Mindfulness, anxiety, and high-stakes mathematics performance in the laboratory and classroom

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Running head: MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 1
Published in Consciousness and Cognition. This paper is not the copy of record and may not exactly replicate
the final, authoritative version of the article. The final article is available via its DOI at
https://doi.org/10.1016/j.concog.2015.09.001
© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0
license http://creativecommons.org/licenses/by-nc-nd/4.0/
Mindfulness, Anxiety, and High-Stakes Mathematics Performance in the Laboratory and
Classroom
David B. Bellinger, Marci S. DeCaro, and Patricia A. S. Ralston
University of Louisville
Author Note
David B. Bellinger and Marci S. DeCaro, Department of Psychological and Brain
Sciences; Patricia A. S. Ralston, Department of Engineering Fundamentals.
Address correspondence to Marci S. DeCaro, Department of Psychological and Brain
Sciences, 2301 S. Third Street, Louisville KY 40292. Email: marci.decaro@louisville.edu

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 2
Abstract
Mindfulness enhances emotion regulation and cognitive performance. A mindful approach may
be especially beneficial in high-stakes academic testing environments, in which anxious thoughts
disrupt cognitive control. The current studies examined whether mindfulness improves the
emotional response to anxiety-producing testing situations, freeing working memory resources,
and improving performance. In Study 1, we examined performance in a high-pressure laboratory
setting. Mindfulness indirectly benefited math performance by reducing the experience of state
anxiety. This benefit occurred selectively for problems that required greater working memory
resources. Study 2 extended these findings to a calculus course taken by undergraduate
engineering majors. Mindfulness indirectly benefited students’ performance on high-stakes
quizzes and exams by reducing their cognitive test anxiety. Mindfulness did not impact
performance on lower-stakes homework assignments. These findings reveal an important
mechanism by which mindfulness benefits academic performance, and suggest that mindfulness
may help attenuate the negative effects of test anxiety.
Keywords: mindfulness; test anxiety; pressure; working memory; mathematics; engineering;
education

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 3
Mindfulness, Anxiety, and High-Stakes Mathematics Performance in the Laboratory and
Classroom
Mindfulness, a mode of attending to present moment experiences without judgment or
elaboration (Bishop et al., 2004; Brown & Ryan, 2003, 2004), has been widely shown to benefit
psychological well-being and cognitive functioning. For example, mindfulness is associated with
reduced stress, stress reactivity, and chronic pain (Creswell & Lindsay, 2014; see Baer, 2003, for
a review) and decreased anxiety and depression (Brown & Ryan, 2003; Chen et al., 2012;
Kumar, Feldman, & Hayes, 2008). Mindfulness is also associated with improved cognitive
control abilities, as evidenced by measures of self-regulation (Bowlin & Baer, 2012; Chambers,
Gullone, & Allen, 2009; Stanley, Schaldach, Kiyonaga, & Jha, 2011; Tang et al., 2007; Teper,
Segal, & Inzlicht, 2013), attention (Chan & Woollacott, 2007; Chiesa, Calati, & Serreti, 2011;
Jha, Krompinger, & Baine, 2007; Napoli, Krech, & Holley, 2005; Tang et al., 2007; van
Leeuwen, Müller, & Melloni, 2009), and working memory (Chambers, Lo, & Allen, 2008; Jha,
Stanley, Kiyonaga, Wong, & Gelfand, 2010; Mrazek, Franklin, Phillips, Baird, & Schooler,
2013). Although mindfulness can be characterized as a trained skill, it is thought to be a capacity
that varies between individuals due to a propensity or willingness to devote attention to the
present moment (Brown & Ryan, 2003).
Due to its positive impact on emotion regulation and cognitive control, mindfulness has
been regarded as foundational to educational practice and a readiness to learn (Bakosh, Snow,
Tobias, Houlihan, & Barbosa-Leiker, 2015; Zenner, Hermleben, & Walach, 2014). Therefore,
researchers and educators have become increasingly interested in examining the impact of
mindfulness in educational settings (e.g., Davidson et al., 2012; Shapiro, Brown, & Astin, 2011).
A few studies have demonstrated a positive relationship between mindfulness and educationally-

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 4
relevant outcome measures such as grades (e.g., Bakosh et al., 2015; Ramsburg & Youmans,
2014). More commonly, school-based mindfulness interventions lead to improvements on a
range of attention, creativity, and social-emotional learning measures (see Zenner et al., 2014). It
is theorized that such improvements translate into greater educational outcomes, although this
mechanism remains largely untested (Shapiro et al., 2011; but see Mrazek et al., 2013).
In addition to improving cognitive control, mindfulness may improve academic
performance by supporting students’ ability to cope with anxiety in high-stakes testing situations
(Napoli et al., 2005; Shapiro et al., 2011). In both controlled laboratory and educational contexts,
we examine the idea that mindfulness improves the emotional response to anxiety-producing
testing situations, freeing working memory resources, and leading individuals to perform at a
higher level on academic exams. High-stakes tests are prevalent, and the corresponding anxiety
is often detrimental to students’ academic performance, despite sufficient preparation and
learning prior to the test (Naveh-Benjamin, McKeachie, & Lin, 1987). By examining how
mindfulness impacts students’ response to high-stakes testing conditions, we may better
understand the mechanisms by which both mindfulness and anxiety impact cognitive control in
academic settings—specifically during test taking. We may also better understand how students
can maximally demonstrate their learning in situations in which they often falter.
Mindfulness, Attention Control, and Emotion Regulation
Mindfulness is associated with decreased negatively-biased cognition and rumination
(Frewen et al., 2008; Kiken & Shook, 2012). One proposed explanation for this relationship is
that mindful individuals are better at decentering—letting anxious and negative thoughts pass
without further elaboration or rumination (Bishop et al., 2004; Ciesla et al., 2012; Evans &
Segerstrom, 2011; Kang et al., 2013). If mindfulness enables individuals to regulate anxious

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 5
thoughts, then their working memory may be less likely to be co-opted from the task at hand.
Working memory is the mental workspace used to attend to thoughts relevant to the task at hand,
while inhibiting irrelevant or intrusive thoughts (Miyake & Shah, 1999). Jha et al. (2010)
demonstrated that working memory decreased during a high-stress situation (i.e., military pre-
deployment). However, individuals who received mindfulness training, and extensively
practiced, did not show this drop in working memory. They also experienced less severe negative
affect.
Mindfulness and Test Anxiety
The benefits of mindfulness may also extend to stressful academic settings. High-stakes
testing situations are known to increase worries and negative ruminations (DeCaro, Rotar,
Kendra, & Beilock, 2010). Moreover, test anxiety is associated with lower academic
performance at every educational level (Chapell et al., 2005; Hembree, 1988; Seipp, 1991) and
can be viewed as a form of emotion dysregulation that contributes to increased worries and
negative self-criticism (Cassady & Johnson, 2002; Cunha & Paiva, 2012). Negative cognitions,
like worries and self-doubt, are thought to consume working memory resources needed for
optimal test performance (Ashcraft, 2002; Ashcraft & Kirk, 2001; Wine, 1971). We propose that
mindfulness may benefit performance in high-stakes academic situations by allowing students to
devote greater attention to the test, rather than to negative anxieties that consume valuable
working memory resources.
Initial support for this idea comes from two sources. First, a few studies have
demonstrated that greater mindfulness is associated with less test anxiety (Cunha & Paiva, 2012;
Napoli et al., 2005). Second, mindfulness is associated with better performance in testing
situations where performance pressure stems from stereotype threat. For example, Weger,

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 6
Hooper, Meier, and Hopthrow (2012) found that a brief mindfulness intervention boosted
women’s math performance when a negative stereotype that “men are better at math” was
activated. There was no relationship between mindfulness and math performance when the
stereotype was not activated. Thus, mindfulness was most helpful in the threatening situation.
Other authors have also separately suggested that the benefits of mindfulness are most visible in
stressful situations, with individuals who are highly anxious, and on tests that require greater
working memory resources (Brunye et al., 2013; Creswell & Lindsay, 2014). However, the
overall relationship linking mindfulness and anxiety to academic performance has not been
tested.
Current Studies
We tested these associations in the current study, examining whether the impact of
dispositional mindfulness on high-stakes math performance is mediated by reduced anxiety. We
predicted that greater dispositional mindfulness would be associated with less anxiety under
pressure and better math performance. The effects of individual differences in mindfulness
should be less evident in situations that present low demands on participants’ working memory
(Study 1) or in low-stakes performance situations (Study 2).
In Study 1, we examined these ideas in a high-stakes testing situation simulated in the
laboratory. In Study 2, we extended our investigation to the classroom, examining performance
in an undergraduate engineering calculus course. Mindfulness may be especially important in the
engineering context, because engineering students frequently experience anxiety related to their
coursework that can impair their attention and memory (Vitasari, Wahab, Herawan, Othman, &
Sinnadurai, 2011). We examined their performance on exams, quizzes, and homework
assignments completed as part of the course. We considered all of these assignments to be

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 7
working memory-demanding. However, exams accounted for 75% of students’ final grade in the
course, and had to be completed in a testing room without notes or books, so these should be
especially stressful (i.e, high stakes) and working memory demanding. Thus, we expected to see
the greatest impact of mindfulness emerge during exams.
Performance on academic exams and standardized tests (e.g., Scholastic Aptitude Test
[SAT], Graduate Record Exam [GRE]) impacts educational retention and determines entry into
degree-granting institutions, greatly affecting students’ lives. Indeed, poor performance in STEM
(Science, Technology, Engineering, and Mathematics) courses, which characteristically use
high-stakes testing situations to assess student learning, is a primary reason for student attrition
in STEM disciplines such as engineering and math (Geisinger & Raman, 2013). Thus,
identifying factors that could facilitate emotion regulation and cognitive control during stressful
academic performance situations is of great practical and theoretical importance. The current
studies contribute to our knowledge of performance in high-stakes situations by helping to
uncover how mindfulness benefits academic performance in anxiety-prone testing situations.
Study 1
Study 1 tested the relationship between dispositional mindfulness and state anxiety, and
subsequent math performance, in a high-pressure testing situation in a controlled laboratory
environment.
Method
Participants
Participants were undergraduate students (N = 112) from the psychology participant pool
who were unfamiliar with modular arithmetic (M age = 20.05 years, SD = 3.97; 69.6% female).
Additional participants were tested, but excluded from the study, for two reasons: (a) reporting

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 8
that they did not believe the cover story designed to create a high-pressure situation (n = 11), or
(b) scoring at or below 50% on either the low-demand or high-demand modular arithmetic
problems in the practice block (n = 6). This minimum accuracy criterion was included to ensure
that participants sufficiently understood the modular arithmetic task prior to the high-pressure
manipulation. One additional participant’s data was identified and removed as a univariate
outlier in the regression analyses. The majority of participants identified themselves as white
(71.4%), with the remaining individuals identifying themselves as black (9.9%), Asian (5.5%),
Hispanic or Latino (3.3%), other (2.2%), or not reported (7.7%).
Modular Arithmetic
We assessed math performance with a task widely-used in previous research examining
performance under pressure: modular arithmetic (e.g., Beilock, Kulp, Holt, & Carr, 2004;
Beilock & Carr, 2005; DeCaro et al., 2010). The modular arithmetic task uses standard
operations (i.e., subtraction and division), but presents them in a novel way. Participants were
asked to judge the truth value of equations such as “19 = 4 (mod 5).” To solve this equation,
participants subtract the second number from the first number (i.e., 19 – 4), which yields “15.”
Next, they must divide this difference by the “mod” number (i.e., 15 ÷ 5 = 3). Finally, they
determine whether the answer is evenly divisible by the “mod” number. In this example, the
equation “19 = 4 (mod 5)” is “true,” because 15 is evenly divisible by the mod “5.” The
statement is “false” if there is a remainder. Participants solved each of these problems without
using a calculator or paper.
To ensure that participants understood the modular arithmetic task, we first instructed
them on the rules and then asked them to solve 12 problems in random order with corrective
feedback. Then participants completed 24 practice problems in random order without feedback.

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 9
Finally, participants completed 24 randomized problems under high pressure (as described
below). Half of the problems required a single-digit subtraction operation (e.g., 7 = 2 (mod 5)),
and thus did not require a borrow operation in the subtraction step. Thus, these items demanded
less working memory resources to complete (low-demand problems). The other half were high-
demand problems that required subtracting double-digit numbers with a borrow operation (e.g.,
63 = 27 (mod 9)), requiring greater working memory resources (Beilock et al., 2004).
Pressure Manipulation
To create a high-stakes testing situation in the laboratory, participants were given two
pressure-inducing incentives used in previous research, performance-based payment and peer
pressure (e.g., Beilock et al., 2004; Beilock & Carr, 2005; DeCaro, Thomas, Albert, & Beilock,
2011). The experimenter explained that the computer had been tracking the participant’s
performance as he or she solved the initial block of modular arithmetic problems and that the
participant needed to improve both problem-solving speed and accuracy by 20% in order to earn
$10. Participants were also told that the study also included an element of teamwork, which
required both the participant and a “partner” to improve their scores to earn the money. The
experimenter noted that the partner had already completed the experiment and did improve by
20%, meaning that earning the reward was entirely contingent upon the present participant
meeting the standard. Participants were told that, if they failed to improve by 20%, neither they
nor their partner would receive the money. At the end of the study, participants were debriefed
and told that the partner was actually fictitious, and were given the $10 regardless of
performance.
Audio Recordings

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 10
In two conditions, participants listened to one of two 15-minute audio recordings
(mindful breathing [n = 41] or progressive muscle relaxation [n = 38]; Feldman, Greeson, &
Senville, 2010). However, these manipulations did not impact any dependent measures,
including ratings of pressure, state mindfulness, state anxiety, or modular arithmetic
performance, compared to a control condition (n = 33) in which no audio recording was used, Fs
< 1. Thus, the audio recording variable was not included in final analyses and will not be
described further.
Self-Report Measures
Mindfulness. The Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003)
was used to assess dispositional mindfulness. The MAAS is a widely-used measure, and includes
15 items (e.g., “I find it difficult to stay focused on what’s happening in the present”; “I find
myself preoccupied with the future or the past”) that are rated on a Likert-type scale of one
(“almost always”) to six (“almost never”) (Cronbach’s  = .84). Scores are computed by
averaging the 15 items. Higher scores indicate higher levels of dispositional mindfulness.
The Toronto Mindfulness Scale – Trait (TMS-T; Davis, Lau, & Cairns, 2009) was also
administered to assess two specific characteristics of dispositional mindfulness: curiosity (6
items;  = .81) and decentering (7 items;  = .63). However, no significant relationship between
these factors and the primary measures of interest were found (i.e., STAI and math accuracy,
controlling for gender; curiosity: STAI r = -.026, p = .788, low-demand math accuracy r = -.061,
p = .522, high-demand math accuracy r = -.062, p = .521; decentering: STAI r = -.150, p = .116,
low-demand math accuracy r = .003, p = .978, high-demand math accuracy r = -.116, p = .225).
Curiosity items were also not significantly correlated with MAAS scores, r = -.112, p = -.240.
However, decentering items were significantly negatively correlated with the MAAS, r = -.255,

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 11
p = .007. This finding is opposite that of previous research, which shows a positive correlation
between decentering scores and the MAAS (Davis, Lau, & Cairns, 2009). For these reasons, and
because the MAAS is more general and standardly used (Bergomi, Tschacher, & Kupper, 2007;
Quaglia et al., 2015), we selected the MAAS as our measure of dispositional mindfulness.
The Toronto Mindfulness Scale – State (TMS-S; Lau et al., 2006) was used to examine
differences in state mindfulness as a result of the audio recordings. This scale also includes
curiosity (6 items;  = .85) and decentering (7 items;  = .66) factors.
State Anxiety. Participants completed the state version of the State-Trait Anxiety
Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1970) to assess state anxiety as a result of
the pressure manipulation. The STAI includes 20 items (e.g., “I feel nervous”; “I am presently
worrying over possible misfortunes”) that are rated on a Likert-type scale of one (“not at all”) to
four (“very much so”) ( = .91). After reverse-coding some items, items are summed to create a
total score. Higher scores indicate higher levels of anxiety.
Post-Experiment Questionnaires. Individuals rated how important it was to them to
perform at a high level on the last set of math problems on a Likert-type scale of one (“not at all
important”) to seven (“extremely important”). Also, individuals rated how much pressure they
felt to perform at a high level on a Likert-type scale of one (“very little performance pressure”)
to seven (“extreme performance pressure”). They were asked to briefly explain their responses to
each of these items in an open-response format. Finally, participants completed a questionnaire
to report demographic information.
Procedure
All participants completed the dispositional mindfulness questionnaires (i.e., MAAS,
TMS-T) and then received instructions on how to solve modular arithmetic problems followed

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 12
by the practice problems. Then, either immediately or following the audio recordings (see
above), participants were told the cover story to induce a high-pressure situation and asked to
complete an additional set of modular arithmetic problems. Finally, all participants completed an
unrelated task
1
for approximately 5 minutes, followed by the TMS-State, STAI, and post-
experiment questionnaires. Finally, participants were debriefed.
Results and Discussion
We hypothesized that, in a high-stakes situation, trait mindfulness would indirectly
benefit math accuracy by reducing state anxiety. We specifically expected this pattern of findings
for high-demand problems, which most depend on working memory (Beilock et al., 2004).
To control for possible speed-accuracy trade-offs in participants’ problem-solving
performance, we included low-demand or high-demand problem reaction times as covariates in
all analyses for low-demand or high-demand accuracy, respectively. These trade-offs were
unlikely, however, as reaction time did not predict either low-demand ( = -.04, t(110) = -2.20, p
= .703) or high-demand ( = .10, t(110) = 1.04, p = .303) problem accuracy.
Preliminary analyses indicated that gender significantly predicted high-demand problem
accuracy ( = -.22, t(109) = -2.20, p = .024, R2 = .055; effects coded: male = -1, female = 1),
with women performing at a lower level than men. Gender did not predict low-demand problem
accuracy ( = -.03, t(109) = -0.28, p = .778). This finding is consistent with extensive research
demonstrating the impact of gender-related stereotypes about math on working memory-
demanding problems (Beilock, Rydell, & McConnell, 2007; Schmader & Johns, 2003) and in
high-stakes testing situations (see Schmader, 2010). Gender did not interact with mindfulness (
1
Following the math task, participants completed an Emotional Stroop task as an exploratory
measure. Because this task was not directly related to the current research question, and no
significant findings were revealed, we do not discuss this task further.

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 13
= -.09, t(107) = -0.86, p = .391). Thus, gender was also included as a covariate in all analyses, to
examine the relationships between the predicted factors beyond any impact of gender.
We began by conducting separate regression analyses to examine the relationships
between each of the variables in the model. Covariates were entered as Step 1, followed by the
predictor variable of interest in Step 2 (ΔR2). Table 1 presents descriptive statistics for these
variables. As shown in Figure 1a, for high-demand problems, greater mindfulness was associated
with significantly better math accuracy ( = .20, t(108) = 2.19, p = .031, ΔR2 = .040, Total R2 =
.095) and significantly lower state anxiety scores ( = -.29, t(108) = -3.25, p = .002, ΔR2 = .083,
Total R2 = .155). In addition, lower state anxiety scores were associated with higher math
accuracy ( = -.25, t(108) = -2.62, p = .010, ΔR2 = .057, Total R2 = .112). In contrast, for low-
demand problems (Figure 1b), mindfulness ( = .05, t(108) = 0.54, p = .590, ΔR2 = .003, Total
R2 = .005) and state anxiety scores ( = -.19, t(108) = -1.94, p = .055, ΔR2 = .034, Total R2 =
.036) did not predict math accuracy.
We used the method described by Hayes (2009, 2013) to test our predicted mediation
model, in which the effect of mindfulness on math accuracy is mediated by state anxiety. A bias-
corrected 95% confidence interval (CI) for this indirect effect was calculated using 10,000
bootstrap samples. Mediation is said to be present if the CI for the indirect effect does not
include zero (Shrout & Bolger, 2002). Scores were standardized before inclusion in the model.
As expected, there was a significant indirect effect of mindfulness on high-demand math
accuracy through the mediator, state anxiety ( = .06, CI [.010, .140]; Figure 1a). The direct
effect was not significant, indicating that trait mindfulness did not influence high-demand
problem accuracy independent of its effect on state anxiety ( = .14, t(107) = 1.51, p = .135).
The full model accounted for 13.0% of the variability in high-demand problem accuracy. There

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 14
were no significant indirect ( = .06, CI [-.005, .173]) or direct ( = -.004, t(107) = -.04, p =
.965) effects of mindfulness on low-demand problem accuracy (Figure 1b).
Thus, Study 1 provides support for a mediation model whereby dispositional mindfulness
reduces state anxiety in a high-pressure testing situation, indirectly improving math accuracy.
The effect of mindfulness was selective to problems that place a high demand on working
memory. These findings are consistent with others demonstrating that high-pressure situations
disrupt the working memory needed to solve high-demand problems (e.g., Beilock et al., 2004;
DeCaro et al., 2010). Moreover, these findings suggest that dispositional mindfulness can reduce
the anxiety experienced in a high-stakes testing situation, freeing the working memory resources
needed for optimal performance.
Study 2
Study 1 supported the proposed mediation model in a controlled laboratory setting. Study
2 was designed to test this model in a more ecologically-valid context, an undergraduate
engineering mathematics course. Study 2 also extended the model to general perceptions of
anxiety toward course test-taking (i.e., test anxiety). In addition, because the course included
both low-stakes (i.e., homework assignments) and high-stakes (i.e., quizzes and exams)
performance measures, we were able to examine the selective impact of mindfulness on these
types of assignments. We predicted that dispositional mindfulness would indirectly benefit
performance by reducing test anxiety, specifically for assignments with higher stakes (quizzes
and exams), but not for assignments with lower stakes (homework).
Method
Participants

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 15
Participants (N = 248; 24% female) were first-time, full-time freshman undergraduate
engineering students enrolled in a calculus course who gave consent to be in the study. Data
from two participants were found to be univariate outliers and removed from the dataset. The
majority of participants identified themselves as white (90%), with the remaining individuals
identifying themselves as Asian (2.8%), black (1.6%), Hispanic or Latino (1.6%), or two or more
races or unreported (4%). Consistent with prior research, three measures of prior mathematics
and science ability were collected as indicators of prior knowledge: standardized college
entrance examination scores (ACT-Math [American College Test]; M = 30.62, SD = 2.52; ACT-
Science; M = 29.90, SD = 3.33), and a department-validated Algebra Readiness Examination
score (M = 75.2%, SD = 16.6%; Hieb, Lyle, Ralston, & Chariker, 2015).
Self-Report Measures
The current research questions were examined as part of a larger, ongoing study
examining the performance and retention of first-year engineering students. Two questionnaires
were relevant to the current study: the Mindfulness Attention Awareness Scale (MAAS; Brown
& Ryan, 2003;  = .87; see Study 1 for details) and the Cognitive Test Anxiety Scale (CTAS;
Cassady, 2004; Cassady & Johnson, 2002). The CTAS measures the cognitive dimension of test
anxiety, including the presence of task-irrelevant and intruding thoughts, inattention to relevant
information, and comparing one’s self to others. The CTAS ( = .93) includes 27 items (e.g., “I
lose sleep over worrying about examinations”; “When I take a test, my nervousness causes me to
make careless errors”) that are rated on a Likert-type scale of one (“not at all typical of me”) to
four (“very typical of me”). Students were asked to respond to this scale with respect to their
math class. Scores are computed by summing responses to all items. Higher scores indicate
greater cognitive test anxiety.

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 16
Academic Performance
Students’ course grades were comprised of the algebra readiness exam (5%), 15
homework assignments (5%), 11 quizzes (15%), and 14 exams (75%). Importantly, all quizzes
and exams were completed without external aides, including books, notes, and calculators. As
specified in the course syllabus, each student was allowed to drop their lowest quiz and exam
scores (excluding the last three exams). After removing the lowest quiz and exam scores, average
homework, quiz, and exam scores were calculated for each student to create three course
outcome measures.
Procedure
The Algebra Readiness Exam was administered to all students during the first week of
the course as part of the course requirements. Students completed the self-report questionnaires
on computer during class, approximately mid-semester. After the semester, academic and
demographic information were connected to survey responses by a third party, and all identifying
information was removed from the data.
Results and Discussion
We hypothesized that trait mindfulness would benefit grades through an association with
reduced trait test anxiety, and this effect would emerge specifically for high-stakes assignments
(quizzes and exams).
ACT-Math, ACT-Science, and Algebra Readiness Exam scores were included as
covariates for all analyses, to control for prior ability. These scores were positive predictors of
course exam and quiz scores (s = .18 to .49, ps <.001 to .004, R2s = .033 to .239), but not
homework scores (s = -.07 to .03, ps = .259 to .797, R2s = .001 to .005).

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 17
Preliminary analyses revealed a significant relationship between gender and performance
on exams ( = .22, t(243) = 3.91, p < .001), quizzes ( = .14, t(243) = 2.65, p = .008), and
homework ( = .13, t(243) = 2.05, p = .041). In contrast to Study 1, females scored at a higher
level than males (effects coded: males = -1, females = 1). These findings are interesting in light
of research demonstrating the impact of negative stereotypes for women in STEM (Schmader &
Johns, 2003), who were a minority of students (24%) in this course. These findings are, however,
consistent with the idea that women who have successfully entered as minorities in engineering
may be less impacted by negative stereotypes, at least as measured by grades (e.g., Crisp, Bache,
& Maitner, 2009; Regner et al., 2010). Gender did not interact with mindfulness to predict any of
the dependent measures (s = -.004 to -.06, ps = .415 to .949). We included gender as a covariate
in all analyses to determine the impact of mindfulness and anxiety beyond any effect of gender.
We first examined the separate relationships between the variables in the model.
Descriptive statistics for these variables are reported in Table 2. The covariates were entered as
Step 1 in all analyses, followed by the predictor variable of interest in Step 2 (ΔR2). As shown in
Figure 2a, mindfulness positively predicted exam scores (β = .11, t(242) = 2.00, p = .047, ΔR2 =
.012, Total R2 = .287) and negatively predicted cognitive test anxiety (β = -.39, t(242) = -6.95, p
< .001, ΔR2 = .143, Total R2 = .285). Cognitive test anxiety also negatively predicted exam
scores (β = -.21, t(242) = -3.60, p < .001, ΔR2 = .037, Total R2 = .312). As shown in Figure 2b,
mindfulness did not significantly predict quiz scores (β = .05, t(242) = 1.01, p = .312, ΔR2 =
.003, Total R2 = .342), but greater cognitive test anxiety was associated with lower quiz scores (β
= -.23, t(242) = -4.14, p < .001, ΔR2 = .044, Total R2 = .383). Finally, as shown in Figure 2c,
neither mindfulness (β = .03, t(242) = 0.45, p = .650, ΔR2 = .001, Total R2 = .025) nor cognitive

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 18
test anxiety (β = .06, t(242) = 0.87, p = .387, ΔR2 = .003, Total R2 = .027) predicted homework
scores.
We tested our predicted mediation model for average exam, quiz, and homework scores
separately, using Hayes’s (2009, 2013) method as in Study 1. As shown in Figure 2a, there was a
significant indirect effect of mindfulness on exam scores through the mediator cognitive test
anxiety ( = .07, BC bootstrap 95% CI [.026, .133]). There was no significant direct effect of
mindfulness on exam performance (= .04, t(241) = .62, p = .537), indicating that mindfulness
did not influence exam scores independent of its effect on cognitive test anxiety. The full model
accounted for 31.4% of the variability in average exam scores.
As shown in Figure 2b, there was also a significant indirect effect of mindfulness on quiz
scores ( = .09, BC bootstrap 95% CI [.046, .155]), and no direct effect ( = -.04, t(241) = -.70, p
= .483). Similar to the exam results, this finding indicates that mindfulness influenced quiz
scores indirectly through an association with reduced cognitive test anxiety. The full model
accounted for 38.4% of the variability in average quiz scores.
For homework scores (Figure 2c), no indirect ( = -.03, BC bootstrap 95% CI [-.090,
.011]) or direct effects ( = .06, t(241) = .89, p = .377) of mindfulness were found.
These findings again support the predicted mediation model. In an undergraduate
engineering mathematics course, higher dispositional mindfulness was associated with better
exam grades. This effect was statistically mediated by cognitive test anxiety, measured as a
dispositional attitude about test-taking in the course. The indirect effect of mindfulness on grades
was present for both exams and quizzes, but not for homework assignments, indicating that
mindfulness is most likely associated with benefits in higher-stakes testing situations.
General Discussion

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 19
Across two studies, we found support for the hypothesis that mindfulness benefits math
performance by reducing anxiety associated with high-stakes testing conditions. In Study 1,
undergraduate students were given a high-pressure scenario in the laboratory prior to completing
novel math problems. Greater dispositional mindfulness was associated with lower levels of
reported anxiety after the math test. In turn, lower anxiety was associated with better scores on
high-demand problems that taxed working memory by requiring multiple mental calculations.
Neither state anxiety nor mindfulness predicted performance on less demanding problems.
Previous research has demonstrated that high-pressure testing situations increase worries
and negative thoughts, selectively impairing performance on working memory-demanding
problems (e.g., Beilock et al., 2004; DeCaro et al., 2010). In the current study, mindfulness was
associated with reduced anxiety and improved high-demand problem accuracy. These findings
are consistent with the idea that mindfulness buffers individuals against the negative impact of
worries and intrusive thoughts, protecting working memory resources (e.g., Jha et al., 2010) so
that individuals can devote these resources to solving more difficult math problems.
Study 2 replicated and extended these findings in a university classroom. Engineering
students enrolled in calculus, a gateway course for the major, reported both their dispositional
mindfulness and cognitive test anxiety, a dispositional measure that reflects cognitive reactions
(e.g., worries and intrusive thoughts) towards test-taking. These measures were examined in
relation to three course components that varied in the stakes associated with performance: exams,
quizzes, and homework. Both exams and quizzes were completed in a traditional testing context,
without the assistance of a calculator, books, or course notes. Exams and quizzes were
considered high-stakes, because they accounted for 75% and 15% of the students’ course grades
respectively. In contrast, homework assignments (5% of the course grade) were considered low-

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 20
stakes. All course material was likely working memory-demanding, given the topic (calculus),
although working memory demand was not explicitly investigated in this study. Higher
dispositional mindfulness was associated with lower dispositional cognitive test anxiety, which
in turn benefited students’ high-stakes exam and quiz performance. Mindfulness was not
associated with performance on lower-stakes homework performance.
The results of Studies 1 and 2 reveal an important mechanism by which mindfulness may
benefit learning and performance. Mindfulness appears to reduce anxious thoughts that otherwise
consume working memory resources. Thus, the immediate benefits of mindfulness for
performance may be most pronounced when the task requires significant working memory
resources to complete. These positive effects of mindfulness may be especially evident in
contexts in which anxious thoughts can impinge on working memory resources, as in high-stakes
testing situations (Beilock et al., 2004; Beilock & Carr, 2005; Beilock & DeCaro, 2007; see also
Cresswell & Lindsay, 2014).
More work is needed to further substantiate the link to working memory, however, as
working memory was manipulated only in Study 1. One way to do this may be to measure
individual differences in working memory capacity. Beilock and Carr (2005; see also Beilock &
DeCaro, 2007) demonstrated that the performance of individuals with higher working memory
capacity was most impacted by performance pressure. Pressure appears to co-opt the working
memory resources these individuals rely on for their typically superior performance. The link
between mindfulness, anxiety, and academic performance might therefore be strongest for
individuals with higher working memory capacity.
These findings contribute to the growing literature on mindfulness in academic settings.
Mindfulness may benefit cognitive or self-regulation abilities that support academic performance

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 21
more generally, although research on the connection of these improvements to educational
outcomes is limited (Shapiro et al., 2011). Research has demonstrated that mindfulness reduces
mind wandering, improving performance on reading comprehension tests such as those found on
the Graduate Record Exam (GRE; Mrazek et al., 2013). We demonstrate a different mechanism
by which mindfulness may benefit academic performance. Mindfulness may allow students to
remain focused on the task in anxiety-producing testing situations. Thus, students with greater
mindfulness may be better able to thrive in important testing situations that might otherwise lead
to underperformance.
This work provides promising initial support for the benefit of mindfulness in academic
testing situations. However, these findings are correlational, based on self-report measures of
mindfulness and anxiety. Future research is needed to test the causal link between mindfulness
and high-stakes test performance more directly by examining the effect of mindfulness training
interventions on anxiety reduction and test performance. Mindfulness interventions have been
successfully implemented in educational contexts, improving emotional functioning and
cognitive performance (Zenner et al., 2014). However, the brief mindfulness intervention used in
our Study 1 did not reduce anxiety or improve performance. This finding corresponds with other
work demonstrating that more extensive mindfulness practice is associated with greater
treatment effects (Zenner et al., 2014). By specifically targeting attention control and decentering
from anxious thoughts, mindfulness training may be an especially useful method for reducing the
impact of test anxiety and pressure felt in high-stakes testing situations.

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 22
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MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 31
Table 1
Descriptive statistics for Study 1
Mean
SD
Dispositional mindfulness (MAAS)
3.69
0.68
State anxiety (STAI)
38.78
10.29
High-demand problem accuracy (%)
78.72
14.71
Low-demand problem accuracy (%)
96.50
5.66
High-demand problem RT (msec)
9223.20
4113.13
Low-demand problem RT (msec)
2261.39
838.28

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 32
Table 2
Descriptive statistics for Study 2
Mean
SD
Dispositional mindfulness (MAAS)
3.85
0.78
Trait cognitive test anxiety (CTAS)
62.81
15.43
Average exam score (%)
73.87
12.44
Average quiz score (%)
74.07
12.09
Average homework score (%)
95.44
9.45

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 33
Figure 1. Relationships among factors in the mediation models predicting (a) high-demand
problem accuracy and (b) low-demand problem accuracy.
Note: Values in parentheses represent zero-order regression coefficients. Values outside
parentheses represent regression coefficients when all variables are included in the mediation
model. Gender and reaction time (not shown) are included as covariates in all analyses. *p < .05,
**p < .01
( = -.25*)
 = -.29**
High-Demand
Problem Accuracy
State Anxiety
Mindfulness
( = .20*)
(a)
(b)
( = -.19)
 = -.30**
Low-Demand
Problem Accuracy
State Anxiety
Mindfulness
( = .05)
 = -.19
 = -.20*
 = .14
 = -.004

MINDFULNESS, ANXIETY, AND MATH PERFORMANCE 34
Figure 2. Relationships among factors in the mediation models predicting average (a) exam
scores, (b) quiz scores, and (c) homework scores.
Note: Values in parentheses represent zero-order regression coefficients. Values outside
parentheses represent regression coefficients when all variables are included in the mediation
model. Gender, ACT-Math, ACT-Science, and Algebra Readiness Exam scores (not shown) are
included as covariates in all analyses. *p < .05, **p < .01, ***p < .001
( = -.21***)
 = -.39***
Exam Scores
Cognitive Test
Anxiety
Mindfulness
( = .11*)
(a)
(b)
( = -.23***)
 = -.39***
Quiz Scores
Cognitive Test
Anxiety
Mindfulness
( = .31)
(c)
( = .06)
 = -.39***
Homework Scores
Cognitive Test
Anxiety
Mindfulness
( = .03)
 = -.19**
 = .04
 = -.24***
 = -.04
 = .09
 = .06