942203
research-article2020
ISCXXX10.1177/1053451220942203Intervention in School and ClinicJohnson et al.
Feature
Intervention in School and Clinic 2021, Vol. 56(3) 163­–171
© Hammill Institute on Disabilities 2020
Article reuse guidelines: sagepub.com/journals-permissions
https://doi.org/10.1177/1053451220942203
DOI: 10.1177/1053451220942203
isc.sagepub.com
A Self-Regulated Learner Framework for Students With
Learning Disabilities and Math Anxiety
Evelyn S. Johnson, EdD1, Anne B. Clohessy, PhD2,
and Pragnyaa Chakravarthy, MEd2
Abstract
Students with math learning disabilities have been shown to experience math anxiety at rates nearly double those of their
typical peers. Anxiety about math is thought to disrupt learning by co-opting attentional resources that could be used in
problem-solving and may be caused by the way in which students interpret their math-related experiences. This article
describes a math intervention designed through a framework of self-regulated learning that defines self-regulated learners
as students who are connected, self-aware, self-determined, strategic, and resilient. Specifically described is an intervention
that helps students regulate anxiety, initiate a problem-solving strategy, and advocate as needed to use approaches they
find effective.
Keywords
math anxiety, math learning disability, self-regulation, emotion regulation, cognitive flexibility
Math anxiety is a state of discomfort caused by performing
math-related tasks (Ma & Xu, 2004) and has been associated with physiological outcomes similar to those when
experiencing pain (Lyons & Beilock, 2012), and high levels
of extreme stress (Pizzi & Kraemer, 2017). Students with
math-related learning disabilities (LDs) tend to have deficits in working memory (Johnson et al., 2010), which makes
math challenging. Students with LD in mathematics have
also been found to experience math anxiety at nearly twice
the rate as that of their typically developing peers (Devine
et al., 2018). Anxiety triggers negative thoughts and emotions that co-opt the working memory needed to solve math
problems (Ashcraft & Kirk, 2001). Put simply, math anxiety contributes to poor math performance.
Students with LD in mathematics tend to struggle with
self-regulated learning, or the ability to select and use
appropriate strategies to achieve learning goals, and this
leads to math anxiety (Duncan & McKeachie, 2005; Jain &
Dowson, 2009; Kramarski et al., 2010), and low math
achievement (Ramirez et al., 2018). When a student does
not have the strategies to approach a math task, their stress
levels can spike, and this further compromises their ability
to perform math tasks. Finally, math anxiety may also be
caused by a student’s negative appraisal of their math learning experiences (Ramirez et al., 2018). When students evaluate their math learning experiences as negative, they can
begin to adopt a “failure as debilitating” mindset (Ramirez
et al., 2018). This thought process creates a negative cycle
of failure–stress–failure that can cause a student to become
highly anxious about math, and significantly compromise a
student’s ability to self-regulate and compromise their math
performance.
Taken together, these findings suggest that intervention
approaches for students with LD in mathematics should
include techniques to manage anxiety, to positively appraise
their math learning experiences, and to develop their strategic thinking in math. This article describes the components
of a multidimensional math and self-regulated intervention
developed to address these issues. The intervention described
1
Boise State University, Boise, ID, USA
Lee Pesky Learning Center, Boise, ID, USA
2
Corresponding Author:
Evelyn S. Johnson, Boise State University, MS 1725, 1910 University Dr.,
Boise, ID 83725-1725, USA.
Email: evelynjohnson@boisestate.edu
164
is grounded in two frameworks, one focused on self-regulated learning and the other on math intervention, integrated
to support students with math anxiety and LD in mathematics (Johnson, Clohessy, & Chakravarthy, 2018; Johnson,
Clohessy, Meek, & Spears, 2018).
Overview of the Frameworks
Because students face different challenges, an understanding of these frameworks provides practitioners a way to
tailor specific components as needed to respond to the
unique needs of their learners. For example, the student
(i.e., D) highlighted in the vignette in this article was identified as having a specific LD that impacted her math performance and experienced high levels of math anxiety that
often prevented her from getting started with a math task.
She also had compromised working memory, low competence with self-regulated learning strategies, low confidence levels, and was struggling with long division in her
fifth-grade math class (see Note 1). Through the use of the
two frameworks to guide intervention design, the specific
strategies can be adapted to support each student’s unique
needs. For example, the math strategy will vary depending
on the math concept to be taught, or the technique to support math anxiety may differ depending on how anxiety
manifests for the student.
Self-Regulated Learner Framework
Self-regulated learning is the ability to regulate one’s thinking, behavior, and emotions in pursuit of a learning goal.
Self-regulation has been strongly linked to successful learning outcomes and it is critical for success in school (Durlak
et al., 2011; Zimmerman & Schunk, 2008). Self-regulation is
a multifaceted, dynamic construct. This means that students
may be able to demonstrate some aspects of self-regulation
but not others; it also means that students may be able to selfregulate in some contexts but not others. To reflect this complexity, self-regulated learners are defined as students who
are connected, self-aware, self-determined, strategic, and
resilient (Johnson, Clohessy, & Chakravarthy, 2018).
Connected learners feel safe, trust their teachers, and
tend to be more engaged during the learning process, which
can lead to higher academic achievement (Leighton &
Bustos Gómez, 2018). Self-aware students understand their
strengths and needs as learners, and they understand which
learning approaches are most effective for them. Selfdetermined learners are able to set goals, make plans, and
monitor their progress to reach their goals. Strategic learners are able to select and effectively use an appropriate strategy to reach their learning goal (Karlen, 2016). Resilient
learners are able to use strategies to recover from setbacks
and are able to adapt to stress or adversity. Cognitive flexibility, the ability to approach a problem in a different way,
Intervention in School and Clinic 56(3)
and emotion regulation are important in developing resilience (Zolkoski & Bullock, 2012).
A key principle of the self-regulated learner framework
is teachers’ flexibility for adapting strategies to meet the
specific needs of their individual students in the moment.
Table 1 includes a definition of each component and the
general approaches to develop students’ self-regulation
skills within the classroom.
Math Intervention Framework
The math intervention framework includes teaching for
conceptual and procedural understanding, developing students’ mathematical reasoning ability, and the ability to
make connections across concepts. The intersection of
these aspects of math is strategic competence with problem-solving, demonstrated when a student can independently identify and apply an effective and efficient approach
to solving a given mathematics problem. Conceptual
understanding is defined as the understanding of the principles that govern a math domain (Rittle-Johnson &
Schneider, 2015). Procedural understanding is the knowledge of action sequences for solving problems (RittleJohnson & Schneider, 2015). Mathematical reasoning is
the ability to develop and evaluate math arguments (Liu &
Xin, 2017). Mathematical connections are a cognitive process in which students relate ideas, concepts, procedures,
or representations (García-García & Dolores-Flores, 2018).
The math instructional framework is not a sequential
instructional preference (Rittle-Johnson & Schneider,
2015). Rather, the goal is to ensure that an intervention is
designed to support students’ math ability through these
four areas. An extensive research base identifies a number
of instructional practices aligned with elements of this
framework that are effective for students with LD to develop
strong competency in math, which include the following:
1.
2.
3.
4.
5.
Explicit instruction of math concepts and procedures (Doabler et al., 2015);
Visual representations to support conceptual understanding and the ability to connect the math concept
to the math procedures (Gersten et al., 2009);
Cognitive strategy instruction to learn to identify
and solve a variety of problem types (Griffin &
Jitendra, 2009);
Explicit inquiry routines to support students’ strategic competence (Impecoven-Lind & Foegen, 2010;
Scheuermann et al., 2009);
Teaching students to verbalize their math reasoning
and use math vocabulary (Gersten et al., 2009).
These evidence-based practices form the basis for the
instructional approaches used in the integrated self-regulation and math intervention.
Johnson et al.
165
Table 1. Self-Regulated Learner Framework: Components, Definitions, and Strategy Approaches.
Component
Definition
Connected
Connected is when learners:
1. Feel safe and that they belong;
2. Feels supported to be themselves and are socially aware;
3. Can take the perspective of others;
4. Can develop, manage, and maintain relationships that are
healthy and helpful.
Self-aware students:
1. Understand their strengths and needs as learners;
2. Can self-monitor during the learning task;
3. Understand how their emotions and actions affect themselves
and those around them;
Self-determined learners:
1. Are self-directed;
2. Make plans and commit to reaching goals;
3. Monitor their progress toward goals;
4. Understand the relationship between short-term and long-term
goals.
Strategic learners:
1. Know how to select and effectively use the appropriate
strategies to reach their learning goals;
2. Create an environment that helps them accomplish their goals.
Self-aware
Self-determined
Strategic
Resilient
Resilient learners:
1. Are able to recover from disappointment;
2. Can adapt to sources of stress or adversity;
3. Persevere through challenges and setbacks.
An Integrated Intervention
In this section, the application of the self-regulated learner
framework within the math instructional environment is
explained. The goal is to create an intervention designed to
(a) reduce math anxiety and build student confidence, (b)
teach a strategy that promotes flexibility in solving problems, and (c) support students’ ability to better appraise
stressful situations, and to adopt a “failure as enhancing
learning” mindset. Figure 1 includes a completed intervention template. Each part of the template is described below
to facilitate application to students with different presenting
concerns. Although the example provided in this article
highlights the application of the framework to a single student, this approach can be used with small groups.
Step 1: Connection
The first part of the intervention design involves creating a
safe and positive learning environment for students. Students
with LD and attention deficits have more distant, more conflictual, and more dependent relationships with their teachers
(Demirkaya & Bakkaloglu, 2015). Establishing a positive rapport and a strong connection supports student well-being
because it can make them feel more understood and less vulnerable about having a disability (Mason et al., 2013). Positive
Strategy approaches
•
•
•
•
•
Intentional planned feedback
Building in interests
Communicate routines/expectations
Responsive to student needs
Frequent opportunities to respond
•
•
•
•
•
•
•
•
Rating scales
Tools to explain emotions
Video self-reflection
Models of proficient performance
Noticing feedback
Goal setting/tracking tools
Choice-making opportunities
Promoting advocacy
• Word problem solving strategies
(schema instruction)
• Use of manipulatives to support
problem-solving
• Step-by-step guides to monitor work
• If-then planning
• Positive emotion building
• Opportunities for success
• Celebratory feedback
emotions and connections have been found to increase student
engagement and this engagement leads to a more positive
learning environment (Leighton & Bustos Gómez, 2018).
Teachers should focus on three main areas to help their students feel connected: (a) trust, (b) empathy, and (c) support.
Trust and empathy are basic attributes for establishing effective relationships and help students feel supported in a positive learning environment (Karreman & Vingerhoets, 2012;
Sabol & Pianta, 2012).
Table 1 provides several approaches to helping students
feel connected. When teachers first begin work with their
student, they should take the time to get to know the student. Some students will readily share information about
themselves, whereas others will be more reluctant. Student
interest inventories can be a great way to learn more about
your student, but a teacher should be sure to read the student’s responses and plan ways to incorporate those
responses into instruction and conversation. For example, if
a student mentions that they play the piano, a lesson on fractions could be related to reading musical notation.
Teachers should also discuss with their students what
goals they have for themselves and teachers should also
share the goals they have for their students. The teacher and
student can each list their goals and then place those goals
on a Venn diagram. This activity can help the student take
more ownership for their learning, and it also demonstrates
166
Intervention in School and Clinic 56(3)
Step 1: Connection
Positive Feedback from Teacher (example Teacher (T) Student (S) dialogue)
T: Let’s check your work together. Can you walk me through the steps you took to solve this problem?
S: This one is 142 ÷ 3. I built 142 on my HTO chart. I split the 100 flat into 10 – 10’s and divided those into 3 groups. Then I split the 10 rod
into 10 ones and divided those into three groups and then I had 52 in this group.
T: You worked really hard to solve that problem.
S: Wait – I see what happened now. I should have had two 10’s left but I moved one before I saw that there wouldn’t be a group of 3 and then
I didn’t move it back.
T: That’s great that you noticed where you made a mistake. Now you can fix it. Can you think of another way to check your work next time?
S: I can count each group to make sure they are equal.
T: That is a great idea. Nice work!
Step 2: Self-Awareness
Anxiety Rating
Calming Strategies
Use anxiety 10-point scale at the start of each session, and as needed
before new material or if D has an upcoming test or assignment that
is challenging
Deep breath, 10 cloud, start with known
Step 3: Self-Determination
Goal Setting
Measures
1.
Reduce time to get started
1.
Clock – goal is < 30 seconds
2.
Complete all assignments on time
2.
Check student gradebook – goal is 100%
3.
Use long division strategy
3.
Record when strategy is used
Step 4: Strategic
Focus
Connections/CRA
Long division
Unifix cubes/HTO chart
Step 5: Resilient
If
Then
If I make a mistake and get upset
Then I can take a break and do my 10 cloud
Figure 1. Self-regulated learner framework implementation sheet.
Note. HTO = Hundreds, Tens, and Ones; CRA = Concrete Representational Abstract.
to students that their teacher is invested in their learning
outcomes.
As intervention begins, teachers should explain how the
lessons will be structured and why. If the intervention session is 30 min long, a teacher can post a schedule of the
planned activities, and should review these with the student.
For example, Today, we will spend about 5 min reviewing
how to divide using manipulatives. Then, we will work for
15 to 20 min on dividing multidigit numbers using manipulatives and connecting our models with written numbers.
During the last 5 to 10 min you’ll get to practice on your
own, check your work, and get feedback from me. This helps
students understand what to expect and why they are engaging in the various learning activities.
Teachers should ensure that their lesson allows for many
opportunities for the student to engage and respond, and
should also let students know through their feedback that
making errors is part of the learning process. Feedback
should be delivered immediately and scaffolded over time
to allow the student to move from a teacher-directed correction to a student-directed correction. The sample script in
Step 1 of Figure 1 provides an example of how the teacher
can implement positive feedback.
Finally, the teacher should also periodically ask for student feedback about what is working for them. For example, a teacher might ask, What strategies are most helpful
for you to understand this math concept? Do you think we
need to slow down so you can have more time to practice?
Are the problems we are doing challenging, too easy, or just
right? This feedback can strengthen what has been termed
the pedagogical alliance (Leighton & Bustos Gómez, 2018),
building trust and empathy between teacher and student as
the student is supported in the learning process.
As the sample dialogue in Figure 1 shows, D’s teacher
provided feedback to encourage her to start with what she
knew how to do and to use known strategies and tools to
solve problems. The goal was to support D’s independent
use of strategies over time, to build her confidence, and to
Johnson et al.
Figure 2. Visual anxiety 10-point scale.
give her a consistent starting point for approaching math
problem-solving. When providing feedback around errors,
D’s teacher emphasized that it was okay not to get the right
answer, and that what was more important was the ability to
review the work and understand why an approach or calculation was incorrect, then take the steps to fix it.
Step 2: Self-Awareness
Math-related anxiety and stress have been shown to compromise working memory and attentional resources
(Ashcraft & Kirk, 2001), indicating that interventions
should include approaches that help students manage their
anxiety during the learning process. An important first step
toward this end is for students to identify, name, and understand their emotions, as this supports emotion regulation.
Emotion awareness and regulation has been demonstrated
to positively impact academic achievement (Arguedas
et al., 2016). Students who are aware of their emotions can
communicate their emotional state to teachers, who in turn
can provide more effective feedback and support (Arguedas
et al., 2016).
One way to support students in developing self-awareness is through the use of a rating scale that gives students a
way to identify and name their emotions (Brackett, 2019).
D used a 10-point anxiety scale, similar to the 10-point pain
scale used in the medical field (Hjermstad et al., 2011). The
anxiety scale we used for D is included in Figure 2. To
introduce the scale, the teacher explained: It is very common to feel stress or anxiety about math, but did you know
that when we feel anxious about math, it can make it harder
for us to do? We are going to work on some ways to ease
anxiety, but to do that, we have to think about how we feel.
This is called a visual anxiety scale. On this end (points to
0–1) we feel okay—not anxious. At this point (points to 5)
we feel pretty nervous. You might be thinking things like “I
can’t do this, I hope math class ends soon. I really don’t
want to be here.” And then on this end (points to 10) we are
feeling very anxious, so anxious that we can’t really focus
on anything else, you might even feel panicky. You might be
thinking things like, “I am not doing this. I am not even
going to try, I hate this.” It is okay for you to be at any level
on this scale, and I want you to be honest about how you are
feeling. That will help us figure out what we need to do next.
167
When students are provided with tools and words to
label their attitude toward the learning task, they can start to
make connections between their frame of mind and the outcomes of their learning (Brackett, 2019). Nonjudgmental
and consistent language can help students understand that it
is okay to experience emotions, but that if they want to be
successful in their learning, they may need to use a strategy
to be more effective. For example, if a student is anxious
about an upcoming math test, it is important for them to
acknowledge their anxiety and those feelings can be validated (e.g., “The pressure of doing well on a test can make
you feel anxious, let’s think of some ways that you can feel
more prepared.”).
In addition, for students who experience significant anxiety, a targeted technique may be needed. Before each session, D would indicate how anxious she was feeling about
math, and on days when she indicated higher levels of math
anxiety, her teacher started with an opening routine that D
could confidently and independently do. D’s intervention
began with a “10 cloud” activity, in which she would draw
a cloud on a white board, and then write out all of the ways
to construct the number 10 (e.g., 3 + 7 = 10; 2 + 8 = 10).
This routine was chosen for D because she had a strong
knowledge of addends to 10 and she enjoyed the opportunity to write on the board as she typically avoided volunteering to do so in front of her classmates in the general
education classroom. The goal of this intervention step was
twofold. First, it served as a reminder to D that she has competency in math. Second, it served as a calming routine that
helped her get started with the lesson, to orient her mind
toward math, and to have an early success in the intervention period.
Step 3: Self-Determination
Self-determination plays a critical role in supporting students with LD to have authentic and personal agency over
their learning and fosters positive academic outcomes.
Students with LD often struggle with setting goals, attributing their effort to achieving outcomes, and therefore, may
need support with goal setting and progress monitoring
(Rogers & Tannock, 2018). Supporting students with LD to
develop self-determination may initially require teacherdirected support that is gradually shifted to become student
directed. For example, teachers can support students by setting realistic goals and connecting the effective use of strategies with goal attainment (Botsas & Padeliadu, 2003).
Goals can be set to focus on a variety of areas, and for
students with LD in mathematics, this process should focus
on small, attainable goals that will lead to improved learning over time. For example, due to her math anxiety, D
initially took a very long time to get started with a math
problem. Together with her teacher, D created a graph to
set and monitor progress toward a goal of reducing the time
168
it took to get started. This promoted both self-awareness
(i.e., helping the student recognize it will be challenging to
develop math skills if they do not use intervention time
effectively) and self-determination (i.e., setting and monitoring progress toward goals can be a strong motivator for
students). After several intervention sessions, D started to
meet this goal and she and her teacher created a second
goal focused on independent completion of her math
homework.
Step 4: Strategy
The next step focuses on teaching a strategy or strategies
to address the particular math domain of focus. The goal is
not to teach students an algorithm that they then apply in
practice sets, but rather to develop their cognitive and
metacognitive processes and strategies to facilitate learning. Cognitive strategy instruction has been highly effective for students with LD in mathematics who typically do
not acquire strategies on their own and have difficulty
selecting and applying those appropriate to the task
(Montague et al., 2011).
Cognitive strategy instruction is a general approach to
teaching that includes six stages: (a) developing and activating background knowledge, (b) discussing the strategy,
(c) modeling the strategy, (d) memorizing the strategy, (e)
supporting the strategy, and (f) independent performance
(Montague & Dietz, 2009). In this particular case, D was
struggling with long division. In her general math class,
students were taught to use a partial quotient model, but
this student did not feel confident in her ability to use the
strategy, and this would trigger a negative cycle of making
lots of errors, which further convinced her that she could
not complete the task. It is highly likely that her working
memory challenges made the use of the partial quotient
model quite difficult. Drawing on evidence-based practices
for math instruction, D was taught a strategy to use a
Hundreds, Tens, and Ones (HTO) chart, and the Concrete
Representational Abstract (CRA; Steedly et al., 2008) progression (see Figure 3). The strategy was first taught using
base-10 blocks and the HTO chart, which D had used to
develop her knowledge of place value and ability to add
and subtract with regrouping. Instruction progressed
through the representational and then abstract, and D was
explicitly shown the connections across the three. This
approach supported D’s conceptual understanding and connected that conceptual understanding to the underlying
procedures. D was supported to make connections across
math concepts (i.e., in this case how to regroup using the
HTO chart, using base-10 blocks to build numbers) using
tools and supports that were familiar to her because she
used them to learn other math concepts. Throughout the
intervention, she was encouraged to explain her reasoning
as she worked through the various steps.
Intervention in School and Clinic 56(3)
Step 5: Resilience
The last component of the self-regulated learner framework
is designed to help students persevere, think flexibly, and
regulate their emotions. High levels of stress associated
with the school environment can make school itself a risk
factor to a child with LD (Bender et al., 1999). When children with LD and attention deficits are not supported, their
accumulated failure to be successful in the classroom can
lead to negative affective characteristics (Rogers &
Tannock, 2018). In order for children to be emotionally
healthy, socially adjusted, and achieve academic success,
they must have the ability to manage their emotions (Durlak
et al., 2011). A proactive approach to supporting students to
become resilient may be the most effective. Creating a positive and safe learning environment, where all students are
encouraged to participate, to take risks, and to support each
other is an important way to help all students cope with
stress. Building a shared vocabulary of words to describe
feelings and emotions allows students to communicate their
anxiety and also allows others to understand how to support
their classmates and peers (Kitzmann, 2012).
It is important for teachers to support children’s emotions and talk to them about their feelings in order for them
to understand their own reactions to stress (Nolan et al.,
2014). Fostering children’s abilities to learn from their mistakes by setting examples, being a positive role model, and
the consistent use of the term “give it a try” strongly support
children’s resilience within the classroom environment
(Nolan et al., 2014). Teachers need to model this behavior
for their students as well (Nolan et al., 2014). Modeling
appropriate responses to stressful situations, communicating, and encouraging collective problem-solving are all
ways that teachers can model behavior that promotes resilience in the classroom.
Students with LD may require more intensive approaches
to develop resilience. To build resilience and give students a
strategy to continue working, an “if-then” approach can be
used. “If-then” planning is used extensively in behavioral
change to provide a plan for managing obstacles encountered in pursuit of a goal (Baumeister et al., 2007). The plan
is developed and rehearsed before challenges are encountered. When intervention first began, D would become very
distraught when she made an error, sometimes shutting
down for the remaining intervention time. D’s teacher first
used an if-then plan connected to D’s calming strategy of the
10-cloud. The if-then plan was, “If I make a mistake, I will
take a break and draw my 10-cloud.” This allowed D to step
away from the work that was frustrating her, but still stay
engaged in a math activity. As D was drawing the 10-cloud,
her teacher would comment, “Remember how the 10-cloud
was difficult for you at first, but you kept practicing and now
you can do it automatically?” This helped D remember that
learning new things was a process.
Johnson et al.
169
Long Division Problem Solving Template
1. Write the Division Problem: 142÷3
Hundreds
Tens
Ones
2. Make groups of 3, starting with hundreds
(0 groups, break the
hundred to 10 tens)
0 groups of 3
4 groups of 3
7 groups of 3, R 1
3. Check my work. Do I have equal groups?
4. Write my answer: 47 R 1
Figure 3. Strategy template using Hundreds, Tens, and Ones and Concrete Representational Abstract for long division.
Over time, D’s teacher helped her use a new if-then
plan to focus on breaking down the steps to identify where
the error occurred, and to be able to take steps to fix it. The
if-then plan was, “If I make an error in solving a problem,
I will use the break it down strategy to review the steps,
and my reasoning.” The break it down strategy is a graphic
organizer to lead the student through the process. This
strategy consists of six steps: (a) write down the problem,
(b) identify what you know, (c) identify what you need to
find and what operation(s) you will use to solve, (d) solve
the problem, and (e) check your answer. This accomplishes two things. First, creating an if-then plan signals to
the student that making errors is an expected part of learning math. Second, the if-then cue card is always placed on
the student’s desk as a prompt for what to do next and
provides the student to use the tools they have learned to
solve problems. This process should be scaffolded by the
teacher, with the goal of supporting the student to become
independent over time.
Conclusion
Students with math-related LD are more likely to experience math anxiety than their typically performing peers
(Devine et al., 2018). Currently, the interventions used with
students with LD in mathematics typically target lower
level skills such as fact recall or competency with procedures only (Marita & Hord, 2017), and most do not address
self-regulation or math anxiety directly. The approach to
intervention as described in this article presents a promising
170
Intervention in School and Clinic 56(3)
way to address the comprehensive needs of students with
significant LDs, anxiety, and self-regulation deficits. The
success of this intervention design will depend on teachers’
understanding of the self-regulation and math intervention
frameworks. This comprehensive model addresses the various aspects of anxiety, self-regulation, and math strategy
knowledge to help students learn to persist in math and
understand that productively struggling in math is a natural
part of learning (Hiebert & Grouws, 2007).
Acknowledgments
The authors acknowledge support from the M.J. Murdock
Charitable Trust and the Cambia Foundation. The opinions
expressed are solely those of the authors.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Note
1.
The vignette reported in this article is based on an authentic situation and only the names have been changed to
pseudonyms.
References
Arguedas, M., Daradoumis, A., & Xhafa, F. (2016). Analyzing how
emotion awareness influences students’ motivation, engagement, self-regulation and learning outcome. Educational
Technology & Society, 19(2), 87–103.
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among
working memory, math anxiety, and performance. Journal of
Experimental Psychology: General, 130(2), 224–237.
Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The strength
model of self-control. Current Directions in Psychological
Science, 16(6), 351–355.
Bender, W. N., Rosenkrans, C. B., & Crane, M. K. (1999). Stress,
depression, and suicide among students with learning disabilities: Assessing the risk. Learning Disability Quarterly, 22(2),
143–156.
Botsas, G., & Padeliadu, S. (2003). Goal orientation and reading
comprehension strategy use among students with and without reading difficulties. International Journal of Educational
Research, 39(4–5), 477–495.
Brackett, M. (2019). Permission to feel. Celadon Books.
Demirkaya, P. N., & Bakkaloglu, H. (2015). Examining the student-teacher relationships of children both with and without
special needs in preschool classrooms. Educational Sciences:
Theory & Practice, 15(1).
Devine, A., Hill, F., Carey, E., & Szűcs, D. (2018). Cognitive and
emotional math problems largely dissociate: Prevalence of
developmental dyscalculia and mathematics anxiety. Journal
of Educational Psychology, 110(3), 431–444.
Doabler, C. T., Baker, S. K., Kosty, D. B., Smolkowski, K.,
Clarke, B., Miller, S. J., & Fien, H. (2015). Examining the
association between explicit mathematics instruction and
student mathematics achievement. The Elementary School
Journal, 115(3), 303–333.
Duncan, T. G., & McKeachie, W. J. (2005). The making of the
motivated strategies for learning questionnaire. Educational
Psychologist, 40(2), 117–128.
Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., &
Schellinger, K. B. (2011). The impact of enhancing students’
social and emotional learning: A meta-analysis of schoolbased universal interventions. Child Development, 82(1),
405–432.
García-García, J., & Dolores-Flores, C. (2018). Intra-mathematical
connections made by high school students in performing Calculus tasks. International Journal of Mathematical
Education in Science and Technology, 49(2), 227–252.
Gersten, R., Chard, D. J., Jayanthi, M., Baker, S. K., Morphy,
P., & Flojo, J. (2009). Mathematics instruction for students
with learning disabilities: A meta-analysis of instructional
components. Review of Educational Research, 79(3), 1202–
1242.
Griffin, C. C., & Jitendra, A. K. (2009). Word problem-solving
instruction in inclusive third-grade mathematics classrooms.
The Journal of Educational Research, 102(3), 187–202.
Hiebert, J., & Grouws, D. A. (2007). The effects of classroom
mathematics teaching on students’ learning. In F. K. Lester
(Ed.), Second handbook of research on mathematics teaching
and learning (Vol. 1, pp. 371–404). Information Age.
Hjermstad, M. J., Fayers, P. M., Haugen, D. F., Caraceni, A.,
Hanks, G. W., Loge, J. H., . . . European Palliative Care
Research Collaborative (EPCRC). (2011). Studies comparing
Numerical Rating Scales, Verbal Rating Scales, and Visual
Analogue Scales for assessment of pain intensity in adults:
A systematic literature review. Journal of Pain and Symptom
Management, 41(6), 1073–1093.
Impecoven-Lind, L. S., & Foegen, A. (2010). Teaching algebra to
students with learning disabilities. Intervention in School and
Clinic, 46(1), 31–37.
Jain, S., & Dowson, M. (2009). Mathematics anxiety as a function of multidimensional self-regulation and self-efficacy.
Contemporary Educational Psychology, 34, 240–249. https://
doi.org/10.1016/j.cedpsych.2009.05.004
Johnson, E. S., Clohessy, A. B., & Chakravarthy, P. (2018).
Mathematical intervention framework. Lee Pesky Learning
Center.
Johnson, E. S., Clohessy, A. B., Meek, J., & Spears, L. (2018).
Self-regulated learner framework: An overview. Lee Pesky
Learning Center.
Johnson, E. S., Humphrey, M. J., Mellard, D. F., Woods, K., &
Swanson, H. L. (2010). Cognitive processing deficits and
specific learning disabilities: A meta-analysis. Learning
Disabilities Quarterly, 33(1), 3–18.
Karlen, Y. (2016). Differences in students’ metacognitive strategy knowledge, motivation, and strategy use: A typology of
self-regulated learners. The Journal of Educational Research,
109(3), 253–265.
Johnson et al.
Karreman, A., & Vingerhoets, A. J. (2012). Attachment and
well-being: The mediating role of emotion regulation and
resilience. Personality and Individual Differences, 53(7),
821–826.
Kitzmann, K. M. (2012). Learning about emotion: Cultural and
family contexts of emotion socialization. Global Studies of
Childhood, 2, 82–84.
Kramarski, B., Weisse, I., & Kololshi-Minsker, I. (2010). How can
self-regulated learning support the problem solving of thirdgrade students with mathematics anxiety? ZDM: International
Journal on Mathematics Education, 42(2), 179–193.
Leighton, J. P., & Bustos Gómez, M. C. (2018). A pedagogical
alliance for trust, wellbeing and the identification of errors for
learning and formative assessment. Educational Psychology,
38(3), 381–406.
Liu, J., & Xin, Y. P. (2017). The effect of eliciting repair of mathematics explanations of students with learning disabilities.
Learning Disability Quarterly, 40(3), 132–145.
Lyons, I. M., & Beilock, S. L. (2012). When math hurts: Math
anxiety predicts pain network activation in anticipation of
doing math. PLOS ONE, 7(10), Article e48076. https://doi.
org/10.1371/journal.pone.0048076
Ma, X., & Xu, J. (2004). The causal ordering of mathematics
anxiety and mathematics achievement: A longitudinal panel
analysis. Journal of Adolescence, 27(2), 165–179. https://doi.
org/10.1016/j.adolescence.2003.11.003
Marita, S., & Hord, C. (2017). Review of mathematics interventions for secondary students with learning disabilities.
Learning Disability Quarterly, 40(1), 29–40.
Mason, P., Timms, K., Hayburn, T., & Watters, C. (2013). How do
people described as having a learning disability make sense
of friendship? Journal of Applied Research in Intellectual
Disabilities, 26(2), 108–118.
Montague, M., & Dietz, S. (2009). Evaluating the evidence base
for cognitive strategy instruction and mathematical problem
solving. Exceptional Children, 75(3), 285–302.
Montague, M., Enders, C., & Dietz, S. (2011). Effects of cognitive strategy instruction on math problem solving of middle
171
school students with learning disabilities. Learning Disability
Quarterly, 34(4), 262–272.
Nolan, A., Taket, A., & Stagnitti, K. (2014). Supporting resilience
in early years classrooms: The role of the teacher. Teachers
and Teaching, 20(5), 595–608.
Pizzi, R. G., & Kraemer, D. J. (2017). Avoiding math on a rapid
timescale: Emotional responsivity and anxious attention in
math anxiety. Brain and Cognition, 118, 100–107.
Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety:
Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145–164.
Rittle-Johnson, B., & Schneider, M. (2015). Developing conceptual and procedural knowledge of mathematics. In R. Cohen
Kadosh & A. Dowker (Eds.), Oxford handbook of numerical
cognition (pp. 1118–1134). Oxford University Press.
Rogers, M., & Tannock, R. (2018). Are classrooms meeting the
basic psychological needs of children with ADHD symptoms? A self-determination theory perspective. Journal of
Attention Disorders, 22(14), 1354–1360.
Sabol, T. J., & Pianta, R. C. (2012). Recent trends in research
on teacher–child relationships. Attachment & Human
Development, 14(3), 213–231.
Scheuermann, A. M., Deshler, D. D., & Schumaker, J. B. (2009).
The effects of the explicit inquiry routine on the performance
of students with learning disabilities on one-variable equations. Learning Disability Quarterly, 32(2), 103–120.
Steedly, K., Dragoo, K., Arafeh, S., & Luke, S. D. (2008).
Effective mathematics instruction. Evidence for education
(Vol. III, Issue I). National Dissemination Center for Children
with Disabilities.
Zimmerman, B. J., & Schunk, D. H. (2008). An essential dimension of self-regulated learning. In D. H. Schunk & B. J.
Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (Vol. 1, pp. 1–30).
Lawrence Erlbaum.
Zolkoski, S. M., & Bullock, L. M. (2012). Resilience in children
and youth: A review. Children and Youth Services Review,
34(12), 2295–2303.