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Journal of Language Teaching, 3(2), 1-10, 2023
https://doi.org/10.54475/jlt.2023.002
ORIGINAL RESEARCH
The relationship between academic boredom and EFL achievement:
Examining the mediating role of behavioral engagement
Yajun Wu 1, Xia Kang 2
School of Humanities and Education, Fos han University, Foshan, Guangdong Province, China
School of Mathematics and Big Data, Fos han Univer sity, Foshan , Guangdong Province, China
Received: December 2, 2022 / Accepted: February 3, 2023 / Published Online: February 15, 2023
© Pioneer Publications LTD 2023
Abstract
In addition to anxiety, academic boredom has also begun to enter the vision of educational researchers in recent
years. However, studies on academic boredom in the English as a foreign language (EFL) domain could be more
comprehensive, especially the mediating mechanism of academic boredom on EFL achievement needs to be further
explored. The present study investigated the direct and indirect effects of academic boredom on EFL achievement
in a sample of two hundred and thirty-five Chinese secondary EFL learners. SPSS Process and Mplus were utilized
to analyze the data. The findings revealed that academic boredom and behavioral engagement scales were valid and
reliable in measuring Chinese secondary EFL learners’ boredom and engagement in learning English. Also,
mediation analysis showed that behavioral engagement partially mediated between academic boredom and EFL
achievement. Implications and directions for future studies are discussed.
Keywords academic boredom; behavioral engagement; EFL achievement; mediation mechanism
1. Introduction
As a kind of negative, deactivating, and activity-
related achievement emotions (Pekrun et al., 2007),
academic boredom has a negative effect on students’
academic outcomes (Sharp et al., 2020; Tze et al.,
2016). Pawlak et al. (2020) documented that academic
boredom was one commonly experienced emotion in
EFL learning. However, due to the inconspicuousness
of academic boredom, it has newly become one
construct in the EFL domain and has received more and
more attention from EFL researchers (Kruk et al., 2021;
Li & Li, 2022). Recently, a few studies have explored
the effect of academic boredom on the key indicators
of academic and well-being outcomes using a
correlational design (Li, 2021; Schwartze et al., 2021;
Wang & Xu , 20 21 ). For example, Wan g and Xu (2021)
explored the influence of academic boredom on foreign
language learning in a sample of 314 Chinese college
EFL learners and identified the negative impact of
academic boredom on EFL learning. However, is
academic boredom necessarily negative for school
outcomes? Hunter et al. (2016) explored the
relationship between boredom proneness and curiosity
and found that boredom proneness positively predicted
curiosity. Given the inconsistency found in the existing
studies (Bench & Lench, 2019; Hunter et al., 2016; Li
& Li, 2022), the relationship between academic
boredom and achievement needs to be further explored,
especially in the EFL learning context in China. Also,
few studies have explored the mediating mechanism
between academic boredom and achievement. To fill
these gaps, the present study endeavored to explore the
direct and indirect effects of academic boredom on
achievement in a sample of 235 Chinese secondary
EFL learners.
2. Literature review
2.1. Academic boredom
Academic boredom refers specifically to the
destructive feeling experienced by secondary school
students in learning EFL, which would negatively
influence their EFL outcomes. As one of the nine most
commonly experienced achievement emotions during
the learning process (i.e., enjoyment, hope, pride,
boredom, anxiety, hopelessness, shame, anger, and
relief), Pekrun et al. (2007) argued that each discrete
achievement emotion could be described from the three
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facets of valence (positive vs negative), activation
(activating vs deactivating), and object focus (activity-
related vs outcome-related). Unlike enjoyment, which
is positive, academic boredom is negative. In terms of
activation, academic boredom is deactivating, for it
might reduce a student’s enthusiasm for learning.
Concerning object focus, achievement emotions are
either activity-related (e.g., boredom and enjoyment) or
outcome-related (e.g., pride, anger, and shame) (Pekrun,
2006; Pekrun et al., 2007; Shao et al., 2020). In sum,
academic boredom is a kind of negative, deactivating,
activity-related achievement emotion.
Driving upon the control-value theory (CVT,
Pekrun, 2006), the antecedents and consequences of
academic boredom have been explored using a
correlational design (Eren & Coskun, 2016; Nakamura
et al., 2021; Pawlak et al., 2020; Tze et al., 2014). For
example, Eren and Coskun (2016) documented that the
level of boredom was negatively correlated with
mathematics performance in a sample of 557 Turkish
high school students. In another study with Saudi
college EFL learners, Shehzad et al. (2021) found that
listening boredom could directly affect listening
performance or indirectly through the mediators of
coping strategies. A meta-analysis by Sharp et al. (2020)
focusing on the relationship among academic boredom,
student engagement and performance in a cohort of
college students found that academic boredom was
negatively associated with student engagement and
academic performance. Also, Nakamura et al. (2021)
explored the possible antecedents of academic
boredom and found that activity mismatch, lack of
comprehension, insufficient L2 skills, task difficulty,
input overload, and lack of ideas all contribute to
boredom.
Existing studies provide a theoretical framework
for the present study. However, there are at least two
deficiencies that need to be resolved. First, the
mediating mechanism between academic boredom and
achievement was seldom explored, especially in the
EFL context (Macklem, 2018). Second, the relationship
between academic boredom and the key indicators of
academic outcomes (e.g., academic achievement) was
inconsistent (Pekrun et al., 2014). To address these two
limitations, the present study explored the direct and
indirect effects of academic boredom on achievement
in a sample of 235 Chinese secondary EFL learners.
2.2. Behavioral engagement
As one of the four aspects of academic
engagement (i.e., behavioral, emotional, cognitive, and
agentic) (Reeve & Tseng, 2011), behavioral
engagement addresses students’ involvement in
academic, social, and extracurricular activities (Reeve,
2013). Specifically, behavioral engagement concerns
secondary school students’ various behaviors in
learning English (e.g., attention, effort and
concentration, teacher-student interaction, frequency of
participation in activities, etc.) (Fredricks et al., 2004).
The present study focused on behavioral engagement
for other types of engagement (e.g., emotional, agentic,
and cognitive engagement) that would indirectly affect
academic achievement via behavioral engagement
(Putwain et al., 2018).
Scholars have extensively studied the antecedents
and consequences of behavioral engagement (e.g.,
Feng & Hong, 2022; Kang & Wu, 2022; Shih, 2018).
For example, Kang and Wu (2022) found that
behavioral engagement mediated the relationship
between academic enjoyment and EFL achievement
among teenagers aged 12 to 15. In a sample of 402
eighth-grade Taiwanese students, Shih (2018)
documented that achievement goals would affect
coping indirectly through engagement. These findings
demonstrated that behavioral engagement might
mediate the relationship between achievement
emotions (e.g., academic boredom) and academic
performance, which provides the theoretical
framework for the present study.
2.3. EFL achievement
As the most important indicator of learning,
academic achievement refers to students’ learning
outcomes, showing the degree to which students,
teachers, and educational institutions have achieved
their goals (Sedaghat et al., 2011; Steinmayr et al.,
2014). Obtaining high academic achievement indicates
that students are successful academically and will be
more successful in completing their studies. Given the
domain specificity of academic boredom and
behavioral engagement (Goetz et al., 2006; Green et al.,
2007), the present study explored EFL achievement and
defined it as the English scores achieved by Chinese
secondary school students.
2.4. The present study
To sum up, the present study attempted to examine
the correlation between academic boredom and EFL
achievement. Meanwhile, the mediating mechanism
between academic boredom and EFL achievement was
also explored. Based on the literature review, academic
boredom could indirectly affect EFL performance
through behavioral engagement. To be specific, the
present study attempted to verify the following two
hypotheses (see Figure 1).
H1: EFL-related boredom negatively predicts EFL
achievement.
H2: EFL-related boredom indirectly affects EFL
achievement via the mediator of behavioral
engagement.
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Figure 1. Proposed model
3. Methods
3.1. Participant s and procedure
A total of 235 secondary school students from one
middle school in Foshan City, China, participated in the
study. There were 121 male students (51.49%) and 114
female students (48.51%). The mean age of the
participants was 12.81 years (SD = .731), ranging from
12 to 14 years old. Participants came from two grades,
with 127 in seventh grade (54.04%) and 108 in eighth
grade (45.96%). Judging by socio-economic status,
participants were mainly from middle-class Chinese
families. Participants provided written informed
consent before the questionnaire survey began.
With the help of a collaborator and the English
teachers, a questionnaire survey was conducted during
English class. Although they signed written informed
consent, participants were informed that they could
withdraw from the questionnaire survey at any point
during the questionnaire process. Under the guidance
of English teachers, participants completed the
questionnaire in approximately fifteen minutes.
3.2. Measures
3.2.1. Academic boredom
The four-item classroom-related boredom that
was adopted from the Achievement Emotions
Questionnaires (Pekrun et al., 2011) was utilized to
measure participants’ emotional experience of boredom.
An example of the academic boredom scale is “I get
bored during English class”. Participants responded to
this scale on a five-Likert scale ranging from 1 =
strongly disagree to 5 = strongly agree. The academic
boredom scale has excellent internal consistency and
construct validity, which has been applied and
examined in previous studies (e.g., Kang & Wu, 2021;
Shao et al., 2020). In this study, the internal consistency
of the academic boredom scale was good, with
Cronbach’s alpha equal to 0.847.
3.2.2. Behavioral engagement
Participants’ engagement in learning English was
measured by the 5-item behavioral engagement scale
developed from the Engagement vs. Dissatisfaction
with Learning Questionnaire (Skinner et al., 2009).
One example of this scale is “I try hard to do well in
English class”. This scale was responded to on a five-
Likert scale (1 = strongly disagree, 5 = strongly agree).
The reliability and validity of the behavioral
engagement scale have been identified in previous
studies (Wu & Kang, 2021; Zhou et al., 2022). The
internal consistency of the behavioral engagement was
good for Cronbach a = 0.909.
3.2.3. EFL achievement
The most recent English final exam scores were
used to represent participants’ EFL achievement. The
examination paper consists of five types of questions,
those are, listening comprehension, vocabulary and
grammar, language communication, reading
comprehension and writing. The total mark of the
examination paper is 120 points. Higher scores
indicated higher EFL achievement among the
participants.
3.3. Pre-test evaluation of items
A pre-test evaluation of items was conducted to
examine the measurement quality of the two scales (i.e.,
behavioral engagement and academic boredom).
According to the size criteria for conducting the pre-
test (Oksenberg et al., 1991), fifty-eight participants
were involved in assessing the discrimination ability of
the items. Accurately, 27 percent of the highest and
lowest scores were selected and analyzed (Kelley,
1939). The results are presented in Table 1. It showed
that the mean values of each item were significantly
different, indicating that all the items in the two studied
scales were discriminative. Thus, all items could be
applied in the formal investigation.
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Ta b l e 1 . The results of item analysis for pre-test
Items
t-test for Equality of Means
Group
N
Mean
SD
t
df
p
Mean Difference
BO1
-8.059
29
.000
-2.133
Low
16
1.000
0.000
High
15
3.133
1.060
BO2
-14.132
29
.000
-2.600
Low
16
1.000
0.000
High
15
3.600
0.737
BO3
-6.660
29
.000
-1.800
Low
16
1.000
0.000
High
15
2.800
1.082
BO4
-7.107
29
.000
-1.800
Low
16
1.000
0.000
High
15
2.800
1.014
EG1
-6.959
41
.000
-1.191
Low
19
3.684
.749
High
24
4.875
.338
EG2
-6.115
41
.000
-.950
Low
19
3.842
.602
High
24
4.792
.415
EG3
-8.236
41
.000
-1.465
Low
19
3.368
.761
High
24
4.833
.381
EG4
-7.771
41
.000
-1.213
Low
19
3.579
.607
-
High
24
4.792
.415
EG5
-7.638
41
.000
-1.340
Low
19
3.368
.597
High
24
4.708
.550
Note: BO is abbreviation for boredom. EG is abbreviation for behavioral engagement
3.4 Data analysis
Data were analyzed with SPSS 23.0 and Mplus 8.3.
First, the measurement instrument was validated. In
this stage, confirmatory factor analysis (CFA) was
conducted using Mplus 8.3 and principal component
analysis with Varimax rotation, the reliability,
convergent validity and discriminant validity were
analyzed by SPSS 23.0. Second, simple regression and
the PROCESS macro (Model 4) (Hayes, 2022) were
utilized to test the hypothesized model (see Figure 1).
The bootstrap approach with bootstrapped confidence
intervals (CI) of 95% was employed to examine the
indirect effects of academic boredom on EFL
achievement via the mediator of behavioral
engagement. If zero did not include in the CI means that
the indirect effect was significant. The results are
presented in the next section.
4. Results
4.1. Principal component analysis (PCA)
Sample adequacy and Bartlett’s test of sphericity
were first conducted to assess that the data were
appropriate for principal component analysis. In this
study, we adopted the criteria that a KMO value
between 0.5 and 0.7 is mediocre, between 0.7 and 0.8
is good, and between 0.8 and 0.9 is excellent (Field,
2013; Hutcheson & Sofroniou, 2006). Results
demonstrated that the Kaiser-Meyer-Olkin (KMO) was
0.900, and Bartlett’s test of sphericity was significant,
with c2 = 1266.595, df = 36, p < .001, suggesting that
the data was appropriate to conduct PCA.
The results of the factor analysis are presented in
Tab le 2. Fir st, the fa ctor load ings of th e studie d it ems
ranged from 0.689 to 0.846, which were satisfactory for
all of them greater than 0.6 (Matsunaga, 2010). In
addition, the results of factor analyses showed that no
cross-loadings of items were above 0.40 (i.e., with less
than 0.4 difference) (Gänswein, 2011), indicating that
no items need to be excluded. The eigenvalue values
for EFL-related engagement and boredom were 5.101
and 1.354, which satisfied the eigenvalues-greater-
than-one criterion proposed by Kaiser (1960). The total
variance explained by the two dimensions of behavioral
engagement and boredom was 71.72%. Specifically,
behavioral engagement explained 56.67% of the total
variance, and academic boredom explained 15.05% of
the total variance (see Table 2).
Ta b l e 2 . PCA results for the two dimensions of
academic boredom and behavioral engagement
Subscale
Factor loading
Boredom
Engagement
BO1
.839
BO2
.836
BO4
.777
BO3
.689
BEG5
.846
BEG4
.842
BEG2
.838
BEG3
.817
BEG1
.747
Eigenvalues
5.101
1.354
Explained variance
56.673
15.050
Total explained
71.723
4.2. Confirmatory factor analysis
Confirmatory factor analysis (CFA) was
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conducted to assess whether the proposed model fits
the data well (see Figure 1). First, skewness and
kurtosis were used to assess the normal distribution of
the data. Byrne (2010) and Hair et al. (2019) proposed
that the absolute value of skewness was smaller than
two and the absolute value of kurtosis was smaller than
seven, indicating that the data was normally distributed.
Data were normally distributed as they satisfied the
criteria of the skewness and kurtosis values (see Table
3). Also, the comparative fit index (CFI), the Tucker-
Lewis index (TLI), the root mean square error of
approximation (RMSEA), and the standardized root
mean square residual (SRMR) were applied to evaluate
the model fit. Typically, RMSEA≤0.06, SRMR≤0.08,
CFI and TLI values greater than 0.90 are considered to
reflect acceptable fit (Hu & Bentler, 1999; Marsh et al.,
2004). The CFA results confirmed the two-factor
structure suggested by PCA, since the model fitted the
data adequately, with c2(26) = 67.460, CFI = .960, TLI
= .945, RMSEA = .089, and SRMR = .049. To sum up,
all factor loadings were greater than 0.6, and the model
indices were adequate, suggesting that satisfactory
construct validity was established.
Ta b l e 3 . Descriptive statistics and factor loadings
Factor
N
Mean
SD
Skewness
Kurtosis
Factor-BO
235
1.87
.905
.939
.281
BO1
235
1.79
1.111
1.480
1.571
BO2
235
2.07
1.113
.737
-.336
BO3
235
1.79
1.073
1.292
.870
BO4
235
1.82
1.076
1.221
.693
Factor-EG
235
4.31
.685
-.751
-.414
EG1
235
4.46
.775
-1.172
.250
EG2
235
4.39
.745
-1.028
.764
EG3
235
4.20
.876
-.902
.400
EG4
235
4.32
.772
-.855
-.075
EG5
235
4.18
.828
-.709
-.021
4.3. Reliability analysis
To te st the reli abilit y of the tw o scale s (i.e., E FL -
related boredom scale and behavioral engagement
scale), inter-item correlation and corrected item-total
correlation for each item were analyzed. According to
the standards that the value of corrected item-total
correlation (above r = 0.40) suggested by Clark and
Watson (1995) and the inter-item correlation need to
exceed 0.30 (Hair et al., 2019), all items satisfied the
criteria, showing that no items need to be omitted from
the related scales. Specifically, the corrected item-total
correlations for the items of EFL-related boredom
ranged from 0.688 to 0.774 and the inter-item
correlations of which were greater than 0.40 (see Table
4). For the items of the EFL-related behavioral
engagement, the corrected item-total correlations
ranged from 0.709 to 0.813, and all the inter-item
correlations were greater than 0.40 (see Table 5).
Ta b l e 4 . Results of reliability for EFL boredom scale
Inter-item Correlation
Internal consistency
BO1
BO2
BO3
BO4
Corrected
item-total
correlation
Cronba
ch’s
alpha
BO1
-
.688
.847
BO2
.700
-
.774
BO3
.503
.612
-
.631
BO4
.544
.603
.517
-
.647
Then, Cronbach’s alpha was calculated
respectively to assess the internal consistency of the
boredom and engagement scales. Comrey and Lee
(2009) proposed that Cronbach’s alpha greater than
0.71 indicated that the reliability of the scale was
excellent. The Cronbach’s alpha of foreign language
boredom was 0.847, and foreign language engagement
was 0.909, which was more than the suggested value of
0.70 (Hair et al., 2019). In sum, the reliability of the two
scales used in this study was excellent.
Ta b l e 5 . Results of reliability for EFL-related behavioral engagement scale
Inter-Item Correlation
Internal consistency
EG1
EG2
EG3
EG4
EG5
Corrected Item-Total Correlation
Cronbach’s alpha
EG1
-
.709
.909
EG2
.642
-
.774
EG3
.595
.678
-
.757
EG4
.665
.670
.669
-
.813
EG5
.598
.689
.693
.785
-
.804
4.4. Convergent validity
A con vergent val idity as ses sment was con ducted
to examine the degree to which the two studied
measures are correlated with each other. Specifically,
average variance extracted (AVE) was calculated to
assess the convergent validity of the two scales. Hair et
al. (2019) suggested that an AVE greater than 0.5
indicated that the related scale has enough convergent
validity. In the present study, as demonstrated in Table
6, the AVE values for the EFL-related boredom scale
and behavioral engagement scale were 0.620 and 0.670,
respectively. The results showed that these two scales
had enough convergent validity.
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Ta b l e 6 . Convergent validity and Discriminant
validity of the scales
Convergent
validity
Mean
SD
Discriminant
validity
AVE
BO
EG
BO
.620
1.867
.905
.787
EG
.670
4.311
.685
-.575
.819
Note: The diagonal bold is the square root of AVE, and
the correlations below the diagonal are Pearson
correlation coefficient.
4.5. The predictive effect of EFL boredom on
achievement
Simple regression analysis was conducted to
examine the potential predictive effect of EFL boredom
on achievement. As shown in Table 7, the regression
analysis is valid with F = 48.840 at p < .001. In this
study, the regression equation was Y = 107.201 – 5.649
X (X = independent variable of EFL boredom, Y =
dependent variable of EFL-related behavioral
engagement). Furthermore, Table 7 demonstrated that
EFL boredom was negatively correlated with EFL
achievement (B = -5.649, t = -6.989, p < .001) and
explained 41.6% variance (R2 = .416) of the EFL
achievement.
Ta b l e 7. Results of regression analysis
Note: *** p < .001.
4.6. Behavioral engagement as a mediator
between EFL boredom and EFL achievement
A si mple me dia tion mo del (Model 4) th rough
SPSS PROCESS computational tool (Hayes, 2022) was
applied to explore the mediating mechanism between
EFL boredom and achievement. Precisely, Model 4 was
applied to examine the mediating effect of behavioral
engagement between EFL boredom and achievement.
The non-parametric bootstrap method with 5000
resamples was adopted to estimate the indirect effect of
EFL boredom on EFL achievement via the mediator of
behavioral engagement. The bootstrap confidence
interval (CI) was used. If 95% CI does not contain zero
(Efron, 1988), indicating that the proposed mediating
effect is significant.
Figure 2. Indirect effect model. All coefficients are unstandardized estimated.
Note: *** p<.001; **p<.01.
As shown in Figure 2, the hypothesized model was
supported. EFL boredom was negatively related to
behavioral engagement (B = -.435, SE = .041, p < .001),
and EFL-related behavioral engagement demonstrated
a positive predictive effect on EFL achievement (B =
3.575, SE = 1.286, p < .01). Also, as demonstrated in
Tab le 8, the indire ct path from EF L bo redom t o EFL
achievement via the mediator of behavioral
engagement was significant with B = 3.575, SE = .714,
95% CI [-3.172, -.379]. Furthermore, the direct effect
of EFL boredom on EFL achievement was also
significant with B = -4.094, SE = .974, 95% CI [-6.012,
-2.176]. Thus, it could be concluded that behavioral
engagement played a partial mediating role between
EFL boredom and achievement.
Model
Unstandardized
Coefficients
t
p
Standardized
Coefficients
R2
F
B
SE
Beta
Academic
achievement
Constant
107.201
1.676
63.948
.000
.416
48.840***
BO
-5.649
.808
-6.989
.000
-.416
7
Table 8 Direct and indirect effects of foreign language learning engagement
Model path
Parameter
estimate
SE
Bia-corrected CIs (95%)
Lower
Upper
Direct effect
-4.094
.974
-6.012
-2.176
Indirect effect: BO→EG→AA
-1.555
.714
-3.172
-.379
Total indirect effect
-5.649
.080
-7.241
-4.056
Note: BO = Foreign language learning boredom; EG = Foreign language learning engagement; AA = Academic
Achievement. Bolded CIs considered significant (value do not include zero).
5. Discussion
This study found that EFL boredom could directly
affect EFL achievement or indirectly affect EFL
achievement through EFL-related behavioral
engagement. That is, H1 and H2 are supported in a
sample of Chinese secondary EFL learners. The finding
that EFL boredom was significantly correlated with
EFL achievement is consistent with existing studies (Li,
2021; Pekrun et al., 2014; Tze et al., 2016). This finding
contributes to the literature by verifying the theoretical
hypothesis of the control-value theory of achievement
emotions (Pekrun, 2006). The control-value theory
postulated that negative achievement emotions (e.g.,
academic boredom) are negatively correlated with
achievement. The present study confirmed the
theoretical hypothesis of the control-value theory in a
sample of Chinese secondary EFL learners. In addition,
the relationships between academic boredom and
school outcomes were inconsistent in the existing
studies (Hunter et al., 2016; Wang & Xu, 2021). This
study clarified the negative relationship between
academic boredom and achievement in a sample of
Chinese secondary EFL learners.
The finding that behavioral engagement mediated
the relationship between academic boredom and
achievement could deepen the understanding of the
mediating mechanism between academic boredom and
achievement. This finding is consistent with the
previous studies (Macklem, 2018; Sharp et al., 2020).
Drawing upon the control-value theory, serial studies
were conducted to investigate the relationships
between achievement emotions (e.g., academic
boredom) and academic performance (e.g., Hunter &
Eastwood, 2021; Shehzad et al., 2021), however, few
studies have explored the mediating mechanism
between these variables, especially in the EFL learning
context in China. The present study found that
behavioral engagement mediated academic boredom
and achievement, which contributed to the
comprehension of the mediating mechanism between
the constructs of academic boredom and achievement.
To pu t it a nother way, be havio ral engagement is
requested to give play to the predictive effect of
academic boredom on EFL achievement.
6. Implications, limitations and
future directions
The present study has both theoretical and
practical implications. First, academic boredom
negatively affected academic achievement, which
indicated that educators and policymakers should focus
on reducing EFL learners’ boredom levels. For example,
cultivating a good teacher-student relationship,
assigning classroom tasks that are comparable to
students’ ability levels, and increasing students’
appraisals of the classroom tasks are effective ways to
reduce students’ academic boredom (Clem et al., 2021;
Nakamura et al., 2021; Pawlak et al., 2020). Second,
behavioral engagement played a partial mediating role
between EFL boredom and achievement, suggesting
that EFL boredom would affect EFL achievement by
acting upon students’ behavioral engagement. This
finding contributed to the achievement emotions
literature by revealing the mechanism by which
achievement emotions (i.e., academic boredom) affect
academic achievement.
The present study investigated the direct and
indirect effects of EFL boredom and achievement in a
sample of 235 Chinese secondary school students,
contributing to the literature by advancing the
understanding of the mediating mechanism between
the studied variables. However, three limitations need
to be addressed. First, the present study was conducted
in a cross-sectional design, and a causal relationship
between the studied variables could not be drawn.
Future studies are suggested to investigate the causal
relationships between academic boredom, behavioral
engagement and achievement in a longitudinal design.
Second, the present study was based on self-reported
data. Although common method bias is ruled out, future
studies are suggested to take significant others’ (i.e.,
parents, teachers, and peers) evaluations into
consideration to gain a more objective insight into the
profiles of the studied variables and the relationships
between the studied variables. Third, we solely
explored the mediating effect of behavioral
engagement between academic boredom and
achievement. Future studies are suggested to identify
the mediating effects of other variables (e.g., learning
strategies and achievement goals) (Daniels et al., 2009;
Kang & Wu, 2022) between academic boredom and
achievement.
8
7. Conclusion
This study attempted to investigate the direct and
indirect effects of academic boredom on academic
achievement in a sample of Chinese secondary EFL
students aged 12 to 14. We found that EFL boredom
could affect EFL achievement directly or indirectly
through behavioral engagement. This study enriched
the literature on academic boredom in the field of EFL
education. Theoretically, we provided empirical
evidence for the hypothesis that negative achievement
emotions (e.g., academic boredom) would generate an
adverse influence on academic achievement.
Furthermore, this study figured out that behavioral
engagement is one of the effective paths for academic
boredom to act upon academic achievement in the EFL
context.
Disclosure statement
All authors declare no conflict of interest.
Yajun Wu (Corresponding Author) is a Senior
Lecturer at the School of Humanities and Education at
Foshan University.
Email: wuyajun1225@163.com
Xia Kang is a Senior Lecturer at the School of
Mathematics and Big Data at Foshan University
(Jiangwan Campus). She is a PhD candidate at the
University of Hong Kong.
E-mail: kangxia15618@163.com.
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