Factors Influencing the Active Engagement of Undergraduate EFL Students in Blended Learning: A Gender-Based Multigroup Analysis

Abstract

Due to the rapid advancement of the Internet and technology, blended learning has gradually gained widespread acceptance among students, teachers, and educational institutions, emerging as the new norm in the post-pandemic era. This study aims to examine the factors influencing active engagement of undergraduate English-as-a-foreign-language (EFL) students in blended learning and the moderating role of gender in these relationships. Self-Determination Theory and Technology Acceptance Model are the theoretical frameworks of this study. A total of 381 questionnaires were collected from six universities in Jiangxi Province. Data were analyzed by Smart-pls 4.0. The results indicate that except for perceived ease of use, perceived autonomy, perceived relatedness, perceived competence, and perceived usefulness were significant predictors of active engagement. Furthermore, the results of the multigroup analysis revealed that there were no significant gender differences in the effects of perceived autonomy, perceived relatedness, perceived competence, and perceived usefulness on active engagement. The details of the results and both theoretical and practical implications have been described in the paper.

Full text

Original Research
SAGE Open
April-June 2025: 1–21
ÓThe Author(s) 2025
DOI: 10.1177/21582440251336512
journals.sagepub.com/home/sgo
Factors Influencing the Active
Engagement of Undergraduate
EFL Students in Blended Learning:
A Gender-Based Multigroup Analysis
Ya n j u n Ya n g
1
and Jun Tan
1
Abstract
Due to the rapid advancement of the Internet and technology, blended learning has gradually gained widespread acceptance
among students, teachers, and educational institutions, emerging as the new norm in the post-pandemic era. This study aims
to examine the factors influencing active engagement of undergraduate English-as-a-foreign-language (EFL) students in
blended learning and the moderating role of gender in these relationships. Self-Determination Theory and Technology
Acceptance Model are the theoretical frameworks of this study. A total of 381 questionnaires were collected from six univer-
sities in Jiangxi Province. Data were analyzed by Smart-pls 4.0. The results indicate that except for perceived ease of use, per-
ceived autonomy, perceived relatedness, perceived competence, and perceived usefulness were significant predictors of
active engagement. Furthermore, the results of the multigroup analysis revealed that there were no significant gender differ-
ences in the effects of perceived autonomy, perceived relatedness, perceived competence, and perceived usefulness on active
engagement. The details of the results and both theoretical and practical implications have been described in the paper.
Plain language summary
Factors influencing the active engagement
Purpose: This study aimed to examine the factors influencing the active engagement of undergraduate English-as-a-
foreign-language (EFL) students in blended learning and the moderating role of gender in these relationships. Self-
Determination Theory and Technology Acceptance Model are the theoretical frameworks of this study. Methods: A
total of 381 questionnaires were collected from six universities in Jiangxi Province. Data were analyzed by Smart-pls
4.0. Conclusions: The present study employs SDT and TAM as the theoretical framework to explore the influencing
factors of EFL students’ AE in blended learning and analyze gender differences in the relationships. The present study
has garnered noteworthy results: PA, PC, PR, and PU are critical predictors of EFL students’ AE in blended learning.
However, there is not a significant connection between PEOU and AE. Furthermore, PC is related directly and
significantly to PU and PEOU, and PR is associated significantly and directly with PU and PEOU. Finally, PA is also
significantly related to PU and PEOU. The research also found that, there was no significant gender difference in the
impact of PA, PR, PC, PU, and PEOU on AE among EFL students in the context of blended learning. Implications: The
theoretical implications of this study primarily encompass two aspects as follows. Firstly, the present research jointly
adopted TAM and SDT as theoretical frameworks to explain EFL students’ AE in the context of blended learning.
1
Nanchang Institute of Technology, Jiangxi, China
Corresponding Author:
Yanjun Yang, School of Foreign Languages, Nanchang Institute of Technology, 289 Tianxiang Avenue, High-tech Development Zone, Nanchang, Jiangxi
330099, China.
Email: Yanjun_yang0624@163.com
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License
(https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of
the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).

Secondly, this study tested the gender difference in the active engagement of EFL students in blended learning.
Limitations of your study: First, the present study solely opted for college students from Jiangxi Province of China,
potentially restricting the generalizability of the research findings. Second, the variables examined in this study account
for 76.2% of the variation in AE; however, it is crucial to note that not all variables capable of affecting AE were
considered. Thirdly, the present study uses quantitative methodologies in the short run, which may not adequately
uncover the underlying impact mechanisms of EFL students’ AE in blended learning.
Keywords
blended learning, active engagement, perceived autonomy, perceived relatedness, perceived competence, perceived
usefulness
Introduction
Blended learning, which refers to using both online and
face-to-face learning experiences, provides students with
both benefits (Al-Obaydi et al., 2023; Wong, 2022), spe-
cifically, offering flexible and personalized learning
approaches, accessing to diverse learning resources, pro-
moting interactivity, and providing timely feedback. For
example, numerous studies prove that blended learning
could effectively improve English language learning per-
formance (Abroto et al., 2021; Chiu, 2021a; Hh et al.,
2022; M€
uller & Mildenberger, 2021). By utilizing
Learning Management Systems (LMS), autonomous
learning applications, and online course platforms, stu-
dents can independently manage their learning schedules
and access educational resources anytime and anywhere
(Huang & Yoon, 2023). The convenience of this technol-
ogy further enhances student engagement, especially in
the process of language learning, where students can
achieve more interaction and immediate feedback
through technology (Xu et al., 2022). Moreover, technol-
ogy helps EFL students overcome the spatial and tem-
poral limitations of traditional classrooms, enabling
them to better balance their studies with their personal
lives (Muthuprasad et al., 2021). Therefore, exploring
the relationship between technology and EFL students is
crucial for understanding their learning behaviors in a
blended learning environment.
However, learners may encounter challenges while
acclimating to the new learning environment, such as the
requirement to adjust to diverse learning scenarios and
paces, as well as the selection and evaluation of online
learning resources (Bayu & Saputra, 2023; Chiu, 2021a;
Hu et al., 2022). To overcome various challenges, exten-
sive efforts have been devoted to investigating the factors
influencing English language proficiency and learning
efficiency in blended learning (Han et al., 2021; Ubu
et al., 2021). The findings consistently highlight that
active engagement (AE), in which students invest energy
in their learning processes, thus rendering it a meaningful
strategy for them, is one of the most crucial factors.
Hence, enhancing college students’ AE and achieving
success in English language learning has become a pri-
mary concern for universities and higher education
institutions.
Empirical studies have revealed that the factors influ-
encing AE in blended learning include multiple task char-
acteristics and teacher roles (Leo et al., 2022), three basic
psychological needs (autonomy, competence, and relat-
edness; Cents-Boonstra et al., 2022; Pan et al., 2023; Zhi
et al., 2024), academic self-efficacy (Pan et al., 2023),
learner’s engagement (Shakki, 2023), the usefulness of
systems and teaching presence (Salas-Pilco et al., 2022),
teacher and parent support, students’ academic self-
efficacy (M.-T. Wang & Hofkens, 2020). Furthermore, to
examine the magnitude of these factors on AE in blended
learning, different theories and models have been
adopted in the literature, such as the self-determination
theory (Chiu, 2021a), social cognitive theory (Panigrahi
et al., 2022), the theory of motivation (Sucaromana,
2013), interactive learning theory (Q. Q. Wang, 2022).
Although existing research has thoroughly explored
various aspects of AE in blended learning, there has been
no concerted effort to simultaneously employ the
Technology Acceptance Model (TAM) and Self-
Determination Theory (SDT) in analyzing student AE
within blended learning contexts. The application of
TAM is instrumental in understanding how students
accept and utilize the technological components inherent
in blended learning, while SDT offers insights into stu-
dents’ intrinsic motivation and psychological needs. The
synthesis of these two theoretical frameworks may afford
a more comprehensive and nuanced perspective, eluci-
dating the complexities of student AE in blended learn-
ing. Such an integrative approach stands to provide
educators with targeted strategies that could enhance the
learning experiences of students, thereby potentially con-
tributing to more effective instructional design and
implementation within the paradigm of blended learning.
In addition, it is approximated that China boasts the
largest percentage of English-as-a-foreign-language
(EFL) learners in Asia, even globally (Li et al., 2018).
2SAGE Open

Hence, investigating the factors that influence AE among
EFL students in blended learning will significantly
enhance the likelihood of achieving success in English
language learning.
Therefore, the objective of this study is to investigate
the factors influencing AE among EFL students in
blended learning, using the TAM and SDT as the theore-
tical framework. Given the complexity of blended learn-
ing, the AE of EFL students should be considered by
considering both technological and psychological factors.
The second objective of this study is to explore the gender
differences in the AE among EFL students in blended
learning. The technical and psychological aspects of lear-
ners may vary depending on gender (Escobar-Viera
et al., 2021; Hsieh et al., 2021; Voss et al., 2021), thus sug-
gesting that the relationship between these factors and
AE may also exhibit gender differences.
The remaining parts of the study are structured as
described below: it begins by outlining its theoretical
foundation and providing a literature review on TAM
and SDT. Following that is a section on methodology
and data analysis results. The final section includes the
discussions and the limitations and future work of the
study .
Literature Review and Hypothesis
Literature Review
Existing Research on Blended Learning. Blended learning,
which combines traditional classroom instruction with
online learning, has been extensively studied and applied
in recent years. Research indicates that blended learning
exhibits significant advantages in flexibility, personalized
learning, and interactivity (M€
uller & Mildenberger,
2021). Through online platforms and Learning
Management Systems (LMS), students can choose their
learning content and pace according to their own needs,
thereby enhancing their capacity for autonomous learn-
ing. Additionally, blended learning provides a diverse
array of learning resources and immediate feedback,
enhancing students’ learning experience and sense of
achievement (Wong, 2022). For example, Abroto et al.
(2021) demonstrated that blended learning effectively
improves students’ academic performance and motiva-
tion, particularly in language learning and other courses
requiring long-term engagement.
Moreover, blended learning has excelled in increasing
student engagement. Studies have shown that courses
employing a blended learning model can inspire greater
student involvement, especially in self-directed learning
and collaborative teamwork (J. C. Sun & Rueda, 2012).
By flexibly scheduling face-to-face and online classes,
blended learning retains the benefits of traditional class-
rooms while overcoming the drawbacks of reduced
interaction in online learning, enabling students to better
manage their learning processes (Garcia-Morales et al.,
2021).
Despite its many advantages in education, the imple-
mentation of blended learning still faces several chal-
lenges. Research indicates that students adapting to a
blended learning environment may encounter technologi-
cal barriers, difficulties in self-management, and issues in
selecting online learning resources (Muthuprasad et al.,
2021). Particularly in regions with high technological
demands, students may face a lack of adequate equip-
ment or unstable internet connections, which can affect
their learning outcomes. Furthermore, although blended
learning can enhance engagement, it also places higher
demands on student self-discipline, especially in the
absence of face-to-face supervision (Xu et al., 2022).
Current research primarily focuses on the implemen-
tation effects of blended learning, technological support,
and student learning outcomes. However, there is still a
research gap in exploring how individual differences,
such as gender and cultural background, affect the learn-
ing experience in blended environments (Y. Wang et al.,
2022). Therefore, further investigation into the learning
behaviors and engagement differences among various
groups within blended learning, particularly gender dif-
ferences, will help provide deeper theoretical support for
personalized education.
Self-Determination Theory. SDT is a prominent macro-level
theory pertaining to studying human motivation (Luo
et al., 2021). The theory identifies two motivations:
extrinsic and intrinsic. According to the SDT, human
beings have three primary psychological requirements:
perceived autonomy (PA; i.e., a feeling of being self-
governed and self-initiating in one’s endeavors), per-
ceived competence (PC; i.e., a sense of being efficient,
which is comparable to the concept of self-efficacy), and
perceived relatedness (PR; i.e., a feeling of closeness with
other people; Deci & Ryan, 2011; Ryan & Deci, 2000). If
these basic psychological demands are met in the class-
room, students are more likely to internalize their moti-
vation and engage in more autonomous learning (Hsu
et al., 2019).
Previous research has concentrated on the validity of
SDT in educational environments, including both tradi-
tional classrooms (Lo & Hew, 2020), online learning
environments, such as K-12 online learning (Chiu,
2021b), MOOCs (Lan & Hew, 2020; Y. Sun et al., 2019),
and blended learning environments (Chiu, 2021a; Hh
et al., 2022). In particular other research, self-
determination factors have been observed as antecedents
that impact learners’ perspectives, for instance, motiva-
tion (Pugh, 2019), continuance intention (Luo et al.,
2021), and attitude (M. Wang et al., 2021).
Yang and Tan 3

Numerous recent studies have found that the satisfac-
tion of these psychological requirements facilitates
engagement and sustains satisfying learning outcomes
(Chiu, 2021a, 2022; Lan & Hew, 2020; Zhou et al.,
2022). For example, Zhou et al. (2022) focused on the
degree of students’ AE in online English learning. They
revealed the basic psychological need to predict students’
four dimensions of online learning engagement, and
competence proved to be the strongest predictor. Chiu
(2022) concluded that remote learning proves to be bet-
ter equipped to adapt to students’ varying needs. The
three psychological needs in SDT hold predictive value
for gaging students’ level of engagement, and relatedness
support are critical. Meanwhile, Y. Sun et al. (2019)
found that competence needs strongly influence students’
psychological engagement, ultimately enhancing beha-
vioral engagement in MOOCs. Moreover, Shah et al.
(2021) proved that the effect of an online learning envi-
ronment on student engagement is moderated by how
students perceive the extent to which their basic psycho-
logical needs are fulfilled or unfulfilled. Similarly,
McEown et al. (2014) discovered that when students’
motivation was more substantial, they were more likely
to desire to keep studying the language.
Technology Acceptance Model. TAM adapted from the
Theory of Reasoned Action (TRA) to better forecast
users’ acceptance of Information technology (IT) and
introduced two crucial concepts: perceived usefulness
(PU) and perceived ease of use (PEOU; Davis et al.,
1989). PU in this study refers to the extent to which stu-
dents perceive that engagement in blended learning will
improve their learning effectiveness. PEOU is the degree
to which students believe engagement in blended learning
is effortless. Furthermore, according to TAM, attitude is
the most critical factor of a user’s behavioral intention
and actual usage, which in turn is a combination of PU
and PEOU. Numerous empirical studies have confirmed
the causal relationship between all of these concepts (Ab
Rahman et al., 2019; Alalwan, 2022; Alismaiel et al.,
2022; Peng et al., 2022).
As technology continues to advance, using technology
for educational purposes is now an integral part of
everyday life, and various learning technologies have
increased (X. Wu & Wang, 2018, 2020). As a result,
research on the relationship between learners’ percep-
tions of new technology and their engagement has been
done (Bernacki et al., 2020; Binder, 2022; Bond et al.,
2020; Code et al., 2020). For example, H. E. Kim et al.
(2021) considered that learners’ perceptions of MOOCs’
usefulness substantially affected learning engagement,
enhancing learning results. Furthermore, the PEOU of
MOOCs learning systems is an essential factor influen-
cing learning engagement. To enhance the explanatory
power of flipped learner engagement, Min (2021) effec-
tively extends TAM by incorporating two additional
external variables, specifically social influence and cogni-
tive instrumental processes. By integrating SDT and
TAM, C. Sun et al. (2020) designed a predictive frame-
work for higher education students’ engagement in
online learning. The findings indicated a significant cor-
relation between PU and significantly influencing emo-
tional and cognitive engagement.
Hypothesis
PA in the context of education refers to individuals
actively choosing learning strategies and regulating the
learning process (Arvanitis, 2017). Several studies have
demonstrated a positive correlation between PA and pos-
itive outcomes (Guay, 2022; Nalipay et al., 2020; Ryan &
Deci, 2020). For instance, Ryan and Deci (2020) pointed
out that in online self-regulated learning, when individu-
als believe that their behavior is motivated by internal
forces, they are more inclined to believe that they have
control over particular objectives and link actions to pos-
itive results. In this study, blended learning offers a multi-
tude of approaches to appropriately address the needs of
diverse learners’ language acquisition in foreign language
education, which enables students to regulate their learn-
ing process without being guided by the teacher, thereby
enhancing their autonomy (Nalipay et al., 2020; Racero
et al., 2020). This way also enhances learning efficacy,
thereby enhancing students’ perceptions of usefulness
and ease of use. As a result, the following hypotheses are
put forth:
H1: PA has a positive effect on PU.
H2: PA has a positive effect on PEOU.
The concept of relatedness involves establishing emo-
tional connections with others (Ahn & Back, 2019;
Gupta, 2020; Luo et al., 2021). Gupta (2020) deemed
that when individuals were in an environment of autono-
mous support coupled with a robust sense of relatedness,
it can engender an augmented incentive for them to
engage in educational undertakings. Thus, PR is a mani-
festation of social influence that parallels the subjective
norm within the field of information systems (Rahi
et al., 2021; Xie et al., 2020). Prior research has indicated
subjective norms’ impact on PU and intrinsic motivation
(Pugh, 2019; Rahi et al., 2021). Nikou and Economides
(2017) also discovered a positive correlation between
relatedness, PU, and PEOU. Based on that, we hypothe-
size that:
H3: PR has a positive effect on PU.
H4: PR has a positive effect on PEOU.
4SAGE Open

PC is analogous to self-efficacy and exhibits common
characteristics (Roca & Gagne
´, 2008). It refers to an indi-
vidual’s belief in whether they can complete a particular
task successfully and accomplish their objectives (Sørebø
et al., 2009). This belief relates to users’ perceptions in
technology-enhanced areas, such as PU and PEOU.
Meanwhile, it is anticipated that PC (an intrinsic motiva-
tion factor) would affect PU (an extrinsic motivation fac-
tor; Lee et al., 2015). In addition, previous research has
demonstrated a positive correlation between self-efficacy
and PEOU using digital systems. Thus, it is assumed by
this study that the perception of system usefulness and
ease of use will increase among Chinese English learners
if they feel confident in utilizing language learning tech-
nology within a blended learning setting. Based on the
above discussion, the following hypotheses are proposed:
H5: PC has a positive effect on PU.
H6: PC has a positive effect on PEOU.
Numerous recent studies have reported that fulfilling
psychological needs increases engagement and fosters
positive learning outcomes and outstanding achievement
(Benlahcene et al., 2021; Conesa et al., 2022; Jin et al.,
2022). For example, Stiegemeier et al. (2022) compared
participants in Small Private Online Course (SPOC) who
completed the course with non-completers and discov-
ered that engagement could be predicted significantly by
basic psychological needs. The research conducted indi-
cated that relatedness was a potent incentive for learning
engagement. The relatedness-supportive learning envi-
ronment in educational settings resulted in greater active
learning engagement. Jin et al. (2022) also concluded that
when individuals perceived that they had the necessary
abilities and resources to complete a task, they were more
inclined to exhibit self-determined behavior and were
more engaged. Conversely, they may lose confidence and
reduce their engagement. Regarding foreign language
learning studies, it had been shown that fundamental
psychological needs could foresee AE among college stu-
dents (Shirvan & Alamer, 2022; Y. Wang et al., 2022).
Therefore, based on the prior literature, we hypothesize:
H7: PA is positively associated with EFL students’
AE in blended learning
H8: PR is positively associated with EFL students’
AE in blended learning.
H9: PC is positively associated with EFL students’
AE in blended learning.
In blended learning, the student’s traits and the learn-
ing platform or social media devices should be the main
focus (H. E. Kim et al., 2021; Panisoara et al., 2020). The
relationship between perceptions of technology and their
actual engagement had been proven by existing research
(Jin et al., 2022; Luo et al., 2021; Xia et al., 2022). For
instance, Rahi et al. (2021) proposed that students’
engagement with blended learning may be influenced by
the characteristic of the platform, particularly its useful-
ness and that this relationship should be explored fur-
ther. In addition, the PEOU of the platform is another
essential factor affecting student engagement (Arvanitis,
2017; Ryan & Deci, 2020; Xia et al., 2022). Hence, the
PU and PEOU are expected to affect EFL students’ AE
in blended learning. As a result, the following hypotheses
are put forth:
H10: PU is positively associated with EFL students’
AE in blended learning
H11: PEOU is positively associated with EFL stu-
dents’ AE in blended learning
Gender is a vital factor to consider when developing a
model of motivation and engagement (Oga-Baldwin &
Nakata, 2017). Similarly, Lietaert et al. (2015) also
believed that females exhibited greater diligence and
engagement in classroom activities and demonstrated
heightened attentiveness and endurance compared to
their male counterparts. Moreover, while learning a lan-
guage, a large body of research shows that females are
more skilled in their native tongue-related fields, and this
trend has also extended to other languages. Females
often fare marginally better than males while learning
English as a foreign language. Meanwhile, differences in
how males and females engage with language learning
may result from differences in basic interaction styles
and identity (Hsieh et al., 2021; Lilleker et al., 2021).
Hence, gender differences warrant careful attention, and
it is necessary to study potential contributing factors that
can inform teaching practices and facilitate educators in
fostering increased student AE in foreign language
acquisition.
Evidence suggests gender differences exist in ability-
related domains (Fu et al., 2022; Niu et al., 2022; Shen
et al., 2022). For instance, compared to males, females
reported a higher level of competence beliefs in language
but a lower level of competence in athletics (Bayu &
Saputra, 2023). To account for these gender gaps in
stereotypical areas, Li et al. (2018) proposed that due to
gender-role socialization differences, males and females
acquire different patterns of competence beliefs and val-
ues, and thus different levels of engagement across a wide
range of activities, which are consistent with their gender
role. Based on the above discussion, this study deems
that the differences in male and female PC will affect
their enthusiasm to participate in foreign language learn-
ing. Therefore, the following hypothesis is proposed:
Yang and Tan 5

H12a: There is a significant gender difference in the
relationship between PC and AE
Several studies have explored gender differences con-
cerning the relationship between PR and AE (Akhrib &
Zohra Mebtouche Nedjai, 2021; Rezaeian &
Abdollahzadeh, 2020; Y. Wu & Kang, 2023). For
instance, Salmela-Aro et al. (2022) considered that PR
had a more significant impact on male engagement than
females. This was primarily attributed to the fact that
males were deemed to be more susceptible to academic
maladjustment. Other studies have reported that, com-
pared to their male counterparts, females often tended to
exhibit more positive attitudes toward their interactions
with instructors and peers (Aditomo & Hasugian, 2018;
Almusharraf et al., 2023). Based on the above discussion,
we deem that the relationship between PR and AE in
blended learning is moderated by gender. Accordingly,
the following hypothesis is proposed:
H12b: There is a significant gender difference in the
relationship between PR and AE
A limited amount of prior research has been con-
ducted regarding gender differences in the correlation
between PA and AE (Akhrib & Zohra Mebtouche
Nedjai, 2021; Hsieh et al., 2021). Conesa et al. (2022)
analyzed a cohort of 274 seventh-grade pupils from
Belgium. They revealed that the correlation between per-
ceptions of PA and behavioral engagement had been
more pronounced in males than females. On the con-
trary, Rezaeian and Abdollahzadeh (2020) found that
females usually exhibit more self-discipline than males
because they are more inclined to plan, set goals, and
self-monitor than males. It is anticipated that there are
substantial gender differences in the correlation between
PA and AE in blended learning. Thus, the following
hypothesis is put forth:
H12c: There is a significant gender difference in the
relationship between PA and AE.
Blended learning is an instructional strategy that
builds on the foundation of conventional classroom
instruction and adds auxiliary teaching means such as
network technology (Du et al., 2022). In this setting,
female students are more likely to pursue high-quality
learning experiences, making them more likely to per-
ceive the utility of foreign language learning and thus
enhance their enthusiasm for participating in blended
learning (Arifin & Ad, 2019; Arvanitis, 2017).
Conversely, male students typically favor individual
learning and self-exploration, placing less emphasis on
the practical application value of learning (Maon et al.,
2021). In light of this, we hypothesize:
H12d: There is a significant gender difference in the
relationship between PU and AE
PEOU refers to the degree to which learners perceive
the convenience of using a specific tool or technology
(Davis, 1989). Research shows that in blended learning,
male learners were more likely to perceive the ease of for-
eign language learning than female learners (Bayu &
Saputra, 2023; Benlahcene et al., 2021). This may be
because males are better at using technical tools, more
confident, and willing to try new technical tools to assist
learning (Chibisa & Mutambara, 2022; Du et al., 2022).
Based on the above discussion, we propose the following
hypothesis:
H12e: There is a significant gender difference in the
relationship between PEOU and AE
Through a comprehensive arrangement and analysis
of the literature and empirical support, we have devised
a research model illustrated in Figure 1.
Methodology
Sample and Data Collection
This study employed a random sampling method to
ensure the representativeness of the sample and the broad
applicability of the research results. Random sampling
minimizes sample selection bias, giving each potential
participant an equal chance of being selected for the
study. Data collection took place from April 10 to May
20, 2023, at six universities in Jiangxi Province, China,
where university students were enrolled in blended EFL
courses. These universities were chosen because they
Figure 1. Theoretical framework.
6SAGE Open

extensively use blended learning as a teaching method for
English courses.
Before the start of data collection, all participants
were informed of the purpose of the study, which aimed
to investigate the factors affecting the active engagement
of EFL students in a blended learning environment.
Researchers assured participants that all personal infor-
mation would be kept confidential and their privacy fully
protected. Participants were also informed of their right
to withdraw from the survey at any time without provid-
ing any reason.
Participants were selected through a random sampling
method from these six universities, ensuring that the
sample adequately represented the broader EFL student
population at these institutions. The questionnaire was
distributed electronically via the professional online sur-
vey platform, Wenjuanxing (https://www.wjx.cn), which
ensured the security and efficiency of data collection.
Before participating in the survey, all participants
provided written informed consent, acknowledging their
understanding of the research purpose and agreeing to
participate in the study. To further enhance the represen-
tativeness of the sample, we ensured it included students
from different academic years (freshmen, sophomores,
juniors, and seniors) and with varying experiences of
blended learning. Specifically, the sample included
56.17% freshmen, 38.06% sophomores, with a balanced
gender distribution of 50.13% male and 49.87% female.
Most respondents (45.95%) had more than 3 months of
blended learning experience, 37.53% had less than
1 month, and 16.54% had 1 to 3 months of experience
(Table 1). This diversity in gender, academic year, and
blended learning experience allows for a comprehensive
exploration of factors affecting student engagement in
blended learning environments, as these variables might
influence students’ attitudes and experiences differently.
For example, newer students or those with less blended
learning experience may differ in autonomy or related-
ness from more experienced students, which could affect
their active participation.
Instruments
The survey comprised two segments. In the first section,
researchers offered demographic information. The sec-
ond section necessitated the respondents to express their
levels of agreement or disagreement toward items by
using a five-point Likert scale ranging from ‘‘strongly
disagree’’ (1) to ‘‘strongly agree’’ (5). Table 2 displays
detailed information on the scale.
Data Analysis
This study employed a cross-sectional research design
aimed at exploring the factors influencing the AE of
Chinese university students in a blended learning envi-
ronment and analyzing the moderating role of gender
among these factors. Cross-sectional designs enable data
collection at a single time point, effectively analyzing the
correlations between different variables (Ringle et al.,
2015). The collected data were processed using various
statistical analysis methods. Initially, descriptive statisti-
cal analysis was used to outline the basic characteristics
of the sample. Subsequently, Smart PLS was utilized to
test the research hypotheses to determine the relation-
ships between independent and dependent variables.
There are several rationales behind employing Smart
PLS in this research: (1) small sample size and non-
normal distribution of data; (2) the ability to handle com-
plex models involving moderating effects; (3) it allows for
model comparison and validation between different
groups to determine model consistency and differences
(J. Hair et al., 2021). Additionally, the moderating role of
gender was examined using Multi-Group Analysis
(MGA).
Results
The Outer Model
To ensure that the data was consistent with the research
model, it was imperative to evaluate the reliability and
validity of each indicator within the questionnaire.
Reliability measurement mainly uses indicator reliability
and internal consistency reliability. Meanwhile, conver-
gence validity and discriminant validity tests are carried
out to evaluate the validity of each indicator.
Factor loadings were used to evaluate the items’ relia-
bility. Table 3 reveals that all items exhibit factor load-
ings surpassing 0.708, indicating a satisfactory level of
reliability (Bagozzi, 1981).
Internal consistency reliability was assessed using
composite reliability (CR) and Cronbach’s alpha (CA).
CA varied from .835 to .885 above the 0.7 threshold rec-
ommended in the literature (Henseler et al., 2009), satis-
fying the value requirements. Furthermore, J. F. Hair
Table 1. Information Regarding the Demographics of the
Respondents (N= 381).
Category Item Frequency Percentage
Gender Male 191 50.13
Female 190 49.87
Grade Freshman 214 56.17
Sophomore 145 38.06
Junior 9 2.36
Seniors 13 3.41
Duration No more than 1 month 143 37.53
1–3 months 63 16.54
More than 3 months 175 45.95
Yang and Tan 7

Table 2. Items of the Constructs.
Construct Item Cronbach’s aSource
PC PC1: I believe that I can quickly accept and adapt to the blended learning mode
(classroom learning and Self-access English Learning systems).
.844 Sørebø
et al. (2009)
PC 2: I can use the Self-access English Learning systems without the assistance of
others.
PC 3: If I encounter technical operational issues during use, I believe I can handle them
well.
PU PU1: The blended learning based on the Self-access English Learning systems can deepen
my understanding of the classroom learning content so that I can quickly complete the
course learning tasks.
.885 H. E. Kim
et al. (2021)
PU2: The blended learning mode encourages my autonomous learning, allowing me to
better organize and manage my learning process.
PU3: The blended learning mode stimulates my learning interest and gives me more
confidence.
PEOU PEOU1: The operation of Self-access English Learning systems for me is very simple .881
PEOU2: The autonomous learning system has clear navigation, a reasonable layout, and
simple operation
PEOU3: The learning resources in the Self-access English Learning systems are easy to
access and download.
PA PA1: I can learn at my own pace in blended learning. Establish good relationships or
partnerships with others
.841 Sørebø
et al. (2009)
PA2: If it were up to me whether or not to do the online English learning task, I would
still have done it.
PA3: I did online English class tasks because I wanted to.
PR PR1: I get along with people when I am learning English. 0.835
PR2: People are pretty friendly toward me when I am learning English.
PR3: I like the people learning English with me.
AE AE1: I am pleased with the effort I have put into this course. .873 J. C. Sun and
Rueda (2012)AE2: When I’m in the English class, I listen very carefully.
AE3:I try hard to do well in blended English classes.
Table 3. Reliability and Validity Evaluation.
Constructs Items Factor loadings
Composite
reliability (CR)
Average variance
extracted (AVE)
PA PA1 0.881 0.841 0.759
PA2 0.877
PA3 0.855
PC PC1 0.857 0.845 0.763
PC2 0.872
PC3 0.891
PEOU PEOU1 0.871 0.883 0.808
PEOU2 0.925
PEOU3 0.900
PR PR1 0.872 0.837 0.752
PR2 0.837
PR3 0.892
PU PU1 0.904 0.886 0.814
PU2 0.925
PU3 0.876
AE SE1 0.909 0.873 0.798
SE2 0.912
SE3 0.859
8SAGE Open

et al. (2017) stated that CR values between 0.60 and 0.70
were acceptable, 0.70 and 0.90 were generally regarded
as satisfactory, and values above 0.90 (particularly
.0.95) were undesirable. The CR in Table 3 ranged
from 0.837 to 0.886, demonstrating the entire dataset
met the requirements above.
Convergent validity, evaluated using the Average
Variance Extracted (AVE), refers to the convergence or
correlation of items to measure the same variable
(Trockel et al., 2018). The AVE values ranged from 0.752
to 0.814 over the recommended value of 0.50, meaning
sufficient convergent validity (Henseler et al., 2015b).
Discriminant validity is the degree to which one con-
struct can be genuinely distinguished from another (Zaitx
& Bertea, 2011). Fornell-Larcker criteria and cross-
loading method could be used to assess the discriminant
validity.
First, according to the Fornell-larcker criterion, the
square roots of the AVE for a particular construct
should outweigh correlation values with other constructs
(Henseler et al., 2015b). Table 4 displays that the square
root of the AVE of each construct is higher than its high-
est correlation with other constructs. Hence, the findings
meet the criteria.
In addition, the research employs a comparative table
of cross-loading items to assess discriminant validity.
Cross-loading among indicators is evaluated by analyz-
ing the inter-relationship between one particular indica-
tor and another. When the relationship between an
indicator and its variables surpasses that of the other
indicators, it is considered to be established (Henseler
et al., 2015a). The findings presented in Table 5 demon-
strate that the aforementioned criteria have been ful-
filled, thereby establishing discriminant validity.
The Inner Model
Collinearity Test. The Variance Inflation Factor (VIF) is a
commonly used metric for detecting multicollinearity
among independent variables. It quantifies the degree of
linear relationship between each independent variable
and all other independent variables, thereby assisting
researchers in determining whether multicollinearity
exists in the model (J. F. Hair et al., 2017). A VIF value
of 5 or more indicates a serious collinearity problem
among indicators (Purwanto, 2021). Therefore, criteria
with values below five were deemed acceptable in this
research. The VIF value, as evidenced in Table 6, dis-
plays a range of 2.132 to 4.426, indicating a lack of colli-
nearity issues.
Significance Testing of Hypotheses. The findings of the
research hypotheses are tabulated in Table 7 and Figure 2.
Table 5. Analysis Outcomes of Discriminant Validity (Cross-loadings).
Constructs Items PA PC PEOU PR PU AE
PEOU PEOU1 0.638 0.679 0.871 0.534 0.642 0.600
PEOU2 0.701 0.689 0.925 0.644 0.704 0.655
PEOU3 0.686 0.621 0.900 0.627 0.637 0.621
PA PA1 0.881 0.623 0.662 0.596 0.734 0.695
PA2 0.877 0.641 0.642 0.601 0.680 0.654
PA3 0.855 0.614 0.659 0.727 0.696 0.718
PR PR1 0.678 0.563 0.588 0.872 0.646 0.695
PR2 0.602 0.520 0.590 0.837 0.605 0.637
PR3 0.636 0.508 0.569 0.892 0.643 0.752
AE SE1 0.727 0.626 0.617 0.734 0.707 0.909
SE2 0.678 0.561 0.601 0.741 0.676 0.912
SE3 0.717 0.630 0.648 0.676 0.755 0.859
PC PC1 0.601 0.857 0.640 0.551 0.677 0.566
PC2 0.665 0.872 0.627 0.502 0.710 0.589
PC3 0.616 0.891 0.666 0.549 0.708 0.621
PU PU1 0.727 0.768 0.693 0.625 0.904 0.687
PU2 0.771 0.708 0.651 0.680 0.925 0.750
PU3 0.687 0.688 0.648 0.664 0.876 0.723
Note: The bolded values represent the outer loadings corresponding to each construct.
Table 4. Analysis Outcomes of Discriminant Validity
(Fornell-Larcker Criterion).
Constructs PA PC PEOU PR PU AE
PA 0.871
PC 0.718 0.873
PEOU 0.751 0.738 0.899
PR 0.737 0.611 0.671 0.867
PU 0.808 0.800 0.736 0.728 0.902
AE 0.792 0.678 0.697 0.803 0.798 0.894
Note. The bold values on the diagonal is the square root of AVE.
Yang and Tan 9

As can be observed, PA was positively linked with PU
(b=5.593, p\.001) and PEOU (b=4.736, p\.001),
supporting H1 and H2 correspondingly. As for PR, it is
positively related to PU (b= 3.997, p\.001) and PEOU
(b=3.229,p\.001), confirming H3 and H4, respectively.
As anticipated in the previous hypothesis, PC positively
influences PU (b= 9.645, p\.001) and PEOU
(b=7.341, p\.001), providing support to H5 and H6.
The analytical results demonstrated that PA was positively
associated with AE (b=5.807, p\.001), confirmed H7.
Additionally, PR meaningfully predicts AE (b= 8.040, p
\.001). Therefore H8 is acceptable. The direct relation
from PC to AE was significant (b= 3.438, p\.001), pro-
viding support H9. PU directly and significantly affected
AE (b=4.067, p\.001), and H10 was supported.
Regarding the relationship between PEOU and AE, the
results demonstrated that PEOU did not influence AE
(b= .617, p..05), and H11 was rejected.
Explanatory Power. The coefficient of determination (R
2
)
value reflects how well the independent variable predicts
the latent dependent variable. As recommended by Chin
(1998), R
2
values of .19, .33, and .67 are regarded as
weak, moderate, and substantial, respectively.
Accordingly, the R
2
values obtained from the
assessments of .661, .773, and .762 show acceptable lev-
els of predictive accuracy (Table 8).
Effect Size (f
2
). The effect sizes (f
2
), based on changes in
R
2
values, were evaluated (J. F. Hair et al., 2017).
Following Cohen (1988)’s guidelines, effect sizes with
values of 0.02, 0.15, and 0.35 are categorized as small,
medium, and large, respectively. PA and PC were found
to have a small substantive effect on students’ engage-
ment, with 0.064 and 0.023, respectively. Moreover, PR
exhibits a moderate effect size (f
2
= 0.243) (Table 9).
The Multigroup Analysis for the Moderator Effects
To test the moderating effect of gender, it is required to
assess the measurement invariance of the female and
male produced in accordance with the variables
(Henseler, 2012). Thereby, the three-step measurement
invariance of composite models (MICOM) procedure
recommended by Henseler et al. (2015a) was used to
assess the measurement invariance for gender before the
PLS-MGA. MICOM configural invariance, compo-
sitional invariance, composite equality. This is an effec-
tive method for determining whether a measurement
model assesses the same attributes across different condi-
tions (Henseler, 2015). According to Leguina (2015),
since the maximum number of arrows pointing toward a
construct in this study is 5, to reach the minimal R
2
of
.10 at a 5% significance level, both subgroups must
exceed 147. Meanwhile, J. F. Hair et al. (2019) emphasize
that although the sizes of two subpopulations need not
be identical, they must be comparable. Consequently, the
criteria have been met by both sample groups included in
this research.
Firstly, the evaluation of configural invariance
involves assessing the measurement model across all
Table 7. Path Coefficients and Results of Hypotheses Testing.
Hypothesis path
Original
sample (O)
Sample
mean (M)
Standard deviation
(STDEV)
Tstatistics
(|O/STDEV|) p-Values Results
PA !PEOU 0.342 0.343 0.072 4.736 .000*** Supported
PA !PU 0.349 0.348 0.062 5.593 .000*** Supported
PA !AE 0.351 0.351 0.060 5.807 .000*** Supported
PC !PEOU 0.377 0.375 0.051 7.341 .000*** Supported
PC !PU 0.417 0.416 0.043 9.645 .000*** Supported
PC !AE 0.150 0.150 0.044 3.438 .000*** Supported
PEOU !AE 0.038 0.041 0.061 0.617 .537 Rejected
PR !PEOU 0.188 0.188 0.058 3.229 .000*** Supported
PR !PU 0.216 0.217 0.054 3.997 .000*** Supported
PR !AE 0.452 0.453 0.056 8.040 .000*** Supported
PU !AE 0.286 0.282 0.070 4.067 .000*** Supported
*** p\.001.
Table 6. Collinearity Statistic (VIF).
Constructs PA PC PEOU PR PU AE
PA 2.923 2.923 3.744
PC 2.132 2.132 3.235
PEOU 2.971
PR 2.258 2.258 2.547
PU 4.426
AE
10 SAGE Open

groups to determine whether the same underlying factor
structure—specifically, the same number of constructs
and their corresponding measurement items—is present
in all groups (Henseler, 2015).Consequently, configural
invariance has been verified using the same data process-
ing, measurement, and structural models, as well as algo-
rithm settings. Secondly, permutation tests are employed
to examine whether statistical evaluations demonstrate
compositional invariance. Table 10 indicated that com-
positional invariance was also proven to exist because
the original correlation is always equal to or higher than
the 5% quantile (Cheah et al., 2020).
The third stage evaluates the composite equality of
mean values and variances across groups. It is essential
to fulfilling two prerequisites in this phase. First, the
mean original difference must fall within the 95% confi-
dence interval. Second, the original variance difference
must be a number that falls within the 95% confidence
interval (Henseler et al., 2015a). Tables 11 and 12 show
that Step 3 was not satisfied.
Partial measurement invariance was built in circum-
stances where Steps 1 and 2 were confirmed, while Step 3
only satisfied one criterion. The partial measurement
invariance proved sufficient to use the PLS-MGA to
compare structural paths between groups (Henseler
et al., 2015a).
Once invariance has been validated, the subsequent
stage necessitates conducting a multigroup analysis
(Table 13). Based on the outcomes of both permutation
tests and PLS-MGA analyses in Table 14, there are no
significant differences between the two genders in
blended learning. Accordingly, H12a, H12b, H12c,
H12d, and H12e are all rejected.
Common Method Bias
Common method bias (CMB) should be considered
while performing quantitative research. Following Kock
(2015)’s recommendation, Harman’s Single Factor Test
was carried out. The principal component factor was
used to analyze each item in SPSS. The findings in Table
15 demonstrated that the first cumulative value was
lower than 50%, indicating that there were no problems
with the CMB in current research (S. B. Kim & Kim,
2016; Podsakoff & Organ, 2016).
Discussion
The present study puts forth an extended model of TAM
that incorporates the motivation factors derived from
SDT. Then empirical research was conducted to examine
the relationship between the constructs in the hypothe-
sized model with gender as a moderating variable. The
findings suggest that 76.2% of the variance in the lear-
ners’ AE is explained by the determinants of SDT and
TAM, which exhibits that the combination of TAM and
SDT offers an appropriate framework for learners’ AE.
The following segments furnish an in-depth analysis of
the study’s findings.
Figure 2. Structural model path coefficients.
Table 8. The Predictive Power of Construct.
Constructs R
2
PEOU .661
PU .773
AE .762
Table 9. The Effect Size.
Constructs PA PC PEOU PR PU AE
PA 0.118 0.184 0.064
PC 0.197 0.358 0.023
PEOU 0.022
PR 0.046 0.091 0.243
PU 0.077
AE
Table 10. MICOM Step 2 Results Report.
Constructs
Original
correlation
Correlation
permutation mean 5.0%
Permutation
p-value
PA 1.000 1.000 1.000 .137
PC 1.000 1.000 0.999 .931
PEOU 1.000 1.000 1.000 .570
PR .999 1.000 0.999 .193
PU 1.000 1.000 1.000 .900
AE 1.000 1.000 1.000 .147
Yang and Tan 11

This research discovered that PA could enhance PU,
which corresponds with the findings of Nambiar (2020).
One plausible justification could be that blended learning
allows EFL students to make choices depending on their
interests and learning goals by offering different learning
means, thereby improving their autonomy. In this auton-
omous learning environment, EFL students are more
Table 11. MICOM Step 3 Results Report—Part 1.
Constructs Mean original difference Permutation mean difference 2.5% 97.5% Permutation p-value
PA 0.018 0.003 20.199 0.203 .070
PC 0.158 0.002 20.206 0.197 .127
PEOU 0.061 0.003 20.209 0.205 .104
PR 0.186 0.003 20.204 0.203 .065
PU 0.175 0.003 20.203 0.205 .104
AE 0.001 0.004 20.188 0.189 .201
Table 12. MICOM Step 3 Results Report—Part 2.
Constructs Variance original difference Variance permutation mean difference 2.5% 97.5% Permutation p-value
PA 20.469 20.003 20.387 0.398 .016
PC 20.355 0.001 20.348 0.343 .046
PEOU 20.435 20.004 20.356 0.338 .015
PR 20.488 0.000 20.334 0.326 .006
PU 20.522 20.002 20.391 0.365 .007
AE 20.589 0.002 20.357 0.352 .000
Table 13. Bootstrapping Results for Males and Females Separately.
Path
Original
(female)
Mean
(female)
STDEV
(female)
t-Value
(female)
p-Value
(female)
Original
(male)
Mean
(male)
STDEV
(male)
t-Value
(male)
p-Value
(male)
PA !AE 0.175 0.172 0.091 1.926 .054 0.283 0.279 0.092 3.094 .002
PC !AE 0.080 0.081 0.090 0.888 .375 20.027 20.025 0.06 0.450 .653
PEOU !AE 0.025 0.029 0.098 0.256 .798 0.058 0.063 0.075 0.776 .438
PR !AE 0.433 0.437 0.098 4.421 .000 0.345 0.347 0.075 4.612 .000
PU !AE 0.197 0.197 0.103 1.907 .057 0.333 0.327 0.092 3.613 .000
Table 15. Total Variance Explained (Herman’s Single Factor Test).
Component Extraction sums of squared loadings
1 Total % Of variance Cumulative %
11.167 44.184 44.184
Note. Extraction method: principal component analysis.
Table 14. Multigroup Analysis Results.
Path
Path coefficients
original (female)
Path coefficients
original (male)
Path coefficients
original difference
(female-male)
Path coefficients
permutation means
difference (female-male) 2.50% 97.50%
Permutation
p-values
PA !AE 0.241 0.438 20.198 20.001 20.257 0.243 .113
PC !AE 0.166 0.143 0.023 20.001 20.171 0.183 .778
PEOU !EI 0.025 0.058 20.033 0.003 20.249 0.248 .800
PR !AE 0.502 0.394 0.108 0.004 20.232 0.238 .355
PU-.AE 0.197 0.333 20.136 20.005 20.288 0.286 .333
12 SAGE Open

likely to experience a sense of achievement and satisfac-
tion in learning, thus believing that blended learning is
beneficial. Therefore, H1 is supported.
PA is confirmed as a powerful predictor for PEOU.
One explanation is that blended learning provides rich
online learning resources, encompassing video tutorials,
interactive exercises, and learning communities. EFL stu-
dents can independently choose and explore these
resources based on their own needs and learning styles.
When students perceive that the platform can provide
this autonomous support, they may think that using the
platform is easy because it matches their learning needs.
This discovery is consistent with several studies that
demonstrate the close relationship between PA and
PEOU (Abou-Khalil et al., 2021; Rezaeian &
Abdollahzadeh, 2020). Therefore, H2 is supported.
Congruent to the findings of Luo et al. (2021), PR
produces significant positive effects on PU. In this study,
blended learning offered a collaborative platform for
EFL learners to interact and engage with each other. By
engaging in active learning, effective communication,
and productive collaboration, students can feel a sense of
connection and belonging to others. This perception of
correlation can enhance the PU of learning resources and
activities, as learners recognize that interaction and colla-
boration with others can improve learning outcomes.
Therefore, H3 is supported.
Our findings strongly supported the hypothesis that
PR positively impacted PEOU. This result is partly linked
to prior research conducted by Y. Wang et al. (2022),
which indicated that PR played a significant role in fore-
casting PEOU. When EFL students perceive an increased
correlation with others, it can promote more opportuni-
ties for communication and feedback. This communica-
tion and feedback can help learners better understand
how to use blended learning tools and resources by pro-
viding practical guidance and suggestions, thus improving
PEOU. Therefore, H4 is supported.
PC was meant to be a good predictor of PU and
PEOU. These results were confirmed by the findings in
prior studies (Cents-Boonstra et al., 2022; Rahi et al.,
2021; Werle et al., 2021). In blended learning, technical
tools and platforms are frequently utilized, including
online learning platforms and speech recognition soft-
ware. When EFL students feel that they have sufficient
technical skills to effortlessly use learning tools and plat-
forms, their learning may become more efficient, enhan-
cing their perception of the usefulness and ease of use of
blended learning. Therefore, H5 and H6 are supported.
The structural model results reveal that PA is the cru-
cial antecedent for AE. This outcome is in line with
research done by Bayu and Saputra (2023) in synchro-
nous online English courses. In blended education, pro-
moting an atmosphere of autonomy can effectively
enhance AE among EFL students. This is because auton-
omous learning gives students a greater sense of control
and autonomy over their learning. Simultaneously, EFL
students are more likely to delve deeper into topics or
issues that pique their interest, and they are also more
likely to experience joy and a sense of accomplishment,
which makes them more willing to engage in blended
courses. Therefore, H7 is supported.
It is affirmed that PR significantly affects learner AE,
which is accordant with results from previous research
(Bayu & Saputra, 2023; Conesa et al., 2022). When
studying a foreign language, EFL students may face var-
ious obstacles and challenges that might induce anxiety
and result in a sense of isolation. In this situation, PR
makes EFL learners feel connected to others, which can
encourage them to exchange ideas with instructors and
peers, share learning experiences, and overcome difficul-
ties together. Consequently, a feeling of relatedness will
motivate them to engage more in foreign language learn-
ing. Therefore, H8 is supported.
PC has positive effects on AE as well. The outcomes
correspond with the results of research conducted by
Conesa et al. (2022) on Chinese EFL learners. This find-
ing may be explained by the fact that Chinese culture
impacts EFL students’ learning styles, which makes them
more dependent on the teacher and less able to study on
their own. Additionally, the traditional one-way educa-
tional approach makes students less confident and timid
in communication and expression. However, blended
learning can provide more opportunities for oral English
practice and communication. In such an environment,
EFL students can perceive their competence to learn
English, which boosts their confidence and passion in
class and enables them to engage in more active English
learning. Therefore, H9 is supported.
For EFL students, PU, while adopting blended learn-
ing, significantly correlated with their AE. This indicates
that if EFL students deem the blended learning approach
to be efficacious in heightening their academic achieve-
ment, it is plausible that they may exhibit heightened
involvement in foreign language learning. One explana-
tion of this result is that blended learning combines tra-
ditional face-to-face teaching with online learning.
Therefore, in blended learning, EFL students can obtain
rich learning resources through online learning plat-
forms, which can help them better understand and mas-
ter language knowledge. The outcome aligned with a
previous study by El-Sayad et al. (2021), which showed
that the usefulness of online learning systems positively
affected emotional and cognitive engagement. Therefore,
H10 is supported.
On the contrary, PEOU, while adopting blended
learning, did not significantly affect AE for EFL stu-
dents. This may be because EFL college students may
Yang and Tan 13

have more technical proficiency and digital literacy than
other age groups. They are generally more familiar with
using computers, the Internet, and various online tools.
Therefore, they may be more concerned with realizing
learning outcomes and the quality of teaching content,
and ease of use may not be their primary concern. This
result is aligned with prior research by Jung and Lee
(2018), who revealed that PEOU is not a significant pre-
dictor when exploring the factors influencing students’
engagement in massive open online courses (MOOCs).
Therefore, H11 is rejected.
There is no significant gender difference in the rela-
tionship between PA and AE for EFL students. One
explanation may be that blended learning offers impar-
tial and equitable learning opportunities where male and
female students receive comparable autonomy support.
This includes the ability to autonomously choose learn-
ing content, control the learning process, and perceive
their learning abilities and effectiveness. This equal learn-
ing environment reduces the moderating effect of gender
on these relationships. This result is aligned with prior
research by Heilporn et al. (2021), who revealed that
regardless of gender, an autonomous learning environ-
ment can promote students’ AE in online learning.
Therefore, H12c is rejected.
Our results showed no significant gender difference in
the relationship between PC and AE for EFL students.
This discovery contradicts the findings of Rinaldi et al.
(2023)’s research, which found that male students
demonstrated a higher inclination than their female
counterparts to self-evaluate their competence positively
in second and foreign language acquisition. A plausible
rationale is that the assessment of competence is predo-
minantly shaped by aspects such as personal cognition,
learning style, learning motivation, and previous learning
encounters, which are not distinctly associated with gen-
der. Furthermore, blended learning often incorporates
technological tools and online resources. This learning
approach facilitates EFL students in modifying their
learning strategies and availing resources as per their
progress and needs, consequently reducing the impact of
gender on their perception of learning competence.
Therefore, H12a is rejected.
Regarding the relationship between PU and AE, there
was no discernible gender difference for EFL students.
That is to say, both male and female youths prefer incor-
porating blended learning to achieve their self-
improvement goals, such as refining oral communication
and collaborative skills, as well as proficiently mastering
and applying the English language. It might be claimed
that in this situation, regardless of gender, EFL students
will adopt any practical techniques to improve English
learning effectiveness. The discovery is demonstrated to
align with the results of Gligor et al. (2022) research.
Therefore, H12d is rejected.
There was no significant gender difference observed in
terms of their levels of PR and AE for EFL students.
There is a plausible explanation that EFL students can
engage in language communication and learning activi-
ties with peers and teachers through online platforms in
blended learning. This virtual environment helps to alle-
viate potential gender stereotypes and biases in face-to-
face interactions. EFL students can freely express their
opinions and ask questions without worrying about the
influence of gender on their acceptance or recognition.
This equal environment encourages EFL students to
actively participate in foreign language learning. The
finding aligns with previous research that has also identi-
fied similar outcomes (Ganotice et al., 2022; Hsiao et al.,
2022). Therefore, H12b is rejected.
The research observed no gender difference concern-
ing the relationship linking PEOU and AE for EFL stu-
dents. The outcome was not unexpected and consistent
with previous research results (Niu & Wu, 2022; X. Wu
& Tian, 2022). As technology advances and learning
platforms are developed, contemporary blended learning
platforms generally provide more user-friendly and easy-
to-use user interfaces. This development has led to less
apparent gender differentiation in users’ perception of
ease of use. Moreover, the proliferation and pervasive
utilization of blended learning may increase EFL stu-
dents’ acceptance and familiarity with learning plat-
forms, thereby reducing gender differences in PEOU.
Therefore, H12e is rejected.
Implications
Even though this study was conducted in China, it has
specific theoretical and practical implications for EFL
students in other countries.
Theoretical Implications
The theoretical implications of this study primarily
encompass two aspects as follows. Firstly, the present
research jointly adopted TAM and SDT as theoretical
frameworks to explain EFL students’ AE in the context
of blended learning. TAM and SDT have been widely
used to describe EFL students’ AE in other contexts
(Hoang Hoa, 2020; Su & Chiu, 2021). This study con-
firmed that SDT and TAM could be employed to expli-
cate the AE of EFL students in blended learning by
testing the relationships between SDT and TAM con-
structs with AE. The results suggested that SDT and
TAM constructs were also important determinants of
EFL students’ AE in blended learning. Secondly, this
14 SAGE Open

study tested the gender difference in the active engage-
ment of EFL students in blended learning. Despite previ-
ous research suggesting gender differences in the effects
of PA, PR, PC, PU, and PEoU on AE in other contexts
(Bayu & Saputra, 2023; Hsieh et al., 2021). However,
this study confirmed that there were no significant gen-
der differences in the relationships between PA, PR, PC,
PU, and PEoU on AE among EFL students in the con-
text of blended learning.
Practical Implications
Firstly, the research results indicate that in blended
learning environments, students’ PA, PR, and PC have a
significant positive impact on AE in learning. Therefore,
educational institutions should strive to provide more
personalized learning environments to meet students’
basic psychological needs. Firstly, educational institu-
tions should ensure the provision of stable and conveni-
ent online learning platforms for students to flexibly
access learning resources. Additionally, institutions
should regularly update platform features to enhance
interactivity and ensure students can seamlessly integrate
online and offline learning. Secondly, educational institu-
tions should offer technical support and training to
teachers and students to ensure they can proficiently
manage and utilize online learning platforms and related
tools. Furthermore, to address technical obstacles stu-
dents may encounter when using technological tools,
institutions should establish dedicated technical support
teams to provide immediate assistance. Thirdly, educa-
tional institutions should develop policies to promote
and implement blended learning models, such as provid-
ing teachers with additional teaching resources and time,
encouraging the use of more online interactive tools, and
enhancing students’ learning experiences.
Secondly, the research results demonstrate that the
role of teachers in blended learning environments signifi-
cantly influences students’ learning experiences.
Particularly, teachers can effectively increase students’
active engagement in learning by enhancing their auton-
omy and competence. Firstly, teachers should encourage
students to exercise autonomy during the learning pro-
cess by providing personalized learning path options,
allowing students to choose learning resources and pace
according to their individual needs. By regularly provid-
ing personalized feedback, teachers can help students
enhance their confidence and competence in learning,
thereby stimulating their motivation to learn. Secondly,
teachers can make greater use of online interactive tools,
such as forums, discussion areas, and virtual classrooms,
to promote interaction and collaboration among stu-
dents, enhancing their sense of relatedness. This not only
helps to improve students’ learning experiences but also
assists students in better integrating into the learning
community and reducing feelings of isolation. Thirdly,
teachers need to combine the advantages of online and
offline teaching to design flexible and diverse teaching
activities. For example, teachers can have students pre-
view course content through videos or online materials
before class, and then focus on interaction and discus-
sion during class to enhance teaching effectiveness.
Finally, the study found that students’ AE in blended
learning is significantly influenced by autonomy, related-
ness, and competence. Firstly, students should learn to
effectively manage their time and tasks, establish reason-
able study plans, and ensure that they can complete
learning tasks within the set timeframe. Secondly, stu-
dents should fully utilize resources available in learning
management systems and online platforms, including
video tutorials, online exercises, and discussion forums.
These tools can help students gain a deeper understand-
ing of the learning material and resolve any uncertainties
through interactions with teachers and peers. Thirdly,
students should actively participate in online discussions
and group collaborations, enhancing their sense of relat-
edness through interactions with classmates and teach-
ers. This not only aids in academic performance but also
enhances the sense of achievement and motivation in
learning.
Limitations
It is necessary to note that this study has a few limits.
First, the present study solely opted for college students
from Jiangxi Province of China, potentially restricting
the generalizability of the research findings. Future stud-
ies should endeavor to broaden the scope of the research
by incorporating a more varied population to explore
further whether the results are robust across different
samples and environments. Second, the variables exam-
ined in this study account for 76.2% of the variation in
AE; however, it is crucial to note that not all variables
capable of affecting AE were considered. Therefore,
future research investigates other variables from the
expanded TAM and SDT frameworks. Thirdly, the pres-
ent study uses quantitative methodologies in the short
run, which may not adequately uncover the underlying
impact mechanisms of EFL students’ AE in blended
learning. Consequently, a mixed-methods research design
and longitudinal tracking of learning activities should be
employed to expand the range of research outcomes.
Finally, this study focused on exploring the gender differ-
ences in the impact of independent variables on active
engagement within a blended learning environment. The
results indicate that in this study sample, gender is not a
significant moderating factor for active participation in
learning. Future research should expand the scope of
Yang and Tan 15

investigation to include a more diverse range of learning
environments and demographic variables to identify the
key factors influencing student active engagement.
Conclusion
The present study employs SDT and TAM as the theore-
tical framework to explore the influencing factors of
EFL students’ AE in blended learning and analyze gen-
der differences in the relationships. The present study
has garnered noteworthy results: PA, PC, PR, and PU
are critical predictors of EFL students’ AE in blended
learning. However, there is not a significant connection
between PEOU and AE. Furthermore, PC is related
directly and significantly to PU and PEOU, and PR is
associated significantly and directly with PU and PEOU.
Finally, PA is also significantly related to PU and
PEOU. The research also found that, there was no sig-
nificant gender difference in the impact of PA, PR, PC,
PU, and PEOU on AE among EFL students in the con-
text of blended learning.
ORCID iD
Yanjun Yang https://orcid.org/0009-0007-2905-006X
Ethics Statement
The researchers confirm that all research was performed in
accordance with relevant guidelines/regulations applicable
when human participants are involved (e.g., Declaration of
Helsinki or similar). This study was approved by the Research
Ethics Committee of Nanchang Institute of Technology
(approval no. NIT-2023-01-0012) on January 10, 2023.
Informed Consent
Informed consent was obtained from all participants involved
in the study.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This research was supported by the Jiangxi Provincial
Social Science Planning Fund (No.:21YY16) and the Jiangxi
Provincial Teaching Reform Project (No.: JXJG-22-18-18).
Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Data Availability Statement
All data relevant to this study are available from the corre-
sponding author upon request.
References
Abou-Khalil, V., Helou, S., Khalife
´, E., Chen, M. A., Majum-
dar, R., & Ogata, H. (2021). Emergency online learning in
low-resource settings: Effective student engagement strate-
gies. Education Sciences,11(1), 24. https://doi.org/10.3390/
educsci11010024
Ab Rahman, R., Ahmad, S., & Hashim, U.R. (2019). A Study
on Gamification for Higher Education Students’ Engage-
ment Towards Education 4.0. In: V. Piuri, V. Balas, S.
Borah & S. Syed Ahmad (Eds.), Intelligent and Interactive
Computing. Lecture Notes in Networks and Systems (Vol 67,
pp. 491–502). Springer.
Abroto, A., Maemonah, M., & Ayu, N. P. (2021). The Influ-
ence of the blended learning method on enhancing motiva-
tion and learning outcomes of elementary school students.
Edukatif: Jurnal Ilmu Pendidikan,3(5), 1993–2000. https://
doi.org/10.31004/edukatif.v3i5.703
Aditomo, A., & Hasugian, E. J. (2018). Indonesian adolescents’
EFL reading comprehension: Gender differences and the
influence of parental background. Indonesian Journal of
Applied Linguistics,8(2), 325–335.
Ahn, J., & Back, K.-J. (2019). The role of autonomy, compe-
tence and relatedness. International Journal of Contemporary
Hospitality Management,31(1), 87–104. https://doi.org/10.
1108/IJCHM-01-2018-0088
Akhrib, M., & Zohra Mebtouche Nedjai, F. (2021). The effects
of situational and perceived interest on EFL reading com-
prehension: A gender-based study at the University of
Algiers 2. Arab World English Journal,12, 480–497. https://
doi.org/10.2139/ssrn.3826863
Alalwan, N. (2022). Actual use of social media for engagement
to enhance students’ learning. Education and Information
Technologies,27(7), 9767–9789. https://doi.org/10.1007/
s10639-022-11014-7
Alismaiel, O. A., Cifuentes-Faura, J., & Al-Rahmi, W. M.
(2022). Social media technologies used for education: An
empirical study on TAM model during the COVID-19 pan-
demic. Frontiers in Education,7, 882831.
Almusharraf, N., Aljasser, M., Dalbani, H., & Alsheikh, D.
(2023). Gender differences in utilizing a game-based
approach within the EFL online classrooms. Heliyon,9(2),
e13136. https://doi.org/10.1016/j.heliyon.2023.e13136
Al-Obaydi, L. H., Shakki, F., Tawafak, R. M., Pikhart, M., &
Ugla, R. L. (2023). What I know, what I want to know,
what I learned: Activating EFL college students’ cognitive,
behavioral, and emotional engagement through structured
feedback in an online environment. Frontiers in Psychology,
13, 1083673. https://doi.org/10.3389/fpsyg.2022.1083673
Arifin, M. A., & Ad, M. (2019). Student engagement, colla-
borative learning, and flipped classroom as a basis for a
blended language learning environment. Asian EFL Journal,
24(4), 38–44.
Arvanitis, A. (2017). Autonomy and morality: A self-
determination theory discussion of ethics. New Ideas in Psy-
chology,47, 57–61. https://doi.org/10.1016/j.newideapsych.
2017.06.001
Bagozzi, R. P. (1981). Evaluating structural equation models
with unobservable variables and measurement error: A
16 SAGE Open

comment. Journal of Marketing Research,18(3), 375–381.
https://doi.org/10.1177/002224378101800312
Bayu, D., & Saputra, E. (2023). EFL undergraduate students’
competence, relatedness, and autonomy in online learning:
A self-determination perspective. Journal of Language, Lit-
erature, and English Teaching,4(1), 1–9. https://doi.org/10.
31629/juliet.v4i1.5557
Benlahcene, A., Kaur, A., & Awang-Hashim, R. (2021). Basic
psychological needs satisfaction and student engagement:
The importance of novelty satisfaction. Journal of Applied
Research in Higher Education,13(5), 1290–1304. https://doi.
org/10.1108/JARHE-06-2020-0157
Bernacki, M. L., Greene, J. A., & Crompton, H. (2020). Mobile
technology, learning, and achievement: Advances in under-
standing and measuring the role of mobile technology in
education. Contemporary Educational Psychology,60,
101827. https://doi.org/10.1016/j.cedpsych.2019.101827
Binder, J. F. (2022). Episodic and semantic memory for interac-
tions with voice-based conversational agents: Developing an
integrative model of technology engagement and cognitive ela-
boration. Design, Operation and Evaluation of Mobile
Communications.
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., &
Kerres, M. (2020). Mapping research in student engagement
and educational technology in higher education: A systema-
tic evidence map. International Journal of Educational Tech-
nology in Higher Education,17(1), 1–30. https://doi.org/10.
1186/s41239-019-0176-8
Cents-Boonstra, M., Lichtwarck-Aschoff, A., Lara, M. M., &
Denessen, E. (2022). Patterns of motivating teaching beha-
viour and student engagement: A microanalytic approach.
European Journal of Psychology of Education,37(1),
227–255. https://doi.org/10.1007/s10212-021-00543-3
Cheah, J.-H., Thurasamy, R., Memon, M. A., Chuah, F., &
Ting, H. (2020). Multigroup analysis using SmartPLS: Step-
by-step guidelines for business research. Asian Journal of
Business Research,10(3), I–XIX. https://doi.org/10.14707/
ajbr.200087
Chibisa, A., & Mutambara, D. (2022). Determinants of high
school learners’ continuous use of mobile learning during
the covid-19 Pandemic. International Journal of Learning,
Teaching and Educational Research,21(3), 1–21. https://doi.
org/10.26803/ijlter.21.3.1
Chin, W. W. (1998). The partial least squares approach for
structural equation modeling. In G. A. Marcoulides (Ed.),
Modern methods for business research (pp. 295–336). Lawr-
ence Erlbaum Associates Publishers.
Chiu, T. K. F. (2021a). Digital support for student engagement
in blended learning based on self-determination theory.
Computers in Human Behavior,124, 106909. https://doi.org/
10.1016/j.chb.2021.106909
Chiu, T. K. F. (2021b). Student engagement in K-12 online
learning amid COVID-19: A qualitative approach from a
self-determination theory perspective. Interactive Learning
Environments,31, 3326–3414. https://doi.org/10.1080/
10494820.2021.1926289
Chiu, T. K. F. (2022). Applying the self-determination theory
(SDT) to explain student engagement in online learning dur-
ing the COVID-19 pandemic. Journal of Research on
Technology in Education,54, S14–S30. https://doi.org/10.
1080/15391523.2021.1891998
Code, J., Ralph, R., & Forde, K. (2020). Pandemic designs for
the future: Perspectives of technology education teachers
during COVID-19. Information and Learning Sciences,
121(5/6), 419–431. https://doi.org/10.1108/ILS-04-2020-0112
Cohen, J. (1988). Set correlation and contingency tables.
Applied Psychological Measurement,12(4), 425–434. https://
doi.org/10.1177/014662168801200410
Conesa, P. J., Onandia-Hinchado, I., Dun
˜abeitia, J. A., & Mor-
eno, M. A
´. (2022). Basic psychological needs in the class-
room: A literature review in elementary and middle school
students. Learning and Motivation,79, 101819. https://doi.
org/10.1016/j.lmot.2022.101819
Davis, F. D. (1989). Perceived usefulness, perceived ease of use,
and user acceptance of information technology. MIS Quar-
terly,13, 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User
acceptance of computer technology: A comparison of two
theoretical models. Management Science,35(8), 982–1003.
https://doi.org/10.1287/mnsc.35.8.982
Deci, E. L., & Ryan, R. M. (2011). Levels of analysis, regnant
causes of behavior and well-being: The role of psychological
needs. Psychological Inquiry,22(1), 17–22. https://doi.org/
10.1080/1047840X.2011.545978
Du, L., Zhao, L., Xu, T., Wang, Y., Zu, W., Huang, X., Nie,
W., & Wang, L. (2022). Blended learning vs traditional
teaching: The potential of a novel teaching strategy in nur-
sing education - A systematic review and meta-analysis.
Nurse Education in Practice,63, 103354. https://doi.org/10.
1016/j.nepr.2022.103354
El-Sayad, G., Md Saad, N. H., & Thurasamy, R. (2021). How
higher education students in Egypt perceived online learning
engagement and satisfaction during the COVID-19 pan-
demic. Journal of Computers in Education,8(4), 527–550.
https://doi.org/10.1007/s40692-021-00191-y
Escobar-Viera, C. G., Melcher, E. M., Miller, R. S., Whitfield,
D. L., Jacobson-Lo
´pez, D., Gordon, J. D., Ballard, A. J.,
Rollman, B. L., & Pagoto, S. (2021). A systematic review of
the engagement with social media–delivered interventions
for improving health outcomes among sexual and gender
minorities. Internet Interventions,25, 100428. https://doi.
org/10.1016/j.invent.2021.100428
Fu, X., Yan, T., Tian, Y., Niu, X., Xu, X., Wei, Y., Hu, Q.,
Ouyang, Z., & Wu, X. (2022). Exploring factors influencing
students’ entrepreneurial intention in vocational colleges
based on structural equation modeling: Evidence from
China. Frontiers in Psychology,13, 898319. https://doi.org/
10.3389/fpsyg.2022.898319
Ganotice, F. A., Jr, Chan, C. S., Chan, E. W. Y., Chan, S. K.
W., Chan, L., Chan, S. C. S., Lam, A. H. Y., Leung, C. Y.
F., Leung, S. C., Lin, X., Luk, P., Ng, Z. L. H., Shen, X.,
Tam, E. Y. T., Wang, R., Wong, G. H. Y., & Tipoe, G. L.
(2022). Autonomous motivation predicts students’ engage-
ment and disaffection in interprofessional education: Scale
adaptation and application. Nurse Education Today,119,
105549. https://doi.org/10.1016/j.nedt.2022.105549
Garcı
´a-Morales, V. J., Garrido-Moreno, A., & Martı
´n-Rojas,
R. (2021). The transformation of higher education after the
Yang and Tan 17

COVID disruption: Emerging challenges in an online learn-
ing scenario [Mini Review]. Frontiers in Psychology,12.
https://doi.org/10.3389/fpsyg.2021.616059
Gligor, D., Russo, I., & Maloni, M. J. (2022). Understanding
gender differences in logistics innovation: A complexity the-
ory perspective. International Journal of Production Econom-
ics,246, 108420. https://doi.org/10.1016/j.ijpe.2022.108420
Guay, F. (2022). Applying self-determination theory to educa-
tion: Regulations types, psychological needs, and autonomy
supporting behaviors. Canadian Journal of School Psychol-
ogy,37(1), 75–92. https://doi.org/10.1177/082957352110
55355
Gupta, K. P. (2020). Investigating the adoption of MOOCs in a
developing country. Interactive Technology and Smart Edu-
cation,17(4), 355–375. https://doi.org/10.1108/ITSE-06-
2019-0033
Hair, J. F., Hult, G., Ringle, C. M., & Sarstedt, M. (Eds.).
(2017). A primer on partial least squares structural equation
modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Hair, J., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M.
(2021). A Primer on Partial Least Squares Structural Equa-
tion Modeling (PLS-SEM) (3rd ed.). SAGE Publications,
Inc.
Hair, J. F., Page, M., & Brunsveld, N. (2019). Essentials of busi-
ness research methods. Routledge.
Han, J., Geng, X., & Wang, Q. (2021). Sustainable develop-
ment of University EFL learners’ engagement, satisfaction,
and self-efficacy in online learning environments: Chinese
experiences. Sustainability,13, 11655. https://doi.org/10.
3390/su132111655
Heilporn, G., Lakhal, S., & Be
´lisle, M. (2021). An examination
of teachers’ strategies to foster student engagement in
blended learning in higher education. International Journal
of Educational Technology in Higher Education,18(1), 25.
https://doi.org/10.1186/s41239-021-00260-3
Henseler, J. (2012). PLS-MGA: A non-parametric approach to
partial least squares-based multi-group analysis. Challenges
at the Interface of Data Analysis, Computer Science, and
Optimization.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015a). Testing
measurement invariance of composites using partial least
squares. Social Science Electronic Publishing,49(3), 41–46.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015b). A new cri-
terion for assessing discriminant validity in variance-based
structural equation modeling. Journal of the Academy of
Marketing Science,43(1), 115–135. https://doi.org/10.1007/
s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use
of partial least squares path modeling in international market-
ing (pp. 277–319). Emerald Group Publishing Limited.
Hh, A., Cjb, B., & Min, Y. (2022). Influences of depression, self-
efficacy, and resource management on learning engagement in
blended learning during COVID-19. The Internet Higher
Education.
Hoang Hoa, C. T. (2020). Integrating culture into teaching
EFL in general education: A context of Vietnam. Language
Related Research,11(5), 227–252. https://doi.org/10.29252/
lrr.11.5.227
Hsiao, J. C., Chen, S. K., Chen, W., & Lin, S. S. J. (2022).
Developing a plugged-in class observation protocol in high-
school blended STEM classes: Student engagement, teacher
behaviors and student-teacher interaction patterns. Comput-
ers & Education,178, 104403. https://doi.org/10.1016/j.com-
pedu.2021.104403Mar.
Hsieh, T.-Y., Simpkins, S. D., & Eccles, J. S. (2021). Gender by
racial/ethnic intersectionality in the patterns of adolescents’
math motivation and their math achievement and engage-
ment. Contemporary Educational Psychology,66, 101974.
https://doi.org/10.1016/j.cedpsych.2021.101974
Hsu, H.-C. K., Wang, C. V., & Levesque-Bristol, C. (2019).
Reexamining the impact of self-determination theory on
learning outcomes in the online learning environment. Edu-
cation and Information Technologies,24(3), 2159–2174.
https://doi.org/10.1007/s10639-019-09863-w
Huang, J., & Yoon, D. (2023). Paradigm model of online learn-
ing experience during COVID-19 crisis in higher education.
Frontiers in Education.8, 1101160. https://doi.org/10.3389/
feduc.2023.1101160
Hu, X., Huang, Z., & Yang, M. (2022). Profiles of EFL lear-
ners’ online self-regulation and their relationship with
dimensions of self-determination motivation in mainland
China. Asia-Pacific Education Researcher,32, 685–694.
https://doi.org/10.1007/s40299-022-00686-x
Jin, W., Zheng, X., Gao, L., Cao, Z., & Ni, X. (2022). Basic
psychological needs satisfaction mediates the link between
strengths use and teachers’ work engagement. International
Journal of Environmental Research and Public Health,19(4),
2330. https://doi.org/10.3390/ijerph19042330
Jung, Y., & Lee, J. (2018). Learning engagement and persis-
tence in massive open online courses (MOOCS). Computers
& Education,122, 9–22. https://doi.org/10.1016/j.compedu.
2018.02.013
Kim, H. E., Song, H. D., & Lee, Y. C. (2021). The effect of aca-
demic self-efficacy, perceived usefulness, perceived ease of use,
and acceptance attitude on learning persistence through stu-
dent engagement in MOOC. EdMedia + Innovate Learning.
Kim, S. B., & Kim, D. Y. (2016). The influence of corporate
social responsibility, ability, reputation, and transparency
on hotel customer loyalty in the U.S.: A gender-based
approach. SpringerPlus,5(1), 1537. https://doi.org/10.1186/
s40064-016-3220-3
Kock, N. (2015). Common method bias in PLS-SEM: A full
collinearity assessment approach. International Journal of e-
Collaboration,11(4), 1–10.
Lan, M., & Hew, K. F. (2020). Examining learning engagement
in MOOCs: A self-determination theoretical perspective
using mixed method. International Journal of Educational
Technology in Higher Education,17(1), 1–24. https://doi.
org/10.1186/s41239-020-0179-5
Lee, Y., Lee, J., & Hwang, Y. (2015). Relating motivation to
information and communication technology acceptance:
Self-determination theory perspective. Computers in Human
Behavior,51, 418–428. https://doi.org/10.1016/j.chb.2015.05.
02151(OCT.
Leguina, A. (2015). A primer on partial least squares structural
equation modeling (PLS-SEM). Taylor & Francis.
18 SAGE Open

Leo, F. M., Mouratidis, A., Pulido, J. J., Lo
´pez-Gajardo, M.
A., & Sa
´nchez-Oliva, D. (2022). Perceived teachers’ behavior
and students’ engagement in physical education: The med-
iating role of basic psychological needs and self-determined
motivation. Physical Education and Sport Pedagogy,27(1),
59–76. https://doi.org/10.1080/17408989.2020.1850667
Li, C., Jiang, G., & Dewaele, J.-M. (2018). Understanding Chi-
nese high school students’ foreign language enjoyment: Vali-
dation of the Chinese version of the foreign language
enjoyment scale. System,76, 183–196. https://doi.org/10.
1016/j.system.2018.06.004
Lietaert, S., Roorda, D., Laevers, F., Verschueren, K., & De
Fraine, B. (2015). The gender gap in student engagement:
The role of teachers’ autonomy support, structure, and
involvement. British Journal of Educational Psychology,
85(4), 498–518. https://doi.org/10.1111/bjep.12095
Lilleker, D., Koc-Michalska, K., & Bimber, B. (2021). Women
learn while men talk?: Revisiting gender differences in politi-
cal engagement in online environments. Information Com-
munication & Society,24(14), 2037–2053. https://doi.org/10.
1080/1369118X.2021.1961005
Lo, C. K., & Hew, K. F. (2020). A comparison of flipped learn-
ing with gamification, traditional learning, and online inde-
pendent study: The effects on students’ mathematics
achievement and cognitive engagement. Interactive Learning
Environments,28(4), 464–481. https://doi.org/10.1080/
10494820.2018.1541910
Luo, Y., Lin, J., & Yang, Y. (2021). Students’ motivation and
continued intention with online self-regulated learning: A
self-determination theory perspective. Zeitschrift f€
ur Erzie-
hungswissenschaft,24(6), 1379–1399. https://doi.org/10.
1007/s11618-021-01042-3
Maon, S. N., Mohd Hassan, N., Mohamad Yunus, N., Abdul
Kader Jailani, S. F. S., & Suzila Kassim, E. (2021). Gender
differences in digital competence among secondary school
students. International Journal of Interactive Mobile Tech-
nologies (iJIM),15(04), 73. https://doi.org/10.3991/IJIM.
V15I04.20197
McEown, M. S., Noels, K. A., & Saumure, K. D. (2014). Stu-
dents’ self-determined and integrative orientations and
teachers’ motivational support in a Japanese as a foreign
language context. System,45, 227–241. https://doi.org/10.
1016/j.system.2014.06.001
Min, Y. D. (2021). Understanding flipped learners’ perceptions,
perceived usefulness, registration intention, and learning
engagement. Bastas Publications.14(1), ep331. https://doi.
org/10.30935/cedtech/11368
M€
uller, C., & Mildenberger, T. (2021). Facilitating flexible
learning by replacing classroom time with an online learning
environment: A systematic review of blended learning in
higher education. Educational Research and Reviews,34,
100394. https://doi.org/10.1016/j.edurev.2021.100394
Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K.
(2021). Students’ perception and preference for online edu-
cation in India during COVID -19 pandemic. Social Sciences
& Humanities Open,3(1), 100101. https://doi.org/10.1016/j.
ssaho.2020.100101
Nalipay, M. J. N., King, R. B., & Cai, Y. (2020). Autonomy is
equally important across East and west: Testing the cross-
cultural universality of self-determination theory. Journal of
Adolescence,78, 67–72. https://doi.org/10.1016/j.adoles-
cence.2019.12.009
Nambiar, D. (2020). The impact of online learning during
COVID-19: Students’ and teachers’ perspective. The Inter-
national Journal of Indian Psychology,8(2), 783–793.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based
assessment: Integrating acceptance and motivational factors
into a combined model of self-determination theory and
technology acceptance. Computers in Human Behavior,68,
83–95. https://doi.org/10.1016/j.chb.2016.11.020
Niu, X., Niu, Z., Wang, M., & Wu, X. (2022). What are the key
drivers to promote entrepreneurial intention of vocational
college students? An empirical study based on structural
equation modeling. Frontiers in Psychology,13, 1021969.
https://doi.org/10.3389/fpsyg.2022.1021969
Niu, X., & Wu, X. (2022). Factors influencing vocational col-
lege students’ creativity in online learning during the
COVID-19 pandemic: The group comparison between male
and female. Frontiers in Psychology,13, 967890. https://doi.
org/10.3389/fpsyg.2022.967890
Oga-Baldwin, W. L. Q., & Nakata, Y. (2017). Engagement,
gender, and motivation: A predictive model for Japanese
young language learners. System,65, 151–163. https://doi.
org/10.1016/j.system.2017.01.011
Panigrahi, R., Srivastava, P. R., Panigrahi, P. K., & Dwivedi,
Y. K. (2022). Role of Internet self-efficacy and interactions
on blended learning effectiveness. Journal of Computer Infor-
mation Systems,62(6), 1239–1252. https://doi.org/10.1080/
08874417.2021.2004565
Panisoara, I. O., Lazar, I., Panisoara, G., Chirca, R., & Ursu,
A. S. (2020). Motivation and continuance intention
towards online instruction among teachers during the
COVID-19 pandemic: The mediating effect of burnout
and technostress. International Journal of Environmental
Research and Public Health,17(21), 8002. https://doi.org/
10.3390/ijerph17218002
Pan, Z., Wang, Y., & Derakhshan, A. (2023). Unpacking Chi-
nese EFL students’ academic engagement and psychological
well-being: The roles of language teachers’ affective scaffold-
ing. Journal of Psycholinguistic Research,52(5), 1799–1819.
https://doi.org/10.1007/s10936-023-09974-z
Peng, Y., Li, Y., Su, Y., Chen, K., & Jiang, S. (2022). Effects
of group awareness tools on students’ engagement, perfor-
mance, and perceptions in online collaborative writing:
Intergroup information matters. The Internet Higher Educa-
tion Research & Development,53, 100845. https://doi.org/10.
1016/j.iheduc.2022.100845
Podsakoff, P. M., & Organ, D. W. (2016). Self-reports in orga-
nizational research: Problems and prospects. Journal of
Management,12(4), 531–544. https://doi.org/10.1177/
014920638601200408
Pugh, C. (2019). Self-determination: Motivation profiles of
bachelor’s degree-seeking students at an online, for-profit
university. Online Learning,23(1), 111–131.
Yang and Tan 19

Purwanto, A. (2021). Partial least squares structural squation
modeling (PLS-SEM) analysis for social and management
research: A literature review. Journal of Industrial Engineer-
ing & Management Research,2(4), 114–123. https://doi.org/
10.7777/jiemar.v2i
Racero, F. J., Bueno, S., & Gallego, M. D. (2020). Predicting
students’ behavioral intention to use open source software:
A combined view of the technology acceptance model and
self-determination theory. Applied Sciences,10(8), 2711.
https://doi.org/10.3390/app10082711
Rahi, S., Othman Mansour, M. M., Alharafsheh, M., & Alghiz-
zawi, M. (2021). The post-adoption behavior of internet
banking users through the eyes of self-determination theory
and expectation confirmation model. Journal of Enterprise
Information Management,34(6), 1874–1892. https://doi.org/
10.1108/JEIM-04-2020-0156
Rezaeian, S., & Abdollahzadeh, E. (2020). Teacher efficacy and
its correlates in the EFL context of Iran: The role of age,
experience, and Gender. International Online Journal of Edu-
cation Teaching,7(4), 1533–1548.
Rinaldi, P., Pasqualetti, P., Volterra, V., & Caselli, M. C.
(2023). Gender differences in early stages of language devel-
opment. Some evidence and possible explanations. Journal
of Neuroscience Research,101(5), 643–653. https://doi.org/
10.1002/jnr.24914
Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation
modeling with the SmartPLS. Brazilian Journal of Market-
ing,13(2), 56–73.
Roca, J. C., & Gagne
´, M. (2008). Understanding e-learning
continuance intention in the workplace: A self-
determination theory perspective. Computers in Human
Behavior,24(4), 1585–1604. https://doi.org/10.1016/j.chb.
2007.06.001
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory
and the facilitation of intrinsic motivation, social develop-
ment, and well-being. American Psychologist,55(1), 68–78.
https://doi.org/10.1037/0003-066X.55.1.68
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic moti-
vation from a self-determination theory perspective: Defini-
tions, theory, practices, and future directions. Contemporary
Educational Psychology,61, 101860. https://doi.org/10.1016/
j.cedpsych.2020.101860
Salas-Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student
engagement in online learning in Latin American higher
education during the COVID-19 pandemic: A systematic
review. British Journal of Educational Technology,53(3),
593–619. https://doi.org/10.1111/bjet.13190
Salmela-Aro, K., Upadyaya, K., Ronkainen, I., & Hietaja
¨rvi,
L. (2022). Study Burnout and engagement during COVID-
19 among university students: The role of demands,
resources, and psychological needs. Journal of Happiness
Studies,23(6), 2685–2702. https://doi.org/10.1007/s10902-
022-00518-1
Shah, S. S., Shah, A. A., Memon, F., Kemal, A. A., & Soomro,
A. (2021). Online learning during the COVID-19 pandemic:
Applying the self-determination theory in the ‘new normal’.
Revista de Psicodida
´ctica (English ed.),26(2), 168–177.
https://doi.org/10.1016/j.psicoe.2020.12.003
Shakki, F. (2023). Investigating the relationship between EFL
learners’ engagement and their achievement emotions. Porta
Linguarum Revista Interuniversitaria de Dida
´ctica de las Len-
guas Extranjeras,39(40), 275–294. https://doi.org/10.30827/
portalin.vi40.27338
Shen, Y., Huang, L., & Wu, X. (2022). Visualization analysis
on the research topic and hotspot of online learning by using
CiteSpace—Based on the Web of Science core collection
(2004–2022). Frontiers in Psychology,13, 1059858. https://
doi.org/10.3389/fpsyg.2022.1059858
Shirvan, M. E., & Alamer, A. (2022). Modeling the interplay of
EFL learners’ basic psychological needs, grit and L2
achievement. Journal of Multilingual and Multicultural
Development,45, 2831–2917. https://doi.org/10.1080/
01434632.2022.2075002
Sørebø, Ø., Halvari, H, Gulli, V. F, & Kristiansen, R. (2009).
The role of self-determination theory in explaining teachers’
motivation to continue to use e-learning technology. Com-
puters & Education,53(4), 1177–1187. https://doi.org/10.
1016/j.compedu.2009.06.001
Stiegemeier, D., Kraus, J., Bringeland, S., & Baumann, M.
(2022). Motivated to use: Beliefs and motivation influencing
the acceptance and use of assistance and navigation systems.
International Journal of Human-Computer Interaction,39,
2926–3016. https://doi.org/10.1080/10447318.2022.2088658
Sucaromana, U. (2013). The effects of blended learning on the
intrinsic motivation of Thai EFL students. English Lan-
guage Teaching,6(5), 141–147. https://EconPapers.repec.
org/RePEc:ibn:eltjnl:v:6:y:2013:i:5:p:141
Su, C.-Y., & Chiu, C.-H. (2021). Perceived enjoyment and
attractiveness influence Taiwanese elementary school stu-
dents’ intention to use interactive video learning. Interna-
tional Journal of Human-Computer Interaction,37(6),
574–583. https://doi.org/10.1080/10447318.2020.1841423
Sun, C., Yao, Y., Wang, R., & Ye, X. (2020). A study on the
influence of scene reality of VR environment on English lear-
ners’ learning engagement and learning effectiveness [Confer-
ence session]. 2020 IEEE 2nd International Conference on
Computer Science and Educational Informatization (CSEI).
Sun, J. C., & Rueda, R. (2012). Situational interest, computer
self-efficacy and self-regulation: Their impact on student
engagement in distance education. British Journal of Educa-
tional Technology,43(2), 191–204. https://doi.org/10.1111/j.
1467-8535.2010.01157.x
Sun, Y., Ni, L., Zhao, Y., Shen, X., & Wang, N. (2019).
Understanding students’ engagement in MOOCs: An inte-
gration of self-determination theory and theory of relation-
ship quality. British Journal of Educational Technology,
50(6), 3156–3174. https://doi.org/10.1111/bjet.12724
Trockel, M., Bohman, B., Lesure, E., Hamidi, M. S., Welle,
D., Roberts, L., & Shanafelt, T. (2018). A brief instrument
to assess both burnout and professional fulfillment in physi-
cians: Reliability and validity, including correlation with
self-reported medical errors, in a sample of resident and
practicing physicians. Academic Psychiatry,42(1), 11–24.
https://doi.org/10.1007/s40596-017-0849-3
Ubu, A., Putra, I. N. A. J. A. J., & Santosa, M. H. (2021). EFL
University student engagement on the use of online
20 SAGE Open

discussion in North Bali. Language Education Journal
Undiksha,4(1), 22–31. https://doi.org/10.23887/leju.v4i1.
29938
Voss, R. C., Donovan, J., Rutsaert, P., & Cairns, J. E. (2021).
Gender inclusivity through maize breeding in Africa: A
review of the issues and options for future engagement. Out-
look on Agriculture,50(4), 392–405. https://doi.org/10.1177/
00307270211058208
Wang, M., Wang, M., Cui, Y., & Zhang, H. (2021). Art teach-
ers’ attitudes toward online learning: An empirical study
using self determination theory. Frontiers in Psychology,12,
627095. https://doi.org/10.3389/fpsyg.2021.627095
Wang, M.-T., & Hofkens, T. (2020). Beyond classroom aca-
demics: A school-wide and multi-contextual perspective
on student engagement in school. Adolescent Research
Review,5(4), 419–433. https://doi.org/10.1007/s40894-019-
00115-z
Wang, Q. Q. (2022). Designing an interactive science exhibit:
Using augmented reality to increase visitor engagement and
achieve learning outcomes. In P. MacDowell, & J. Lock
(Eds.), Immersive education: Designing for learning (pp. 15–
30). Springer International Publishing.
Wang, Y., Derakhshan, A., & Azari Noughabi, M. (2022). The
interplay of EFL teachers’ immunity, work engagement,
and psychological well-being: Evidence from four Asian
countries. Journal of Multilingual and Multicultural Develop-
ment,45, 3241–3317. https://doi.org/10.1080/01434632.2022.
2092625
Werle, D., Winters, K. L., & Byrd, C. T. (2021). Preliminary
study of self-perceived communication competence amongst
adults who do and do not stutter. Journal of Fluency Disor-
ders,70(12), 105848. https://doi.org/10.1016/j.jfludis.2021.
105848
Wong, R. (2022). Basis psychological needs of students in
blended learning. Interactive Learning Environments,30(6),
984–998. https://doi.org/10.1080/10494820.2019.1703010
Wu, X., & Tian, Y. (2022). Predictors of entrepreneurship
intention among students in vocational colleges: A struc-
tural equation modeling approach. Frontiers in Psychology,
12, 6179. https://doi.org/10.3389/fpsyg.2021.797790
Wu, X., & Wang, M. (2018). Selection of cooperative enter-
prises in vocational education based on ANP. Educational
Sciences Theory & Practice,18(5), 1507–1511. https://doi.
org/10.12738/estp.2018.5.047
Wu, X., & Wang, M. (2020). Influence of professional identity
and core self-evaluation on job satisfaction of vocational
education teachers and the mediating effect of work stress.
Revista argentina de clinica psicologica,29(2), 31. https://doi.
org/10.24205/03276716.2020.204
Wu, Y., & Kang, X. (2023). Conceptualisation, measurement,
and prediction of foreign language learning psychological
capital among Chinese EFL students. Journal of Multilingual
and Multicultural Development,46, 654–714. https://doi.org/
10.1080/01434632.2023.2193601
Xia, Q., Chiu, T. K. F., & Chai, C. S. (2022). The moderating
effects of gender and need satisfaction on self-regulated
learning through Artificial Intelligence (AI). Education and
Information Technologies,28, 8691–8713. https://doi.org/10.
1007/s10639-022-11547-x
Xie, Z., Wu, N., Yue, T., Jie, J., Hou, G., & Fu, A. (2020).
How leader-member exchange affects creative performance:
An examination from the perspective of self-determination
theory. Frontiers in Psychology,11, 573793. https://doi.org/
10.3389/fpsyg.2020.573793
Xu, L., Duan, P., Padua, S. A., & Li, C. (2022). The impact of
self-regulated learning strategies on academic performance
foronlinelearningduringCOVID-19.Frontiers in Psychol-
ogy,13, 1047680. https://doi.org/10.3389/fpsyg.2022.1047680
Zaitx, A., & Bertea, P. (2011). Methods for testing discriminant
validity. Management Marketing Journal,9(2), 217–224.
Zhi, R., Wang, Y., & Derakhshan, A. (2024). On the role of
academic buoyancy and self-efficacy in predicting teachers’
work engagement: A case of Chinese English as a foreign
language teachers. Perceptual and Motor Skills,131(2),
612–629. https://doi.org/10.1177/00315125231222398
Zhou, S., Zhu, H., & Zhou, Y. (2022). Impact of teenage EFL
learners’ psychological needs on learning engagement and
behavioral intention in synchronous online English
courses. Sustainability,14(17), 10468. https://doi.org/10.
3390/su141710468
Yang and Tan 21