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Mathematics anxiety among STEM and social sciences students: the roles of mathematics self-efficacy, and deep and surface approach to learning


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Background Although mathematics anxiety and self-efficacy are relatively well-researched, there are several uninvestigated terrains. In particular, there is little research on how mathematics anxiety and mathematics self-efficacy are associated with deep (more comprehensive) and surface (more superficial) approaches to learning among STEM and social sciences students. The aim of the current work was to provide insights into this domain. Results Bivariate correlation analysis revealed that mathematics anxiety had a very high negative correlation with mathematics self-efficacy. However, while mathematics anxiety correlated positively with surface approach to learning in the STEM student sample, this association was not statistically significant in the social sciences student sample. Controlled for age and gender, regression analysis showed that lower mathematics self-efficacy and female gender predicted higher mathematics anxiety, while only mathematics self-efficacy predicted mathematics anxiety in the social sciences student sample. Interestingly, approaches to learning were not statistically significant predictors in multivariate analyses when mathematics self-efficacy was included. Conclusions The results suggest that mathematics self-efficacy plays a large role in mathematics anxiety. Therefore, one potential takeaway from the results of the current study is that perhaps improving students’ mathematics self-efficacy could also be helpful in reducing mathematics anxiety. Since the current study was cross-sectional, it could also be that reducing students’ mathematics anxiety could be helpful in boosting their mathematics self-efficacy. Future studies should aim to clarify the causal link in this relationship.
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S H O R T R E P O R T Open Access
Mathematics anxiety among STEM and
social sciences students: the roles of
mathematics self-efficacy, and deep and
surface approach to learning
Dmitri Rozgonjuk
, Tiina Kraav
, Kristel Mikkor
, Kerli Orav-Puurand
and Karin Täht
Background: Although mathematics anxiety and self-efficacy are relatively well-researched, there are several
uninvestigated terrains. In particular, there is little research on how mathematics anxiety and mathematics self-
efficacy are associated with deep (more comprehensive) and surface (more superficial) approaches to learning
among STEM and social sciences students. The aim of the current work was to provide insights into this domain.
Results: Bivariate correlation analysis revealed that mathematics anxiety had a very high negative correlation with
mathematics self-efficacy. However, while mathematics anxiety correlated positively with surface approach to
learning in the STEM student sample, this association was not statistically significant in the social sciences student
sample. Controlled for age and gender, regression analysis showed that lower mathematics self-efficacy and female
gender predicted higher mathematics anxiety, while only mathematics self-efficacy predicted mathematics anxiety
in the social sciences student sample. Interestingly, approaches to learning were not statistically significant
predictors in multivariate analyses when mathematics self-efficacy was included.
Conclusions: The results suggest that mathematics self-efficacy plays a large role in mathematics anxiety. Therefore,
one potential takeaway from the results of the current study is that perhaps improving studentsmathematics self-
efficacy could also be helpful in reducing mathematics anxiety. Since the current study was cross-sectional, it could
also be that reducing studentsmathematics anxiety could be helpful in boosting their mathematics self-efficacy.
Future studies should aim to clarify the causal link in this relationship.
Keywords: Mathematics anxiety, Mathematics self-efficacy, Approaches to learning, STEM, Social sciences
One could argue that mathematics is an important com-
ponent in science, technology, engineering, and math-
ematics (STEM) education, since most domains rely on
applying mathematical thinking. Research on teaching
and learning mathematics has received a lot of attention
over the years, as mathematical knowledge is a crucial
factor for studentssuccessful future careers (Claessens
& Engel, 2013; Konvalina, Wileman, & Stephens, 1983).
As mathematics is commonly perceived to be difficult
(Fritz, Haase, & Räsänen, 2019), it has been proposed
that instead of instructing the content and practices of
mathematics, the main focus should be on studentsex-
perience of the discipline and providing mathematical
sense-making (Li & Schoenfeld, 2019). Research in ter-
tiary mathematics education is also a growing field as
the role of mathematics in learning other disciplines is
widely acknowledged.
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article are included in the article's Creative Commons
licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons
licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit es/by/4.0/.
* Correspondence:
Department of Molecular Psychology, Institute of Psychology and
Education, Ulm University, Helmholtzstraße 8/1, 89081 Ulm, Germany
Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
Full list of author information is available at the end of the article
International Journal of
STEM Education
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46
Little research has investigated the relationships be-
tween mathematics anxiety, mathematics self-efficacy,
and approaches to learning in the context of mathemat-
ics education among STEM and social sciences students.
Do students with higher mathematics anxiety also have
a more superficial approach to learning? Or does math-
ematics self-efficacy also contribute to a more thoughtful
and integrative learning process? Are there significant
differences in mathematics self-efficacy, mathematics
anxiety, and approaches to learning between STEM and
social sciences students? Thus far, these questions have
not received a lot of attention in the academic literature.
Therefore, the main aim of this study is to provide some
insights into the relationships between mathematics anx-
iety, mathematics self-efficacy, and approaches to learn-
ing, and the potential differences in those variables
between STEM and social sciences students. While sev-
eral associations have been investigated in earlier works
(see below), this is the first study where the relationships
between all these variables are compared among STEM
and social sciences students.
Literature overview
Mathematics anxiety has been described as experiencing
feelings of panic and helplessness when asked to solve a
mathematical task or problem (Tobias & Weissbrod,
1980). Psychological as well as physiological symptoms
may appear when feeling anxious about mathematics
(Chang & Beilock, 2016). Mathematics anxiety is known
as a common problem in K-12 as well as tertiary educa-
tion (Ashcraft & Moore, 2009; Luttenberger, Wimmer,
& Paechter, 2018; Yamani, Almala, Elbedour, Woodson,
& Reed, 2018) and, therefore, has received considerable
attention as a researched topic among educational scien-
tists (Dowker, Sarkar, & Looi, 2016; Hoffman, 2010; Jan-
sen et al., 2013). For instance, in the Programme for
International Student Assessment (PISA) 2012, across
the 34 participating Organisation for Economic Co-
operation and Development (OECD) countries, 59% of
the 15-year-old students reported that they often worry
that math classes will be difficult for them and 31% re-
ported they get very nervous doing math problems
(OECD, 2013b).
Mathematics anxiety can be caused by several different
factors. For instance, unpleasant teaching and assess-
ment strategies for students, like time testing (Ashcraft
& Moore, 2009) and assigning mathematics as punish-
ment (Oberlin, 1982), that are still widely in use in all
school levels, may influence the spread of mathematics
anxiety. Although mathematics anxiety may have been
appearing relatively early in life, it has been shown that
there are possibilities to reduce mathematics anxiety in
all levels of schooling (Hembree, 1990). As appropriate
mathematics-related instruction and teachers
enthusiasm toward mathematics are important in the
development of mathematics anxiety of students (Jack-
son & Leffingwell, 1999), reduction of pre-service
teachersown mathematics anxiety is crucial and it could
be helpful in reducing the studentsmathematics anxiety
(Gresham, 2007; Vinson, 2001). Applying more active
learning (such as group work) may also reduce anxiety
(Cooper, Downing, & Brownell, 2018).
Mathematics anxiety has been shown to be associated
with poorer performance in mathematics (Ashcraft &
Faust, 1994; Devine, Fawcett, Szűcs, & Dowker, 2012;Fan,
Hambleton, & Zhang, 2019). In addition, it has been
shown, that mathematics anxiety also correlates with
other variables (e.g., learning behavior, self-efficacy) that
influence academic performance (Feng, Suri, & Bell, 2014;
McMullan, Jones, & Lea, 2012). For example, Paechter,
Macher, Martskvishvili, Wimmer, and Papousek (2017)
investigated psychology students and showed a correlation
between mathematics and statistics anxiety and learning
behavior. In addition, Royse and Rompf (1992) compared
social work and non-social work university students and
found that the former had higher levels of mathematics
anxiety than the latter group. Nevertheless, there are no
studies comparing STEM and social sciences students
with regard to mathematics anxiety.
Attitudes toward mathematics is another construct
that plays an important role in mathematical studies, as
well as its outcomes (Ahmed, Minnaert, Kuyper, & van
der Werf, 2012; House, 2005). Mathematics attitudes
and anxiety are often studied together; nevertheless, they
cannot be equated with each other. As Zan and Martino
(2007) describe, many studies about mathematics atti-
tudes do not provide a clear definition for the construct.
It always has an emotional dimension (positive or nega-
tive emotional disposition toward mathematics), usually
also involving conceptualization of mathematics (Dow-
ker et al., 2016), and/or mathematics-related behavior,
depending on the specific research problem. In addition,
one may argue that, to some extent, attitudes toward
mathematics also reflect mathematics self-efficacy (Yusof
& Tall, 1998). Self-efficacy could be defined as ones be-
lief in ones ability to succeed in specific situations. The
academic aspect of this concept is called academic self-
efficacy, and is described as an individuals belief that
they can successfully achieve at a designated level on an
academic task (Bandura, 1997). Mathematics self-efficacy
is ones belief about how their own action and effort
could lead to success in mathematics (Luttenberger
et al., 2018; OECD, 2013b). Higher mathematics self-
efficacy has been shown to be correlated with lower
mathematics anxiety, more positive, and less negative at-
titudes toward mathematics (Akin & Kurbanoglu, 2011).
In addition, higher mathematics anxiety is related to
more negative attitudes toward mathematics (Vinson,
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 2 of 11
2001). These findings underscore the importance of
mathematics anxiety in attitudes toward mathematics, as
well as mathematics self-efficacy.
More general attitudes toward learning are also im-
portant to be considered. Marton and Säljö (1976) re-
ferred to a co-existence of intention and process of
learning and described deep and surface learning ap-
proaches. Students with a deep approach to learning
look for the meaning of the studied material and try to
relate new knowledge with prior information, whereas
students with a surface approach to learning use rote
learning and un-meaningful memorization. How stu-
dents approach to learning in higher education is an im-
portant factor when speaking about educational
outcomes (Duff, Boyle, Dunleavy, & Ferguson, 2004;
Fryer & Vermunt, 2018; Maciejewski & Merchant, 2016).
Deep approach to learning is associated with better gen-
eral academic outcomes, as well as, specifically, better
mathematical performance (Murphy, 2017; Postareff,
Parpala, & Lindblom-Ylänne, 2015). Although it is not
the sole factor influencing mathematics achievement, it
is still important to determine studentsapproaches to
learning mathematics, as it enables educators to analyze
and shape the studentsclassroom experience toward
more effective learning.
Little research has been done in the domain of ap-
proaches to learning in relation to mathematics anxiety
and self-efficacy in tertiary education. Anxiety in general
is associated with higher surface and lower deep ap-
proach to learning (Marton & Säljö, 1984). In one study,
surface approach to learning has been found to correlate
with mathematics anxiety (Bessant, 1995). It has also
been demonstrated that students with positive attitudes
toward mathematics tend to use more deep and less sur-
face approach when learning mathematics (Alkhateeb &
Hammoudi, 2006; Gorero & Balila, 2016). Another com-
mon finding in educational research is that students
who have higher self-efficacy adopt more deep approach
to learning (Papinczak, Young, Groves, & Haynes, 2008;
Phan, 2011; Prat-Sala & Redford, 2010).
There are not many studies investigating the role of
deep and surface approaches to learning in mathematics
anxiety. Although a study by Bessant (1995) showed that
mathematics students scored lower on mathematics anx-
iety measure than psychology/sociology students, the re-
lations between mathematics anxiety and approaches to
learning in STEM and social sciences students is a
largely unexplored area.
Conceptual framework
Several studies have aimed to explain the potential
causes for mathematics anxiety. It has been proposed
that the origins of mathematics anxiety could be catego-
rized into three groups (Baloglu & Kocak, 2006):
situational, dispositional, and environmental factors.
Situational factors are direct stimuli related to feelings of
anxiety in relation to mathematics. Dispositional factors
include individual characteristics, such as personality
traits; for instance, it has been shown that people with
higher trait neuroticism (the tendency to experience
negative effect; McCrae & Costa, 2003) worry more and
tend to be more anxious in general (Costa & McCrae,
1985), although this typically decreases with age (Mõttus
& Rozgonjuk, 2019). Finally, environmental factors in-
clude prior perceptions, attitudes, and experiences that
may have affected the individual (Baloglu & Kocak,
In the current work, mathematics self-efficacy as well
as approaches to learning could be conceptualized as en-
vironmental factors that could potentially affect the de-
velopment of mathematics anxiety. Furthermore,
studentsage, gender, and the curricula could be consid-
ered as environmental factors potentially affecting math-
ematics anxiety (Baloglu & Kocak, 2006).
Aims and hypotheses
The general aim of this study is to investigate how math-
ematics anxiety and self-efficacy, as well as approaches
to learning (deep and surface), are related to each other.
Furthermore, these relationships are also compared
across STEM and social sciences student samples. Based
on the previous literature, we have posed some hypoth-
eses that are rather confirmatory of previous findings.
Based on the previous literature, we hypothesize the
H1: Mathematics anxiety and mathematics self-efficacy
are negatively correlated.
Previously it has been demonstrated that mathematics
anxiety and self-efficacy are inversely associated (Akin &
Kurbanoglu, 2011; Vinson, 2001).
H2: Mathematics anxiety is positively correlated with
surface approach to learning and negatively with deep
approach to learning. Even though one study found
that mathematics anxiety correlates positively with
surface approach to learning (Bessant, 1995), it would
also be natural to assume that deep approach to
learning is negatively associated with mathematics
anxiety, since typically surface and deep approaches to
learning are inversely correlated (Rozgonjuk, Saal, &
Täht, 2018).
H3: Mathematics self-efficacy is positively associated
with deep and negatively with surface approach to
learning. It has previously been shown that high self-
efficacy, in general, is associated with more deep and
less surface approach to learning (Chou & Liang, 2012;
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 3 of 11
Papinczak et al., 2008). Therefore, it would be logical to
assume that also in the context of mathematics, these
constructs would be correlated.
H4: STEM students have less mathematics anxiety than
social sciences students. Previously, Bessant (1995) have
demonstrated that mathematics students had lower
scores on mathematics anxiety measure than
psychology/sociology students. However, our study
goes beyond comparing only mathematics students,
and includes students from other disciplines (e.g.,
biology) as well, forming a more heterogeneous STEM
student group.
H5: Approaches to learning and mathematics self-
efficacy predict mathematics anxiety when age and gen-
der are controlled for. Based on previous research and
hypotheses mentioned above, there is evidence to
believe that the associations between approaches to
learning, mathematics self-efficacy, and mathematics
anxiety would hold also when covariates are included.
There is relatively little research in this domain and
knowing the associations between these variables may
help educators to improve and adjust their teaching
strategies to potentially improve the learning process.
The results of this study aim to outline the important
predictors of mathematics anxiety, and, therefore, ex-
pand the existing research in this field of study, and in-
fluence future teaching strategies. The results of this
work could be helpful for the teacher/lecturer when he/
she aims toward reducing mathematics anxiety by, e.g.,
using teaching strategies that could enhance deeper (and
less surface) approach to learning, increase mathematics
self-efficacy, or both.
Material and methods
Sample and procedure
The study participants were students who either took an
introductory calculus course (dealing with more elabor-
ate topics than in secondary education) for university
students or an introductory statistical modeling course
at a major Estonian university. Importantly, these
courses were mandatory in order to complete the stu-
dents curriculum and, in most cases, were prerequisite
courses for other courses in the curriculum. While most
of the students in the introductory calculus course were
STEM curricula students, mainly psychology and polit-
ical sciences majors were enrolled in the statistical mod-
eling course. However, because it is possible to take
these courses as electives as well, students with various
backgrounds could participate in these courses. This
means that, theoretically, both student groups could en-
roll in either the calculus or statistical modeling course.
For instance, as could be seen in Supplementary Table 1
there are some Economics students who enrolled in a
Calculus course, while all other social sciences students
were enrolled in the statistical modeling course. Stu-
dentsresponses across variables of interest across cur-
ricula are depicted in Supplementary Figures 1to 4.
The data were collected during the start of both
courses, in September 2019. Students were asked to take
part in a web survey which aimed to investigate the role
of different factors in mathematics education. Participa-
tion in the study was voluntary, anonymous, and in line
with the Helsinki Declaration.
In total, there were 358 responses. However, many rows
were empty or most of the data were missing, after some
initial data cleaning, 234 rows of responses were kept. The
reason for the aforementioned missing datalies in the
fact that whenever a person opens the questionnaire en-
vironment, this gets logged as a response row. However, it
does not necessarily mean that a person provides any re-
sponses to the questionnaire. Therefore, as mentioned,
out of 358 rows logged, only 234 were actually partially or
fully filled in with responses. Finally, because n=3people
did not specify their major, we excluded those rows.
Therefore, the effective sample comprised 231 students
(age M = 21.39, SD = 5.12; 79 (34.2%) men, 152 (65.8%)
women). There were 147 (63.6% of total sample) STEM
students (age M = 20.55, SD = 4.51; 57 men, 90 women),
and 84 (36.4%) social sciences students (age M = 22.87,
SD = 5.78; 22 men, 62 women).
We queried about the study participantssocio-
demographic variables (e.g., age, gender, curriculum/
major), mathematics anxiety and mathematics self-
efficacy, and approaches to learning (deep and surface).
Mathematics anxiety
Mathematics anxiety was measured with the 5-item
mathematics anxiety questionnaire used in the inter-
national PISA 2012 survey (OECD, 2013a). Students
were asked to assess on a 4-point scale (1 = strongly dis-
agree to 4 = strongly agree) the extent of agreement with
the following statements: (1) I often worry that mathem-
atics classes will be difficult for me; (2) I get very tense
when I have to do mathematics homework; (3) I get very
nervous doing mathematics problems; (4) I feel helpless
when doing a mathematics problem; (5) I worry that I
will get poor grades in mathematics. The psychometric
properties of this scale in an adolescent population could
be found in OECD report (2014; Table 16.7 on page
320). As a side comment, we opted for using this meas-
ure as opposed to, e.g., the Abbreviated Mathematics
Anxiety Scale (AMAS; Hopko, Mahadevan, Bare, &
Hunt, 2003), because the PISA-study mathematics anx-
iety scale fits better with contemporary classroom where
the role of digitalization is increasing (e.g., the AMAS
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 4 of 11
items include words like bookand blackboard,but
not digital resources). Secondly, PISA mathematics anx-
iety scale has demonstrated good psychometric proper-
ties, it has probably been administered in a larger variety
of cultural settings (as opposed to the AMAS), and it
has been validated against mathematics aptitude test in
all these cultures (e.g., see the report by OECD (2014),
p. 320, Table 16.7, ANXMAT). Finally, in all PISA sur-
vey questionnaires, stringent quality-assurance mecha-
nisms are implemented by experts in translation,
sampling, and data collection, resulting in a high degree
of reliability and validity (OECD, 2017). Cronbachs
alpha for the effective sample of this measure was very
good, α= 0.90.
Mathematics self-efficacy
Mathematics self-efficacy was measured with three
items, measuring the extent of agreement on a four-
point scale (1 = strongly disagree to 4 = strongly agree)
from Yusof and Tall (1998). All items from the mathem-
atics self-efficacy scale (Yusof & Tall, 1998) were trans-
lated into Estonian by the members of our mathematics
education team and were reviewed by a professional Es-
tonian philologist. The questionnaire was then translated
back into English by another translator, and the back-
translated English version was reviewed by an English-
speaking student in order to estimate the content and
the similarities between the original and the back-
translated items. The items were the following: (1) I usu-
ally understand a mathematical idea quickly; (2) I have
to work very hard to understand mathematics; (3) I can
connect mathematical ideas that I have learned. Cron-
bachs alpha for this three-item measure was α= 0.83.
Approaches to learning
Approaches to learning were measured with the Esto-
nian adaptation of the Revised Study Process Question-
naire (Biggs, Kember, & Leung, 2001; Valk & Marandi,
2005). It is a 16-item measure (8 items for deep and 8
items for surface approach to learning) that measures
deep and surface approaches to learning on a five-point
scale (1 = do not agree at all to 5 = totally agree). Ex-
ample items for the deep approach to learning scale are
as follows: I find most new topics interesting and often
spend extra time trying to obtain more information about
them, and I learn because I want to understand the
world. Example items for the surface approach to learn-
ing scale are as follows: I see no point in learning mater-
ial which is not likely to be in the examination,andIn
case of difficult topics, learning by rote is one way to pass
an exam. The internal consistency of deep and surface
approaches to learning were acceptable, Cronbachsα=
0.71 for both scales.
Data analysis was conducted in the R software version
3.5.3 (R Core Team, 2020). As mentioned in the Sample
and proceduresection, we first removed the data rows
that were not valid responses (empty rows) or where
people did not specify their major (n= 3). After this pro-
cedure, there were no missing data in key variables. In-
ternal consistency statistics were calculated with the
alpha() function from the psych package (Revelle, 2018).
Since the sample sizes were not equal, Mann-Whitney U
tests to analyze the potential group differences between
STEM and social sciences students in age, math anxiety
and self-efficacy, and deep and surface approach to learn-
ing were used. Chi-square test was used to see if there are
differences in gender distribution among those student
We then computed descriptive statistics and con-
ducted Spearman correlation analysis (with pvalues ad-
justed for multiple testing with the Holms method),
using the rcorr.adjust() function from the RcmdrMisc
package (Fox, 2020). Finally, we computed regression
models where mathematics anxiety was treated as the
outcome variable, either surface or deep approach to
learning as the predictor, age and sex were covariates,
and we also computed additional regression models
where mathematics self-efficacy was additionally in-
cluded as a predictor variable. We ran these analyses for
the whole sample, as well as for STEM and social sci-
ences students separately.
The data as well as the analysis script are included
with this work as Supplementary Materials.
Firstly, we analyzed if STEM and social sciences students
had group differences in key variables. There were no sta-
tistically significant group differences in deep and surface
approaches to learning, mathematics anxiety, as well as in
gender distribution (all ps > 0.01). However, the social sci-
ences student group was slightly older (M=22.87,SD=
5.78) than the STEM student group (M=20.55,SD=
4.51), W= 9742, p< 0.001. In addition, STEM students
(M= 8.39, SD = 1.92) had higher mathematics self-
efficacy scores than social sciences students (M=7.77,SD
=2.03),W= 5080.50, p=0.023.
Descriptive statistics and correlations for mathematics
anxiety and self-efficacy, and approaches to learning
The descriptive statistics and Spearman correlation coef-
ficients between the variables are in Table 1.
According to Table 1, mathematics anxiety was very
strongly negatively correlated to mathematics self-
efficacy across all samples. Additionally, surface learning
was positively significantly associated with mathematics
anxiety in the total and STEM student sample, but it
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 5 of 11
was not significant among social sciences students. Deep
approach to learning and age were not statistically sig-
nificantly associated with mathematics anxiety.
Mathematics self-efficacy was negatively significantly
associated with surface approach to learning in total and
STEM student samples, but not in social sciences stu-
dents. Mathematics self-efficacy did not correlate with
deep approach to learning.
Surface approach to learning was negatively correlated
to deep approach to learning in total and STEM student
samples (but not in social sciences student sample) and
had a statistically significant negative correlation with
age only in social sciences student sample. Deep ap-
proach to learning did not correlate with age.
Which factors predict mathematics anxiety?
Next, we computed several regression models where
mathematics anxiety was treated as the outcome vari-
able. We computed models for three samples of stu-
dents: the full sample (N= 231), the STEM student
sample (N= 147), and the social sciences students (N=
84). For each sample of students, we computed two
models. Model 1 included age, gender, and surface and
deep approaches to learning as predictors. In model 2,
mathematics self-efficacy was added as an additional
predictor. For the full sample, we also included the stu-
dent group (STEM vs social sciences) as a predictor.
According to results in Table 2, when regression
models are computed across the full sample, female stu-
dents tend to have greater mathematics anxiety than
male students. Approaches to learning, age, and being a
STEM versus social sciences student did not predict
mathematics anxiety. Finally, including the mathematics
self-efficacy variable was negatively associated with
mathematics anxiety in this multivariate model. In
addition, it seems that mathematics self-efficacy explains
a large proportion of mathematics anxiety, as inclusion
of this variable improved the explained variance by al-
most 50% in the regression model full sample level.
However, the regression analysis results are somewhat
different when the sample is broken down into the
STEM and social sciences student group. In STEM stu-
dents, it seems higher mathematics anxiety is associated
with older age, female gender, and more surface and less
deep approach to learning. However, when mathematics
self-efficacy is included in the model, only gender and
mathematics self-efficacy effects are significant.
Interestingly, when mathematics self-efficacy is not in-
cluded as a predictor of mathematics anxiety, there are no
statistically significant predictors in the social sciences stu-
dent sample; however, once it is included in the regression
Table 1 Descriptive statistics and correlations for math anxiety and self-efficacy, approaches to learning, and age
Sample (N) M SD Min Max 1 2 3 4
Total sample (N= 231)
1. Math anxiety 11.06 3.77 5 20 1
2. Math self-efficacy 8.17 1.98 3 12 0.769*** 1
3. SAL 19.00 4.53 9 33 0.251*** 0.210* 1
4. DAL 27.51 4.06 12 36 0.115 0.085 0.318*** 1
5. Age 21.39 5.12 17 51 0.128 0.202* 0.113 0.167
STEM sample (N= 147)
1. Math anxiety 10.73 3.64 5 20 1
2. Math self-efficacy 8.39 1.92 3 12 0.759*** 1
3. SAL 19.03 4.50 10 31 0.317*** 0.233* 1
4. DAL 27.17 4.23 12 36 0.215 0.160 0.351*** 1
5. Age 20.55 4.51 17 45 0.125 0.206 0.025 0.083
Social sciences sample (N= 84)
1. Math anxiety 11.64 3.94 5 20 1
2. Math self-efficacy 7.77 2.03 3 12 0.802*** 1
3. SAL 18.93 4.60 9 33 0.148 0.182 1
4. DAL 28.11 3.68 20 36 0.031 0.005 0.246 1
5. Age 22.87 5.78 19 51 0.059 0.059 0.319* 0.208
SAL surface approach to learning; DAL deep approach to learning
Pvalues are adjusted for multiple testing with the Holms method
*p< 0.05
**p< 0.01
***p< 0.001
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 6 of 11
model, mathematics self-efficacy is statistically signifi-
cantly and negatively associated with mathematics anxiety.
The aim of the current study was to investigate the rela-
tionships between mathematics anxiety, mathematics
self-efficacy, and approaches to learning (deep and sur-
face) among STEM and social sciences students. We had
posed several hypotheses to meet that aim.
Based on the literature (Akin & Kurbanoglu, 2011;
Vinson, 2001), we expected mathematics anxiety and
mathematics self-efficacy to have a negative association
(H1). This hypothesis found support from the data. The
very high negative correlation of r=0.768 (across the
full sample) suggests that these variables explain each
others variance relatively well. These results were ex-
pected, since students who perceive that they can suc-
ceed in mathematics and who have a more positive
attitude toward this topic, should experience less anx-
iety; furthermore, as mentioned earlier, these findings
are coherent with previous research (Akin & Kurbano-
glu, 2011).
Our second hypothesis (H2) regarded the relationship
between mathematics anxiety and approaches to learn-
ing. Specifically, we expected that mathematics anxiety
correlates positively with surface approach to learning
and negatively with deep approach to learning. While
surface approach to learning should be associated with
increased and deep approach to learning with decreased
anxiety in general, a study found that only higher levels
of surface approach to learning correlated with more
mathematics anxiety (Bessant, 1995). The results of the
current study supported this hypothesis on the full and
STEM student sample level; however, surface approach
to learning did not correlate significantly with mathem-
atics anxiety in social sciences students. Furthermore,
deep approach to learning was negatively correlated with
mathematics anxiety in the STEM student sample. This
is the first study demonstrating that there are discrepan-
cies in approaches to learning in association with math-
ematics anxiety between STEM and social sciences
students. Although it is hard to explain these discrepan-
cies based on our data, it is certainly a topic that needs
to be pursued further.
According to the third hypothesis (H3), we expected
mathematics self-efficacy to be positively correlated with
deep and negatively with surface approach to learning,
in line with some previous findings (Alkhateeb & Ham-
moudi, 2006; Gorero & Balila, 2016). This hypothesis
found partial support from the data. Deep approach to
learning was not associated with mathematics self-
efficacy, while surface approach to learning had a nega-
tive correlation with mathematics self-efficacy on the full
and STEM student sample level.
We expected that STEM students have less mathemat-
ics anxiety than social sciences students in our fourth
hypothesis (H4). Royse and Rompf (1992) compared
groups of students who did and did not study social
Table 2 Results for regression models where age and gender, approaches to learning, and mathematics self-efficacy predict
mathematics anxiety in STEM and social sciences student samples
Dependent variable: math anxiety
Full sample STEM sample Social sciences sample
Predictors Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Intercept 5.853* (2.562) 20.756*** (1.888) 5.274 (2.865) 20.432*** (2.313) 4.993 (4.716) 21.688*** (3.206)
Age 0.086 (0.048) 0.008 (0.032) 0.147* (0.062) 0.016 (0.045) 0.007 (0.077) 0.038 (0.047)
Gender 1.407** (0.510) 1.207*** (0.335) 1.927** (0.576) 1.682*** (0.397) 0.284 (0.995) 0.271 (0.605)
SAL 0.183** (0.056) 0.037 (0.038) 0.179** (0.066) 0.053 (0.047) 0.185 (0.100) 0.013 (0.062)
DAL 0.074 (0.064) 0.036 (0.042) 0.150* (0.072) 0.065 (0.050) 0.089 (0.126) 0.018 (0.077)
Student group 0.628 (0.507) 0.104 (0.335)
Math self-efficacy 1.427*** (0.083) 1.350*** (0.107) 1.560*** (0.134)
Model statistics
N 231 231 147 147 84 84
0.116 0.622 0.183 0.615 0.046 0.652
Adjusted R
0.097 0.612 0.160 0.601 0.002 0.630
Residual SE (df) 3.584 (225) 2.350 (224) 3.338 (142) 2.300 (141) 3.945 (79) 2.399 (78)
F (df) 5.927*** (5; 225) 61.352*** (6; 224) 7.968*** (4; 142) 45.044*** (5; 141) 0.961 (4; 79) 29.205*** (5; 78)
Regression coefficients are displayed (with standard errors in parentheses)
SAL surface approach to learning; DAL deep approach to learning
*p< 0.05
**p< 0.01
***p< 0.001
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 7 of 11
work and found that the former had higher mathematics
anxiety. However, this was not the case in the current
study. STEM and social sciences students did not differ
from each other in group comparison analysis. There-
fore, this hypothesis did not find support from data.
These results are surprising, since one may logically
think that if a student chooses to major in a subject that
has a strong mathematics component, the students anx-
iety toward mathematics could be lower than among
students who choose a curriculum where the share of
mathematics may be rather small (on an undergraduate
level). Furthermore, STEM students are more likely to
have mathematics in different courses throughout their
studies as well as professionally after graduation. There-
fore, these results are certainly interesting, since they
demonstrate that STEM and social sciences students are
as much or as little anxious toward mathematics.
Finally, to understand how mathematics anxiety would
be predicted from approaches to learning and mathem-
atics self-efficacy when age and gender are controlled
for, we conducted regression models on the total, STEM,
and social sciences student samples. We hypothesized
that approaches to learning and mathematics self-
efficacy predict mathematics anxiety, also when age and
gender are controlled for (H5).The regression model re-
sults showed that among STEM student sample, older
age, female gender, higher surface, and lower deep ap-
proach to learning predicted higher mathematics anx-
iety. However, when mathematics self-efficacy was
included in the model, only female gender and lower
mathematics self-efficacy were significant predictors of
mathematics anxiety. Gender differences are somewhat
in line with research finding that female students tend to
experience more anxiety in STEM classroom settings
(Pelch, 2018). Interestingly, only lower mathematics self-
efficacy predicted higher mathematics anxiety in social
sciences student sample.
One potential takeaway from the results of this study
is that in order to lower ones mathematics anxiety, it
could be necessary to boost ones mathematics self-
efficacy. However, this may prove to be a rather difficult
task, since there is a potential problem of a vicious cir-
cle:ones mathematics self-efficacy may be dependent
on ones performance in mathematics, and vice versa
(Carey, Hill, Devine, & Szücs, 2016). Therefore, if a stu-
dent performs well on a mathematics task, their self-
efficacy may get a boost, consequently lowering
mathematics-related anxiety. On the other hand, if a stu-
dent performs poorly, their self-efficacy may drop,
followed by increased anxiety. Mathematics anxiety, in
turn, could further hamper ones mathematics perform-
ance, resulting in poorer perceived self-efficacy. It would
be, therefore, necessary to further studypreferably ex-
perimentally and in a longitudinal study designhow
working with ones mathematics self-efficacy could be
helpful against mathematics anxiety.
While we discussed the association between mathem-
atics anxiety and self-efficacy, it is nevertheless note-
worthy that approaches to learning seem to play a
significant role in mathematics anxiety among STEM
students. Somewhat coherent with previous findings,
more surface approach to learning predicted more math-
ematics anxiety (Bessant, 1995). These results suggest
that perhapsat least among STEM studentsthere is a
possibility to tailor the classroom experience so that it
would promote more synthesis of study materials, and
decrease fact-based, rote-learning. STEM subjects likely
have more universal facts (e.g., equations, proofs) to be
learned, possibly promoting superficial learning. Here,
too, could be a potentially vicious circle in play: a stu-
dent who has to study materials that may seemingly be
isolated facts, could implement rote-learning. This re-
sults in superficial knowledge, which may not prove to
be useful when synthesis with other materials is needed.
In turn, this may lead to poor performance and higher
mathematics anxiety due to that. As discussed earlier,
mathematics self-efficacy also likely plays a crucial role
in this process. On the other hand, this reasoning does
not entirely explain why approaches to learning did not
predict mathematics anxiety among social sciences stu-
dents. It could be that STEM students differ in how they
perceive mathematics in general due to having to use
this more in their studies. We believe that this should
receive more attention in future research.
The main contribution of this study is providing insights
into the potential role of mathematics self-efficacy, and
deep and surface approaches to learning in mathematics
anxiety in STEM and social sciences students. All in all, it
could be inferred from this study that while surface ap-
proach to learning may be, to some extent, an important
factor possibly predicting mathematics anxiety, the role of
mathematics self-efficacy should be further studied in
combination with approaches to learning in order to
understand mathematics anxiety. It could be further hy-
pothesized that by improving mathematics self-efficacy, it
could also be helpful in reducing mathematics anxiety, as
well as surface approaches to learning. Interestingly, while
STEM and social science students differ in attitudes to-
ward mathematics (with STEM students scoring higher),
there were no differences in mathematics anxiety between
these student groups.
There are limitations that need to be mentioned.
Firstly, we used self-reports in our study. It could be
helpful to include other important variables, such as
grades and test scores, to complement the results. In
addition, methods such as experience sampling may also
provide more valid results (Lehtamo, Juuti, Inkinen, &
Lavonen, 2018). Secondly, there were significantly fewer
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 8 of 11
social sciences students than STEM students in the total
sample, and social sciences students were slightly older
than STEM students. Although age was accounted for in
multivariate analyses, future studies should aim toward
equal sample sizes as well as higher similarity in other
demographic characteristics (e.g., age, gender). A third limi-
tation was the absence of controlling for studentsprior
academicability(e.g.,gradepoint average, course grades,
ability test results). It could be that there are inherent differ-
ences between the past performance in mathematics-
related courses and mathematics self-efficacy and anxiety.
Future works should include variables of prior academic
ability as control variables. In addition, future works could
also collect data among STEM and social sciences students
across multiple semesters, providing more robust results.
The fourth limitation regards the use of the mathematics
anxiety scale that has been validated in a sample of adoles-
cents. Some additional measures of mathematics anxiety
designed for tertiary-education settings, such as the AMAS
(Hopko et al., 2003), could further validate the findings. Fi-
nally, future studies could also include other external fac-
tors to models predicting mathematics anxiety (Martin-
Hansen, 2018).
In conclusion, we found that STEM and social sciences
students do not differ largely with regard to mathematics
anxiety, while STEM students do have higher mathemat-
ics self-efficacy. It may be that surface approach to learn-
ing plays a larger role in mathematics anxiety in STEM
students than in social sciences students. This is the first
work to investigate the differences between STEM and
social sciences students in mathematics anxiety and self-
efficacy, as well as deep and surface approaches to learn-
ing. The results could be helpful for mathematics educa-
tors, as it is relevant for them to learn about and
understand the interplay between deep and surface ap-
proach to learning, mathematics anxiety and self-
efficacy, and studentscurricula. It could be that improv-
ing studentsmathematics self-efficacy, as well as facili-
tating more synthesis among the learned materials could
help as a remedy against mathematics anxiety. This,
however, should be investigated in future research that,
preferably, implements an experimental and longitudinal
study design.
Supplementary information
Supplementary information accompanies this paper at
Additional file 1: Supplementary Table 1 Grouping of students to social
sciences/STEM by self-reported curricula, and the distribution of students'
curricula by course taken Notes. LT_Calc1 = Calculus I (LTMS.00.003);
MT_Calc1 = Calculus I (MTMM.00.340); SH_StatM = Statistical Modeling
(SHSH.00.002). Supplementary Figure 1: Students' mathematics anxiety
summed scores plotted by curricula. Note: points are jittered on the
graph (with the geom_jitter() function). Supplementary Figure 2: Stu-
dents' mathematics self-efficacy summed scores plotted by curricula.
Note: points are jittered on the graph (with the geom_jitter() function).
Supplementary Figure 3: Students' deep approach to learning summed
scores plotted by curricula. Note: points are jittered on the graph (with
the geom_jitter() function). Supplementary Figure 4: Students' surface ap-
proach to learning summed scores plotted by curricula. Note: points are
jittered on the graph (with the geom_jitter() function). Math anxiety
DR designed the study, collected and analyzed the data, and wrote the first
draft; TK designed the study, collected the data, and revised the manuscript;
KOP collected the data and revised the manuscript; KM collected the data,
and revised the manuscript; KT collected the data, and revised the
manuscript. The author(s) read and approved the final manuscript.
This work did not receive funding. Open access funding provided by Projekt
Availability of data and materials
The data as well as analysis script are available among the supplementary
Competing interests
The authors declare that they have no competing interests.
Author details
Department of Molecular Psychology, Institute of Psychology and
Education, Ulm University, Helmholtzstraße 8/1, 89081 Ulm, Germany.
Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia.
Institute of Psychology, University of Tartu, Tartu, Estonia.
Received: 3 March 2020 Accepted: 9 August 2020
Ahmed, W., Minnaert, A., Kuyper, H., & van der Werf, G. (2012). Reciprocal
relationships between math self-concept and math anxiety. Learning and
Individual Differences,22(3), 385389.
Akin, A., & Kurbanoglu, I. N. (2011). The relationships between math anxiety, math
attitudes, and self-efficacy: A structural equation model. Studia Psychologica,
53(3), 263273.
Alkhateeb, H. M., & Hammoudi, L. (2006). Attitudes toward and approaches to
learning first-year university mathematics. Perceptual and Motor Skills,103(1),
Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and mental arithmetic
performance: An exploratory investigation. Cognition & Emotion,8(2), 97125.
Ashcraft, M. H., & Moore, A. M. (2009). Mathematics anxiety and the affective drop
in performance. Journal of Psychoeducational Assessment,27(3), 197205.
Baloglu, M., & Kocak, R. (2006). A multivariate investigation of the differences in
mathematics anxiety. Personality and Individual Differences,40(7), 13251335.
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Bessant, K. C. (1995). Factors associated with types of mathematics anxiety in
college students. Journal for Research in Mathematics Education,26(4), 327.
Biggs, J., Kember, D., & Leung, D. Y. (2001). The revised two-factor study process
questionnaire: R-SPQ-2F. Br J Educ Psychol,71(Pt 1), 133149.
Carey, E., Hill, F., Devine, A., & Szücs, D. (2016). The chicken or the egg? The
direction of the relationship between mathematics anxiety and mathematics
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 9 of 11
performance. Frontiers in Psychology,6.
Chang, H., & Beilock, S. L. (2016). The math anxiety-math performance link and its
relation to individual and environmental factors: A review of current
behavioral and psychophysiological research. Current Opinion in Behavioral
Chou, C., & Liang, J. (2012). Exploring the structure of science self-efficacy: A
model built on high school studentsconceptions of learning and
approaches to learning in science. The Asia-Pacific Education Researcher,21(1),
Claessens, A., & Engel, M. (2013). How important is where you start? Early
mathematics knowledge and later school success. Teachers College Record,
115, 060306.
Cooper, K. M., Downing, V. R., & Brownell, S. E. (2018). The influence of active
learning practices on student anxiety in large-enrollment college science
classrooms. International Journal of STEM Education,5(1), 23.
Costa, P. T., & McCrae, R. R. (1985). Hypochondriasis, neuroticism, and aging:
When are somatic complaints unfounded? American Psychologist,40(1), 19
Devine, A., Fawcett, K., Szűcs, D., & Dowker, A. (2012). Gender differences in
mathematics anxiety and the relation to mathematics performance while
controlling for test anxiety. Behavioral and Brain Functions,8,33http://www.
Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we
learned in 60 years? Frontiers in Psychology,7.
Duff, A., Boyle, E., Dunleavy, K., & Ferguson, J. (2004). The relationship between
personality, approach to learning and academic performance. Personality and
Individual Differences,36(8), 19071920.
Fan, X., Hambleton, R. K., & Zhang, M. (2019). Profiles of mathematics anxiety
among 15-year-old students: A cross-cultural study using multi-group latent
profile analysis. Frontiers in Psychology,10, 1217.
Feng, S., Suri, R., & Bell, M. (2014). Does classical music relieve math anxiety? Role
of tempo on price computation avoidance. Psychology & Marketing,31(7),
Fox, J. (2020). RcmdrMisc: R Commander Miscellaneous Functions (2.7-0) [Computer
Fritz, A., Haase, V. G., & Räsänen, P. (2019). International handbook of mathematical
learning difficulties: From the laboratory to the classroom.
Fryer, L. K., & Vermunt, J. D. (2018). Regulating approaches to learning: Testing
learning strategy convergences across a year at university. British Journal of
Educational Psychology,88(1), 2141.
Gorero, L. G., & Balila, E. A. (2016). Mediated moderation effects of gender, year
level and learning approaches on attitude, teaching efficacy and
mathematics achievement of education students. Journal of International
Scholars Conference - EDUCATION/SOCIAL SCIENCES,1(2), 96105.
Gresham, G. (2007). A study of mathematics anxiety in pre-service teachers. Early
Childhood Education Journal,35(2), 181188.
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal
for Research in Mathematics Education,21(1), 33.
Hoffman, B. (2010). I think I can, but Im afraid to try: The role of self-efficacy
beliefs and mathematics anxiety in mathematics problem-solving efficiency.
Learning and Individual Differences,20(3), 276283.
Hopko, D. R., Mahadevan, R., Bare, R. L., & Hunt, M. K. (2003). The abbreviated
math anxiety scale (AMAS): Construction, validity, and reliability. Assessment,
10(2), 178182.
House, J. (2005). Mathematics beliefs and achievement of adolescent students in
Japan: Results from the TIMSS 1999 assessment. Psychological Reports,97,
Jackson, C. D., & Leffingwell, R. J. (1999). The role of instructors in creating math
anxiety in students from kindergarten through college. The Mathematics
Teacher,92(7), 583586.
Jansen, B. R. J., Louwerse, J., Straatemeier, M., Van der Ven, S. H. G., Klinkenberg, S.,
& Van der Maas, H. L. J. (2013). The influence of experiencing success in math
on math anxiety, perceived math competence, and math performance.
Learning and Individual Differences,24, 190197.
Konvalina, J., Wileman, S. A., & Stephens, L. J. (1983). Math proficiency: A key to
success for computer science students. Communications of the ACM,26(5),
Lehtamo, S., Juuti, K., Inkinen, J., & Lavonen, J. (2018). Connection between
academic emotions in situ and retention in the physics track: Applying
experience sampling method. International Journal of STEM Education,5(1),
Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning
mathematics as givenin STEM education. International Journal of STEM
Education,6(1), 44, s40594-019-01970199.
Luttenberger, S., Wimmer, S., & Paechter, M. (2018). Spotlight on math anxiety.
Psychology Research and Behavior Management,Volume 11, 311322. https://
Maciejewski, W., & Merchant, S. (2016). Mathematical tasks, study approaches, and
course grades in undergraduate mathematics: A year-by-year analysis.
International Journal of Mathematical Education in Science and Technology,47(3),
Martin-Hansen, L. (2018). Examining ways to meaningfully support students in
STEM. International Journal of STEM Education,5(1), 53.
Marton, F., & Säljö, R. (1976). On qualitative differences in learning: IOutcome
and process. British Journal of Educational Psychology,46(1), 411.
Marton, F., & Säljö, R. (1984). Approaches to learning. In F. Marton, D. Hounsell, &
N. J. Entwistle (Eds.), The Experience of Learning (pp. 3655). Scottish Academic
McCrae, R. R., & Costa, P. T. (2003). Personality in adulthood: A five-factor theory
perspective. New York: Guilford Press.
McMullan, M., Jones, R., & Lea, S. (2012). Math anxiety, self-efficacy, and ability in
British undergraduate nursing students. Research in Nursing & Health,35(2),
Mõttus, R., & Rozgonjuk, D. (2019). Development is in the details: Age differences
in the big five domains, facets, and nuances. Journal of Personality and Social
Murphy, P. E. L. (2017). Student approaches to learning, conceptions of
mathematics, and successful outcomes in learning mathematics. In L. N.
Wood & Y. A. Breyer (Eds.), Success in Higher Education (pp. 7593). Springer
Oberlin, L. (1982). How to teach children to hate mathematics. School Science and
Mathematics,82(3), 261261.
OECD. (2013a). Mathematics self-beliefs and participation in mathematics-related
activities. In OECD, PISA 2012 Results: Ready to Learn (Volume III) (pp. 87112).
OECD. (2013b). PISA 2012 results: Ready to learn (Volume III): Studentsengagement,
drive and self-beliefs. OECD.
OECD. (2014). PISA 2012 technical report.
PISA%202012%20Technical%20Report_Chapter%2016.pdf. Accessed 24 Apr
OECD. (2017). What is PISA? In OECD, PISA 2015 assessment and analytical
framework (pp. 1118). OECD.
Paechter, M., Macher, D., Martskvishvili, K., Wimmer, S., & Papousek, I. (2017).
Mathematics anxiety and statistics anxiety. Shared but Also Unshared
Components and Antagonistic Contributions to Performance in Statistics.
Frontiers in Psychology,8, 1196.
Papinczak, T., Young, L., Groves, M., & Haynes, M. (2008). Effects of a
metacognitive intervention on studentsapproaches to learning and self-
efficacy in a first year medical course. Advances in Health Sciences Education,
13(2), 213232.
Pelch, M. (2018). Gendered differences in academic emotions and their
implications for student success in STEM. International Journal of STEM
Education,5(1), 33.
Phan, H. P. (2011). Interrelations between self-efficacy and learning approaches: A
developmental approach. Educational Psychology,31(2), 225246. https://doi.
Postareff, L., Parpala, A., & Lindblom-Ylänne, S. (2015). Factors contributing to
changes in a deep approach to learning in different learning environments.
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 10 of 11
Learning Environments Research,18(3), 315333.
Prat-Sala, M., & Redford, Paul. (2010). The interplay between motivation, self-
efficacy, and approaches to studying. British Journal of Educational
Psychology,80(2), 283305.
R Core Team. (2020). R: A language and environment for statistical computing (3.6.
3) [Computer software]. R Core Team.
Revelle, W. R. (2018). psych: Procedures for personality and psychological research.
Royse, D., & Rompf, E. L. (1992). Math anxiety: A comparison of social work and
non-social work students. Journal of Social Work Education,28(3), 270277.
Rozgonjuk, D., Saal, K., & Täht, K. (2018). Problematic smartphone use, deep and
surface approaches to learning, and social media use in lectures. International
Journal of Environmental Research and Public Health,15(1).
Tobias, S., & Weissbrod, C. (1980). Tobias, S., & Weissbrod, C. Anxiety and
mathematics: An update. Harvard Educational Review,50(1), 6370.
Valk, A., & Marandi, T. (2005). How to support deep learning at a university. (F. E. H.
Tay, T. S. Chuan, & S. Han-Ming, Eds.; Vol. 200, pp. 191196). National
University of Singapore.
Vinson, B. M. (2001). A comparison of preservice teachersmathematics anxiety
before and after a methods class emphasizing manipulatives. Early Childhood
Education Journal,29(2), 8994.
Yamani, M., Almala, A., Elbedour, S., Woodson, K., & Reed, G. (2018). Math anxiety:
Trends, issues and challenges. Journal of Psychology and Clinical Psychiatry,
9(1), 00503.
Yusof, Y. bt. M., & Tall, D. (1998). Changing attitudes to university mathematics
through problem solving. Educational Studies in Mathematics,37(1), 6782.
Zan, R., & Martino, P. (2007). Attitude toward mathematics: Overcoming the positive/
negative dichotomy. The Montana Mathematics Enthusiast,3(1), 157168.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Rozgonjuk et al. International Journal of STEM Education (2020) 7:46 Page 11 of 11
... To the list, the attitudes of students towards STEM classes and their motivation towards science are also added [17]. However, students' feelings towards math-related activities have shown to be one of the most important factors influencing the selection and persistence on STEM careers [1,18,19], and therefore this research focus is on math self-efficacy and math anxiety levels. ...
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Studies have reported that there is a gender disparity wherein women do not study equally to men in bachelor’s degrees in science, technology, engineering, and math (STEM) areas, although they lead the race of having a better terminal efficiency rate in higher education. This research explores engineering students’ math anxiety and math self-efficacy levels, aiming to determine if there is a gender gap for this specific population. Data were collected from 498 students using adapted items from existing surveys. These items were translated to Spanish, and validity tests were used to establish content validity and reliability. A multivariate analysis of variance (MANOVA) was used to determine possible differences between male and female math anxiety and math self-efficacy levels. Male engineering students reported higher self-efficacy and lower math anxiety levels, and this difference was shown to be significant according to the MANOVA results. Findings of this research could help engineering educators to better understand how their students feel when they are practicing and performing math-related activities and what type of strategies could be designed when aiming to ameliorate female students’ math anxiety feelings.
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The role of mathematics in every aspect of life is beyond any doubt. Despite of its positive contributions there are several challenges which are attributed to it. The present study is an attempt to examine the Mathematics anxiety and its impact on the attitude of avoidance by the MPhil research scholars currently enrolled in public private universities in Karachi Sindh. This study is quantitative in nature as it examines the relationship between variables by using Likert scale survey questionnaire. Simple random sampling was used and in total 400 questionnaires were floated in public and private sector universities in Karachi. The gathered data was analyzed by using correlation and regression analysis. The findings of the study confirm the presence of impact of math anxiety on attitude of avoidance in Pakistani students. This requires concerned corners to examine and manage the situation for effective contribution of MPhil scholars at every level in the country. Results of the study paved need for exploring other dimensions like culture impact on this relationship. Further need for out of the box thinking in teaching students challenged by math anxiety also appeared as fruition of this effort.
... Likewise, Tapia and Marsh (2004) found that students with lower math anxiety had significantly more confidence than students with high math anxiety. Rozgonjuk et al. (2020) concluded that reducing students' mathematics anxiety could be helpful in boosting their mathematics confidence. Broadly speaking, the literature indicates that confidence in mathematics has a significant impact on math anxiety. ...
... The tree diagrams of the CRT method revealed temporal variations of these factors over the course of the semester. Specifically, from weeks 1-9, there was an interchange between the level of confidence and motivation (less than three or four) as the significant predictors of math anxiety which is consistent with the results of previous research (Akin & Kurbanoglu, 2011;Rozgonjuk et al., 2020;Tapia & Marsh, 2004;Zakaria & Nordin, 2008). There was an exception for week 4 and week 5, where the number of hours studied, and course level became the primary predictors of math anxiety. ...
... Tobias). There is a lack of current research on math anxiety at the university level (Andrews & Brown, 2015;Rozgonjuk et al., 2020). Increasing understanding of correlates of math anxiety may help to decrease this barrier to higher education and pursuit of STEM majors. ...
... Beilock & Maloney, 2015;Brunyé et al., 2013). Our results contradict those of a recent study that examined levels of math anxiety in STEM and social sciences students (Rozgonjuk et al., 2020). The authors found that type of major did not predict levels of math anxiety in their sample. ...
Many undergraduate students avoid mathematics classes due to math anxiety. This curtails options, particularly STEM majors where workers are needed and jobs are prevalent. This study aimed to investigate whether self-efficacy, mindfulness, and self-compassion predicted math anxiety. Participants of this study were undergraduate students (N = 345) from the Mathematics Department at a large Southeastern U.S. university. There was a significant difference in math anxiety scores between students pursuing STEM and non-STEM degrees. Non-STEM majors had higher scores on a measure of math anxiety. Hierarchical multiple regression results suggested that self-efficacy and self-compassion predicted math anxiety. There was a significant correlation, but not a predictive relationship, between mindfulness and math anxiety.
... This program provided synchronous and asynchronous curriculum resources, which were fully available online, and promoted teaching interaction through real-time video conferences, lectures, and discussions. been used to measure student change with respect to science (Bryan et al., 2011), technology (Shank & Cotten, 2014), engineering (Brown & Burnham, 2012), and mathematics (Rozgonjuk et al., 2020), and it has also played a vital role in students' career development (Luo et al., 2021b). Computational thinking uses computer science to solve a range of cognitive activities, such as problem-solving and system design, and it is a must-have skill for everyone, just like reading, writing, and arithmetic (Wing, 2006). ...
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As a result of COVID-19, various forms of education and teaching are moving online. However, the notion of an online STEM camp is still in its beginnings, and there is little relevant research and experience in this context. At the beginning of April 2021, the research team launched an online STEM charity camp with the theme of "Shen Nong Tastes Herbs." Participants included 113 third- and fourth-grade primary school students ranging from 8 to 12 years of age from four schools in Karamay, Xinjiang Uygur Autonomous Region with weak educational capabilities. The camp lasted for 3 days and included 7 activities, while remote teaching was accomplished through Dingtalk. Pre- and post-test questionnaires and interviews were used to explore the impact of this camp on students. We found that online STEM camps could improve students' self-efficacy, computational thinking, and task value, and there is a significant improvement in the self-efficacy (p = 0.000) and task value (p = 0.001) dimensions. In addition, students with high self-efficacy had higher scores in the other two dimensions. Finally, we summarized the experiences and gains of students and teachers and proposed suggestions for developing online camps based on this experience. [Table: see text]. Supplementary information: The online version contains supplementary material available at 10.1007/s10956-022-09967-y.
... Despite the problems defining how to improve STEM education, it is well understood that these degrees based on science and mathematics solve problems through the design and use of technology [5]. Thus, students who have developed good mathematical skills and can use this knowledge to solve problems are more common in STEM majors [6,7]. Due to the importance of mathematical skills among students studying STEM degrees today, finding programs with educational design strategies encouraging middle and high school students to engage in mathematics skills activities is more common [8]. ...
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Trained professionals in science, technology, engineering, and mathematics (STEM) are needed for a robust, science-based economy that incorporates various technologies' design, construction, and commercialization to address societal problems. However, keeping students interested in STEM subjects and achieving optimal performance is a challenging task. Math self-efficacy has shown to be one of the most important factors affecting students' interest in STEM majors and assessing this factor has been a great challenge for education researchers around the world due to the lack of calibrated and culturally adapted instruments. Observing this need, this seminal study conducted psychometric validation tests and cultural adaptations to the Mathematic Self-Efficacy Survey (MSES) aiming to measure this instrument in Spanish-speaking students in different STEM areas in Mexico. Data collected from 877 students were tested for validity using sequential exploratory factor analyses, and contextual modifications were performed and analyzed aiming to achieve cultural equivalency. Suggestions for continuing the adaptation and validation process of the MSES to Spanish language and STEM students' context are presented with the results of the exploratory factor analyses.
... Learners will gain confidence due to being equipped with fundamental ideas from the lower grade level when they advance to a higher level. As a result, anxiety about studying mathematics changed from having a good attitude towards it, resulting from the spiral progression approach in teaching (Rozgonjuk, 2020). Likewise, learners highly accepted the researcher-made flipped videos since the language used, intonation, and accent is familiar to them compared to the foreign recorded instructional videos taken from any social media platform (Ulker et al., 2021). ...
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Students need to be anxiety-free when it comes to Mathematics. This phenomenon is an unpleasant sensation that interferes with someone’s ability to communicate well with Mathematics. Although several studies have investigated the relationship between Mathematics anxiety and Mathematics proficiency, none has explored the effect of Mathematics anxiety on individual students’ Mathematics scores, particularly of Biology education students. A correlational analysis was performed with quantitative research method to explore the association between Mathematics scores and Mathematics anxiety. 68 participants were recruited for the study, using non-probability sampling, voluntarily and purposively. A student’s mathematics anxiety was assessed using a mathematical anxiety scale. The data was analysed by correlation and regression analysis to determine whether Mathematics anxiety was associated with the students’ success. The mathematical model is the best approximation of the relationship between variables described by the equation (3.6 + 0.0 A1 - 0.5 A2 - 1.6 A3 - 2.3 A4 - 2.9 A5). The results suggest that poor Mathematics skills increase with levels of anxiety. Students with the least amount of anxiety are predicted to get passing grades of A and B, whereas those with more anxiety are likely to get lower grades of D and E.
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During the last decades of higher education research, new student-centred learning environments have emerged with the emphasis on students’ own activity, responsibility, and independence for learning. Still, in the context of university mathematics, teacher-led instruction remains the most frequent instructional practice. Although the urgent need for developing more student-centred university mathematics learning environments is acknowledged in the literature, research focusing on this area is scarce. This doctoral dissertation addresses the research gap by creating new knowledge on how student-centred learning environments can support mathematics students’ quality of learning at university. To offer a holistic perspective, quality learning is conceptualised with three theoretical concepts, namely students’ approaches to learning, academic self-efficacy, and self-regulation of learning. The students’ approaches to learning (SAL) tradition comprehends an approach to learning as a combination of students’ aims for learning and the processes used to achieve them. Typically, two distinctive approaches are considered, a deep approach aiming to understand, and a surface approach aiming to reproduce knowledge. The tradition values a deep approach to learning and its development during university studies. The notion of academic self-efficacy refers to a person's belief in their ability to perform a specific task in a specific context. Self-efficacy has been identified as the strongest indicator of study success in higher education. In addition, self-efficacy has a central role in the disciplinary context of mathematics, as it increases especially women’s retention in mathematics-related majors. The notion of self-regulation of learning (SRL) characterises how students regulate their cognition, behaviour, motivation, and emotions to enhance their personal learning processes. In this doctoral dissertation, self-regulation of learning is viewed as both an individual and a social practice, and in this vein, the notion of co-regulation refers to a transitional process of acquiring self-regulation skills. Learning environment refers to “the social, psychological and pedagogical contexts in which learning occurs and which affect student achievement and attitudes” (Fraser, 1998). In this doctoral dissertation, the same students are investigated in two parallel student-centred mathematics learning environments, offering an opportunity to address the role of the context on students’ quality of learning. The two learning environments were chosen for their well-established but different student-centred instructional practices; Course A functioned within a typical lecture-tasks-small groups framework with the inclusion of student-centred elements, and Course XA was implemented with Extreme Apprenticeship, a form of inquiry-based mathematics education with a flipped learning approach. The results of this doctoral dissertation are based on both quantitative and qualitative data. The quantitative data consists of students who answered an electronic questionnaire in both courses (N=91). The questionnaire included items measuring students’ approaches to learning, self-efficacy, self-regulation of learning, and experiences of the teaching-learning environment. In addition, data collected during the courses (number of completed tasks, participation, and course exam results) were merged with the questionnaire data. All participants of the prior quantitative data collection point were invited for an interview on a voluntary basis. The qualitative data consists of 16 semi-structured interviews where the students reflected on their experiences in both learning environments. This doctoral dissertation summarises four studies, each articulating the quality of learning in the university mathematics context from different perspectives. Study I quantitatively contrasts students’ approaches to learning, self-efficacy, and perceptions of the learning environments in the two learning environments. In addition, the study identifies three student subgroups: 1) students applying a deep approach to learning, 2) students applying a surface approach to learning, and 3) students applying a context-sensitive surface approach to learning. Study II is a follow-up of Study I and takes a qualitative approach when contrasting the student subgroups and their aims for learning and the actualised learning processes in the two learning environments. Study III quantitatively examines gender-specific differences in self-efficacy, and Study IV takes a mixed-methods approach when contrasting students’ self- and co-regulation of learning in the two learning environments. The results of this doctoral dissertation show that there can be substantial variation in students’ quality of learning between different student-centred learning environments. The central elements of the learning environment contributing to the quality of learning were tasks, lectures, scaffolding, and student collaboration. In particular, student collaboration was focal in supporting students to move away from undesired learning practices, such as applying a surface approach to learning or unregulated learning. Moreover, the results demonstrate that disrupting the typical course structure by a flipped learning approach elicited various benefits for the quality of students’ learning. In this vein, this doctoral dissertation argues for a holistic approach to design university mathematics learning environments and promotes pedagogical development as a significant factor in supporting students to learn mathematics within higher education. Overall, this doctoral dissertation demonstrates how discipline-based higher education research can advance both the fields of university mathematics education and higher education towards the development of research-based student-centred learning environments.
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Mathematics is fundamental for many professions, especially science, technology, and engineering. Yet, mathematics is often perceived as difficult and many students leave disciplines in science, technology, engineering, and mathematics (STEM) as a result, closing doors to scientific, engineering, and technological careers. In this editorial, we argue that how mathematics is traditionally viewed as “given” or “fixed” for students’ expected acquisition alienates many students and needs to be problematized. We propose an alternative approach to changes in mathematics education and show how the alternative also applies to STEM education.
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We examined the extent to which the Big Five domains, 30 facets, and nuances (uniquely represented by individual questionnaire items) capture age differences in personality, expecting domains to contain the least and nuances the most age-related information. We used an Internet sample (N = 24,000), evenly distributed between ages of 18 and 50 years and tested with a 300-item questionnaire. Separately based on domains, facets, and items, we trained models to predict age in one part of the sample and tested their predictive accuracy in another part. Big Five domains predicted age with an accuracy of r = .28, whereas facets' (r = .44) and items' (r = .65) predictions were more accurate. Less than 15% of the sample was needed to train models to their optimal accuracy. Residualizing the 300 items for all facets had no impact on their predictive accuracy, suggesting that age differences in specific behaviors, thoughts, and feelings (i.e., items) were not because of domains and facets but mostly unique to nuances. These findings replicated in a multisample dataset tested with another questionnaire. We found little evidence that age differences only appeared nuanced because items referred to age-graded roles or experiences. Therefore, a substantial part of personality development may be uniquely ascribed to narrow personality characteristics, suggesting the possibility for a many-dimensional representation of personality development. Besides theoretical implications, we provide concrete illustrations of how this can open new research avenues by enabling to study systematic variations between traits. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Using PISA 2012 data, the present study explored profiles of mathematics anxiety (MA) among 15-year old students from Finland, Korea, and the United States to determine the similarities and differences of MA across the three national samples by applying a multi-group latent profile analysis (LPA). The major findings were that (a) three MA profiles were found in all three national samples, i.e., Low MA, Mid MA, and High MA profile, and (b) the percentages of students classified into each of the three MA profiles differed across the Finnish, Korean, and American samples, with United States having the highest prevalence of High MA, and Finland the lowest. Multi-group LPA also provided clear and useful latent profile separation. The High MA profile demonstrated significant poorer mathematics performance and lower mathematics interest, self-efficacy, and self-concept than the Mid and Low MA profiles. Same differences appeared between the Mid and Low MA profiles. The implications of the findings seem clear: (1) it is possible that there is some relative level of universality in MA among 15-year old students which is independent of cultural context; and (2) multi-group LPA could be a useful analytic tool for research on the study of classification and cultural differences of MA.
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Abstract A strong, positive science, technology, engineering, and mathematics (STEM) identity is a predictor of future career choice in a STEM field. In this commentary, major concepts are explored within and among four different research studies with implications regarding STEM or science identity. This commentary describes ways in which one can view STEM identity as its own construct—and how different experiences affect positive or negative influences upon the formation and continuation of STEM identity. A summary of external and internal factors is included with discussion of the pertinent points regarding facilitation and development of STEM identity within educational settings.
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Background Understanding student anxiety is an important factor for broadening the gender diversity of STEM majors due to its disproportionate and negative influence on women. To investigate how student anxiety is related to other academic emotions I conducted open-ended interviews with 19 university students and analyzed the data using emergent grounded theory. Emergent grounded theory uses inductive and deductive reasoning to develop a model of cognition and human behavior. Results Data analysis led to the development of a detailed theoretical model outlining connections among student anxiety, positive and negative academic emotions, self-regulated learning, and performance. In addition, the data highlight important emotional differences between men and women that have the potential to influence retention in STEM. Specifically, the model elaborates on the concept of a self-deprecating cycle driven by negative academic emotions and suggests that women may be more likely to become trapped in this cycle. Conclusion The model incorporates students’ emotions as a powerful influence on performance and can be used to inform strategies aimed at changing how university students experience and deal with emotions such as student anxiety.
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Anxiety disorders are some of the most widespread mental health issues worldwide. In educational settings, individuals may suffer from specific forms of test and performance anxiety that are connected to a knowledge domain. Unquestionably, the most prominent of these is math anxiety. Math anxiety is a widespread problem for all ages across the globe. In the international assessments of the Programme for International Student Assessment (PISA) studies, a majority of adolescents report worry and tension in math classes and when doing math. To understand how math anxiety takes effect, it has to be regarded as a variable within an ensemble of interacting variables. There are antecedents that facilitate the development of math anxiety. They concern environmental factors such as teachers’ and parents’ attitudes toward their students’ and children’s ability in math, societal stereotypes (eg, on females’ math abilities), or personal factors such as traits or gender. These antecedents influence a number of variables that are important in learning processes. Math anxiety interacts with variables such as self-efficacy or motivation in math, which can intensify or counteract math anxiety. Outcomes of math anxiety concern not only performance in math-related situations, they can also have long-term effects that involve efficient (or not-so-efficient) learning as well as course and even vocational choices. How can math anxiety be counteracted? A first step lies in its correct diagnosis. Questionnaires for the assessment of math anxiety exist for all age groups, starting at primary education level. Help against math anxiety can be offered on different levels: by educational institutions, by teachers and a change in instructional approaches, by parents, or by the affected person. However, much more research is needed to develop effective measures against math anxiety that are tailored to an individual’s characteristics and needs.
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Background There is a lack of students enrolling in upper secondary school physics courses. In addition, many students discontinue the physics track, causing a lack of applicants for university-level science, technology, engineering and mathematics (STEM) programmes. The aim of this research was to determine if it is possible to find a connection between academic emotions in situ and physics track retention at the end of the first year of upper secondary school using phone-delivered experience sampling method. We applied experience sampling delivered by phone to one group of students in one school. The sample comprised 36 first-year upper secondary school students (median age 16) who enrolled in the last physics course of the first year. Students’ academic emotions during science learning situations were measured using phones three times during each of four physics lessons. Results The logistic regression analysis showed that lack of stress predicted retention in the physics track. Conclusions Via questionnaires delivered by phone, it is possible to capture students’ academic emotions in situ, information on which may help teachers to support students emotionally during their physics studies. In addition, reflecting their situational academic emotions, students could perhaps make better-informed decisions concerning their studies in STEM subjects.
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Background Over the past decade, the prevalence of anxiety has increased among college-aged students and college counseling centers have become increasingly concerned about the negative impact of anxiety on students. While college in general can be stressful, college science classrooms have the potential to be especially anxiety-inducing because of the sometimes chilly and competitive environment of the class. Further, college science courses are increasingly being transitioned from traditional lecture to active learning where students take an active role in their learning, often through participating in activities such as clicker questions and group work. There is emerging evidence that suggests active learning activities may cause students to feel anxious, but no studies have thoroughly explored why active learning activities in science courses may increase students’ anxiety. Further, no studies have explored whether active learning activities can reduce students’ anxiety. In this exploratory interview study of 52 students enrolled in large-enrollment active learning college science courses, we investigate how three active learning practices, clicker questions, group work, and cold call/random call, increase and decrease students’ anxiety. Results Students reported that clicker questions and group work had the potential to both increase and decrease their anxiety. The way the active learning activity is implemented and the extent to which students perceive they benefit from the activity seems to influence the effect of the activity on students’ anxiety. Conversely, students reported that cold call and random call only increased their anxiety. From our interviews, we identified the fear of negative evaluation, or the sense of dread associated with being unfavorably evaluated while participating in a social situation, as the primary construct underlying students’ high levels of anxiety associated with speaking out in front of the whole class when they do not volunteer. Conclusion This study illustrates that active learning can both increase and decrease students’ anxiety depending on the way active learning is implemented. We hope that this study encourages instructors to create more inclusive active learning science courses by implementing active learning in ways that minimize students’ anxiety.
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Several studies have shown that problematic smartphone use (PSU) is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding) and positively correlated with a surface approach to learning (defined as superficial learning). The study participants were 415 Estonian university students aged 19–46 years (78.8% females; age M = 23.37, SD = 4.19); the effective sample comprised 405 participants aged 19–46 years (79.0% females; age M = 23.33, SD = 4.21). In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU.
This comprehensive volume provides teachers, researchers and education professionals with cutting edge knowledge developed in the last decades by the educational, behavioural and neurosciences, integrating cognitive, developmental and socioeconomic approaches to deal with the problems children face in learning mathematics. The neurocognitive mechanisms and the cognitive processes underlying acquisition of arithmetic abilities and their significance for education have been the subject of intense research in the last few decades, but the most part of this research has been conducted in non-applied settings and there’s still a deep discrepancy between the level of scientific knowledge and its implementation into actual educational settings. Now it’s time to bring the results from the laboratory to the classroom. Apart from bringing the theoretical discussions to educational settings, the volume presents a wide range of methods for early detection of children with risks in mathematics learning and strategies to develop effective interventions based on innovative cognitive test instruments. It also provides insights to translate research knowledge into public policies in order to address socioeconomic issues. And it does so from an international perspective, dedicating a whole section to the cultural diversity of mathematics learning difficulties in different parts of the world. All of this makes the International Handbook of Mathematical Learning Difficulties an essential tool for those involved in the daily struggle to prepare the future generations to succeed in the global knowledge society.