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Leading scholars in the field of second language (L2) moti-
vation have speculated whether learning an L2 is a “special
case” (Ushioda, 2012a, 2012b). This speculation stems
from a long-standing assumption in the language motiva-
tion field that learning an L2 is different from other school
subjects. Historically, the genesis of this notion may be
traced back to the work of Gardner and Lambert (1959; see
Al-Hoorie, 2017, for a historical overview). According to
. . . the second language course is very different from other
courses in the student’s curriculum. Other courses such as
mathematics, history, and geography, all involve aspects of the
student’s own culture, or at least perspectives of his or her own
culture . . . When confronted with modern languages, however,
students face material from another cultural community.
Moreover, students are not asked simply to learn about the
language; they are required to learn the language, to take it in, as
it were, and make it part of their behavioural repertoire. The
words, sounds, grammatical principles and the like that the
language teacher tries to present are more than aspects of some
linguistic code; they are integral parts of another culture. (p. 6)
That learning a second language is, at the motivational
level, distinct from learning other school subjects has become
a central assumption in the language motivation field. For
example, Dörnyei (2005) argued that this idea “has been
accepted by researchers all over the world, regardless of the
actual learning situation they were working in” (p. 67). He
also described it as a “breakthrough” that has “rightfully
influenced the motivation research [for] decades” (Dörnyei,
1994b, p. 519). (For similar arguments, see Dörnyei, 2003,
pp. 3–4; 2009, p. 9; Williams, 1994, p. 77.)
These views and conceptualizations point to a central
conclusion: The motivation to learn an L2 is qualitatively
different from learning other school subjects, and thus L2
motivation requires theories that suit its distinctive nature. In
this article, we refer to this assumption as the fundamental
difference hypothesis in language motivation. We adapted
this term from Bley-Vroman’s (1990) fundamental differ-
ence hypothesis, which posits that L1 and L2 acquisition are
fundamentally different because younger learners use
domain-specific linguistic mechanisms, whereas older learn-
ers can only use domain-general problem-solving skills. The
fundamental difference hypothesis in language motivation
945702SGOXXX10.1177/2158244020945702SAGE OpenAl-Hoorie and Hiver
1Royal Commission for Jubail and Yanbu, Jubail, Saudi Arabia
2Florida State University, Tallahassee, USA
Ali H. Al-Hoorie, English Language and Preparatory Year Institute, Royal
Commission for Jubail and Yanbu, Jubail Industrial City 31961, Saudi Arabia.
The Fundamental Difference Hypothesis:
Expanding the Conversation in Language
Ali H. Al-Hoorie1 and Phil Hiver2
In this study, we examine the fundamental difference hypothesis in language motivation, which suggests that language
learning—at the motivational level—is qualitatively different from learning other school subjects. Despite being a long-
standing assumption, few investigations have directly examined it. Using a comparative cross-sectional approach, we adapted
the L2 Motivational Self System and collected data from South Korean high school students (N = 644) related to their
motivation to learn English (L2), Chinese (L3), and mathematics (a nonlanguage subject). Contrary to the fundamental
difference hypothesis, the L2 Motivational Self System fit these three subjects well and did not reveal clear uniqueness
pointing toward a qualitative difference in favor of language learning motivation. We use these findings to discuss the
possibility of a more global and parsimonious learning motivation theory to accommodate multiple languages in addition to
nonlanguage subjects. We also discuss the need for language learning researchers to reengage with other learning sciences.
fundamental difference hypothesis, language motivation, mathematics motivation, L2 Motivational Self System, ethnocentrism
2 SAGE Open
posits that learning a language is fundamentally different
from learning other school subjects.
In this article, we start by examining the basis of this
assumption and how it was rationalized by L2 motivation
theorists. We then review comparative research investigating
the motivational processes of learning second languages and
of learning other nonlanguage subjects. Building on this
analysis, we finally present our study, which compares learn-
ing English (L2), Chinese (L3), and mathematics (a nonlan-
guage subject) to further shed light on this assumption.
What Makes L2 Motivation Different?
Theorists positing unique motivational processes in the con-
text of L2 learning consistently evoke the role of identity.
Identity accounts revolve around two major grounds: Identity
construction and identity subtraction. In terms of identity
construction, “acquisition involves making the language part
of the self” (Gardner, 2010, p. 7). van Lier (2007) further
explained that L2 learning “involves a struggle to forge a
new identity” (p. 47).
From this perspective, language learning is not a matter of
discrete linguistic elements of a communication code to be
learned. Instead, language learning is presumed to be deeply
social requiring incorporation of social elements belonging
to a different linguistic community (Dörnyei, 2003). To suc-
cessfully learn an L2, the learner is expected to go beyond
skills, rules, and grammar; they are expected to additionally
engage in alteration of self-image, adoption of cultural and
social behaviors, and new ways of being (Williams, 1994).
Similarly, Noels and Giles (2009) pointed out that there is
general agreement about the involvement of self and identity
processes in L2 learning regardless of scholars’ ontological,
epistemological, and methodological standpoints. Simply
put, “This view has been broadly endorsed by L2 research-
ers” (Dörnyei, 2003, p. 4).
These conceptualizations have colored language motiva-
tion theories and frameworks. For example, Gardner’s (1979,
1985) early work postulated four different aspects of the
learning process: social milieu (cultural and educational
backgrounds), individual differences (intelligence, aptitude,
motivation, and anxiety), acquisition contexts (formal vs.
informal), and outcomes (linguistic vs. nonlinguistic). The
integrative motive (Gardner, 2010) suggests that identifica-
tion with L2 speakers and a positive outlook toward them
contributes to L2 learning success. Following these steps,
Dörnyei (2009) argued that developing an ideal L2 self simi-
larly involves a critical cultural element related to the speak-
ers of the L2 group: “it is difficult to imagine that we can
have a vivid and attractive ideal L2 self if the L2 is spoken by
a community that we despise” (Dörnyei, 2009, p. 28).
This argument was taken a step further with the advent of
globalization. Learning English is argued to be qualitatively
different from learning other languages since English has
become a global language not associated with a specific
language community (see Dörnyei & Al-Hoorie, 2017). In
recognition of the increasing globalization in English learn-
ing, Lamb (2004) argued that L2 learners no longer affiliate
with a specific (i.e., Anglophone) community but a more
global community of multilingual language speakers. Going
even further, Norton (2013) argued that L2 learners may
identify with imagined communities. These communities
may be historical reconstructions or completely imagined
communities that promise identity enhancement opportuni-
ties, thus investment in L2 learning.
The second process presumed to make L2 learning
unique is identity subtraction. According to Lambert
(1973), L2 learning may trigger uneasiness in learners who
feel that L2 learning is associated with L1 loss. Although
relatively less attention has been paid to identity subtrac-
tion processes relative to identity construction, the conse-
quences of identity subtraction can be found in a number of
theoretical accounts (see Al-Hoorie, 2016a, 2016b). For
instance, Clément’s (1980) sociocontextual model empha-
sizes the acquisition of norms, values, and behaviors of the
L2 culture. Clément, following Lambert’s lead, argued that
language learning involves “a delicate balance” (p. 148)
between the status of the L1 and L2 communities, which
then leads either to openness to the L2 community and cul-
ture or to fear of assimilation. Schumann’s (1975) accul-
turation model also underscores openness to the L2 group,
since the successful L2 learner is expected to be “non-eth-
nocentric, non-authoritarian and non-machiavellian” (p.
218). More recent approaches, while expanding under-
standing in the field, remain allied with this characteriza-
tion. Emphasizing openness to L2 speakers and cautioning
against ethnocentrism and fear of assimilation, Yashima
(2002) introduced the notion of international posture,
which, among other things, involves a nonethnocentric atti-
tude toward different cultures.
Thus, for decades, L2 motivation scholars have argued
that L2 motivation is unique and inherently different from
other school subjects due to the involvement of identity con-
struction and subtraction processes. This view has naturally
resulted in an abundance of “native” motivation theories that
are specific to L2 learning. On the flip side, this view has
also resulted in relatively sparse engagement with motiva-
tion theories in other fields (see Oga-Baldwin et al., 2019).
Such engagement is sometimes characterized by wariness
given that those motivation theories need to be adapted and
filtered through an L2 lens, which Dörnyei (2009) considers
a “challenge” (p. 34). From this perspective, when L2 moti-
vation researchers do engage with and adapt theoretical
frameworks originally developed for other subjects, they still
have to account for the underlying assumption that L2 moti-
vation is special, unique, and different. If a motivation theory
successfully accounts for motivation in, say, mathematics, it
may still need to be somehow “tailored” to fit the distinctive
nature of L2 learning. For example, Noels et al. (2000)
adapted their intrinsic motivation scale to reflect the belief
Al-Hoorie and Hiver 3
that “an individual’s motivation to learn an L2 is sustained
by both attitudes toward the L2 community and the goals, or
orientations, sought through the acquisition of the L2” (p.
36). Curiously, motivation research into other school sub-
jects has expressed sentiments similar to various tenets found
in the L2 field, as explained in the next section.
Motivation in Nonlanguage Subjects
Looking on the other side of the fence, so to speak, we find
that education scholars outside of the language sciences have
over the decades studied topics that are parallel to topics
associated with L2 motivation. Work in school subjects, such
as mathematics, acknowledges that social, and cultural, and
even political dimensions shape education and learning out-
comes in classrooms (e.g., Clements et al., 2013). Just like
L2 learning, forging a new identity has been argued to play a
key role in learning mathematics (e.g., Boaler, 2002; Darragh,
2016; Gutiérrez, 2013; Nasir & de Royston, 2013). In fact, it
is exactly the lack of identity engagement in mathematics
that is thought to have led many students to dislike this sub-
ject and fail to achieve satisfactory competence in it. As
Boaler and colleagues explain,
It is our contention that any explanation of what happens in
mathematics classrooms will be incomplete if it ignores the
essentially social nature of schooling. The students who are
learning mathematics in secondary schools are also trying to
negotiate conflicting constraints in developing their identities . .
. Most students want to be successful at school, not least to avoid
conflict with parents, but they also want to negotiate a way of
being successful that does not alienate them from groups with
whom they feel affinity. In some cases, the playing out of these
social [processes] will lead students towards particular
individuals or groups, while in others, it will be influenced by a
desire not to be like an individual or a group. (Boaler et al., 2000
p. 10, original emphasis)
The same applies to the argument that L2 learning
involves notions of power, conflict, and social struggles
(e.g., Noels & Giles, 2009; Pavlenko & Blackledge, 2004).
Again, a very similar situation is found in STEM (science,
technology, engineering, and mathematics) subjects. For
instance, systematic inequalities based on gender, ethnicity,
and socioeconomic status continue to abound both in the
United States (National Science Foundation, 2017, pp. 6–9)
and the United Kingdom (State of the Nation, 2016, pp.
59–62), two of the most progressive countries in the world.
In fact, the commission of the State of the Nation (2016)
expressed this notion very clearly: “Over recent years there
has been growing concern in our country—and across many
developed nations—that the link between demography and
destiny is not weakening, but strengthening” (p. 1).
There is now a wealth of research into STEM indicating
that learning experiences structured interactively in collab-
orative contexts positively impact learners’ values and
achievement (Webel, 2013; Wood & Kalinec, 2012), and
that race, gender, and identity impact how students navigate
the educational landscape (Nasir & de Royston, 2013;
Nosek et al., 2002). Just like L2 learning, research has also
demonstrated that mathematics learning implicates anxiety
along with various other emotions (Hannula, 2015; Zan
et al., 2006). Research on mathematics has also shown how
developing key learning strategies contributes to learners’
classroom success (Murayama et al., 2013) and the way
self-concept and other self-relevant beliefs influence on-
task behaviors and classroom engagement with the subject
matter (Skaalvik et al., 2015; Usher & Pajares, 2009). The
debate regarding the optimal age to commence deliberate
learning is also equally robust in STEM education (e.g.,
Cannon & Ginsburg, 2008). Similarly, layperson notions
that mathematics requires more direct instruction and has
only distant connections to the daily activities of learners
are not at all consistent with the social-constructivist turn in
education research (e.g., Lerman, 2000). Finally, work
explicitly examining the challenges teachers experience as
they dynamically transition from being a learner of mathe-
matics to a teacher of mathematics—concepts language
teacher research would find germane and current—is well-
established (Jones et al., 2000).
This review shows that when considering the literature on
learning other school subjects, similarities reveal themselves
and point to a great deal of overlap with the literature on L2
motivation. This raises the question as to what makes L2
motivation unique and special. The following section reviews
empirical research investigating this issue.
Perhaps the most direct way to examine uniqueness of L2
learning motivation is to empirically compare the motivation
across different school subjects. Some comparative research
has shown that, interestingly, L2 motivation models may
actually be applicable to other school subjects as well. For
example, in their paper “Statistics as a Second Language,”
Lalonde and Gardner (1993) applied Gardner’s socio-educa-
tional model to learning statistics and found that the model
fit the data and explained both effort and eventual achieve-
ment in statistics over and above mathematical aptitude.
Lalonde and Gardner argued that
. . . the social factors involved in learning statistics are very
similar to those involved in the acquisition of a second language
. . . For example, both statistics and a second language are
associated with a particular group of individuals who use them
(e.g., the French vs. individuals who engage in empirical
research), both involve new vocabularies that are foreign to the
learner (e.g., “le plus-que-parfait” vs. “sampling distributions”),
and both are capable of eliciting affective responses when they
are spoken to an individual learner (e.g., anxiety). (Lalonde &
Gardner, 1993, pp. 111–112)
4 SAGE Open
Another, more recent, study by MacIntyre et al. (2012)
tested the socio-educational model on learning music. Music
integrativeness in MacIntyre et al.’s study, as is the case with
L2 integrativeness, involves an interest in taking on the char-
acteristics of musicians. Despite the sharp contrast between
statistics and music—one being a representation of the math-
ematical sciences and the other the arts—the same conclu-
sion was reached. The socio-educational model showed
strong fit with the data and accounted for both perceived
competence in music and self-reported achievement level.
Another longitudinal study by Fryer and Oga-Baldwin
(2017) compared self-efficacy in the context of L1, L2, and
mathematics. Their results showed similar dynamics among
the three subjects. Analysis of change in self-efficacy
revealed a consistent pattern of decline over time across all
subjects (see Al-Hoorie, 2019). In another longitudinal
study on these three subjects, Fryer and Oga-Baldwin
(2019) found, again, that their results supported the shared
role of intrinsic motivation and self-efficacy in achieve-
ment. Across all three subjects, they further found recipro-
cal relations between learner motivation and beliefs, on one
hand, and perceptions of instruction, on the other (see also
Oga-Baldwin & Fryer, 2020).
Some research did point out some fine-grained, domain-
specific results. In their longitudinal study, Arens et al.
(2019) found L2 learning and mathematics to actually be
more similar to each other than L1 learning. For the L1, prior
value perceptions (intrinsic value and attainment value) had
an impact on later self-perceptions of competence. For L2
and mathematics, the opposite pattern was observed in that
former perceptions of competence had an impact on later
value perceptions. To quote Arens et al. (2019), L2 and math-
ematics “are perceived as defined, homogenous, sequential,
and static school subjects” (p. 678). Such patterns do not
strike us as pointing toward any fundamental difference in
one domain over another. They can be explained by a
Chomskyan principles versus parameters metaphor: The
parameters governing motivational dynamics in each domain
might vary, but they are well within the general principles
(e.g., competence, intrinsic value, attainment value) of con-
ventional theories of educational motivation (see also Huang,
2008; Trautwein & Lüdtke, 2007).
Some comparative research has also been conducted
within the L2 Motivational Self System framework (L2MSS;
Dörnyei, 2005, 2009) Unlike the socio-educational model,
the L2MSS has its roots in psychological research rather than
language learning-specific research. Therefore, it would be
reasonable to expect that it is possible to apply it “back” in
other non-L2 domains (see, for example, Henry, 2010, 2017;
Henry & Thorsen, 2018). Indeed, in a study by Taylor et al.
(2013), the researchers found similar patterns in L2 and
mathematics in four European countries: Bulgaria, Germany,
the Netherlands, and Spain. Thus, there seems to be little
empirical evidence suggesting that the patterns and processes
underlying L2 motivation are unique.
The Present Study
In this article, we do not dispute the association between
language learning and identity or a community of multilin-
gual speakers (local, global, or imagined), findings that
draw on decades of research (Kramsch, 2008; van Lier,
2004). Instead, our aim was to investigate the question of
whether—at the motivational level—there is any evidence
that such patterns and processes are unique to L2 learning
versus other school subjects. We investigated this question
by comparing motivation to learn English (a global L2),
Chinese (a nonglobal L3), and mathematics (a nonlanguage
subject). As reviewed above, L2 learning has been con-
strued as inherently different from learning other highly
valued subjects, such as mathematics, in terms of some core
educational characteristics including social implications,
type of activities and tasks involved, teacher’s role and
teaching approach, and cognitive demands (Dörnyei &
Ryan, 2015). Furthermore, it has been argued that English
in particular has acquired a special status relative to other
languages, making it qualitatively different (Dörnyei &
In the context of this study (South Korea), there are some
key structural contingencies that make the classroom learn-
ing of foreign languages—primarily English, but to a lesser
extent also Chinese—of equal instrumental utility to learning
mathematics. In the present setting, there is also relative par-
ity with regard to the social value ascribed to mathematics
and L2 achievement, an occurrence influenced by the out-
sized weighting each is given on standardized assessments in
compulsory education, and in the perceived importance of
each in tertiary education that serves as a metric of success
for subsequent entry to the workplace. In practice, then, these
both serve as social stratification metrics because success in
classroom language learning and in mathematics is key to
success at various stages of life and in many areas of society,
and failure in either one of these target domains could rele-
gate an individual to lower-tier learning institutions and
types of employment perceived as being less desirable.
In our study, we investigated the following research
Research Question 1 (RQ1): Does the L2MSS model
of language learning motivation achieve fit with each
of the three school subjects (English, Chinese, and
For this research question, we investigated the model rep-
resented in Figure 1. Following previous research (e.g.,
Al-Hoorie, 2016b; Lamb, 2012), Figure 1 hypothesizes
that while intended effort is predicted by all exogenous
variables in the model, actual achievement is primarily
predicted by the learner’s prior achievement. Meta-analytic
research (Al-Hoorie, 2018) has shown that the ideal L2
self, the ought-to L2 self, the L2 learning experience, and
Al-Hoorie and Hiver 5
intended effort are all weak and nonsignificant predictors
of L2 achievement. However, as some recent evidence
suggests that the L2 learning experience might be a signifi-
cant predictor of achievement in multilingual research
(Huang, 2019), we hypothesized a link between the L2
learning experience and achievement. Furthermore, some
research has suggested that it might actually be higher
achievement that fosters intended effort, rather than vice
versa (Hiver & Al-Hoorie, 2020a). To reflect this ambigu-
ous causal directionality, we allowed the residuals of these
two variables to covary. (We additionally explored the pos-
sibility that achievement is predicted by the ideal L2 self
and the ought-to L2 self.) We tested this model on each of
the three school subjects separately and examined their
model fit indices. We reasoned that if the L2MSS was cap-
turing uniqueness in the motivation for language learning,
the model would fit well in the case of L2 English and
L3 Chinese, but poorly in the case of mathematics.
Alternatively, if it fit all three subjects, this would provide
no evidence for a fundamental difference.
Research Question 2 (RQ2): Do ethnocentrism and fear
of assimilation predict intended effort and achievement
equally across the three school subjects?
For this research question, we investigated the model in
Figure 2. We reasoned that if language learning was indeed
distinct in that it is related to cultural impact and identity
subtraction, then—logically—ethnocentrism and fear of
assimilation would exhibit a negative association with lan-
guage-related outcome variables. At the same time, there
would be no association between mathematics-related out-
comes and the extent to which the learner espouses ethnocen-
tric and fear of assimilation tendencies.
Having two language subjects, rather than just one, serves
as a more rigorous test of any fundamental difference
between learning a language versus learning another subject.
This is because, if this hypothesis was valid, differences
would have to be consistent: The two language subjects
should be more similar to each other than to mathematics. By
testing our comparisons with two languages, we therefore
intended to minimize the likelihood of Type I error.
These two research questions were preregistered prior to
data collection (a time-stamped copy can be obtained at
https://osf.io/h63sb). Preregistration involves specifying in
advance research questions, detailed study design, as well as
the analytical strategy and statistical models. This aims to
demarcate exploratory versus confirmatory research and to
minimize researcher degrees of freedom, which can bias the
results in favor of preferred or anticipated outcomes.
For completeness, we also performed exploratory fol-
low-up analyses. We examined whether the paths in Figures
1 and 2 vary across the three school subjects. We also exam-
ined whether intended effort, the ideal self, and the ought-to
self predict achievement. These follow-up analyses were not
preregistered and are therefore exploratory.
Figure 1. The model tested in the first research question.
6 SAGE Open
The participants in this study were 10th through 12th grade
students (N = 644, female = 349, age range = 16–18) sam-
pled from eight high schools in the most populous regions of
South Korea—the capital (n = 222), the province surround-
ing the capital (n = 308), and the southern-most province
(n = 114). In the Korean educational system, students take
compulsory mathematics and English (as an L2) classes. All
Korean high schools also offer some form of mandatory L3
class, and although European languages have tended to dom-
inate, in the past several years, Mandarin Chinese has become
an increasingly popular choice for this high school gradua-
tion requirement (Kim, 2014) due in part to Korea’s geo-
graphic proximity to China and the perceived current and
future importance of the language.
Seven-point Likert-type scales were adapted from the
L2MSS literature to measure the ideal self, the ought-to self,
the learning experience, and intended effort. Following the
design of previous comparative studies (e.g., Trautwein &
Lüdtke, 2007), these scales used parallel wording to address
each school subject (English, Chinese, and Mathematics),
ensuring the data elicitation measures are comparable. Two
further scales, related to ethnocentrism and to fear of assimi-
lation, were also administered (see the appendix for all
items). Table 1 presents the reliabilities of all scales. A higher
score in each scale reflects a higher level in the respective
trait. The participants also reported their final grades from
the previous year and from the current year for each of the
three school subjects.
The questionnaire was first translated into the respondents’
L1 (Korean) by a nonaffiliated researcher and then back-
translated by the authors for consistency. Following institu-
tional review board (IRB) approval, we formally approached
administration and teaching faculty at a number of schools to
obtain both institutional and parental consent. Students from
the schools that had agreed to participate completed the
Figure 2. The model tested in the second research question.
Al-Hoorie and Hiver 7
survey in the week following their winter finals (i.e., a period
of time typically used for remedial work or to wrap up the
semester). The research assistant administering the question-
naire reminded the respondents that participation was volun-
tary and assured them of the confidentiality of their responses.
Throughout all data collection, the participants were treated
in accordance with APA ethical guidelines.
The response rate was satisfactory (81%). However, stu-
dents from one school (n = 87) had the choice to opt out of
the regular high school L3 requirements, and thus students
at this school (an STEM-track high school) were not taking
Mplus 7 (Muthén & Muthén, 1998–2012) was used for all
analyses. Missing data were handled using the default func-
tion in Mplus, which estimates the model under missing
data theory using all available data. Standard errors and chi
square tests were corrected to account for nonindependence
of observations, as participants came from 26 classrooms.
A robust weighted least squares (WLSMV) estimator using
a diagonal weight matrix was used, which is a standard
approach to handle ordinal data. We allowed the predictors
to covary, and the residuals of the two outcome variables to
covary, to reflect their noncausal and unclear causal rela-
To answer the first research question, we examined the
model fit for each school subject. For the second research
question, we constrained the paths from ethnocentrism and
from fear of assimilation to be equal across the three school
subjects. We then examined (using the Wald test) whether the
model fit deteriorated significantly as a result of this equality
constraint. Deterioration of model fit would indicate that the
paths are not equal across the three school subjects. We fol-
lowed standard structural equation modeling (SEM) guide-
lines for model fit indices: comparative fit index (CFI),
Tucker–Lewis index (TLI) (more than .95, or at least .90),
and root mean square error of approximation (RMSEA; less
than .06, or at least .08).
In our data analysis, we followed our preregistration
protocols. We, however, deviated from these protocols with
respect to one point. While testing the model represented in
Figure 2 to answer the second research question, we
obtained an error related to the matrix not being positive
definite. This error was resolved only after excluding the
ideal self, the ought-to self, and the learning experience.
However, for the purpose of the second research question—
which is concerned specifically with ethnocentrism and
fear of assimilation—these three variables are not relevant.
We therefore do not believe that excluding these variables
had an impact on our results.
Finally, in our preregistration form, we described the pro-
cedure of the second research question as a “multiple-group
SEM.” This may be misleading, as multiple-group SEM
requires independent samples responding to the same vari-
ables (e.g., related to one school subject). In our case, we
had the same sample responding to different school sub-
jects. However, the actual analytical procedures we fol-
lowed are the same as those described in our preregistration
form and in this article.
We first conducted a confirmatory factor analysis to investi-
gate the measurement model for each school subject sepa-
rately. Tables 2 to 4 present the factor loadings for the three
subjects, showing that most standardized factor loadings are
in excess of .70 and higher. Table 5 presents the reliability
and validity of each construct, again showing equivalent lev-
els across the three school subjects. Finally, the overall fit
(Table 6) shows little difference across the three subjects.
Table 1. Scales Used in This Study and Their Reliabilities for Each School Subject.
Scale No. of items αAdapted from
Ideal Self 5 .89 (L2)
Taguchi etal. (2009)
Ought-to Self 5 .84 (L2)
Learning Experience 5 .91 (L2)
Intended Effort 4 .88 (L2)
Ethnocentrism 4 .71 Neuliep & McCroskey (1997)
Fear of Assimilation 4 .78 Ryan (2009)
8 SAGE Open
Table 2. Standardized and Unstandardized Factor Loadings,
Standard Errors, and z Ratios of the Measurement Model for
Path βB SE(β)z
Ideal Self →Ideal1 .75 — 0.020 37.08
Ideal2 .85 1.14 0.012 71.51
Ideal3 .84 1.14 0.016 53.16
Ideal4 .85 1.15 0.018 47.68
Ideal5 .84 1.12 0.016 53.60
Ought-to Self →Ought1 .80 — 0.015 54.86
Ought2 .79 0.99 0.016 48.29
Ought3 .78 0.98 0.023 33.69
Ought4 .68 0.85 0.025 27.46
Ought5 .74 0.93 0.023 31.83
→Learning1 .90 — 0.008 106.43
Learning2 .84 0.93 0.011 78.83
Learning3 .92 1.02 0.009 103.07
Learning4 .81 0.90 0.014 57.68
Learning5 .77 0.86 0.019 40.75
Intended Effort →Intended1 .83 — 0.019 42.98
Intended2 .87 1.04 0.019 45.86
Intended3 .80 0.97 0.017 47.97
Intended4 .77 0.98 0.015 53.36
Ethno1 .59 — 0.019 30.60
Ethnocentrism →Ethno2 .73 1.25 0.028 26.05
Ethno3 .69 1.18 0.029 23.97
Ethno4 .70 1.19 0.024 28.53
FoA1 .83 — 0.021 40.28
→FoA2 .85 1.03 0.020 43.27
FoA3 .72 0.87 0.029 24.91
FoA4 .55 0.67 0.024 22.83
Note. All coefficients significant at the .001 level.
Table 3. Standardized and Unstandardized Factor Loadings,
Standard Errors, and z Ratios of the Measurement Model for
Path βB SE(β)z
Ideal Self →Ideal1 .75 — 0.018 42.66
Ideal2 .87 1.15 0.011 82.81
Ideal3 .83 1.11 0.016 53.66
Ideal4 .86 1.14 0.009 92.55
Ideal5 .81 1.08 0.014 59.63
Ought-to Self →Ought1 .78 — 0.020 39.62
Ought2 .77 1.00 0.016 47.09
Ought3 .75 0.97 0.025 30.36
Ought4 .70 0.90 0.022 31.23
Ought5 .77 0.99 0.022 35.02
→Learning1 .91 — 0.007 122.26
Learning2 .85 0.94 0.011 78.26
Learning3 .91 1.00 0.008 117.68
Learning4 .85 0.94 0.009 95.82
Learning5 .83 0.91 0.011 72.07
Table 4. Standardized and Unstandardized Factor Loadings,
Standard Errors, and z Ratios of the Measurement Model for
Path βB SE(β)z
Ideal Self →Ideal1 .79 — 0.012 67.91
Ideal2 .88 1.12 0.011 79.25
Ideal3 .90 1.14 0.009 96.86
Ideal4 .89 1.14 0.009 95.98
Ideal5 .89 1.13 0.010 88.00
Ought-to Self →Ought1 .81 — 0.014 59.06
Ought2 .84 1.03 0.015 57.08
Ought3 .87 1.07 0.012 73.76
Ought4 .74 0.91 0.021 35.71
Ought5 .85 1.05 0.014 62.27
→Learning1 .91 — 0.007 127.98
Learning2 .87 0.96 0.010 86.32
Learning3 .91 1.01 0.007 136.33
Learning4 .83 0.92 0.009 88.46
Learning5 .81 0.90 0.016 50.34
Intended Effort →Intended1 .90 — 0.009 94.56
Intended2 .90 1.01 0.009 105.80
Intended3 .90 1.01 0.009 103.42
Intended4 .89 1.00 0.009 104.25
Ethnocentrism →Ethno1 .61 — 0.020 29.99
Ethno2 .72 1.20 0.029 24.50
Ethno3 .69 1.14 0.034 20.14
Ethno4 .69 1.14 0.026 26.55
→FoA1 .82 — 0.024 34.33
FoA2 .86 1.04 0.023 37.55
FoA3 .72 0.87 0.029 24.81
FoA4 .55 0.67 0.025 21.81
Note. All coefficients significant at the .001 level.
Table 3. (continued)
Path βB SE(β)z
Intended Effort →Intended1 .81 — 0.014 57.37
Intended2 .87 1.07 0.008 102.32
Intended3 .81 1.00 0.011 72.15
Intended4 .84 1.04 0.012 67.61
Ethnocentrism →Ethno1 .58 — 0.023 25.31
Ethno2 .73 1.07 0.034 21.80
Ethno3 .70 1.00 0.028 24.58
Ethno4 .70 1.04 0.024 29.50
→FoA1 .82 — 0.023 35.83
FoA2 .85 1.27 0.020 42.04
FoA3 .73 1.21 0.031 23.62
FoA4 .55 1.21 0.025 21.80
Note. All coefficients significant at the .001 level.
These results suggest that, had the L2MSS been originally
developed for L3 or Mathematics, it would have served that
purpose equally well.
Al-Hoorie and Hiver 9
RQ1: Model Fit Across the Three Subjects
The first research question aimed to investigate the structural
model of each of the three school subjects. Table 7 presents
the path coefficients for each subject, whereas Table 8 pres-
ents the model fit. Generally speaking, the results are very
similar across the three subjects. CFI, TLI, and RMSEA
showed adequate fit, with the only exception of a minor mis-
fit for L3 which is nonetheless within the acceptance range.
A notable observation from Table 7 is that the Learning
Experience was a significant predictor of Post Achievement
only in the case of Mathematics.
Finally, our model proposed no association between
Intended Effort and Post Achievement (see Figure 1). To find
out whether this was indeed the case, we inspected their
residuals in each of the three school subjects. The residuals
did not covary significantly either for L2 (θ = −.095, p =
.095) or L3 (θ = .081, p = .151). However, it did reach sig-
nificance in the case of Mathematics (θ = −.14, p = .019),
though the relationship was negative (rather than positive as
implied by using Intended Effort as a criterion measure).
This pattern suggests that those who report an intention to
work harder are those who are already struggling.
RQ2: Ethnocentrism and Fear of Assimilation
Across the Three Subjects
The second research question was concerned with whether
Ethnocentrism and Fear of Assimilation would be significant
predictors in the language subjects but not in Mathematics.
The results in Table 9 showed that both Ethnocentrism and
Fear of Assimilation were weak and nonsignificant predic-
tors of either Intended Effort or Post Achievement. The only
exception was a positive association between Ethnocentrism
and Post Achievement in the case of Mathematics. Post hoc
analysis showed that the coefficient for Mathematics is sig-
nificantly larger than both L2 and L3. This finding is con-
trary to the expectation that Ethnocentrism and Fear of
Assimilation would be negative predictors of performance in
the language subjects.
Our model proposed that neither the ideal self nor the ought-
to L2 self had a direct effect on achievement. We conducted
exploratory follow-up analyses to find out whether this is
indeed the case. We examined whether the Ideal Self and
the Ought-to Self would predict Post Achievement in their
respective subject. Table 10 presents the path coefficients
and Table 11 presents the model fit for each model.
Table 5. Reliability and Validity of the Constructs in the Measurement Model and Their Inter-Construct Correlations for Each School
CR AVE 1 2 3 4 5 6
L2 1. Ideal Self .92 .69 .83
2. Ought-to Self .87 .57 .69 .76
3. Learning Experience .93 .72 .80 .58 .85
4. Ethnocentrism .77 .46 −.02 .21 .02 .68
5. Fear of Assimilation .83 .56 .02 .05 .07 .56 .75
6. Intended Effort .90 .68 .81 .69 .83 −.04 −.03 .83
L3 1. Ideal Self .92 .68 .83
2. Ought-to Self .87 .57 .74 .75
3. Learning Experience .94 .76 .87 .66 .87
4. Ethnocentrism .77 .46 .04 .34 .03 .68
5. Fear of Assimilation .83 .56 .00 .15 .07 .56 .75
6. Intended Effort .90 .70 .92 .75 .93 .04 .56 .83
Math 1. Ideal Self .94 .76 .87
2. Ought-to Self .91 .68 .78 .82
3. Learning Experience .94 .75 .88 .67 .87
4. Ethnocentrism .77 .46 .07 .19 .10 .68
5. Fear of Assimilation .83 .55 .09 .09 .15 .55 .74
6. Intended Effort .94 .81 .87 .79 .86 .01 .04 .90
Note. Values in the diagonal are the square roots of their respective AVE. CR = construct reliability. AVE = average variance extracted.
Table 6. Fit of the Measurement Model for the Three School
χ²(309) CFI TLI
Estimate 90% CI p
L2 702.331 .976 .973 .044 [.040, .049] .982
L3 684.923 .987 .985 .043 [.039, .048] .993
Math 739.395 .988 .986 .047 [.042, .051] .908
Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA =
root mean square error of approximation; CI = confidence interval.
10 SAGE Open
The Wald test showed no significant differences among
the three school subjects in either model. The Ideal Math Self
did predict Post Achievement, but its magnitude was small
and not significantly larger than that of the other two lan-
guage subjects (i.e., it was significantly different from zero
but not from the two language subjects).
Table 7. Standardized and Unstandardized Coefficients, Standard Errors, and z Ratios for the Structural Model of the Three School
Path βB SE z
L2 Ideal Self →Intended Effort .25 0.28 0.053 4.71***
Ought-to Self .22 0.23 0.039 5.69***
Learning Experience .50 0.47 0.034 14.72***
Prior Achievement .001 0.001 0.028 0.05
Learning Experience →Post Achievement .06 0.15 0.035 1.80†
Prior Achievement .78 0.78 0.040 19.38***
L3 Ideal Self →Intended Effort .39 0.42 0.044 8.80***
Ought-to Self .13 0.13 0.033 3.78***
Learning Experience .51 0.46 0.035 14.39***
Prior Achievement −.004 −0.001 0.016 0.25
Learning Experience →Post Achievement .03 0.08 0.034 0.91
Prior Achievement .89 0.88 0.024 37.02***
Math Ideal Self →Intended Effort .21 0.24 0.065 3.33***
Ought-to Self .30 0.33 0.034 8.88***
Learning Experience .44 0.43 0.050 8.73***
Prior Achievement .09 0.03 0.020 4.44***
Learning Experience →Post Achievement .28 0.72 0.063 4.44***
Prior Achievement .56 0.52 0.041 13.58***
†p = .072. ***p < .001.
Table 8. Fit of the Structural Model for the Three School Subjects.
χ²(178) CFI TLI
Estimate 90% CI p
L2 472.885 .980 .977 .051 [.045, .056] .406
L3 626.774 .984 .981 .067 [.062, .073] <.001
Math 598.253 .987 .985 .061 [.055, .066] .001
Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; CI = confidence interval.
Table 9. Standardized Coefficients and Wald Tests of Parameter Constraints for Ethnocentrism and Fear of Assimilation.
Path L2 L3 Math Wald test
Ethnocentrism →Intended Effort −.02 .06 .04 1.92
Post Achievement .04 −.04 .12** 11.40**
→Intended Effort −.01 .01 −.02 0.56
Post Achievement −.02 .05 .01 2.92
Note. df = 2.
**p < .01.
Table 10. Standardized Coefficients and Wald Tests of Parameter Constraints for the Ideal and Ought-to Selves.
Path L2 L3 Math Wald test
Ideal Self →Post Achievement .05 .02 .126* 2.66
Ought-to Self →Post Achievement −.003 .01 .01 0.15
Note. df = 2.
*p = .040.
Al-Hoorie and Hiver 11
In this article, we set out to test empirically what we called the
fundamental difference hypothesis in language motivation.
This hypothesis suggests that—at the motivational level—
language learning is qualitatively different from learning
other school subjects, as second and additional foreign lan-
guages have a social component and belong to another
community. This assumption has dominated the language
motivation field since its inception.
Our results showed that, overall, the three subjects exhibit
very similar patterns. The results further offered no evidence
that language learning is unique given its relationship to
identity subtraction. As with results from the socio-educa-
tional model (Lalonde & Gardner, 1993; MacIntyre et al.,
2012) and from self-determination theory (Fryer & Oga-
Baldwin, 2017, 2019), our comparisons using the L2MSS
revealed few clear or systematic differences between the
two language subjects and mathematics. The only exception
was in the learning experience and ethnocentrism predicting
achievement, but this occurred in mathematics rather than
in the two language subjects. Because it is in the opposite
direction, any conclusions remain tentative until this pattern
is replicated in future comparative research. Apart from
this, empirically, we are so far unable to find support for
the validity of a fundamental difference hypothesis either
between language versus nonlanguage subjects or between
global English versus Chinese.
Nevertheless, we are cautious not to accept the null, as
future research might indeed reveal some uniqueness to
learning languages. However, it is also likely that such dif-
ferences might be better characterized as various manifesta-
tions of the same underlying motivational process, rather
than a fundamental difference, per se, between language and
nonlanguage learning. As an illustration, Norton and Toohey
(2001) argued that the “good language learner” is not just
someone with a certain constellation of personality charac-
teristics, cognitive styles, attitudes, motivations, or past
learning experiences. Instead, successful language learners
are the ones who are additionally able to exercise their
agency and have access to social networks and resources in
their language communities (see, for example, Henry, 2017).
While this might be true, it is not clear why this should be
unique to language learning. The same principle applies to
mathematics (to take one side of the spectrum) where access
to a community of math experts would certainly facilitate
learning the subject; it also applies to the arts (to take the
other side of the spectrum) where access to the artistic exper-
tise of musicians, painters, photographers, and so on, would
also facilitate excelling in one’s respective field.
In the absence of empirical support for cross-subject
uniquenesses, it might be more constructive to move toward
a unified theory of learning that is inclusive of the various
psycho-social factors at play (cf. Baumeister, 2016). This
theory would involve more general principles and common
terminology applicable to both language and nonlanguage
subjects. Research into this unified theory would also
involve comparative analysis of multiple subjects, rather
than an exclusive focus on language learning phenomena.
Until clear evidence for qualitative difference between lan-
guage and nonlanguage subjects becomes available, a uni-
fied theory drawing on the rich body of evidence in
educational psychology and the learning sciences appears to
be more parsimonious.
The very same topics and constructs that feature in
research on language learning are equally prominent out-
side of it, but the current publication practices in our field
are characterized by a disconnect from educational psy-
chology and other learning sciences (see Al-Hoorie et al., in
press; Hiver et al., in press; Oga-Baldwin et al., 2019).
Relatively few applied linguists or second language schol-
ars would venture to publish in mainstream educational
journals. Similarly, very few scholars in educational psy-
chology or the learning sciences publish in or are familiar
with applied linguistics journals on language learning and
motivation. This isolation further extends to citations, with
little overlap in reference lists between second language
acquisition journals and contemporary educational psy-
chology and learning sciences journals. We would suggest
that this rift on topics of mutual interest is neither justified
nor conducive to scholarly progress and advancement.
This L2 focus has also caused a rather confused state in
the language motivation field. While researchers have
asserted for decades that language learning motivation is dif-
ferent from learning other school subjects, many scholars
have at the same time also called for “catching up” with
advances in educational psychology and the learning sci-
ences (e.g., Crookes & Schmidt, 1991; Dörnyei, 1994a,
1994b; Oxford, 1994; Oxford & Shearin, 1994). If language
learning is different from other school subjects, then main-
stream educational psychology would be, by definition, the
Table 11. Fit of the Structural Model for the Two Models.
χ²(df) CFI TLI
Estimate 90% CI p
Ideal Self 420.804(168) .977 .972 .048 [.043, .054] .674
Ought-to Self 498.974(165) .963 .953 .056 [.050, .062] .038
Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; CI = confidence interval.
12 SAGE Open
“wrong” place to look for insight. Fortunately, research into
the psychology of language learning is increasingly drawing
from educational psychology and the learning sciences in
recent theorizing (Joe et al., 2017; MacIntyre et al., 2016;
Mercer & Kostoulas, 2018; Oxford, 2017; Yun et al., 2018).
There is also increasing interest in complexity theory (e.g.,
Dörnyei et al., 2015; Hiver & Al-Hoorie, 2016, 2020b),
which originates from the mathematical sciences and also
contradicts the assumption that language learning is qualita-
tively distinct. Because complex dynamic systems have
found relevance in many social domains, it is highly unlikely
that they are applicable only to language learning (see, for
example, Capra & Luisi, 2014). Thus, there is reason to
believe that common motivational principles would apply
equally well to language learning as to other school subjects.
As Ushioda explains,
once we begin to consider motivation from the experiential
perspective of the person engaged in the business of L2
learning, it becomes evident that we need to broaden our
theoretical focus beyond features of motivation distinctive to
language learning. Indeed, it would seem surprising if more
generic concepts of motivation that apply to all areas of
conscious and intentional human learning did not apply also to
language learning. (Ushioda, 2012a, p. 16)
Limitations and Conclusion
This article has introduced the fundamental difference
hypothesis in language motivation. Our results do not point
toward a qualitative difference between mathematics and the
language subjects. Instead, differences seem to be in degree
rather than in kind, and potentially explainable by a unified
theory of educational motivation.
Nevertheless, future comparative research should apply
more rigorous designs to shed light on these cross-subject
differences. In our case, for example, there did not seem to be
a direct way to formally compare the fit of different SEM
models. Neither were we able to combine the three school
subjects into one model due to the large size of the resulting
model and the close parallel of its items. It would therefore
be informative for future research to zoom in on specific
areas that have the potential to reveal such cross-domain
The ideal self
I can imagine myself using _____ effectively in the future.
I can imagine myself in the future being so good at _____
that I can even teach it.
I can imagine a situation where I am successfully working in
a career that requires______.
I can imagine myself in the future mastering ______.
I can imagine myself using ______ to do the things that I
want to do in the future.
The ought-to delf
Studying ______ is important to me to gain the approval of
Studying ______ is important to me to gain the approval of
Studying ______ is important to me to gain the approval of
I study ______ because close friends of mine think it is
I consider learning ______ important because the people I
respect think that I should do it.
The learning experience
I really like the actual process of learning ______.
I find learning ______ really interesting.
I really enjoy learning ______.
I always look forward to ______ classes.
I think time passes faster while studying ______.
I am prepared to expend a lot of effort in learning ______.
I would like to spend lots of time studying ______.
I would like to concentrate on studying ______ more than
any other topic.
I intend to do my best in learning ______.
I have greater respect for cultures that are most similar to my
Other cultures should try to be more like my culture.
My culture should be the role model for other cultures.
The survival of our society depends on the Korean people
preserving the Korean language and Korean culture.
Fear of assimilation
As a result of internationalization, there is a danger that
Korean people may forget the importance of Korean lan-
guage and culture.
As a result of internationalization, Korean society is in dan-
ger of losing the Korean language and culture.
Korean language and culture have been influenced by glo-
balization in a negative way.
I’m afraid to use English in front of other Koreans, because I
will be thought of as less Korean.
Note. Blank spaces in all items contained each of the three
subjects (i.e., L2, L3, Math).
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
Al-Hoorie and Hiver 13
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
study was supported by the Florida State University Open Access
IRB approval was obtained from the authors’ institution as
explained within the manuscript.
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