al., 2014; Taylor & Stanton, 2007; Thoits, 2011; Umaña-Taylor, Wong, Gonzales, & Dumka, 2012). Such research should examine whether perceived stress is reduced, how biological stress responses change, and whether and how intervention effects cascade through the pathways of the model.

Success in sleep intervention studies indicates that parents and school-based programs can change children’s sleep behavior, at least in the short term (Cassoff, Knäuper, Michaelsen, & Gruber, 2013; Hiscock et al., 2015; Sadeh et al., 2003). Morning exercise regimens can also improve sleep, mood, and concentration in healthy adolescents (Kalak et al., 2012). Changes to school start times could also lead to widespread increases in sleep quantity and improved academic outcomes (Carrell, Maghakian, & West, 2011; Edwards, 2012; Heissel & Norris, 2017).

One important note is that the expansion of research into the importance of stress exposure and stress responses on student performance could exacerbate the achievement gap if already-advantaged families are more likely to act on such research. To reduce the achievement gap, information and policies must be particularly targeted to those from disadvantaged backgrounds.

Finally, we reiterate that not every low-SES and racial-/ethnic-minority individual will face the types of stressors that affect their HPA axis and sleep, and given the same event, some may perceive a situation as more stressful than others. Policymakers and practitioners should consider within-group variability as they attempt to address disparities in stress exposure and stress responses, in terms of both the policies they pursue and how they interact with and interpret the test scores of individual students. Between-group variability may matter as well. Future studies should examine outcomes separately by sex, age, and pubertal status; for instance, given differences in stress responsivity and sleep by pubertal status, the processes may work differently in different stages of the pubertal transition.

Conclusion

Differences in stress exposure are not the only causes of the academic achievement gap, but they are a potentially important and understudied part of the story. Human biological systems, such as the HPA axis and sleep, both respond to external stressors and affect one another—and in turn affect outcomes like test performance. Stress exposure can change how students gain knowledge during the learning process and how students respond to the acute stress of high-stakes testing. Differences in stress exposure and stress response may explain why two students with the same level of knowledge may have different results on the same standardized tests—or why one student does not accumulate the same level of knowledge in the first place. Developmental increases in HPA axis reactivity and changes to the circadian rhythm, combined with increased stress exposure, may make the stress gap proposed in the present paper even more relevant for adolescents than for younger children.

High-stakes tests, such as end-of-course tests, course placement exams, high school competency tests, and college entrance exams, are used to make important decisions. The current focus on test-based accountability means that some students, teachers, and schools are penalized and may receive sanctions for standardized test scores that are influenced by stress-related factors outside of their control. The passage of the Every Student Succeeds Act (ESSA) means that states have more authority over their accountability systems and how testing results are used. As states design their new policies under ESSA or its eventual replacement, they should pay particular attention to ways that stress exposure might affect student learning and test-taking performance. More holistic measures of student achievement, such as projects or interim unit tests, may reduce acute stress, although this requires additional study. Still, students’ learning may be affected by stress-related learning inhibitors, and using alternative measures would not solve this problem. Thus, the overall goal of preventing excess stress exposure for children and adolescents remains important.

Given that stress exposure is not randomly distributed across the population, the effects of stress have important implications for interpreting racial-ethnic and socioeconomic achievement gaps measured by standardized tests. The current paper is as much a call for additional research as it is a call to action. Nonetheless, we believe that an accumulating body of research supports the claims that factors outside the classroom—including students’ stress exposure and their biological responses to stress—may play an important and understudied role in America’s achievement gaps.