Computers & Education 157 (2020) 103963
Contents lists available at ScienceDirect
Computers & Education
journal homepage: http://www.elsevier.com/locate/compedu
Understanding the determinants of learner engagement in
MOOCs: An adaptive structuration perspective
Yongqiang Sun a, Yanping Guo a, Yiming Zhao b, *
a
b
School of Information Management, Wuhan University, China
Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, Hubei Province, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
MOOC
Adaptive structuration theory
Consensus of appropriation
Learner engagement
As an innovative educational paradigm, the massive open online course (MOOC) has received
extensive attention from both academia and industry. Extant literature has made much effort to
investigate the technological supports of MOOCs but how learners understand and master these
technologies is still unclear. Moreover, given the collaborative features (e.g. discussion forum and
peer assessment) of MOOCs, it is vital to ascertain the mechanism through which group learners
reach a consensus on the technology usage and thereby engage in MOOCs. To advance this line of
research, the study proposes a theoretical model leveraging adaptive structuration theory. Spe­
cifically, we identify three contextualized factors (i.e., collaborative spirit, task interdependence,
and social interaction ties) as the antecedents of consensus of appropriation. Through an online
survey recruiting 374 Chinese University MOOC learners, this study demonstrates that collabo­
rative spirit, task interdependence and social interaction ties are positively related to consensus of
appropriation, which will facilitate commitment and learner engagement. In addition, commit­
ment is also confirmed to enhance learner engagement. Implications and limitations are further
discussed.
1. Introduction
The recent years have witnessed a flourish of Massive Open Online Courses (MOOCs) (Almatrafi et al., 2018; Chen & Chen, 2015;
Joo et al., 2018). In 2012, many MOOC platforms, such as Coursera, edX and Udacity, were launched, giving rise to “the year of
MOOC”. Till the end of 2018, more than 900 universities had created 11,400 MOOCs involving over 101 million learners around the
world. As a novel mode leveraging the technology affordances in higher education (Joo et al., 2018; Zhu et al., 2018), the MOOC
renders education resources accessible to anyone anywhere and thus raises the potentials for learner engagement.
Although MOOCs were held up as an educational innovation (Joo et al., 2018), the problem of high dropout rate also emerged (Hew
& Cheung, 2014). As argued by Reich and Ruiperez-Valiente (2019), the low completion rates of MOOCs had never been improved
over 6 years. Such issues raised serious concerns (e.g., Hew & Cheung, 2014; Hone & El Said, 2016) and many researchers demon­
strated that participants with a lower level of engagement were more likely to drop out (Wang et al., 2019). Hence, there is an urgent
need for investigating how to improve learner engagement in MOOCs.
Extant scholars have made initial effort to investigate engagement issues within MOOCs (Li & Baker, 2018). Nevertheless, they still
regarded technological tools as the primary drivers for learner engagement. This is evident in that many existing MOOC studies
* Corresponding author. Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, Hubei Province, China
E-mail addresses: sunyq@whu.edu.cn (Y. Sun), guoyanping@whu.edu.cn (Y. Guo), zhaoyiming@whu.edu.cn (Y. Zhao).
https://doi.org/10.1016/j.compedu.2020.103963
Received 2 January 2020; Received in revised form 21 June 2020; Accepted 23 June 2020
Available online 26 June 2020
0360-1315/© 2020 Elsevier Ltd. All rights reserved.
Computers & Education 157 (2020) 103963
Y. Sun et al.
advocate the technological determinism, and they discuss how the Internet and computer technologies can maximize the effectiveness
of MOOCs and support participant learning (Xie, 2019; Zhu et al., 2018). However, the opinion that technologies naturally drive
certain actions may hide designers’ intention and it cannot account for how people learn to use technologies (Oliver, 2011). That is,
learners exercise a high level of agency for setting forth their ideas (Cacciamani et al., 2012). In this respect, more attention should be
paid to how learners utilize technologies rather than how technologies govern learner behaviors.
Furthermore, although a few scholars have emphasized the importance of collaboration in MOOCs (Razmerita et al., 2018), the
mechanisms underlying this collaboration process are yet unclear. It should be noted that in this paper, the collaboration pertains to a
broad concept that includes any collaborative activities in the MOOC context, such as participants’ attending peer assessments and
exchanging their ideas on the discussion forum in MOOCs (Formanek et al., 2017; Wise et al., 2017). Generally, MOOCs can provide
technological supports to broadcast centralized materials like pre-recorded video lectures, slide presentations, e-textbooks assign­
ments, and automatic assessments. In addition, MOOCs can also provide collaborative tools like discussion board that enables question
& answering and opinion debating for students to grasp a deep understanding of the material (Almatrafi et al., 2018). Moreover, the
peer assessment function in MOOCs allows learners to rate their classmates in a reciprocal learning manner. From this perspective,
learning in MOOCs is regarded as a social process in which group learners are responsible to construct knowledge, solve problems, and
cultivate skills together (Razmerita et al., 2018). Hence, understanding the mechanism through which learners reaches a consensus on
the MOOC system usage is crucial. On these accounts, our research question is: How will group learners reach a consensus on the tech­
nology usage and subsequently engage in MOOCs?
To address this issue, the study draws upon adaptive structuration theory (AST) because it is suitable for capturing the features of
learners’ collaborative process. Originated from the organizational research, AST illustrates how an advanced information technology
(IT) is appropriated by group members (Desanctis & Poole, 1994). Central to AST is the concept consensus of appropriation, which
represents the degree of agreement among group members regarding how the technology should be used and how it fits the group’s
work (DeSanctis et al., 2008). Moreover, AST offers a comprehensive framework to interpret three sources of technology appropriation
including IT structures, task features, and the group’s internal system (Desanctis & Poole, 1994). Contextualized to the MOOC setting,
we identify collaborative spirit, task interdependence, and social interaction ties as the antecedents for consensus of appropriation.
Then we establish a research model to illuminate the mechanism of learner engagement and test it using the survey data from Chinese
University MOOC learners.
This study can yield several implications. First, we clarify the mechanism of group learners’ engagement in the MOOC context.
Given the fact that most previous scholars pay attention to the technological influences on learner engagement, we offer both theo­
retical and practical insights on how to boost MOOC learners’ collaboration. Second, we provide new understanding on MOOC
learners’ behaviors through the lens of AST. In particular, current work is among the first to expound the process of collaboration in
MOOCs ingeniously using the concept consensus of appropriation. Such a theorization presents a clear picture about how group
learners collaboratively harness technologies to facilitate their learning. Third, current study also identifies the contextualized
characteristics of the MOOC system as the antecedents for consensus of appropriation. This complements the deficiencies of the
context-specific factors among the MOOC research (Hood et al., 2015), and supplies practitioners with concrete suggestions on
improving the completion rate of MOOC.
2. Literature review
2.1. Learner engagement in MOOCs
MOOCs are an online learning environment available for anyone via the Internet (Hood et al., 2015). In MOOCs, instructors deliver
content to learners through pre-recorded video lectures, courseware resources, and interactive activities on the discussion forum, and
learner performance are in general assessed through quizzing and peer assessment (Formanek et al., 2017). By virtue of the Internet
and computer technologies, MOOCs expand traditional education to a global scope. Millions of learners around the world have
registered on MOOC platforms, like edX, Coursera, and Udacity, to share knowledge and create collaborative learning opportunities
(Cohen et al., 2019). However, the issue of high dropout rate also annoyed many scholars (e.g. Hew & Cheung, 2014; Hone & El Said,
2016). Since recent literature concluded that learners with higher levers of engagement were less likely to drop out (Wang et al., 2019),
more investigations on the mechanism of learner engagement are needed.
Learner engagement is defined as the amount of physical and psychological energies that student devotes to the academic expe­
rience (Oh et al., 2017). Following the definition, learner engagement embodies the psychological state that learners are emotionally
and cognitively active in their course learning and the behavioral effort that learners expend in MOOCs to master the knowledge and
pursuit high-quality performance (Sun et al., 2019). Conceptualizing learner engagement as the combination of psychological and
behavioral dimensions is reasonable because this study investigates how learners reach agreement on the system usage, during which
learners not only need to understand and accept the system design on the psychological level, but also make endeavor to participate in
course-related activities via the MOOC system. Specifically, psychological engagement refers to the level of an individual’s positive,
fulfilling, and action-related state of mind that is characterized by vigor, dedication, and absorption in MOOCs (Oh et al., 2017); while
behavioral engagement pertains to learners’ sustained behaviors that extend beyond typical or expected in-role actions in MOOCs (Oh
et al., 2017). On the whole, having a higher degree of engagement indicates that learners may have more enthusiasm for using
technological tools to communicate and learn in MOOCs.
Most extant works have focused on the patterns and measurements of learner engagement in MOOCs (Hew & Cheung, 2014).
Another proportion of empirical research employs technology acceptance model (TAM) and task-technology-fit (TTF) to examine
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Y. Sun et al.
learners’ adoption and usage of MOOCs (Joo et al., 2018). These studies position technological tools as the primary drivers of learner
behaviors in MOOCs. However, the technology determinism cannot explain how people learn to use technologies because people are
not simply mechanical conveyers of animating external sources of influence (Oliver, 2011). Instead, learners possess a high level of
agency and they are responsible for constructing new skills and knowledge (Cacciamani et al., 2012). In this regard, MOOCs are
learner-centered in which learners decide how they should use the technologies to aid their learning.
Furthermore, researchers have pointed out that education should pay attention to not only the use of new technological solutions,
but also the collaborative methods for students’ skill development (Sung & Hwang, 2013). Existing studies have also explored MOOC
learners’ engagement from a social interaction perspective (Hone & El Said, 2016; Wang et al., 2019), including the discussion forum
(Wise et al., 2017) and peer assessment (Formanek et al., 2017). In the online environment, learner interaction becomes particularly
important because the traditional learning mode are shifting to the learner-centered approach that emphasizes collaboration (Zhao,
Wang, & Sun, 2020). Nevertheless, prior conceptual works fail to theorize and empirically examine the collaborative process in
MOOCs. Hence, understanding how learners can jointly handle the technologies to finish their assignments and subsequently engage in
MOOCs is crucial, which calls for an integrated theoretical perspective.
2.2. Adaptive structuration theory (AST)
Since MOOCs can take advantage of the Internet resources and technological tools to facilitate learners’ communications (Xie,
2019; Zhu et al., 2018), investigating learner engagement requires a thorough consideration of the technologies, tasks, and social
interactions among learners. AST is a suitable theory to capture these features and portray learners’ behavioral mechanisms. Origi­
nated from the organizational research, AST was proposed as a blend thought of decision-making and institutional schools positing that
group members’ task performance hinged on the appropriation of information technologies (ITs) under the social structures (Desanctis
& Poole, 1994). Such social structures comprise of rules and resources provided by the characteristics regarding the technologies,
tasks, and group norms (Desanctis & Poole, 1994; Kang et al., 2012). The process of creating such structures is so-called structuration
(Kang et al., 2012). Structuration is not consequentially a reactive and passive adaptation to the environment, but it involves creative
and proactive aspects that group members persistently refine and renovate throughout their membership careers (Kang et al., 2012).
Due to its remarkable explanative power, AST has been used by previous scholars who adopt multi-faceted perspectives to
investigate factors influencing system use and user performance across various contexts including organizational systems (Kang et al.,
2012; Liang et al., 2015), health information technologies (Romanow et al., 2018), and customer relationships management (Johnson
et al., 2012). For instance, Liang et al. (2015) drew on AST to examine how system complexity, task characteristics (i.e., job autonomy
and task variety), and innovation climate jointly affect employees’ exploration and extended use of enterprise systems. Similarly, AST
can provide an integrated framework to investigate how the features of the system, tasks, and group learners facilitate their consensus
on technology usage and subsequent engagement, as elaborated next.
AST concentrates on the impact of social structures, including the structures of ITs (e.g., structural features and spirits), other
sources of structures (e.g., tasks and the organizational environment), and the group’s internal system, on the appropriation of
structures which in turn leads to decision outcomes (e.g., efficiency, quality, and commitment) (Desanctis & Poole, 1994). To be
specific, the structures of an advanced IT can be described in terms of its spirit, which refers to the general intent with respect to values
and goals underlying a given set of structural features. The unconditional access of MOOCs grants global learners bountiful courses,
facilitating them to share knowledge and collaborate (Cohen et al., 2019). Moreover, previous studies have shown that the MOOC’s
functions, such as the discussion forum (Wise et al., 2017) and peer assessment (Formanek et al., 2017), can promote learners’
collaboration. Therefrom, in the MOOC context, collaborative spirit can be identified as the design philosophy of this learning system.
Besides, the contents and constraints of a given task are another major source of structures. MOOCs remind learners the importance of
joint effort and encourage them to complete the learning tasks through collaboration. Thus, task interdependence can be viewed as an
important source of structures. Moreover, a certain structure may be appropriated differently relying on the group’s internal system,
which refers to the nature of members and their relationships inside the group (DeSanctis et al., 2008). Since learners in MOOCs are
favored to have a stable construction of their own knowledge through interactions with peers, social interaction ties are suitable to
represent the structure of group’s internal system.
這個研究只做年輕⼈
延伸研究可以做其他年齡層
Fig. 1. Research model.
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Y. Sun et al.
Further, whether group members can leverage these structures to achieve learning outcomes largely relies on the consensus of
appropriation, which is the extent of agreement among group members concerning how the technology should be used and how it fits
the group’s work (DeSanctis et al., 2008). In the context of collaborative technology use, the benefits can only be realized once the
entire learning group or an adequately large number of members collectively adopt the technology (Kang et al., 2012). At first,
members may have divergent opinions until they reach a consensus on how to utilize the Internet and computer technologies to
accomplish their tasks (Desanctis & Poole, 1994). They gradually settle such disagreements by learning and communicating with each
other. Subsequently, improvements in these decision processes are expected to result in desirable outcomes like strong commitment
and high degree of engagement.
3. Research model and hypotheses
According to the arguments above, we draw upon adaptive structuration theory (AST) to develop a research model as depicted in
Fig. 1. Next, we will provide detailed justifications about the proposed hypotheses.
3.1. Structures of appropriation
AST claims that in a specific information system, structures reflect the beliefs, values, and goals as understood by the designers. In
other words, they are designed to reflect a spirit (Desanctis & Poole, 1994). By extension, the spirit is the “official line” that the
technology presents to the users regarding how to behave when using the system, how to interpret its characteristics, and how to fill in
gaps in the process which are not explicitly specified. Learners are unconditionally supported by MOOCs to attend courses, share
knowledge and collaborate (Cohen et al., 2019). Prior researchers have indicated several means that the MOOC system can afford to
support learners’ collaboration including the discussion forum (Wise et al., 2017) and peer assessment (Formanek et al., 2017). Thus,
we identify collaborative spirit as an essential concern for the MOOC system.
In this study, we define collaborative spirit as the general intent with respect to collaborative values and goals underlying the
system design of MOOCs. The spirit provides the “legitimation” for the technology by supplying a normative frame with regard to
behaviors that are appropriate in a specific system. Meanwhile, spirit can function as a means of expression, because it assists users in
understanding and interpreting the meaning of the technology (Desanctis & Poole, 1994). Specific to our study, the more collaborative
spirit it demonstrates, the more group learners will stick to the goals and values offered by the MOOC system. In this way, they are more
likely reaching an agreement on the system usage. The first hypothesis is put forward as follows:
H1. Collaborative spirit is positively related to consensus of appropriation
In addition to IT structures, group members can utilize the structural potentials offered by other sources, among which an
important element is the contents or constraints of the group’s tasks (DeSanctis et al., 2008). MOOCs improve communications and
interactions that can drive learners to consider other’ demands and to evaluate the impacts of their learning patterns on other learners
(Zhu et al., 2018). In this regard, group members are more likely to realize the importance of collaborative tasks (Sharma & Yetton,
2007). That is, the completion of learning tasks in MOOCs is dependent on the group’s joint effort. We thus consider interdependence
as a critical constraint for the MOOC tasks.
Task interdependence is defined as the extent to which group members rely on one another to perform and complete their works
(Van de Ven et al., 1976). In the MOOC context, as the dependence on one person shifts to multiple individuals, technologies are
supposed to accommodate different needs for these task variations (Razmerita et al., 2018). Hence, the features of tasks can affect
which technological structures are chosen for using, how the results are explained, and how they are applied. Thus, a high level of task
interdependence will lead to a more consistency on how the Internet and computer technologies should be used by MOOC learners. H2
is formulated below:
H2. Task interdependence is positively related to consensus of appropriation
For MOOCs, building relations with these learners who share the same interests is a key to their success. Further, extant scholars
also argue that learning in MOOCs is a social process where individuals take the responsibility for constructing their own under­
standing about knowledge through social interactions (Chen & Chen, 2015). Therefore, apart from collaborative spirit and task
interdependence, the third premise for adaptive structuration is the social interaction ties within the group’s internal system, defined
as the strength of the relationships, the amount of learning time spent, and the communication frequency among members in MOOCs
(Chiu et al., 2006).
Early research has suggested that the relationships among group members may appropriate given structures inside their internal
system (DeSanctis et al., 2008). During the learning process in MOOCs, this adaptation may happen in two ways. For one thing, the
increasing frequency of contact can strengthen the trust and attachment among group members. Learners will be more open for others’
opinions and suggestions on the MOOC system usage. For another, enhanced communications and interactions will breed the sense of
belonging to the tasks and courses. In such a condition, learners are willing to discuss how to maximize the utilities of the technologies
and they tend to reach an agreement easily. Therefore, we propose the next hypothesis:
H3. Social interaction ties are positively related to consensus of appropriation.
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3.2. Outcomes for appropriation
Promotion in decision outcomes will emerge only if the group members come to an agreement that the focal technology is suitable
for the tasks at hand (Desanctis & Poole, 1994). Commitment is a typical outcome for appropriation that can be defined as the learner’s
sense of belonging and positive feeling toward the MOOC system (Ellemers et al., 1999). Basically, consensus of appropriation plays
dual roles. On one hand, it lessens the uncertainty about which structures of the technological interventions are appropriate for a given
task. At this point, users can obtain utilitarian benefits and the feelings of reliability from the MOOC system, which may prompt the
establishment of affective bonds between them. On the other hand, the consensus is also associated with less ambiguity and conflict
over technology using patterns (Im, 2014). Resolving this concern can improve learners’ efficiency and thus facilitate their sense of
involvement in the MOOC system. On these accounts, group members will develop the tendency to emotionally attach themselves to
the MOOC system, which is the foundation of commitment (Allen & Meyer, 1990). Therefore, we hypothesize:
H4. Consensus of appropriation is positively related to commitment
In addition to commitment, the process of technology appropriation can also contribute to straightforward participation and system
usage (Im, 2014; Kang et al., 2012). Specific to the MOOC context, consensus may serve as a social pressure among the learners in a
group that arouses their intense enthusiasm and motivations to fulfill the learning tasks. Moreover, the social learning theory (SLT)
(Bandura, 1977) can provide a theoretical explanation for our prediction from another angle. SLT posits that individuals are more
likely to adopt a modelled behavior with functional values if the model is similar to the observer’s and has an admired status (Bandura,
1977). Generally, people are exposed to alternative learning opportunities by observing others’ experiences. When the system usage
leads to positive results, other members are likely to imitate that behavior once there is strong consensus among the group (Im, 2014).
On account of these justifications, if MOOC learners experience less uncertainty and ambiguity concerning appropriations, they will be
more prone to engagement in the MOOC system. Hence, H5 is proposed:
H5. Consensus of appropriation is positively related to learner engagement
Lastly, we assume that affective outcomes have a direct relationship with the psychological and behavioral ones. That is,
commitment will also render the learning experiences more meaningful and thus facilitate learner engagement. Commitment em­
bodies an individual’s emotional attachment, identification, and involvement toward the object (Allen & Meyer, 1990). Rather than
driven by interests, commitment is based on the affective bonds between a focal person and the target in general (Meyer et al., 2002).
As a consequence, people with this sense of belonging often demonstrate the tendency of maintaining long-term relationships (Hashim
& Tan, 2015). Correspondingly, engagement has been postulated as a psychological process and behavioral manifestation that can lead
to the formation of loyalty (van Doorn et al., 2010). Specific to the MOOC context, engagement is viewed as co-occurring with the
construction of group knowledge, which can facilitate task accomplishment and goal realization (Fang et al., 2019). Owing to these
benefits, committed group learners are willing to develop longstanding association with the MOOC system through a joint regulation
on the task comprehending (i.e., psychological engagement), as well as the endeavor investment and on-task persistence (i.e.,
behavioral engagement) (Fang et al., 2019). Therefore, we put forward the last hypothesis:
H6. Commitment is positively related to learner engagement
4. Methodology
4.1. Research setting
To test the proposed hypotheses, our data were collected using an online survey from the users of the Chinese University MOOC
(http://www.icourse163.org/). The platform embraces functions such as bulletin board, discussion board, courseware, test and
assignment, final exam, peer assessment, and sharing courses to social media. Chinese University MOOC was launched in 2014 and it
has been one of the most authoritative MOOC platforms in China with more than 11 million active learners. Moreover, it involves 612
outstanding universities and institutions to provide thousands of high-quality courses that cover a broad range of disciplines.
Considering these merits, Chinese University MOOC is suitable to be used in this investigation.
4.2. Measures
All the measures except for collaborative spirit were adapted from prior studies and had been confirmed to be reliable and valid. To
fit the MOOC context, we made some slight wording modifications. Seven-point Likert scales anchored with “strongly disagree” to
“strongly agree” were adopted for multiple items of all latent constructs. Specifically, learner engagement was taken as a second-order
reflective construct composed of psychological and behavioral dimensions, both of which were measured with the items adapted from
Stumpf et al. (2013). In addition, the items measuring social interaction ties and commitment were adapted from Chiu et al. (2006).
Besides, consensus of appropriation was measured with five items adapted from Kang et al. (2012) while task interdependence was
measured with four items adapted from Sharma and Yetton (2007). Further, because there were no validated items of collaborative
spirit among previous studies, we developed the initial instruments including three items based on its definition that the general intent
with respect to collaborative values and goals underlying the system design. The face validity of the instruments was first assessed by
several PhD students majoring in education and then by several actual users of Chinese University MOOC. Statistical validity was
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further assessed and reported in the next section. All the constructs and measures were elaborated in Table 1.
4.3. Data collection procedure
Our survey was conducted via wjx.cn, the largest online questionnaire distribution and collection platform in China. This platform
has been widely adopted by more than 30,000 companies and over 90% of Chinese universities. On average, more than one million
people fill in questionnaires on it every day. The platform is effective and powerful, with abundant sources of respondents, and it
supports online viewing, screening, as well as data transformation in a variety of formats. Given these advantages, we adopted wjx.cn
to collect our survey data.
We first did a pilot test among 20 random-selected students who had experience for learning on Chinese University MOOC platform
and we collected their comments on the length and wording of the instruments, which helped to improve the quality of the ques­
tionnaire and to ensure its clarity and validity. Functions that reflect the nature of collaboration, such as the discussion forum and peer
assessment, are inherent in the MOOC platform. The questionnaires were sent to students in a certain course taught by us via the e-mail
addresses they provided at the Chinese University MOOC platform. The purpose of this survey was claimed in the email, and
participating in this survey was voluntary and anonymous. In order to motivate more students to answer the questionnaire, we
guaranteed participants extra credits by checking their screenshots of the questionnaire completion page. We were informed of all the
participants, but we did not ascertain the exact questionnaires corresponding to them. In this way, we could offer the participants
credit reward and meanwhile ensure the anonymous of our survey process. Finally, we obtained 374 valid samples and the de­
mographics were shown in Table 2.
Since online survey was adopted to collect data, the response bias may exist. In order to solve this problem, we checked the de­
mographic difference between the first third and the last third of respondents (Kim et al., 2016). The results uncovered that there were
no significant differences between the two sub-groups in demographics except for the login frequency. Moreover, we compared the
construct means for these two sub-groups and found that there was no significant difference. These examinations demonstrated that
response bias was not a serious concern for this study.
5. Data analysis
Partial least squares (PLS) was used in the data analysis based on several advantages of this technique. As a second-generation
structural equation modeling (SEM) approach, PLS can estimate the loadings of indicators on constructs and the causal relation­
ships among constructs (Fornell & Bookstein, 1982). Besides, compared to covariance-based SEM approach, PLS is more appropriate to
deal with small sample size (Hair et al., 2011), which fits our study. Moreover, whereas CB-SEM is regarded as being more appropriate
Table 1
Measures for the constructs in the research model.
Constructs
Items
Collaborative Spirit (Self-developed)
CLS1: The MOOC system is designed with the spirit of facilitating cooperation between students.
CLS2: The MOOC system is designed with the spirit of facilitating collaboration between students.
CLS3: The MOOC system is designed with the spirit of facilitating students’ teamwork.
TID1: The learning tasks in the MOOC system can be performed fairly independently of others.*
TID2: The learning tasks in the MOOC system can be planned with little need to coordinate with others.*
TID3: It is rarely required to obtain information from others to complete the learning tasks in the MOOC system.*
TID4: The learning tasks in the MOOC system is relatively unaffected by the performance of other individuals.*
SIT1: I maintain close social relationships with some members in the MOOC system.
SIT2: I spend a lot of time interacting with some members in the MOOC system.
SIT3: I have frequent communication with some members in the MOOC system.
SIT4: I know some members in the MOOC system on a personal level.
CAP1: Overall, MOOC members agreed on how we should use the MOOC system for learning.
CAP2: MOOC members reached mutual understanding on how we should use the MOOC system to perform our
learning tasks.
CAP3: There was no conflict in MOOC members regarding how we should use the MOOC system in our learning.
CAP4: MOOC members were able to reach consensus on how to apply the MOOC system to learn.
CAP5: MOOC members were able to reach consensus on how we should use the MOOC system to perform our
learning tasks.
CMT1: I feel a sense of belonging towards the MOOC system.
CMT2: I am proud to be a member of the MOOC system.
CMT3: I have the feeling of emotional attachment to the MOOC system.
PENG1: When participating in MOOCs, I am energized by the work I do.
PENG2: When participating in MOOCs, I am enthusiastic about my work.
PENG3: When participating in MOOCs, my work really interests me.
PENG4: When participating in MOOCs, the work that I do is very satisfying to me.
PENG5: When participating in MOOCs, my work is personally fulfilling.
BENG1: When participating in MOOCs, I often take extra initiative to get things done.
BENG2: When participating in MOOCs, I actively seek opportunities to contribute.
BENG3: When participating in MOOCs, I often put more effort into my work than is required.
Task Interdependence (Sharma & Yetton,
2007)
Social Interaction Ties (Chiu et al., 2006)
Consensus of Appropriation (Kang et al.,
2012)
Commitment (Chiu et al., 2006)
Psychological Engagement (Stumpf et al.,
2013)
Behavioral Engagement (Stumpf et al., 2013)
Note: * The item was reverse coded.
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Table 2
Demographic statistics.
Characteristics
Levels
Frequency
Percentage (%)
Gender
Male
Female
�22
23–25
26–28
�29
Below bachelor
Bachelor
Master or above
�2 years
3–4 years
5–6 years
7–8 years
>8 years
1
2–3
4–5
�6
199
175
171
68
19
116
33
260
81
20
66
66
58
164
66
173
61
74
53.2
46.8
45.7
18.2
5.1
31.0
8.8
69.5
21.7
5.4
17.6
17.6
15.5
43.9
17.6
46.3
16.3
19.8
Age
Education
Internet experience
Number of courses enrolled in
for theory confirmation, PLS does provide a good approximation of CB-SEM in terms of final estimates (Hair et al., 2011). From the
above considerations, PLS was used in the analysis. Specifically, SmartPLS was adopted as the analytic tool.
5.1. Measurement model
All the constructs were reflectively measured, so the measurement model for the constructs was evaluated by examining their
reliabilities, convergent and discriminant validities. The reliability of a construct could be assessed by checking its average variance
extracted (AVE), composite reliability, and Cronbach’s Alpha (Fornell & Larcker, 1981). As shown in Table 3, the AVEs, composite
reliabilities, and Cronbach’s Alpha coefficients for all the constructs were greater than 0.6, 0.8, and 0.8, exceeding the suggested
threshold values of 0.5, 0.7, and 0.7, respectively (Fornell & Larcker, 1981). Therefore, all of these constructs were with suitable
reliabilities.
Convergent validity can be assessed by checking whether or not the item loadings on their respective constructs were high enough
while discriminant validity can be assessed by checking whether or not the item loadings on their respective constructs were higher
than the loadings on the other constructs (i.e., cross-loadings). As shown in Table 4, the item loadings on their respective constructs
were higher than 0.7 and these loadings were higher than the cross-loadings, suggesting that these constructs had adequate convergent
and discriminant validities.
Another method to evaluate the discriminant validity is to compare the square root of AVE for a construct and the correlation
coefficients related to this construct. As shown in Table 5, the square roots of AVE for all the constructs were greater than the cor­
relation coefficients, proving that these constructs were with good discriminant validities (Bock et al., 2005).
As discussed in the preceding paragraph, learner engagement was taken as a second-order reflective construct consisting of psy­
chological engagement and behavior engagement. As shown in Table 6, the loadings for the two first-order constructs were above 0.8
with the significance at p < 0.01 level, proving a good validity of learner engagement.
Finally, since the correlations between constructs were relatively high, a multicollinearity concern could arise. However, the
regression analysis results indicated that the variance inflation factor (VIF) values for collaborative spirit, task interdependence, social
interaction ties, consensus of appropriation, commitment, psychological engagement, and behavioral engagement were 1.338, 1.305,
1.451, 1.529, 1.883, 2.612, and 2.098, respectively, lowering than the suggested threshold value 3.3 (Petter et al., 2007). Therefore,
multicollinearity was not a serious problem in this study.
Table 3
Construct reliability.
Construct
AVE
Composite Reliability
Cronbach’s Alpha
BENG
CAP
CLS
CMT
PENG
SIT
TID
0.805
0.792
0.906
0.848
0.684
0.882
0.779
0.925
0.950
0.967
0.943
0.915
0.968
0.933
0.878
0.934
0.948
0.910
0.881
0.955
0.905
Note: BENG¼Behavioral engagement; CAP¼Consensus of appropriation; CLS¼Collaborative spirit; CMT¼Commitment; PEN­
G¼Psychological engagement; SIT¼Social interaction ties; TID ¼ Task interdependence.
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Y. Sun et al.
Table 4
Cross-loadings.
BENG1
BENG2
BENG3
CAP1
CAP2
CAP3
CAP4
CAP5
CLS1
CLS2
CLS3
CMT1
CMT2
CMT3
PENG1
PENG2
PENG3
PENG4
PENG5
SIT1
SIT2
SIT3
SIT4
TID1
TID2
TID3
TID4
BENG
CAP
CLS
CMT
PENG
SIT
TID
0.867
0.931
0.892
0.262
0.276
0.195
0.243
0.240
0.276
0.264
0.232
0.451
0.482
0.481
0.458
0.687
0.665
0.620
0.522
0.198
0.190
0.237
0.109
0.227
0.252
0.212
0.252
0.255
0.243
0.240
0.834
0.912
0.891
0.930
0.880
0.356
0.291
0.312
0.355
0.327
0.345
0.411
0.289
0.268
0.218
0.258
0.482
0.443
0.447
0.426
0.350
0.317
0.393
0.302
0.250
0.249
0.230
0.260
0.329
0.275
0.301
0.338
0.963
0.953
0.939
0.448
0.383
0.363
0.274
0.243
0.249
0.310
0.315
0.344
0.312
0.314
0.249
0.058
0.045
0.087
0.093
0.478
0.473
0.425
0.292
0.353
0.316
0.351
0.341
0.432
0.409
0.389
0.907
0.924
0.932
0.461
0.505
0.516
0.492
0.539
0.392
0.372
0.416
0.281
0.229
0.248
0.230
0.204
0.643
0.688
0.606
0.324
0.324
0.276
0.296
0.289
0.331
0.333
0.287
0.548
0.561
0.565
0.652
0.887
0.880
0.869
0.826
0.275
0.267
0.321
0.175
0.330
0.318
0.267
0.304
0.278
0.149
0.102
0.452
0.427
0.451
0.438
0.363
0.332
0.278
0.317
0.418
0.282
0.379
0.494
0.227
0.161
0.134
0.206
0.943
0.960
0.948
0.903
0.286
0.265
0.379
0.152
0.253
0.237
0.224
0.370
0.335
0.392
0.333
0.300
0.109
0.055
0.059
0.237
0.215
0.261
0.339
0.311
0.268
0.258
0.263
0.292
0.296
0.349
0.244
0.904
0.921
0.889
0.811
Notes: BENG¼Behavioral engagement; CAP¼Consensus of appropriation; CLS¼Collaborative spirit; CMT¼Commitment; PENG¼Psychological
engagement; SIT¼Social interaction ties; TID ¼ Task interdependence.
Table 5
Means, standard deviations, and correlations.
BENG
CAP
CLS
CMT
PENG
SIT
TID
Mean
SD
BENG
CAP
CLS
CMT
PENG
SIT
TID
5.589
4.378
5.223
5.235
5.384
3.298
4.725
1.122
1.422
1.344
1.340
1.067
1.757
1.562
0.897
0.274
0.271
0.512
0.722
0.198
0.266
0.890
0.337
0.372
0.346
0.481
0.391
0.952
0.432
0.333
0.326
0.080
0.921
0.606
0.391
0.259
0.827
0.288
0.347
0.939
0.315
0.882
Notes: SD¼Standard deviation; BENG¼Behavioral engagement; CAP¼Consensus of appropriation; CLS¼Collaborative spirit; CMT¼Commitment;
PENG¼Psychological engagement; SIT¼Social interaction ties; TID ¼ Task interdependence. Boldfaced diagonal elements are the square roots of
AVEs.
Table 6
Second-order constructs.
High-order construct
Low-order construct
Loading
t-statistic
Learner engagement
Psychological engagement
Behavioral engagement
0.954
0.896
146.951
53.582
5.2. Structural model
PLS results of the structural model were illustrated in Fig. 2. The results showed that collaborative spirit (β ¼ 0.210, t ¼ 3.770), task
interdependence (β ¼ 0.270, t ¼ 5.233), and social interaction ties (β ¼ 0.326, t ¼ 6.683) were all positively related to consensus of
appropriation, thus supporting H1, H2, and H3. Additionally, consensus of appropriation was found to be significantly related to
commitment (β ¼ 0.372, t ¼ 7.023) and learner engagement (β ¼ 0.128, t ¼ 2.719), confirming H4 and H5. Finally, commitment was
proved to have a significantly positive relation with learner engagement (β ¼ 0.583, t ¼ 13.976), lending support to H6.
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Y. Sun et al.
Fig. 2. PLS results.
6. Discussion
6.1. Key findings
Drawing upon adaptive structuration theory (AST), the study examines how learners adapt themselves to the technology and thus
engage in MOOCs. Our results can elicit several crucial findings. First, collaborative spirit, task interdependence, and social interaction
ties are all strong predictors of consensus of appropriation. This verifies AST’s basic presumption that group members’ adaptation for
an advanced technology depends on the joint effects of IT structures, task characteristics, and the group’s internal system (DeSanctis
et al., 2008). Second, consensus of appropriation is confirmed to significantly enhance both commitment and learner engagement,
demonstrating that when learners reach an agreement about how to use the technology, emotional bonds and behavioral tendencies
are likely to develop (Romanow et al., 2018). Under the circumstances, positive outcomes toward the MOOC system will emerge.
Although several extant studies have investigated the antecedents of engagement in MOOCs (Sun et al., 2019), they mostly focused on
learners’ characteristics, goals, and motivations. This research is unique in that the construct consensus of appropriation is utilized to
theorize learners’ collaborative process in MOOCs and we empirically confirm its positive relation with learner engagement. Lastly, the
results also validate commitment as a facilitator for learner engagement. A possible reason lies in that learner engagement denotes
persistent participation in the MOOC system, which fits prior conclusions that commitment is conducive to the formation of long-term
relationships (Hashim & Tan, 2015).
6.2. Theoretical implications
This study contributes to related literature in multiple ways. First, we clarify the mechanism shaping learner engagement in the
MOOC context. Although prior scholars have demonstrated the significant role of learner engagement in MOOCs (Fang et al., 2019; Li
& Baker, 2018; Wang et al., 2019), they mainly investigate the effects of technologies and intrinsic motivations, nevertheless failing to
interpret how learners collaborate with each other in MOOCs. Under the MOOC setting, the degree of productive participation hinges
on the quality of collaboration in shared activities. Therefore, ascertaining the mechanism underlying learner collaboration can shed
light on how to facilitate effective engagement in the MOOC system.
Second, we make a fresh attempt to address the collaboration process in MOOCs through the concept consensus of appropriation.
With the paradigm shift from technological determinism to human agency, researchers moreover pay attention to whether and how
people can handle the system rather than how the system should be designed (Oliver, 2011). In other words, if users cannot construct
sufficient understanding on the using patterns, a well-built system will probably become a waste. Particularly in terms of the MOOC,
only when learners collaboratively comprehend the system rules and are able to use the learning tools can the MOOC system realizes its
maximum utility (Razmerita et al., 2018). However, a proper theoretical perspective to explain the collaborative mechanism is still
lacking among extant research. Consensus of appropriation, a key concept of AST, represents the extent of agreement among group
members concerning how the technology should be used to match the group’s works (DeSanctis et al., 2008). Consequently, it fits the
process of collaboration in MOOCs and can provide new understanding for related scholars on group learners’ behaviors.
Third, we identify the antecedents of consensus of appropriation by contextualizing the system-, task-, and learners-related factors
in the MOOC context. Notwithstanding extant scholars have been struggling to investigate how to better facilitate group learners’
collaboration (Chen & Chen, 2015), they conducted case study or content analysis but failed to theoretically identify the prerequisites
of learners’ collaboration. This leaves a considerable blank to comprehensively understand what leads to learners’ consensus on the
use of MOOC system. As argued previously, MOOCs pertain to a multi-faceted schema where group learners take active control of
studying and social interactions with the help of the Internet and computer technologies (Xie, 2019). Accordingly, AST suggests that
the major sources of structure for groups as they interact with an advanced information technology include the system’s spirit, tasks,
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Computers & Education 157 (2020) 103963
Y. Sun et al.
and members’ interaction in the environment (Desanctis & Poole, 1994). Therefore, AST offers a sound framework to account for the
drivers of group learners’ consensus on the MOOC system usage.
6.3. Practical implications
The study also provides abundant insights for practice regarding how to encourage learners to reach an agreement about tech­
nology usage and thereafter engage in the MOOC system. First of all, since collaboration has been proven to be an important part of the
MOOC system, practitioners should continue to strengthen this advantage and optimize technological structures, thus fostering
learners’ collaborative consciousness and improving their participating intention. Second, MOOC practitioners must keep an eye on
the task characteristics within the learning system. Specifically, task interdependence is another critical determinant for group
learners’ consensus which indicates that learners can only complete their tasks when they rely on each other. Hence, MOOC operators
are supposed to enrich the assignments and activities throughout the courses to maintain task interrelations. Finally, given the fact that
group internal factor plays an essential role as well, we suggest MOOC designers refine the communication strategies to expedite user’s
social interaction in the system and provide functional supports for teamwork, such as a real-time discussion group embedded in each
course.
6.4. Limitations and future research
Although this study yields several important implications for academia and industry, some limitations still need to be acknowl­
edged for future research. First, to test the proposed hypotheses, our study employed a cross-section survey. Although multicollinearity
was not verified to be a serious concern, mixed-methods and longitudinal design are strongly suggested to better capture the causal
relationships. Second, it is notable that current study was conducted in the setting of Chinese University MOOC. Since previous
research indicated that persons with different cultural background may have different behavioral motives (Hofstede, 1980). Specif­
ically, Chinese people hold the collectivism that may better meet the group learning pattern. Future scholars can take cross-cultural
issues into account to reach more interesting findings. Third, to explore why learners engage in MOOCs, we draw on AST to incorporate
the features of technologies, tasks, and the group’s internal system. However, other essential factors, such as the interface design,
group climate, as well as learners’ motivations and experiences are also worth investigating to explain the mechanism underlying
learner behaviors in MOOCs.
CRediT authorship contribution statement
Yongqiang Sun: Conceptualization, Methodology, Writing - original draft. Yanping Guo: Formal analysis, Writing - original draft.
Yiming Zhao: Investigation, Writing - review & editing.
Acknowledgments
The work described in this paper was partially supported by the grants from the National Natural Science Foundation of China
(Grant No. 71974148, 71874130, 71420107026, and 71921002), the Humanities and Social Sciences Foundation of the Ministry of
Education, China (Project No. 16YJC870011, 17YJC630157, 18YJC870026), the world-class discipline project “Library, Information
and Data Science”, and the China Association for Science and Technology (Project No. 2017QNRC001).
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