The Australian Educational Researcher
https://doi.org/10.1007/s13384-024-00689-1
Online learning environment and student engagement:
the mediating role of expectancy and task value beliefs
Hoi Vo1,2 · Hang Ho2
Received: 13 June 2023 / Accepted: 2 January 2024
© The Author(s) 2024
Abstract
This study contributes to a more nuanced understanding of student engagement in
online learning by exploring the effects of salient online learning environment con‑
ditions on student engagement and the motivational pathways through which they
affect engagement. Survey data were collected from 351 undergraduate students
enrolled in various online undergraduate programs at a large open university in
Vietnam. Results of structural equation modelling revealed that course clarity and
task relevance had significant indirect effects on students’ behavioural, cognitive,
and affective engagement via their expectancy and task value beliefs. Teacher sup‑
port was found to have indirect effect on student engagement only via expectancy
beliefs whereas student connectedness predicted neither students’ motivation nor
engagement in online learning. Results of the study are discussed in light of existing
theoretical and empirical evidence on the intricate relationships between learning
environment, motivation, and student engagement. Implications for practice are also
offered to help create an online learning environment that has potential to foster stu‑
dent engagement and alleviate disengagement and dropout.
Keywords Online learning · Student engagement · Online learning environment ·
Expectancy value beliefs · Basic psychological needs
* Hoi Vo
hoi.vo@scu.edu.au
Hang Ho
hang.hl@ou.edu.vn
1
TeachLab, Faculty of Education, Southern Cross University, 144 Coolangatta Rd, Bilinga,
QLD 4225, Australia
2
Faculty of Foreign Languages, Ho Chi Minh City Open University, 35‑37 Ho Hao Hon Street,
Co Giang Ward, District 1, HCMC, Vietnam
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H. Vo, H. Ho
Introduction
Online learning has been gaining in popularity evidenced by the growing number
of online student enrolments and online education providers. This growing
trend has been fuelled by the recent outbreak of the COVID-19 pandemic which
forced educational institutions worldwide to move their classes online. Due to
its capability to break down geographical, spatial, and temporal barriers, online
learning affords the anytime, anywhere and for everyone philosophy of education
and offers students a viable alternative to the traditional face-to-face learning
mode (Kaufmann & Buckner, 2019; Naidu, 2017). Given the importance of
lifelong learning that has been increasingly recognised by global governments,
online learning is expected to be one of the fastest growing areas of education
(Farrell & Brunton, 2020). A critical challenge remains as to how to increase
student retention in online learning since the attrition rate in this learning
environment is by far greater than that in the face-to-face learning mode (Hoi,
2022; Hoi & Hang, 2021a; Redmond et al., 2018). This high attrition rate is
mainly attributable to students’ feelings of isolation and disconnection, and hence
lack of motivation and engagement in a learning environment where students
are expected to exercise greater self-regulatory skills and responsibility for their
own learning than in the conventional brick-and-mortar classroom (Hartnett,
2015; Hsu et al., 2019; Lee et al., 2015; Redmond et al., 2018). In this context,
understanding salient online learning environment factors and how they affect
students’ motivation and engagement plays a crucial role in enhancing student
retention and reducing drop-out rates in online learning.
To this end, Wang et al. (2020) proposed the development-in-sociocultural
context model of student engagement as an integrative theoretical framework to
delineate the engagement process of students in developmental contexts. This
model is a comprehensive theoretical framework that synthesises research over
the past 40 years on student engagement in various contexts and across various
disciplines. It contributes to reducing the fragmentation associated with student
engagement research by presenting a systematic and clear conceptualisation
and clarification of the multidimensional student engagement construct. In this
model, engagement is positioned in an ecological process during which various
contextual and motivational factors interact to shape the learning process. Within
this process, engagement plays multiple roles in the educational development
of students and manifests itself to be a prime target for intervention programs
that have potential to enhance students’ academic achievements and well-being
and reduce students’ disengagement and drop-out. The model is graphically
illustrated in Fig. 1 and further discussed in Sect. "Student engagement", "Key
characteristics of student engagement", and "The development-in-sociocultural
context model of student engagement".
Drawing on this model, this study aims to make contribution to the online
learning engagement literature by examining the effects of four online learning
environment variables, namely teacher support, course clarity, student connect‑
edness, and task relevance on student engagement in online learning, and what
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Online learning environment and student engagement: the…
Fig. 1 The development-in-sociocultural context model. (Reproduced with permission from Wang et al.
(2020)
roles do students’ expectancy and task value beliefs play in these relationships.
A detailed discussion of these variables and how their relationships are informed
by the development-in-sociocultural context model of student engagement is pre‑
sented in the next section.
Literature review
Student engagement
Student engagement is a critical psychological concept that emerged out of the
drop-out prevention and school reform research traditions almost four decades ago
(Wong & Liem, 2021). Much empirical evidence in this line of research suggests
that engagement is a proximal predictor of school success, student achievement,
and retention, thus manifesting itself to be a prime target for school interventions
(Fredricks et al., 2004). Wang et al. (2020) define engagement as a student’s active
involvement and interactions with learning activities, processes and contexts, and
conceptualise it as a multidimensional construct consisting of behavioural, cogni‑
tive, and affective aspects (Fredricks et al., 2004; Wang et al., 2020). Behavioural
engagement refers to the observable behaviours such as effort, concentration, persis‑
tence, attention, and hard work that students exhibit while participating in learning
(Fredricks et al., 2004; Redmond et al., 2018). Cognitive engagement is concerned
with students’ mental efforts and investment in learning including self-regulation, a
desire to go beyond what is required, and cognitive strategies to understand complex
concepts (Wang et al., 2016). Affective engagement is defined as students’ positive
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H. Vo, H. Ho
emotional reactions to learning activities, context, and their own participation in
those activities (Fredricks et al., 2016). Although students might display different
levels of each engagement component in a specific learning activity, engagement
necessitates all three components, thereby painting a clear portrait of how students
act, feel, and think during learning (Wang et al., 2020).
Key characteristics of student engagement
In addition to the multidimensional structure of engagement, the research literature
highlights three other evidence-based characteristics of the engagement construct.
First, there is ongoing debate as to whether engagement and disengagement are dis‑
tinct and thus should be measured along a continuum with engagement constitut‑
ing one end and disengagement constituting the other end or should be placed on
two separate continua. This study follows the majority of recent studies on online
learning environments by measuring engagement and disengagement along a sin‑
gle scale with higher values on the scale indicating engagement and lower values
indicating disengagement (see, for example, Chiu, 2021a; Deng et al., 2020; Hoi &
Hang, 2021a). Second, engagement operates across multilevel contexts and various
timescales. The former refers to the sociocultural, family, school, classroom, peer,
and task factors that shape student engagement while the latter is concerned with
the moment-to-moment, day-to-day, or longitudinal timeframes in which engage‑
ment is measured. Since this study aims to identify online classroom variables that
might have immediate effects on student engagement in the online learning class‑
room, the contextual variables of interest are those directly related to teachers, peers,
and learning tasks that students interact with. Finally, engagement is a malleable
concept, which is responsive to contextual and personal factors. It is this feature of
engagement that attracts great research interest as researchers seek to understand
factors that affect engagement and how to orchestrate those factors to enhance stu‑
dent engagement in the classroom.
The development‑in‑sociocultural context model of student engagement
As per the development-in-sociocultural context model (see Fig. 1), engagement
plays multiple roles in the educational development of a student (Wang et al., 2019,
2020). More specifically, it is considered a pathway to student resilience, a facilitator
of academic achievement and well-being, a mediating factor channelling the effects
of contextual variables on students’ learning outcomes, and an academic outcome
in its own right. In this study, engagement is examined as a desirable academic out‑
come, and thus the study focus is on how this outcome can be fostered in the online
learning environment (i.e., the left portion of the model in Fig. 1). As an academic
outcome, engagement is directly influenced by students’ developmental competen‑
cies and psychological beliefs. The former refers to students’ evolving social, emo‑
tional, and cognitive skills in their interactions with the social environments while
the latter refers to their self-appraisals about learning and learning-related tasks that
create a motivational context informing students about whether to remain persistent
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Online learning environment and student engagement: the…
or give up in the face of setbacks. To understand this motivational context, Wang
et al. (2020) integrated several salient motivational theories such as basic psycholog‑
ical needs theory (Ryan & Deci, 2017) (see Sect. "Online learning environment vari‑
ables and student engagement") and expectancy-value theory (Eccles & Wigfield,
2002) (see Sect. "Expectancy-value beliefs as mediators") within the developmentin-sociocultural context model. These motivational constructs are placed within
the motivational beliefs and self-appraisals box that leads to the engagement box
in Fig. 1. These psychological beliefs, in turn, are shaped by the students’ socialisa‑
tion experience in class, school, family, or peer contexts. As this study is focused on
the motivational mechanisms through which online learning environment variables
influence student engagement, it explicitly draws on the basic psychological needs
theory to identify salient online learning environment variables and then employs
expectancy-value theory to hypothesise the motivational pathways through which
these variables affect engagement.
Online learning environment variables and student engagement
Engagement does not exist in a vacuum, but instead emerges out of the interaction
between the learners, the learning contexts, and learning outcomes (Skinner, 2016;
Wang et al., 2020). Therefore, it is a highly situation-specific and context-dependent
construct which is shaped by the objective environmental conditions and students’
subjective self-appraisals of those objective conditions. Identifying these contextual
variables in the online learning environment and the motivational pathways through
which they exert effects on student engagement plays a crucial role in enabling
teachers to design and apply instructional strategies that have potential to foster
student engagement in online learning. To this end, the development-in-sociocultural
context model (Wang et al., 2020) posits that engagement flourishes in learning
environments where students’ needs for competence, autonomy, and relatedness are
fulfilled. Competence refers to one’s need to feel a sense of mastery and efficacy
in one’s interaction with the social environment (Elliot & Dweck, 2005; Van den
Broeck et al., 2016). Autonomy is concerned with students’ need to have a sense
of ownership, volition, and self-endorsement of their own behaviours in learning
rather than being controlled by external forces (Ryan & Deci, 2021). Relatedness
is defined as one’s need to feel connected to others and a sense of belonging in
one’s community (Ryan & Deci, 2000). Previous studies have identified various
contextual variables that met students’ basic needs of competence, autonomy, and
relatedness, thereby promoting student engagement in online learning environments
(for a comprehensive review, see Chiu, 2021a; Hoi, 2022; Martin et al., 2018). Four
such variables are the central foci of this study, namely teacher support, student
connectedness, task relevance, and course clarity (i.e., these variables can be placed
within the school context portion in the sociocultural characteristics box that leads
to the motivational beliefs and self-appraisals box in Fig. 1). These variables are
selected because they represent essential online learning classroom variables that
directly influence students’ experience in the online classroom and are under the
teachers’ control. By focusing specifically on these variables, the study has potential
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H. Vo, H. Ho
to generate pedagogical implications for the development and delivery of an
engaging online learning classroom.
Course clarity refers to the extent to which the course organisation and instruc‑
tional information are accessible and enable students to easily navigate and master
the course content (Heilporn et al., 2021; Kaufmann et al., 2016). Course clarity
fulfills students’ need for competence by giving them clear guidance about what
it takes to become successful and achieve their desired learning outcomes in the
online learning environment (Hoi, 2022). Studies have found that online learning
courses which bolster students’ sense of competence by offering clear communica‑
tion of expectations for student behaviours and clear guidance for learning positively
affected all dimensions of student engagement (Chiu, 2021a; Martin et al., 2018).
Task relevance is related to the design and delivery of online learning tasks
that match students’ personal interests and future goals beyond the online courses
(Alamri et al., 2020; Lee et al., 2015). For example, students who intend to become
a tour guide in the future are likely to have high perceived relevance of tasks that
require them to make a presentation to introduce their classmates to historical and
scenic spots in their hometown. Students are likely to feel a sense of autonomy and
show greater participation in online learning when they are given tasks that are not
only academically, professionally, and personally intriguing but also relevant to their
future goals (Heilporn et al., 2021; Lee et al., 2015). Indeed, studies have found
that students exerted greater effort, enthusiasm, and commitment to online learning
when they were presented with relevant tasks and learning materials (Alamri et al.,
2020; Chiu, 2021a; Martin et al., 2018).
Students’ need for relatedness is fulfilled in online learning environments where
they perceive teachers to have supportive behaviours and feel connected to other
peers. Teachers perceived to have supportive behaviours are those who show con‑
cern, care, respect, and understanding toward students and who are approachable
(Kaufmann et al., 2016; Lan & Hew, 2020). Similarly, student connectedness refers
to the extent to which students perceive their peers to be supportive, cooperative,
and respectful (Kaufmann et al., 2016). Once students feel that they have a sense
of personal contact and someone to go to when they have learning difficulties or
personal problems, they are likely to form a positive relationship with teachers and
peers and a sense of belonging in the online learning community (Stone & Springer,
2019). Teacher support and student connectedness were found in previous studies to
positively predict students’ behavioural, cognitive, affective, and social engagement
in online learning (Chiu, 2021a; Cole et al., 2019; Luan et al., 2020).
Expectancy‑value beliefs as mediators
The development-in-sociocultural context model also proposes the expectancyvalue theory (Eccles & Wigfield, 2002) as a motivational construct that informs
our understanding of the student engagement process. According to the expec‑
tancy-value theory, the values that students attach to the learning activities and
instructional practices (i.e., subjective task value) and their beliefs about the
immediate or future success in such practices (i.e., expectancy belief) play a
mediational role in the relationship between contextual conditions and students’
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Online learning environment and student engagement: the…
achievement-related outcomes (i.e., student engagement) (Eccles & Wigfield,
2020). While previous research provided strong evidence for the mediating role
of basic psychological needs in the relationship between online learning environ‑
ment conditions and student engagement, little is known about whether students’
perceived expectancy value beliefs also play the same mediating mechanism.
As one of the most prominent motivational frameworks, the expectancy value
theory has been widely employed by educational psychologists to explain stu‑
dents’ achievement-related choices (e.g., engagement and persistence) and aca‑
demic performance in both traditional and online learning environments (Eccles
& Wigfield, 2020). Expectancy refers to students’ beliefs about how well they
will do on an upcoming task while subjective task value refers to the utility,
attainment, and interest values that students ascribe to a given task (Eccles &
Wigfield, 2002, 2020). Utility value refers to the perceived usefulness of taking
a certain course of action or participating in a certain activity (e.g., a student
intending to become a tour guide might find a presentation task useful because
it helps them improve their oral presentation skills and enrich their knowledge
of their own hometown). Attainment value implies the importance of performing
well on a given task (e.g., a student intending to become a tour guide might find
a presentation task important because being able to deliver a presentation about a
tourist attraction is part of their daily job as a tour guide). Finally, interest value
is the enjoyment one gains from doing a task (e.g., a student intending to become
a tour guide might find a presentation task interesting because introducing peo‑
ple to new and interesting places is what makes them want to become a tourist
guide) (Gladstone et al., 2022). These beliefs operate in tandem to inform stu‑
dents’ decision-making as to whether to persist or give up on a task (Wang et al.,
2020). Indeed, growing evidence in online learning environments has indicated
that the more confidence students have in their ability to successfully complete an
online task (i.e., expectancy belief), the more effort, energy, and enthusiasm they
commit to completing the task (i.e., behavioural and affective engagement) (Fryer
& Bovee, 2016; Jung & Lee, 2018; Pellas, 2014). Similarly, it was found that stu‑
dents employed more advanced cognitive strategies and engaged more deeply in
the online learning process (i.e., cognitive engagement) if they perceived online
learning tasks to be enjoyable, useful, and important (i.e., task value beliefs) for
their future goals and interests (Joo et al., 2013; Lai, 2021; Manwaring et al.,
2017; Zhang & Liu, 2019). For these reasons it is hypothesised that:
Hypothesis 1 Students’ expectancy belief positively and significantly influences
their behavioural, cognitive, and affective engagement in online learning. (see
Fig. 2)
Hypothesis 2 Students’ task value belief positively and significantly influences their
behavioural, cognitive, and affective engagement in online learning
Students’ expectancy-value beliefs are in turn shaped by their experience and
interpretation of various social influences such as their past achievement-related
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H. Vo, H. Ho
Fig. 2 The hypothesised structural model
events or other socialisers’ behaviours, attitudes, and expectations (Putwain et al.,
2019; Trautwein et al., 2012). In the online classroom, factors such as course
clarity, task relevance, teacher support, and student connectedness were all found
to be proximal predictors of students’ expectancy and task value beliefs. For
example, students were found to be more confident in their ability to succeed in
online learning if the course is well-structured and offers clear guidelines for the
navigation and mastery of course content (Martin et al., 2018; Trespalacios et al.,
2023). On the other hand, students feel less competent and discouraged in online
classes where they find the instructional videos unclear or the lesson content too
difficult to understand (Chiu, 2021b). In terms of task relevance, Lan and Hew
(2020) reported that students enjoyed a higher sense of competence and became
more confident when they could see a clear connection between knowledge
presented in the online course and how it could be applied in real-world contexts.
Studies also indicated a positive relationship between student expectancy beliefs
and the extent to which they perceived teachers and peers to be supportive and
cooperative (Farrell & Brunton, 2020; Xu et al., 2020). Therefore, the following
hypothesis is fomulated:
Hypothesis 3 Course clarity, task relevance, teacher support, and student
connectedness positively and significantly influence students’ expectancy belief.
(see Fig. 2)
Task value beliefs too are shaped by students’ interpretation of their own
socialisation experience in the online classroom. Several studies have suggested
that online learning activities that have immediate connections with real-world
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Online learning environment and student engagement: the…
contexts or students’ personal interests and future career goals are considered
by students to be enjoyable, useful, and important (Lee, 2015; Vanslambrouck
et al., 2018). Similarly, studies have also highlighted the relationships between
teacher support and student connectedness and the values that students attached
to online learning tasks (Edwards, 2021; Farrell & Brunton, 2020; Lai, 2021;
Mullen & Tallent-Runnels, 2006). In terms of course clarity, it is unknown
whether course clarity influences students’ perception of the values of online
learning. However, evidence from school-based research suggests that when stu‑
dents are provided clear guidelines for learning and consistent responses to their
queries, they come to know what it means and what it takes to thrive in that con‑
text, thus attaching greater values to their learning (Assor et al., 2002; Wang &
Eccles, 2013). It is believed that a similar proposition is also supported in online
learning given students’ need to self-navigate their own learning journey in an
environment where they have minimal face-to-face interaction with other social
partners. For the reasons discussed above, it is hypothesised in the current study
that:
Hypothesis 4 Course clarity, task relevance, teacher support, and student
connectedness positively and significantly influence students’ task value belief. (see
Fig. 2)
Hypothesis 5 Expectancy belief mediates the effects of teacher support, course
clarity, student connectedness, and task relevance on students’ behavioural,
cognitive, and affective engagement respectively.
Hypothesis 6 Task value belief mediates the effects of teacher support, course
clarity, student connectedness, and task relevance on students’ behavioural,
cognitive, and affective engagement respectively.
Methodology
Study context
The study was conducted in the context of various online undergraduate pro‑
grams at a large open university in southern Vietnam. Students were from dif‑
ferent academic disciplines including English language, business administration,
finance and banking, law, and accounting. The online programs in which these
students enrolled were offered over a 12-week period during which students
were provided with learning and instructional materials, engaged in weekly dis‑
cussion boards, peer review activities and exercises, and participated in three
synchronous video conferences where they could discuss the course content
with the instructors and their peers. Students’ course performance was assessed
via three assignments which, along with their weekly activities, accounted for
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H. Vo, H. Ho
35% of the course outcome while the final exam, either organised face-to-face or
online, constituted the remaining 65%.
Participants
Data collection took place at the end of the second semester of the 2020–2021 aca‑
demic year during which an online questionnaire was distributed to 517 students
who gave their consent to participate in the study. Three hundred and fifty-one stu‑
dents completed and submitted the questionnaire yielding a response rate of 68%.
Of these participants, 67.5% was female (N = 237) and 32.2% was male students
(N = 113) with an age range of 19 to 65 (M = 32,64, SD = 8.03); first-year students
constituted 43.9% (N = 154) of the sample while 26.5% (N = 93), 9.1% (N = 32), and
20.5% (N = 72) respectively were second, third-, and fourth-year students. All par‑
ticipants reported to have been fully equipped with computers and internet access
and to have a minimum required level of computer literacy for online learning.
Instruments
Students’ perception of the online learning classroom (i.e., course clarity, task rel‑
evance, teacher support, and student connectedness) was measured by four scales
adapted from previously validated instruments (Hoi, 2022; Kaufmann et al., 2016;
Wang & Eccles, 2013). Course clarity was assessed by five items eliciting students’
perception of the extent to which the course provided clear guidelines for learning
and instructions for the navigation and mastery of course content (a sample item is
Based on my experience with and perception of this online course, I think the organisation of the course was clear). Three items were used to assess students’ percep‑
tion of the relevance of the online learning tasks to their daily life and career goals
(A sample item is How often do you learn things that are related to your life outside
the online classroom). Teacher support was measured by six items about the degree
to which students perceived their online teachers to be approachable and emotion‑
ally supportive (a sample item is Based on my online class interactions with the
instructors, I perceived my instructor as respectful to me). Finally, students’ sense
of connectedness with their peers in the online classroom was evaluated by four
items (a sample item is Based on my online class interactions with the students in
my class, I perceived students as cooperative with one another).
Students’ expectancy value beliefs in the online learning environment were meas‑
ured by two scales adapted from Lazarides et al., 2020 and Midgley et al., 2000. The
expectancy belief scale consists of five items assessing students’ perception of their
ability to thrive in the online learning environment (a sample item is I’m certain I
can master the skills taught in the online classes this year). The value belief scale
includes four items measuring students’ perception of the utility (e.g., Learning the
content of this online course is useful for what I want to do after I graduate and go
to work.), importance (e.g., It is important for me to be someone who is good at the
subject matter that I am learning in this online course), and interest (e.g., Learning
the material covered in this online course is enjoyable.).
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Online learning environment and student engagement: the…
Student engagement in online learning was measured by a 12-item scale from Hoi
and Hang (2021a, 2021b). Students’ observable behaviours in online learning was
assessed by four items (e.g., I take notes when I participate in video conferences and
discussion boards). Four items were used to gauge students’ cognitive investment
and use of advanced strategies to understand online course content (e.g., I try to
connect what I am learning in course materials with things I learn before). Finally,
students’ affective responses to the online learning activities were indexed by four
items (e.g., I enjoy online learning activities).
Data analysis
Structural equation modelling, a latent variable modelling technique, was employed
to test the hypothesised model. A two-step modelling procedure was adopted: vali‑
dation of a measurement model in which the relationship among the observed indi‑
cators and their underlying latent factor was scrutinised, followed by evaluation of
a structural model in which the causal relationships among the latent factors were
examined (Byrne, 2016). The former was handled through confirmatory factor
analysis that allowed for the inspection of the model fit, item properties, convergent
validity, and discriminant validity of the students’ perception of the online learn‑
ing environment, expectancy value beliefs, and engagement scales. Model fit was
evaluated based on well-established model fit indices suggested by Hu and Bentler
(1999) and Byrne (2016): normed χ2 (< 3), Comparative Fit Index (CFI > 0.90),
Tucker Lewis Index (TLI > 0.90), Root Mean Square Error of Approximation
(RMSEA < 0.08), and Standardised Root Mean Square Residuals (SRMR < 0.07).
Satisfactory item properties (i.e., item loadings, variance, and covariance) are char‑
acterised by statistically significant values and the absence of Heywood cases (i.e.,
negative variances or correlations higher than 1). Convergent validity, the extent to
which the items converge to measure a latent construct, is achieved if the Average
Variance Extracted (AVE) value for each construct is higher than 0.50 (Fornell &
Larcker, 1981) while discriminant validity, the extent to which the latent constructs
are distinguished from one another, is satisfied if the Heterotrait-Monotrait Ratio of
Correlations (HTMT) is lower than 0.85 (Kline, 2016). To examine the reliability of
the measures used in the study, composite reliability was computed for each meas‑
ure. This reliability coefficient is computed based on the factor loadings of indica‑
tors obtained from confirmatory factor analysis, using the formula: CR = (sum of
standardised loadings)2 / (sum of standardised loadings)2 + (sum of indicator meas‑
urement error) (Hair et al., 2014). Composite reliability values higher than 0.70 are
considered acceptable (Hair et al., 2014).
Evaluation of the structural model followed the same model fit testing procedure
as the measurement model. Once this fit is established, the structural paths among
the latent factors could be examined. Standardised values were used to determine
the relative strength of the direct relationship among the variables while the
indirect effects of online learning environment variables on student engagement
were assessed via the bias-corrected bootstrapped 95% confidence intervals (CIs)
based on 5000 random samples (Preacher et al., 2007). Bootstrapped CIs that do
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H. Vo, H. Ho
not cross zero suggest significant indirect effects. All analyses were conducted
in AMOS 28 using the maximum likelihood estimation method. To enable the
comparison among the path coefficients in the structural model and to provide
the context for interpreting the magnitude of the effects on learning outcomes in
educational research, we follow Keith (2019) in interpreting effect sizes as too
small to be meaningful (β < 0.05), small but meaningful (0.05 < β < 0.10), moderate
(0.10 < β < 0.25), and large (β > 0.25) (Keith, 2019, p. 63).
Results
Table 1 presents descriptive statistics and parameter estimates of the measurement
model. The skewness and kurtosis values were all within the acceptable range of
|3| and |7|, suggesting the univariate normal distribution of data. The measure‑
ment model in which all latent constructs were allowed to covary with each other
showed adequate fit (χ2/DF = 1.879; CFI = 0.934; TLI = 0.927; RMSEA = 0.050, CI
[0.046—0.054]; SRMR = 0.049).
All standardised parameters were statistically significant, and no Heywood cases
were identified, verifying the psychometric qualities of the scale items. The conver‑
gent validity of the scales was supported by strong loadings (0.488—0.891) of each
item set onto their respective factor and acceptable AVE values (0.544—0.712) for
all scales (except for the behavioural engagement scale but see below). The lowerthan-optimum AVE value of the behavioural engagement scale was of little concern
because the composite reliability measure of this scale (0.784) was higher than 0.60
(Fornell & Larcker, 1981). The discriminant validity of the scales was also sup‑
ported given that all HTMT correlations were lower than 0.85 (0.217—0.699) (see
Table 2).
The structural model (see Fig. 3) achieved an acceptable level of fit to data
(χ2/DF = 1.996; CFI = 0.923; TLI = 0.917; RMSEA = 0.053, CI [0.049—0.057];
SRMR = 0.064). Subsequent examination of the hypothesised relationships among
the study variables revealed that students’ expectancy and subjective task value
beliefs positively predicted all dimensions of student engagement. Expectancy
belief was a more robust predictor of students’ behavioural, cognitive, and affective
engagement than value belief (βexpt = 0.40, 0.45, 0.55 respectively; p < 0.001,
large effect size; βvalue = 0.22, 0.12, 0.26 respectively; p < 0.05, moderate effect
size). Of the four online learning classroom variables, only course clarity and task
relevance had positive direct effects on both expectancy (βcourse = 0.248, p < 0.001;
βtask = 0.317, p < 0.001, moderate effect size) and value beliefs (βcourse = 0.209,
p < 0.01; βtask = 0.349, p < 0.001, moderate effect size). Teacher support only had
positive effect on expectancy belief (βteacher = 0.209, p < 0.01, moderate effect size)
while student connectedness was found to predict neither expectancy nor value
beliefs.
The indirect effects of course clarity (95% CIs = [0.072—0.264], β = 0.144; [0.063—
0.262], β = 0.137; [0.096—0.345], β = 0.191, moderate effect sizes) and task relevance
(95% CIs = [0.112—292], β = 0.202, moderate effect size; [0.108—0.277], β = 0.186,
moderate effect size; [0.156—0.381], β = 0.265, large effect size) respectively on
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Online learning environment and student engagement: the…
Table 1 Parameter estimates of the measurement model
The measurement model
Constructs
Items
Mean SD
Teacher support
INS1
3.58
0.861 − 0.177
0.164
0.771
INS2
4.02
0.721 − 0.679
1.342
0.799
INS3
3.84
0.822 − 0.603
0.829
0.819
INS4
3.41
0.930 − 0.346
0.286
0.780
INS5
3.85
0.790 − 0.291 − 0.160 0.849
INS6
3.65
0.922 − 0.333 − 0.112 0.857
CC1
3.83
0.772 − 0.704
1.456
0.789
CC2
3.92
0.774 − 0.679
1.255
0.809
CC3
3.78
0.821 − 0.601
0.795
0.853
CC4
3.91
0.732 − 0.438
0.920
0.891
CC5
3.90
0.750 − 0.704
0.395
0.873
PC1
3.96
0.696 − 0.305
0.331
0.704
PC2
3.70
0.774 − 0.059 − 0.057 0.851
PC3
3.76
0.801 − 0.106 − 0.209 0.886
PC4
3.66
0.779 − 0.100
TR1
3.00
1.120 − 0.240 − 0.665 0.668
TR2
2.97
1.117 − 0.296 − 0.584 0.817
TR3
3.35
0.962 − 0.506
0.449
0.816
EB1
3.58
0.721
0.102
0.204
0.734
EB2
3.56
0.757 − 0.199
0.574
0.807
EB3
3.47
0.817
0.122
0.761
EB4
3.75
0.801 − 0.115 − 0.183 0.744
EB5
3.51
0.865 − 0.320
VB1
4.05
0.721 − 0.081 − 1.068 0.850
VB2
3.87
0.742 − 0.006 − 0.726 0.858
VB3
4.05
0.758 − 0.116 − 1.139 0.904
VB4
4.05
0.731 − 0.291 − 0.215 0.734
Course clarity
Student connectedness
Task relevance
Expectancy belief
Task value belief
Skew
0.008
Kur
0.365
0.366
Factor loadings CR
0.983 − 0.639
0.219
0.790
0.852 − 0.852
1.319
0.807
BEH3 3.12
1.057 − 0.270 − 0.213 0.649
BEH4 4.11
0.683 − 0.244 − 0.463 0.488
COG1 3.72
0.804 − 0.454
COG2 3.81
0.847 − 0.415 − 0.066 0.753
COG3 3.92
0.734 − 0.349 − 0.029 0.802
COG4 3.90
0.698 − 0.467
0.762
0.669
AFF1
3.84
0.791 − 0.753
1.185
0.819
AFF2
3.74
0.844 − 0.826
1.390
0.705
AFF3
3.88
0.744 − 1.01
2.233
0.733
AFF4
3.89
0.737 − 1.16
2.924
0.778
Affective engagement
0.925 0.712
0.901 0.697
0.813 0.593
0.875 0.584
0.772
BEH2 3.91
0.374
0.921 0.661
0.885
Behavioural engagement BEH1 3.72
Cognitive engagement
AVE
0.720
0.904 0.703
0.784 0.484
0.826 0.544
0.845 0.577
0.921 0.661
CR Composite reliability, AVE Average variance extracted, Skew Skewness, Kur Kurtosis
13
H. Vo, H. Ho
Table 2 HTMT correlations among the constructs
INS
INS
CC
PC
EB
VAL
BEH
COG
AFF
TR
1
CC
0.699
1
PC
0.695
0.641
1
EB
0.560
0.558
0.470
1
VAL
0.503
0.534
0.537
0.479
1
BEH
0.331
0.419
0.434
0.489
0.408
1
COG
0.217
0.364
0.352
0.484
0.324
0.608
1
AFF
0.569
0.472
0.509
0.637
0.493
0.549
0.512
1
TR
0.492
0.504
0.566
0.528
0.539
0.559
0.358
0.539
1
Fig. 3 The structural model with standardised estimates
students’ behavioural, cognitive, and affective engagement via expectancy and task
value beliefs were all significant. On the other hand, teacher support indirectly affected
student engagement only via expectancy belief (95% CIs = [0.025—0.170], β = 0.097,
small but meaningful effect size; [0.024—0.149], β = 0.109, moderate effect size;
[0.026—0.201], β = 0.134, moderate effect size) respectively on behaviour, cognitive,
13
Online learning environment and student engagement: the…
Table 3 Summary of the research findings in relation to the research hypotheses
Research hypotheses
Research findings
Fully supported
Hypothesis 1:Students’ expectancy belief
positively and significantly influences their
behavioural, cognitive, and affective engagement
in online learning
Fully supported
Hypothesis 2: Students’ task value belief
positively and significantly influences their
behavioural, cognitive, and affective engagement
in online learning
Hypothesis 3: Course clarity, task relevance,
teacher support, and student connectedness
positively and significantly influence students’
expectancy belief
Hypothesis 4: Course clarity, task relevance,
teacher support, and student connectedness
positively and significantly influence students’
task value belief
Partially supported: Only course clarity, task rel‑
evance, and teacher support significantly predicted
expectancy belief
Partially supported: Only course clarity and task
relevance significantly predicted task value beliefs
Hypothesis 5: Expectancy belief mediates the
Partially supported: Both expectancy and task value
effects of teacher support, course clarity, student
beliefs significantly mediate the effects of course
connectedness, and task relevance on students’
clarity and task relevance on all dimensions of
behavioural, cognitive, and affective engagement
student engagement whereas only expectancy
respectively
belief significantly mediated the effect of teacher
support on student engagement. There was no
Hypothesis 6: Task value belief mediates the
mediating effect of expectancy and value beliefs
effects of teacher support, course clarity, student
on the relationship between student connectedness
connectedness, and task relevance on students’
and three dimensions of student engagement
behavioural, cognitive, and affective engagement
and affective). Finally, both students’ expectancy and task value beliefs were found to
have no mediating effect on the relationship between student connectedness and student
engagement.
Discussion
This study was situated in an online learning context characterised in the litera‑
ture by the isolated and disconnected feelings that students frequently experience
and the high dropout rate that online education providers are trying to reduce (Hoi
& Hang, 2021a). Its purpose was to unpack salient online learning classroom var‑
iables that might influence student engagement and the motivational mechanisms
through which these influences are exerted, thereby suggesting methods to lever‑
age student engagement and retention in online learning. Table 3 summarises the
research findings in relation to the research hypotheses.
The findings largely supported the integration of the basic psychological need
theory and expectancy value theory into an overarching framework of engage‑
ment that has potential to inform research on student engagement. Students’
expectancy and task value beliefs were found to be significant predictors of stu‑
dent engagement in online learning and in turn were significantly predicted by
13
H. Vo, H. Ho
online learning classroom variables that fulfill students’ needs for competence,
autonomy, and relatedness. However, contrary to the general literature on the
expectancy value theory that attested to the role of expectancy belief as a more
robust determinant of academic achievements (e.g., test results, GPA) and the
role of task value belief as a stronger predictor of achievement-related choices
(e.g., engagement and persistence) (Marsh et al., 2005; Wigfield & Eccles, 2000),
the current study found that students’ expectancy belief predicted student engage‑
ment in online learning with larger magnitude than task value belief. While this
finding represented a departure from the general literature, it might be explained
by the contextual conditions that shaped expectancy and task value beliefs. In this
respect, it was found in this study that students’ expectancy belief was signifi‑
cantly predicted by course clarity, task relevance, and teacher support while task
value belief was influenced only by course clarity and task relevance. The differ‑
ential effects of expectancy and task value beliefs on student engagement might
also be explained by variables that had not been included in the current study. For
example, in a recent study, Lai (2021) reported that the effect of task value on
student engagement was weakened if students perceived learning tasks to be dif‑
ficult – a variable that can be offset by high expectancy belief. Had perceived task
difficulty been included as a contextual variable in this study, the results might
have been different.
Task relevance was found to be the most robust determinant of both students’
expectancy value beliefs and engagement. This finding not only reenforced the pos‑
itive effect of providing relatable and meaningful tasks on student engagement in
online learning as reported in previous studies (see, for example, Chiu, 2021a; Lee
et al., 2015; Martin et al., 2018; Vanslambrouck et al., 2018) but also extended on
those studies by shedding light on the motivational mechanisms via which this effect
was exerted. As such, students are likely to attach greater values to and become
more confident in tasks that are directly relevant to their life and future career, which
in turn promotes their engagement in online learning (Lee, 2015). Therefore, the
design and delivery of tasks in the online learning environment should take this fea‑
ture into account and should be exercised with caution because variables such as
task difficulty might nullify the values of task relevance (Lai, 2021).
Similar to task relevance, course clarity indirectly influenced all dimensions of
student engagement with moderate to large effect sizes via both expectancy and task
value beliefs. This construct can be likened to the construct of structure, a dimen‑
sion of teacher support identified in self-determination theory for classroom prac‑
tice, which involves clear communication of expectations for student success, wellstructured lessons, strong guidance during learning, and effective learning materials
(Lietaert et al., 2015). While structure emphasises the role of teachers in delivering
these types of support in the classroom, course clarity involves the organisation of
course information in the online classroom that can be accessed by students at the
beginning of the course. Just as structure was reported in previous classroom-based
studies to promote different dimensions of student engagement (Skinner & Belmont,
1993; Wang & Eccles, 2013), it was found in this study that the extent to which the
course organisation provided students with clear instructions on the use of learning
materials and technology, and clear guidelines for assignments, positively predicted
13
Online learning environment and student engagement: the…
their engagement in online learning through the consolidation of their expectancy
and task value belief. Therefore, an important element to consider when designing
online courses is the clear communication of the course objectives as well as guide‑
lines for learning and assessment.
Contrary to task relevance and course clarity, teacher support and student con‑
nectedness were found to have no or only partial effect (with small effect sizes)
on student engagement via expectancy and task value beliefs. While this finding
diverged from those consistently reported in conventional face-to-face classroom
research that highlighted the positive effects of teacher support and student con‑
nectedness on student engagement (Sulis & Philp, 2020; Wang & Eccles, 2013), it
received support from several studies in online learning environments (Chiu, 2021b;
Edwards, 2021; Lan & Hew, 2020). Consistent with these studies, the finding sug‑
gests that students’ sense of connectedness with peers was largely undermined in
the online learning context. Although the online courses were designed in a way
that required students to collaborate for task completion, it could be inferred from
the course information that this collaboration was largely asynchronous. The limited
synchronous interactions, therefore, might have deprived students of opportunities
to get regular support and engage in constant interaction with peers. In a context
where students need to exercise more regulation and self-navigate their own learn‑
ing journey through the entire course, lack of constant peer support and interaction
when learning difficulties occur is likely to dampen their sense of connectedness and
their subsequent engagement (Kaufmann & Vallade, 2020). Teacher support, how‑
ever, was found to influence student engagement via expectancy belief rather than
task value beliefs. A plausible explanation could be that the type of teacher sup‑
port, as perceived by students, was of a reactive nature rather than proactive. That
is, online teachers, due to limited interactions with students and to the nature of the
course structure, might have provided support only when learning problems occur
or when students approach them for advice or feedback during their self-regulated
learning. This type of support might enable students to solve problems, give them
confirmation as to whether they are on the right track, or give them more confidence
in their self-regulated learning journey (i.e., expectancy belief). However, this type
of support is not likely to arouse their interest, enjoyment, or perceived importance
and usefulness of the tasks at hand (i.e., task value belief) compared to proactive
support wherein teachers are actively involved in students’ work and offer support
on a regular basis. While teacher support has been well researched in the literature
on student engagement, the results of this study suggest that more research needs to
be done to gain a richer understanding of the right type of teacher support that lever‑
ages student engagement in a specific context such as online learning.
Implications
The study findings offer important suggestions for the development of online
courses that have potential to foster student engagement. First, since task relevance
manifested itself to be the most salient determinant of student engagement in online
learning, course designers and teachers should take students’ learning preferences,
13
H. Vo, H. Ho
learning habits, personal interests, current professional practices, and future career
orientation into account in an attempt to provide them with authentic and engaging
tasks. Tasks that align course content and skills with students’ professional prac‑
tices or daily life and that stimulate a genuine need among students to use skills
and knowledge from the online course for task completion might be particularly
engaging. Teachers making videos in which successful entrepreneurs or alumni are
invited to share their start-up experience and then showing these videos in a busi‑
ness course, or projects that require students to make a video interview with native
speakers of a foreign language in a language course are some examples of task
relevance. In addition, since student connectedness was undermined in the online
learning environment, the interactive nature of course content and activities should
be improved. Some strategies that have been successfully employed in previous
studies to enhance students’ sense of connectedness include teachers setting aside
regular hours per week to answer students’ questions and provide feedback to com‑
mon issues raised by students synchronously (Lan & Hew, 2020), or setting up and
managing informal online space through social networks parallel to the online learn‑
ing management system where they can make themselves more available to students
(Hoi & Hang, 2021b). Tasks that necessitate interactions among students need to
be manipulated in ways that students need to genuinely collaborate with each other
synchronously for fulfillment rather than just to take turns and complete their parts.
Encouraging students to contribute to discussion forums by building on others’
posts and rewarding them with plus points when their contribution is acknowledged
by other classmates might also help improve students’ sense of competence in the
online community.
Conclusions
This study sheds light on some salient online learning classroom variables that hold
strong potential for fostering student engagement. It also contributes to theorising
and conceptualising online learning engagement from an ecological perspective.
Among the online learning environment variables, course clarity and task relevance
play critical roles in motivating students to engage in online learning. On the other
hand, online learning designers and teachers should put more effort into innovat‑
ing the online course design, instructional strategies, and support features in ways
that instigate stronger task value attachment and perceived competency among stu‑
dents, which in turn will contribute to more effective participation, collaboration,
and involvement in online learning.
Limitations
Despite the positive findings, there are several limitations in this study that could
be addressed in future research. First, the cross-sectional nature of data collection
in this study did not allow for the examination of fluctuation of student engagement
over the entire semester or academic year and how students develop their
13
Online learning environment and student engagement: the…
developmental competencies and psychological beliefs in response to the evolving
online learning environment to remain engaged in learning and persistent in the
face of setbacks. Future studies might need to investigate this longitudinal aspect
of student engagement in online learning environments. Second, from an ecological
perspective, student engagement constitutes a central role in the bidirectional or
reciprocal relationships among the learning context, the self, and relevant learning
outcomes. Therefore, hypothesising and testing the causal relationship among the
online learning classroom variables, motivational beliefs, and engagement as done
in the current study might have obscured the reciprocal effect of engagement on
future motivational beliefs and learning environment. As suggested by Wang et al.
(2020), engagement begets future engagement by bolstering students’ psychological
beliefs and reinforcing students’ positive experience of the learning environment.
This reciprocal relationship deserves further research into the online learning
environment in future studies. Third, the study only focused on the online learning
classroom variables, expectancy and task value beliefs, and online engagement.
There are other contextual (e.g., sociocultural and family factors), developmental
(e.g., social, emotional, and cognitive skills), and motivational (e.g., mindset)
variables suggested in the development-in-sociocultural context model that are
beyond the scope of the current study. Future studies are encouraged to examine
these variables to paint a more comprehensive picture of the student engagement
process. Finally, the study did not examine the effects of demographic variables
such as gender, grades, and academic majors due to the unequal distribution of
these variables in the convenience sample. Future studies might take these factors
into account to afford a richer understanding of the individual differences that might
affect the engagement process in online learning.
Appendix 1: The questionnaire items
Teacher support behaviour scale (Kaufmann et al., 2016)
Based on my online class interactions with the instructor, I perceived my instructor
1. as understanding.
2. as respectful to me.
3. as supportive.
4. as responsive (provide feedback on assignment and discussion boards).
5. as engaged in the course.
6. as approachable.
Peer connectedness scale (Kaufmann et al., 2016)
Based on my online class interactions with students in my class, I perceive:
13
H. Vo, H. Ho
1. students as respectful of one another.
2. students as cooperative with one another.
3. student as comfortable with one another.
4. student as supportive to one another.
Task relevance scale (Wang & Eccles, 2013)
1. How often do you discuss problems and issues that are meaningful to you?
2. How often do you learn things that are related to your life outside the online
classroom?
3. How often do you learn things that are useful for your future jobs?
Course clarity scale (Kaufmann et al., 2016)
Based on my experiences with and perceptions of this online course:
1. the organisation of the course was clear.
2. the instructions for the use of technology were clear.
3. the instructions for assignments were clear.
4. Course objectives were clear.
5. Course content was clear.
Expectancy belief scale (Midgley et al., 2000)
1. I’m certain I can master the skills taught in the online classes this year.
2. I’m certain I can figure out how to do the most difficult task in the online
classes.
3. I can do almost all the work in the online classes if I don’t give up.
4. Even if the work is hard, I can learn it.
5. I can do even the hardest work in the online classes if I try.
Task value belief scale (Lazarides et al., 2020; Wang & Eccles, 2013)
1. It is important for me to be someone who is good at the subject matter that I am
learning in this online course.
2. Learning the material covered in this online course is enjoyable.
3. The concepts and principles taught in this online course are interesting.
4. Learning the content of this online course is useful for what I want to do after I
graduate and go to work.
13
Online learning environment and student engagement: the…
Behavioural engagement scale (Hoi & Hang, 2021a)
1. I take notes when I participate in video conferences and discussion boards.
2. I stay focused during video conferences and discussion board.
3. I talk about course topics (posted by teachers and other students) outside the
online classes.
4. I complete all assignments and discussion board topics on time.
Cognitive engagement scale (Hoi & Hang, 2021a)
1. I try to connect what I am learning in course materials with things I learn before.
2. I try to find extra learning resources (from TV, internet) to understand a con‑
cept that I don’t understand.
3. I try to understand my mistakes if I get something wrong during online learn‑
ing activities.
4. When I have trouble understanding something, I go over it again until I under‑
stand it.
Affective engagement scale (Hoi & Hang, 2021a)
1. I enjoy online learning activities.
2. I look forward to online learning activities.
3. I feel comfortable participating in online discussions.
4. I feel inspired to improve my skills in online classes.
Acknowledgements The authors would like to thank all the students for their participation in this
research and the teachers who allowed them to conduct data collection in their classes. Special thanks
also go to the anonymous reviewers for the manuscript.
Funding Open Access funding enabled and organized by CAUL and its Member Institutions. The authors
receive no funding for this research project.
Data availability The datasets used and/or analysed during the current study are available from the cor‑
responding author on reasonable request.
Declarations
Conflict of interest The authors declare that there is no conflict of interest.
Ethical approval Ethics was obtained from the relevant university and all participants provided informed
consent to participate in the study. Project No E2022.05.1CD.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in
this article are included in the article’s Creative Commons licence, unless indicated otherwise in
13
H. Vo, H. Ho
a credit line to the material. If material is not included in the article’s Creative Commons licence and
your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need
to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
and institutional affiliations.
Hoi Vo is a Vice Chancellor’s Senior Research Fellow in the Faculty of Education, Southern Cross
University. He obtained a PhD in education from Queensland University of Technology, Australia. His
research interests are learner engagement, motivation, and teacher professional development.
Hang Ho is a lecturer at Ho Chi Minh City Open University. Her research interests include technologymediated learning, educational psychology, and language education.
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