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)
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Yulök Revista de Innovación Académica, ISSN 2215-5147, Vol. 7, N.º 1
Enero-Junio 2023, pp. 107-119
Bula, O. Problematizing Speaking Anxiety in Language Learning Settings.
tics of trait anxiety, Leal et al. (2017) claimed that “trait
anxiety is, therefore, relatively stable over time and con-
sidered a central characteristic of patients with anxiety
disorders, as they present higher trait anxiety in compari-
son to healthy individuals” (p. 148). A person exhibiting
trait anxiety might not be able to perform under regular
circumstances. In addition, people with trait anxiety will
react differently – that is, personality traits have different
connections and reactions to circumstances. Remarkably,
MacIntyre and Gardner (1991) affirmed that “for most in-
dividuals, some situations will provoke anxiety whereas
others will promote feelings of relaxation. Within a large
group of people, the situations provoking anxiety will di-
ffer, even among individuals showing similar trait anxie-
ty scores” (MacIntyre & Gardner, 1991, p. 88). Specific
features of the learners’ personality determine the reac-
tions to a given situation. Finally, the concept of time also
plays a significant role in trait anxiety; “trait anxiety es-
sentially is the idea that future anxiety propensities can be
inferred from past anxiety experiences by the assumption
of a continuity in the frequency and the intensity of anxie-
ty behavior from past to future” (Reiss, 1997, p. 211).
Interestingly, there is a connection between trait and state
anxiety “… trait anxiety refers to a trait of personality,
describing individual differences related to a tendency to
present state anxiety” (Leal et al., 2017, p. 148). Research
on the topic also established the connection; “in VMST,
trait anxiety correlated to state anxiety (psychological pa-
rameters) in all test phases” (Leal et al., 2017, p. 147).
Reiss established the connection too; “trait anxiety, or a
propensity to experience state anxiety, cannot be directly
observed but is manifested as state anxiety when stressed
is experienced” (Reiss, 1997, p. 204).
Anxiety as an emotional state. A second perspective
considers anxiety as an emotional state, the here-and-
now approach. In certain individuals, it is a response tri-
ggered by a specific situation, for example, performing
in a role-play in front of a class, “state anxiety is appre-
hension experienced at a particular moment in time, for
example, prior to taking examinations” (MacIntyre &
Gardner, 1991, p. 90). For Leal et al. (2017), state anxie-
ty “… reflects the psychological and physiological tran-
sient reactions directly related to adverse situations in a
specific moment” (p. 148). In comparing these previous
constructs, Horwitz (2001) posited that “…trait anxiety is
conceptualized as a relatively stable personality charac-
teristic while state anxiety is seen as a response to a par-
ticular anxiety provoking stimulus such as an important
test” (p. 113). Stressful situations or moments are a sa-
lient feature of state anxiety. For MacIntyre and Gardner
(1991), “state anxiety is a blend of the trait and situational
approaches” (p. 90). When establishing the connection
between these perspectives, Reiss (1997) concluded that
“the concept of trait anxiety requires an objective spe-
cification of the circumstances under which the inferred
propensity for state anxiety can be observed” (p. 211).
Research also established a correlation to state anxiety. In
this sense, MacIntyre and Gardner (1991) said that “the
moderately strong correlation usually found between sta-
te and trait anxiety suggests that increased levels of trait
anxiety are associated with higher state anxiety” (MacIn-
tyre & Gardner, 1991, p. 90).
Anxiety as a situation-specific construct. A third
approach deals with situation-specific anxieties. Parti-
cularly, the term specific anxiety or situation-specific
anxiety has been used to describe language learning si-
tuations since; “situation specific studies can offer more
to the understanding of anxiety because the respondents
are queried about various aspects of the situation. A key
difference is that respondents are required to make at-
tributions of anxiety to particular sources” (MacIntyre
& Gardner, 1991, p. 91). Also, MacIntyre and Gardner
(1991) imply that research results offer reliable data when
using this approach, “it seems plausible to suggest that
the more meaningful and consistent results have emer-
ged from the latter group [situation-specific anxiety]” (p.
92). Additionally, the classification of foreign language
anxiety as a situation-specific type of anxiety is discus-
sed by Horwitz (2010); “typically referred to as language
anxiety or foreign language anxiety (FLA), this anxiety
is categorized as a situation-specific anxiety, similar in
type to other familiar manifestations of anxiety such as
stage fright or test anxiety” (Horwitz, 2010, p. 154). This
concept is essential in terms of research purposes as it
establishes foreign language anxiety as a situation-speci-
fic anxiety. Finally, this approach has been criticized for
both its broadness and specificity. MacIntyre and Gard-
ner (1991) pointed out that “a criticism of this approach
is that the situation under consideration can be defined
very broadly, more narrowly, or quite specifically” (Ma-
cIntyre & Gardner, 1991, p. 91). It can be argued that the
situation-specific type of anxiety is not established. To
this end, MacIntyre and Gardner (1991) explained that “it
is the researcher’s responsibility to define a situation that
is sufficiently specific to be meaningful for the purpose at
hand, yet to have reasonable generality to permit genera-