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Australasian Journal of Educational Technology, 2022, 38(6).
75
Need satisfaction and collective efficacy in undergraduate blog-
driven classes: A structural equation modelling approach
Shantanu Tilak, Michael Glassman, Joshua Peri, Menglin Xu
The Ohio State University
Irina Kuznetcova
Akita International University
Lixiang Gao
Peking University
This paper investigates how psychological needs spurring self-determined motivation relate
to collective efficacy for flourishing in online learning communities. Self-determination
theory posits individuals experience intrinsic motivation to flourish at educational tasks
because of targeted satisfaction of the three psychological needs: autonomy, relatedness, and
competence. However, studies conducted to investigate collective, technology-assisted
learning processes suggest competence and relatedness may play a pivotal role in online
community engagement and knowledge-sharing. Moreover, informal gaming experiences
may mirror the collaborative skills needed in online educational/professional communities.
These insights suggest confidence in one’s abilities to contribute to a community, the
perception of a strong, supportive social culture in the online classroom, and informal online
experiences may lead to self-determined motivation enabling agents in distributed,
technology-assisted classrooms to collectively flourish. Little work has been done to examine
effects of need satisfaction on collective efficacy in using online technologies. To fill this
research gap, we used structural equation modelling to investigate perceptions of 636
undergraduate students enrolled in classes within an education department at a midwestern
university employing weekly asynchronous blogging. Our results suggest students’
experience with multiplayer gaming, and need satisfaction towards competence and
relatedness correlate with higher collective efficacy in technology-assisted classrooms
employing discussion forums.
Implications for practice or policy:
• For instructors, student usership and design can spur motivation in online classrooms.
• For researchers, understanding student perceptions of collaboration using technology
can help understand how to design better technology-assisted classrooms.
• The design of collaborative online educational communities should focus on creating
positive social cultures and fostering competence for students.
Keywords: self-determination theory, collaborative learning constructivism, self-efficacy,
cybernetics
Introduction
One of the great riddles of online education, and online behaviour in general, is the development of vibrant
knowledge-building collectives. The development of joint agency through electronic connections was
always one of the great promises of the Internet (Bush, 1945), from early communities like the Whole Earth
Lectronic Link (WELL) and early open-source coding communities, such as Linux and Apache. There have
been multiple attempts to study emergent online communities in educational contexts through frameworks
including classroom community (Rovai, 2002), communities of inquiry (Garrison et al., 2010)
connectivism (Clarà & Barberà, 2013; Siemens, 2005), and knowledge forums (Lei & Chan, 2018;
Scardamalia, 2004). These contributions paved the way for further understanding the creation and
maintenance of online communities on demand (necessary for transient educational initiatives). Recently,
it has been hypothesised that an important attribute of a well-functioning online community is a sense of
collective efficacy among users; the belief among community members that they have something of value
to offer the community, and that the other community members place importance and value on each other’s
Australasian Journal of Educational Technology, 2022, 38(6).
76
participation (Glassman et al., 2021). A big question, however, surrounds the mechanisms of online
community formation.
A critical aspect of forming these communities is whether to place emphasis on those designing and leading
communities (Kirschner et al., 2004) to create collective efficacy, or on users’ innate desires to be members
of well-functioning online collectives. In this paper, we focus on the latter perspective, that is; the users’
self-determined motivations to be part of well-functioning communities. Hur et al.’s (2013) model suggests
willingness to participate in online communities is mostly based on the potential for these communities to
meet the needs of potential users. While self-determined motivation and self-efficacy have shown strong
relationships in previous studies (see Ryan & Deci, 2020) there has been little investigation into the
relationships between distributed forms of efficacy in producing self-determined motivation, and the role
of these relationships in advancing online communities. We used the idea of the basic psychological needs,
deemed as essential nutrients to adaptive functioning in the social world in Deci and Ryan’s (2000) self
determination theory to understand forms of need satisfaction that heighten collective efficacy, to augment
functioning of distributed educational communities.
Our study used structural equation modelling to examine relationships between undergraduate students’
need satisfaction in online collaborative learning involving blogging and online discourse, and self-
determined motivation to participate in sustainable, productive communities, operationalised within a
collective efficacy framework. We suggest two of the three psychological needs, namely relatedness and
competence play important roles in (potential) learner attitudes towards pursuing membership in a well-
functioning online community using asynchronous blogging. Our paper expands the self-determination
theory framework into the realm of collaborative learning in a highly interconnected online universe.
The first part of this paper explores theoretical concepts of collective efficacy and self-determination theory,
outlines their integration, and reviews studies examining relationships between self-determined motivation
and online community formation in students and professionals. The second part focuses on inferential
development of our hypothesis based on our literature review, and outlines our structural equation model.
The third part of the paper presents results of our Structural Equation Model, and potential answers this
might hold for our hypothesis. We then discuss future possibilities for research to expand the scope of self-
determination theory into the study of technology-mediated distributed learning communities.
Collective efficacy and self-determination theory
There are two points of origin for the framework of collective efficacy used in this paper. The first, better
known in the field of education is situated in Bandura’s (2000) social-cognitive framework. The second is
a sociological approach where collective efficacy is used to describe community in general. This latter
model was developed primarily by Sampson et al. (1999) and Morenoff et al. (2001). Initially the two
concepts had differences, but began to merge with Bandura’s (2000) explication (and we would argue, re-
working) of his social-cognitive model of collective efficacy. The Sampson model was developed primarily
through research in criminology, attempting to understand why some neighbourhoods are stable with few
dysfunctional behaviours, while others sometimes situated close by (from a material distance perspective,
few blocks away) are transient, having more crime. This sociological model is an extension of social capital
(Coleman, 1994), except, while social capital defines potential resources, collective efficacy defines task-
specific activities related to social flourishing and heightened capability in negotiating one’s educational
experiences.
Sampson et al. (1999) hypothesised stable, safer neighbourhoods had higher collective efficacy. Here, we
refer to collective efficacy as the belief among residents that other members of the community care about
its ability to survive and prosper, are willing to engage with and within the community to make this happen
(shared expectations), and that their own behaviours will frame the way others act in response to the same
task (reciprocal local exchange). Sampson et al. (1999) used collective efficacy mostly for social
comparison, suggesting neighbourhoods with higher collective efficacy are more likely to be successful
from both a sustainability and a quality of life orientation (the two are intertwined), with intergenerational
closure between children and adults playing an important role. However, this factor has little value for the
transient online communities we explored. Sampson et al. (1999) believed collective efficacy was not a
deficit model where individuals did not fail to form sustainable communities because they were lacking
Australasian Journal of Educational Technology, 2022, 38(6).
77
some individual attribute. Rather it was a task-oriented, community model, where individuals were willing
to put aside other needs to maintain shared expectations and reciprocal local exchange.
Bandura (2000) also developed a theoretical framework for collective efficacy, based more on development
and maintenance of transient communities (e.g., sports teams) and individual’s perceptions of their own
and their fellow team members, to build a well-functioning community. Bandura’s (2000) early writings
on collective efficacy portrayed collective efficacy as resembling individual self-efficacy, in which groups
developed perceptions of abilities to achieve goals in much the same way individuals did, through building
of cognitive filters based on information, actions, and subsequent successful experience homeostatically
recalibrating behaviours (Tilak et al., 2022). Bandura’s (2009) later ideas on collective efficacy were more
nuanced and qualitatively different from self-efficacy. In many ways, Bandura’s (2000, 2009) extension of
collective efficacy reflected Sampson’s (1999) components of shared expectations and reciprocal local
exchange, placing them in the age of mass media. These ideas suggest individuals may believe other
community members have the same approach to shared tasks as they do, and that engagement in task-
specific activities will lead other members to recognise their contribution and reciprocate. Collective
efficacy is based in individual members’ perceptions about specific tasks related to the community rather
than general group belief systems, involving constantly shifting individual perceptions arising from both
satisfaction and dissatisfaction with immediate circumstances.
Our framework of collective efficacy in using online technologies (Glassman et al., 2021) aligns with
Bandura’s later ideas, suggesting individual contributions to a group, and perceptions of the group’s
dynamic functioning contribute to development of collective efficacy. We suggest common perspectives
and orientations (Hipp et al., 2018) towards tasks, developed over time, can reduce social distance, resulting
in collective efficacy (it might be more difficult to develop community between participants with different
backgrounds and reasons for taking courses). Individuals who have experienced successful knowledge-
sharing contexts may be more likely to believe it is possible to thrive in an environment of shared
expectations and reciprocal local exchange, and collectively build successful online communities. This
prompts asking why students, or really any potential community members would want to do this in the first
place. In the next section, we explore how self-determination theory provides a sound framework to
understand intentions guiding community-building.
The role of need satisfaction in online community building
Collective efficacy cannot develop unless potential community members desire to develop and/or sustain
ongoing relationships for achieving shared goals. We live in an individualistic society, and education often
focuses on individual actions and achievements (Grollios et al., 2015). Entire motivation theories are
focused on developing educational contexts where individual learners achieve some self-defined value, and
learners will not participate unless that value is apparent (Eccles & Wigfield, 2020; Urdan & Kaplan, 2020).
The types of orientations and intentions needed for individual processes may be different from aspects
required to function in a shared context. Self-determination theory may align with the development of
collective efficacy, suggesting individuals may be motivated by opportunities to engage with others in
shared activities.
Self-determination theory suggests we are driven by three innate needs/desires and engage in activities to
satisfy these needs: relatedness (psychological need to feel a strong social connection to others),
competence (need to be effective at using one’s abilities and interacting with others), and autonomy (need
to act out of individual volition) (Ryan & Deci, 2020). In collective processes, we experience satisfaction
by working with others we feel some sense of relationship with. The desire for relationships can present a
dilemma for educators, as such relationships can be dependent on common goals, but this is difficult to
establish in transient groups where individuals have limited shared history, entering classrooms having
undergone an array of diverse experiences (Coleman, 1994).
Teachers attempt to develop shared cultures or social capital in classrooms, but this can lead to exclusionary
behaviours, where some students become central and foster greater competence, while others become
marginalised, many times based on superficial resemblance and/or belief systems. Even when teachers can
develop shared languages, it can take extended, well-planned interactions that, especially in high-school
and college, educators do not have at their disposal based on distributed school structures and/or face-to-
face academic experiences. Students can gain access to opportunities for organic community formation, but
Australasian Journal of Educational Technology, 2022, 38(6).
78
it is usually outside of educational experiences, and has little to do with academic goals (e.g., all the students
in a high-school or college cheering together for the football team; attending house parties; going to prom
together; chatting in online text threads, organising LAN parties to play video games).
Online educational communities can many times lead to development of superficial ties (limited personal
information used based on one’s individual volition) (Tilak & Glassman, 2020). If at least some individuals
have a sense of online collective efficacy, and believe that these online communities are a place to find
relationships (a belief in much social media activity outside of educational contexts), they might be more
motivated to be part of these communities and work towards sustaining them.
Competence may be more directly related to social interaction and agentic contribution within online
communities. While competence in face-to-face, individual learning scenarios is often related to abilities
to successfully accomplish tasks (Miller & Prior, 2010), competence in online communities has a strong
social component, interweaving abilities with an individual’s belief system that those abilities are
meaningful and constructive for the functioning of the larger community. Collective efficacy suggests
individuals have enough confidence (self-efficacy) in their own abilities recognising that they will find a
way to merge their thinking and capabilities with others. For example, in early Open-Source programming
communities, new members felt an inherent sense of competence in their own abilities, so much so that
they would be recognised as worthy members by the larger expert community (Kelty, 2008).
One's individual contributions are only part of the competence equation in online learning communities.
Another factor is the ability to recognise and trust in the competence of others, and the idea they will be
able to add to the knowledge base. There is need satisfaction not only in being able to add to the community,
but being part of a dynamic community that can work with you in a relatable way to solve problems and
function as a knowledge building community. Collective efficacy may be related to both social cultures or
relatedness, and confidence we feel in contributing to a group and functioning within a group (perceived
self-competence). In the next section, we review the handful of studies that have been conducted in the
realm of self-determination theory and online community formation.
Current research in need satisfaction and online community formation
Online networks or communities involve constantly unfolding processes defined by both individual
(contribution to a group) and collective (dynamic group functioning) level activity. Such communities allow
individuals to both interact, and take up information presented to them (Tilak & Glassman, 2020).
Supportive online environments, for example social networking websites, allow individuals to pursue their
goals, develop curiosity, and engage in agentic interactions with one another. Frameworks adhering to SDT
suggest that satisfaction of the three needs leads to heightened tendency for individuals to engage with
online technologies (Miller & Prior, 2010). Chen and Jang (2010) conducted a study of 267 special
education pre-service teachers in an online synchronous class comprising chat sessions and asynchronous
discussions. Results suggested that need satisfaction directed towards the three needs, arising from
contextual support provided by instructors in the online classroom leads to individual self-determined
motivation, predicting expected grades, and hours spent studying. Roca and Gagne (2008) suggested that
the three psychological needs are related to behavioural intention to use technology, and perceived
usefulness; all pivotal facets of Davis’ (1989) technology acceptance model. We studied 140 high school
seniors taking a biodiversity course. The course operationalised QR code technology to ask multiple choice
questions to students as they navigated a botanical garden. Results suggested that the three psychological
needs led to individual student’s perception of the usefulness of the technology, and perception of ease of
use. This, in turn, predicted behavioural intention of students to use the software.
The effects of need satisfaction on individual’s self-determined motivation has been examined extensively
in informal technology use. Ryan et al. (2006) saw that autonomy and competence of undergraduate
students predicted engagement and future play in individual platform gaming titles with some multiplayer
capacities. All three needs contributed to individual intentions to continue play in massive multiplayer
online games. Gender differences were seen in perceived intuitiveness of the gestural commands used in
gaming titles. While we examined individual tendencies in informal online groups, we extend Ryan et al.’s
findings by understanding whether informal collective behaviour (gaming experiences) may affect formal
collaborative education. We also aim to understand the role of gaming experiences in developing collective
efficacy, drawing inference from literature (see Petter et al., 2020) asserting that skills accrued via gaming
Australasian Journal of Educational Technology, 2022, 38(6).
79
experience are in line with the ingredients required to sustain professional/educational communities (e.g.,
accountability, cooperation, openness to learning).
The skills explored in gaming communities may also correlate with the use of social networking platforms.
Wang and Li (2016) conducted a study on 221 undergraduate students in an entry-level business course and
understood how creating a culture of relatedness led to satisfaction in the use of social networking
platforms. They found that belief in one’s competence to use social media predicted relatedness, leading to
overall satisfaction in engaging with others using these platforms. We extend these findings by
understanding undergraduate students’ contribution to online blogs (a form of social media) embedded
within an educational environment.
Kuem et al.’s (2020) study further extends findings of Wang and Li’s (2016) results. The authors
investigated how need satisfaction influences community engagement on Instagram. Their study involved
a sample of 152 individuals at a market research firm. The results of their Structural Equation Modelling
analysis suggested that both relatedness and knowledge self-efficacy (used interchangeably with
competence) predicted community engagement, and were mediated by prominence in the community.
However, autonomy had no direct effect on community engagement. Both Kuem et al. (2020), and Wang
and Li (2016) controlled for gender in their models a priori, but found no significant effect on need
satisfaction and engagement. We include gender in our hypothesis testing to test its effects in our model
without prior manipulation. Yoon and Rolland (2012) studied 209 Internet users participating in online
forums and communities, to understand facets of need satisfaction affecting online knowledge-sharing.
They found that perceived competence and relatedness influenced knowledge-sharing, while autonomy did
not. Familiarity in these environments had a more positive effect on need satisfaction as opposed to
anonymity.
Familiarity is developed in classrooms through shared social and educational histories and ongoing
experiences. In blended learning settings, the Internet forms a bridge between in-person and online
attendees. Butz and Stupinsky (2017) investigated the interconnective capacities of the Internet in a class
of 83 graduate students in their mixed methods study, and found students attending online and in-person
students were able to develop a productive social culture (relatedness) through interactions mediated by the
Internet. Zhao et al. (2011) conducted a study on 3475 high school students in China, and found that while
teacher autonomy support led to curiosity, parental support did not. However, relatedness (operationalised
in terms of peer influence) and competence (used interchangeably with Internet self-efficacy) fully
predicted both curiosity and enjoyment in online educational communities. These findings suggest
confidence in one’s own abilities, and a vibrant classroom culture may more decisively predict productive
collaborative online activity. Our literature review provided the foundation to infer competence and
relatedness play a central role in online community formation, and clarified the possible role of informal
Internet experiences and gender on community interactions. We used these insights to construct the
hypothesis guiding our Structural Equation Modelling analysis.
Method
Hypothesis development
Our literature review suggested that when adolescents and college age students feel they can function
effectively, contribute to online communities, and sense a strong social culture, they may be more likely to
develop collective efficacy, and function as a cohesive group to create new knowledge, working on projects
together. We used this inference to construct the hypothesis for our structural equation model. We also
investigated the role of gaming experience and gender on relationships between need satisfaction and
collective efficacy. These additions, focusing on students’ sociodemographic characteristics/experiences
were derived from studies outlining the existence of difference in technology use based on gender (Sun et
al., 2020) coupled with Internet experiences (Jung, 2020), suggesting these variables may act as covariates
in understanding distributed learning. We also relied on Petter et al.’s (2020) ideas about the strong
resemblance between collective behaviours in gaming experience, and the ingredients for successful
educational/professional communities, and the consideration of gender as a covariate in studies on self-
determined motivation and need satisfaction in online environments (Kuem et al., 2020; Ryan et al., 2006;
Wang & Li, 2016). We hypothesised:
Australasian Journal of Educational Technology, 2022, 38(6).
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H1: While controlling for the experience students have with massive multiplayer online and role-
playing games, and gender, the satisfaction of the needs for competence and relatedness correlate
with heightened collective efficacy.
Figure 1. Hypothesised model.
By theorising collective efficacy is an outcome of need satisfaction devoted towards competence and
relatedness, we suggest distributed self-determined motivation may manifest in technology-supported
collaborative college classrooms because of strong social cultures and feelings of perceived capacity to
thrive and perform.
Participants
In total, 636 undergraduate students took part in our study (30.6% male, 69.4% female). Students were
enrolled in in-person classes at the education department of a large midwestern university, and syllabi in
these classes involved use of the asynchronous discussion forum on the Canvas platform for posting and
interacting to discuss weekly topics, and create an online learning community that informed discussions in
class lectures through commenting and interaction on the class discussion forum. Participants were asked
to give their consent to partake in the study. Every participant was offered a chance to win a $50 Amazon
gift card, and received extra credit for participation. Research assistants entered the classrooms at the end
of the semester and took 15 minutes of instructional time to distribute surveys to students via a Qualtrics-
generated link.
Instruments
In this study, we consider responses to two scales to test the hypothesised relationships between collective
efficacy, relatedness, and competence in online community-driven classrooms. The first scale, which
measured students’ collective efficacy in technology use by looking at engagement, social presence, and
collaboration at both I and We levels, has been recently validated with undergraduate students in
technology-assisted classrooms (Glassman et al., 2021). In this study we retained the factor structure of the
collective efficacy scale. In total, all 636 students responded to this scale (Table 1).
Table 1
Online collective efficacy scale (Glassman et al., 2021)
Scale factor
“I” items
“We” items
Social
presence
CE1. I can create comments and posts in
online learning communities that others
connect with.
CE7. Members of our online learning
community can create posts and
comments that we can all connect with.
Australasian Journal of Educational Technology, 2022, 38(6).
81
CE2. I can create comments and posts that
others respond to.
CE8. Members of our online learning
community can be responsive to each
other.
CE3. I can comment and post in ways that
make other members of the online
learning community respond thoughtfully.
CE9. Members of our online learning
community can respond to each other
thoughtfully.
CE4. I can relate to the other members of
the online learning community by reading
their posts and comments.
CE10. Members of our online learning
community can recognise each other's
personalities through posting and
commenting.
CE5. I can sense there is an audience for
my thinking when I post and comment
online.
CE11. Members of our online learning
community are able to interact with each
other openly and freely.
CE6. I can make comments and posts that
other members find interesting.
CE12. Members of our online learning
community can be an attentive audience
for posts and comments.
Engagement
CE13. I can get myself to engage in the
online learning community when there are
other interesting things to do.
CE20. Members of our online learning
community are capable of developing a
common goal of knowledge-building.
CE14. I can influence the online learning
community to develop a common goal of
knowledge-building.
CE21. Any member of our online
learning community is capable of
making an important contribution to our
common goal.
CE15. I can become so interested in the
online learning community that I log on
just to see what others are posting.
CE22. Members of our online learning
community can respond thoughtfully to
the ideas others are posting.
.
CE16. I can immerse myself in this online
learning community without the fear of
being judged.
CE23. Members of our online learning
community can become interested in
each other's posts.
CE17. I can post and comment in ways
that make other members of the online
learning community respond in a timely
manner in genuine way.
CE24. Members of our online learning
community can respond to each other's
posts in a timely manner.
CE18. I can come back to the online
learning community even when I am
disappointed in it.
CE25. Members of our online learning
community can create a judgment-free
posting environment.
CE19. I can post and comment in ways
that make other members of the online
learning community respond in a timely
manner in genuine way.
CE26. Members of our online learning
community can continue to be
responsive to each other, even when
there are disagreements.
Collaboration
CE27. I can contribute to the online
learning community in whatever way
needed.
CE32. Members of our online learning
community can change each other's
thinking through posts.
CE28. I can move the thinking of the
online learning community forward
through my contributions.
CE33. Members of the online learning
community can be open to the ideas of
other members.
CE29. I can learn more from posting and
reading online than I can just by exploring
topics by myself.
CE34. Any member of our online
learning community is capable of
contributing to the group in whatever
way needed.
CE30. I can offer constructive feedback to
the ideas of other group members.
CE35. Members of our online learning
community can offer constructive
feedback on each other's posts.
CE31. I can work well with other
members of the online learning
community to solve a problem.
CE36. Members of our online learning
community can work well together in
order to solve a problem.
Australasian Journal of Educational Technology, 2022, 38(6).
82
The second instrument was Hur et al.’s (2013) democratic classroom scale, which incorporates elements of
self-determination theory into its items. The scale has been validated with undergraduate students more
than five years prior to this study, warranting refinement of the factor structure to better explain facets of
self-determination theory (autonomy, competence, relatedness), and explain trends in community formation
with current samples. In total, 334 students in the sample responded to this scale. Missing data was handled
using maximum likelihood estimation. The factor structure is provided in Table 2.
Table 2
Initial democratic classroom scale (Hur et al., 2013)
Scale factor
Items
Performance
orientation
DC2. The most important thing for me in taking this class is getting a good
grade.
DC15. The most important thing you get out of taking a class is the credits and
the grade.
External motivation
DC3 I am very concerned with what my professor thinks of me and how he will
judge me.
DC4. I am very concerned with what the teacher thinks of my classmates and
how he will judge them.
Cooperation
DC1. I feel like I can work well with others to achieve a goal.
DC5 I believe my own abilities and knowledge are important when I am solving
a problem.
DC6 I believe my ability and willingness to work with others are important
when solving a problem.
DC7. I believe the abilities and willingness of others to work together is
important when solving a problem.
Goals before trust
DC11. I think my classmates would betray me to get a better grade.
DC12. I would betray my classmates if it meant I could get a better grade.
DC13. My classmates would cheat for a better grade if they knew they would
not get caught.
DC14. I would cheat for a better grade if I knew I would not get caught.
Integrated activity
DC8. I tend to trust my peers when we work together on a project.
DC9. I believe others will do their best when I am working together with them
on a project.
DC10. I am willing and ready to depend on others when doing work required
for a course.
DC16. The most important things you get out of a class is knowledge.
DC17. The most important thing you get out of a class is a chance to work with
others.
Analysis
We first conducted exploratory factor analysis using principal axis factoring in SPSS, to understand factor
structure of the democratic classroom scale. Our data-driven approach did not make assumptions about
patterns in the data, and allowed assessment of dimensionality of the scale in the latest phase of data
collection, occurring more than 5 years after initial creation (and accompanied by drastic amplification in
online technologies) (Knekta et al., 2019). We then conducted confirmatory factor analysis in Mplus to
assess scale validity, and obtained factor solutions (Worthington & Whittaker, 2006). Since confirmatory
factor analysis was used as an evaluation of the measurement model before proceeding to analyse the
structural equation model, we did not use cross-validation procedures (Vodanovich et al., 2005) for
exploratory factor analysis or confirmatory factor analysis. In total, 167 of the 334 responses were analysed
for validation. The resultant factor structure mapped onto the three facets of need satisfaction examined by
self-determination theory: autonomy, competence, and relatedness.
We used two subscales from the democratic classroom scale to test our hypothesis for the relatedness and
competence subscales. These were used in a path model to examine relationships between collective
efficacy and need satisfaction. The model represented a multiple regression to understand if need
satisfaction directed towards relatedness and competence led to collective efficacy in technology use, while
controlling for gender and gaming experience with multiplayer and role-playing games as covariates. Our
Australasian Journal of Educational Technology, 2022, 38(6).
83
results elaborate our exploratory factor analysis and confirmatory factor analysis conducted using half the
responses, to refine the democratic classroom scale to focus on need satisfaction in online community
formation, and the path model we constructed using all 636 responses, to measure relationships between
collective efficacy and need satisfaction.
Results
Refinement of democratic classroom scale
We first refined the factor structure of the democratic classroom scale to map directly onto self-
determination theory, and for effective further use to gain insights into need satisfaction. The exploratory
factor analysis was conducted in SPSS using half the scale responses (167 out of 334). The Kaiser-Meyer-
Olkin test (0.742) and Bartlett’s test for sphericity (χ2(df) = 2306.14(136), p < 0.001) showed that chosen
items and sampling was adequate to conduct exploratory factor analysis. We analysed the 17-item scale
using a principal axis factoring method with varimax rotation. To correspond with the established tripartite
framework of need satisfaction offered by self-determination theory, we extracted a fixed number of factors
(3) in the exploratory factor analysis. The results of the exploratory factor analysis are presented below
(Table 3). We found items 4 and 16 showed inadequate factor loadings, leading to removal. The remaining
items showed strong correspondence with ideas related to autonomy, competence and relatedness, and
satisfaction of these needs in educational settings.
Table 3
Rotated factor matrix from exploratory factor analysis
Items
Competence
Autonomy
Relatedness
DC1
.495
-
-
DC2
-
.305
-
DC3
-
.312
-
DC4
-
-
-.382
DC5
.740
-
-
DC6
.925
-
-
DC7
.811
-
-
DC8
-
-
.777
DC9
-
-
.837
DC10
-
-
.738
DC11
-
.736
-
DC12
-
.771
-
DC13
-
.664
-
DC14
-
.665
-
DC15
-
.478
-
DC16
-
-
-
DC17
-
-
.474
Following the exploratory factor analysis, confirmatory factor analysis was conducted in Mplus, with the
remaining 15 items. We allowed for error covariances conforming with substantive theory based on
Sörbom’s (1989) recommendations to improve model fit. Additionally, Byrne (2013) suggested similar
sounding survey items may lead to correlated errors. We adjusted for these correlations upon examining
items. Two items showed poor loadings, leading to elimination. We then ran the model using the remaining
13 items. The resultant factor structure is provided below (Figure 2).
Australasian Journal of Educational Technology, 2022, 38(6).
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Figure 2. Measurement model for the restructured democratic classroom scale
This configuration produced a good model fit: χ2(df) = 116.83(59), p < 0.05, CFI = 0.95, TLI = 0.93,
RMSEA = 0.077, SRMR = 0.065. The McDonald’s omega was 0.738, suggesting the scale was reliable for
use with undergraduate populations in technology-supported classrooms. This process helped refine the
factor structure of the democratic classroom scale to map directly onto self-determination theory. Further
use of the scale within the self-determination theory framework warranted the use of items retained after
the confirmatory factor analysis. The updated factor structure of the scale is presented below (Table 4). It
was seen that items from the initial scale belonging to the cooperation factor, focusing on one’s perception
of their own skill, and that of others to succeed at tasks mapped onto the competence factor. Items in the
integrated activity factor in the old scale focusing on trusting in others mapped onto the relatedness factor,
which focuses on the social culture of an environment. Four items from the goals before trust factor, which
focus on attaining one’s goals before relying on others, and one item from the performance orientation
factor (focusing on the importance of individual grades and credit hours in educational environments)
mapped onto the autonomy factor in the new scale. This new structure mapped well onto self-determination
theory.
Australasian Journal of Educational Technology, 2022, 38(6).
85
Table 4
Updated factor structure of the democratic classroom scale
Scale factor
Items
Previous factor
Competence
DC1. I feel like I can work well with others to
achieve a goal.
Cooperation
DC5 I believe my own abilities and knowledge are
important when I am solving a problem.
Cooperation
DC6 I believe my ability and willingness to work
with others are important when solving a problem.
Cooperation
DC7. I believe the abilities and willingness of others
to work together is important when solving a
problem.
Cooperation
Relatedness
DC8. I tend to trust my peers when we work
together on a project.
Integrated activity
DC9. I believe others will do their best when I am
working together with them on a project.
Integrated activity
DC10. I am willing and ready to depend on others
when doing work required for a course.
Integrated activity
DC17. The most important thing you get out of a
class is a chance to work with others.
Integrated activity
Autonomy
DC11. I think my classmates would betray me to
get a better grade.
Goals before trust
DC12. I would betray my classmates if it meant I
could get a better grade.
Goals before trust
DC13. My classmates would cheat for a better grade
if they knew they would not get caught.
Goals before trust
DC14. I would cheat for a better grade if I knew I
would not get caught.
Goals before trust
DC15. The most important thing you get out of
taking a class is your credits and your grade.
Performance orientation
Path model
After factor analyses were conducted on the democratic classroom scale, we tested relationships between
relatedness, competence, and collective efficacy, controlling for gender and gaming experience, using a
multiple regression path model in Mplus. The items of the collective efficacy scale were converted into
factors, used as composite latent variables representing the original 6-factor structure. This approach was
taken to simplify the model, and to capture relationships between the specific subscales of the democratic
classroom scale, and the construct produced from uniting the six factors of collective efficacy (I/We social
presence, I/We engagement, I/We collaboration, and augmentation). The model terminated normally, and
produced good model fit: χ2 (df) - 266.85 (93), p < .001, CFI = 0.962, TLI = 0.951, RMSEA = 0.054, SRMR
= 0.058. The correlation matrix (Table 5) and model results (Figure 3) are shown below.
Australasian Journal of Educational Technology, 2022, 38(6).
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Table 5
Correlation matrix for path model
DC1
DC5
DC6
DC7
DC8
DC9
DC
10
DC
17
CEF1
CEF2
CEF3
CEF4
CEF5
CEF6
GEN
GEX
DC1
1
DC5
.358
1
DC6
.481
.661
1
DC7
.394
.594
.777
1
DC8
.243
.210
.249
.283
1
DC9
.231
.108
.137
.139
.738
1
DC
10
.141
.038
.179
.212
.603
.637
1
DC
17
.117
.073
.203
.219
.383
.375
.381
1
CEF1
.403
.371
.455
.431
.352
.242
.252
.246
1
CEF2
.352
.351
.412
.380
.423
.340
.300
.294
.763
1
CEF3
.212
.129
.211
.234
.405
.383
.363
.287
.665
.735
1
CEF4
.322
.257
.327
.358
.393
.330
.280
.275
.656
.762
.696
1
CEF5
.266
.213
.315
.347
.386
.368
.314
.333
.676
.713
.753
.764
1
CEF6
.332
.269
.391
.419
.452
.382
.340
.350
.62
.704
.662
.783
.783
1
GEN
.075
.149
.138
.220
.062
.045
.078
.122
.092
.007
-.035
.01
.017
.018
1
GEX
.009
.054
.107
.225
.058
.133
.050
.116
.187
.04
.095
.138
.146
.099
.481
1
Figure 3. Path model examining relationship between need satisfaction and CE for engagement with online
communities
The path model showed increase in need satisfaction for relatedness by one standard deviation, when
controlling for competence, gaming experience and gender produced increases in collective efficacy by
0.418 standard deviations. This relationship was statistically significant (β = 0.418, t = 8.06, p < 0.05). An
increase in need satisfaction for competence by one standard deviation, when controlling for relatedness,
gender and gaming experience produced an increase in collective efficacy by 0.347 standard deviations.
This relationship was also statistically significant (β = 0.347, t = 6.378, p < 0.05). An increase in gaming
experience by one standard deviation produced an increase in collective efficacy by 0.161 standard
deviations, when controlling for competence, relatedness, and gender. This relationship was statistically
significant (β = 0.161, SE = 2.473, p = 0.013). There were no significant differences seen in collective
efficacy based on participants’ gender.
To summarise, we saw need satisfaction targeting relatedness and competence spurred individual’s
perceived capacity to meet classroom goals through individual contributions and group work in dynamic
Australasian Journal of Educational Technology, 2022, 38(6).
87
online communities. Those with greater gaming experience with massive multiplayer online and role-
playing games were seen to show greater collective efficacy, suggesting tenets of group activity in larger
online systems may permeate formal educational environments. The informal online experiences that
undergraduate students may have to create action-oriented communities on gaming platforms, as discussed
in our literature review, may help foster accountability, cooperation, digital literacy, and openness to
learning among other skills (Petter et al., 2020). These are all possible ingredients of a cohesive online
learning community. The commonalities that Petter et al. (2020) describe may have explained the covariate
effect of gaming experiences on the relationships between need satisfaction and collective efficacy. Overall,
our results suggested that the creation of supportive social cultures (relatedness) and students’ competence
over educational material, fostered a cohesive learning community which displayed collective efficacy.
Discussion and limitations
This study showed need satisfaction targeted towards relatedness and competence produced increases in
collective efficacy for undergraduate students’ use of online blogs, Online collective efficacy comprises of
both I and We levels of social presence, engagement, and collaboration. Theoretical frameworks suggest
that satisfaction of all three needs (competence, relatedness, and autonomy) leads to self-determined
motivation in online settings (Miller & Prior, 2010). While traditional online learning/work environments
requiring students/workers to function as individual units show that need satisfaction at three levels leads
to adaptive technology use (Chen & Jang, 2010; Roca & Gagne, 2008), results from empirical studies
investigating distributed activity in such settings portray a different picture. By ascertaining relationships
between relatedness, competence and collective efficacy, our findings add another layer to existing
literature showing how relatedness and competence, rather than autonomy, predict knowledge-sharing
behaviours pivotal to online learning community development (Butz & Stupinsky, 2017; Kuem et al., 2020;
Wang & Li, 2016; Yoon & Rolland, 2012; Zhao et al., 2011).
We suggest satisfaction of two of the three needs contribute towards a distributed self-determined
motivation, which fuels ongoing interactions in collective classroom settings utilising new media platforms
like discussion forums. Our inquiry taps into the potential of social-cognitive approaches focusing on
constantly evolving community-level interactions. While Bandura (2000, 2009) ruminates over the
potential to better understand collective agency through creation of a nomenclature for social-cognitive
theory (the concept of collective efficacy), there has been little done to understand how contemporary
motivation theories map onto this concept. Our study adds to the few existing studies examining the role of
need satisfaction and motivation in both informal (online gaming, social media) (Kuem et al., 2020; Wang
& Li, 2016; Yoon & Rolland, 2012) and formal (learning, work) (Butz & Stupinsky, 2017; Zhao et al.,
2011) online collaboration. We aimed to fill this gap by empirically extending the tenets of self-
determination theory (Ryan & Deci, 2020) into an age of heightened digital interconnectivity. Our goal was
to understand relationships between online collective efficacy and need satisfaction in tool-mediated
classrooms, and whether these relationships are affected by experiences in informal online environments,
and by individual demographic characteristics shown to influence online activity, for example gender. Our
results suggest informal gaming experiences, that augment skills required for collective participation in
educational and professional settings (Petter et al., 2020), may act as a covariate to psychological
mechanisms of online community formation. Incorporating collaborative projects and avenues for peer-to-
peer critical discourse into classrooms using online technologies may help capitalise on the opportunity for
students to tap into forms of social capital they acquire in their everyday, informal activities . Engaging in
such activities can spur distributed self-determined motivation.
The higher proportion of female students in our classrooms may explain non-significant negative effects of
gender as a control variable on collective efficacy. A more balanced distribution of students may help
understand how gender identity can affect functioning in online educational environments. Achieving such
a distribution in naturalistic educational environment subject to constraints of educational institutions can
be difficult, and calls for further rounds of surveying. A second limitation is that scales and variables helping
understand social processes provide self-reports of observable community-building activity. Bandura
suggests collective understandings of motivation are more dynamic than individual-level analyses, subject
to vicissitudes of interacting groups and moving classroom contexts (Glassman et al., 2021). Self-report
data used to derive our insights provided a snapshot of these community-building processes. Direct
observations of social phenomena leading to strengthening of community ties may supplement results
produced by self-report approaches.
Australasian Journal of Educational Technology, 2022, 38(6).
88
Conclusion
Research in self-determination theory suggests satisfying the three psychological needs in formal and
informal learning leads individuals to be motivated in performing tasks (Ryan & Deci, 2020). However,
there has been little work done to understand how communities can thrive within this framework, learning
together as a company of actors. With the Internet expanding exponentially, ingredients for thriving in
informal online communities may intersect with skills for problem-solving and learning in technology-
supported classrooms. Our results on blog-driven undergraduate classes, suggest that to enable technology-
supported learning communities to thrive, individuals participating in them need to feel confident in their
skills (competence), and perceive a strong social culture comprising trust and belongingness (relatedness).
The students in our sample reported that such factors enabled them to perceive that they could create a
strong learning community with high collective efficacy in online interactions and discourse. The online
experiences that young adults and adolescents have may inform the nature of their participation in
distributed educational processes, with experiences in gaming environments towards community-building
influencing and augmenting capacity for online posting and discussions for educational purposes. This idea
is reflected in our study through the salient effect of gaming experience on relationships between need
satisfaction and collective efficacy in blog-driven classes. To tap into the potential of informal experiences
in augmenting distributed self-determined motivation for educational technology use, teachers need to craft
curricula capitalising on the traits of students’ online realities. Using project-based approaches allowing
increased collaborative interaction, and setting up forums of discussion (like blogs) may spur productive
collective conversations, pointing towards the need for student-centred approaches to instruction. Further
directions for research involve incorporating such informal aspects of online experience in a complex
information age into classroom curricula.
References
Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in
Psychological Science, 9(3), 75-78. https://doi.org/10.1111/1467-8721.00064
Bandura, A. (2009). Social cognitive theory of mass communication. In J. Bryant, & M. B. Oliver (Eds.),
Media effects (pp. 110-140). Routledge.
Bush, V. (1945). As we may think. The Atlantic Monthly, 176(1), 101–108.
Butz, N. T., & Stupnisky, R. H. (2017). Improving student relatedness through an online discussion
intervention: The application of self-determination theory in synchronous hybrid
programs. Computers & Education, 114, 117-138. https://doi.org/10.1016/j.compedu.2017.06.006
Byrne, B. M. (2013). Structural equation modeling with EQS: Basic concepts, applications, and
programming. Routledge.
Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination
theory. Computers in Human Behavior, 26(4), 741-752. https://doi.org/10.1016/j.chb.2010.01.011
Clarà, M., & Barberà, E. (2013). Learning online: Massive open online courses (MOOCs), connectivism,
and cultural psychology. Distance Education, 34(1), 129-136.
https://doi.org/10.1080/01587919.2013.770428
Coleman, J. (1994). Foundations of social theory. Belnap Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information
technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-
determination of behavior. Psychological Inquiry, 11(4), 227-268.
https://www.jstor.org/stable/1449618
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory:
A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary
Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry
framework: A retrospective. The Internet and Higher Education, 13(1-2), 5-9.
https://doi.org/10.1016/j.iheduc.2009.10.003
Glassman, M., Kuznetcova, I., Peri, J., & Kim, Y. (2021). Cohesion, collaboration and the struggle of
creating online learning communities: Development and validation of an online collective efficacy
scale. Computers and Education Open, 2, 1-12. https://doi.org/10.1016/j.caeo.2021.100031
Grollios, G., Giroux, H. A., Gounari, P., & Macedo, D. (2015). Paulo Freire and the curriculum.
Australasian Journal of Educational Technology, 2022, 38(6).
89
Routledge.
Hipp, J. R., Williams, S. A., & Boessen, A. (2018). Disagreement in assessing neighboring and collective
efficacy: The role of social distance. Socius, 4. https://doi.org/10.1177/2378023118769536
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary
Journal, 6(1), 1-55.
Hur, E. H., Glassman, M., & Kim, Y. (2013). Finding autonomy in activity: Development and validation
of a democratic classroom survey. Educational Assessment, Evaluation and Accountability, 25(4),
303-320. https://doi.org/10.1007/s11092-013-9173-y
Jung, C. W. (2020). Role of gamers’ communicative ecology on game community involvement and self-
identification of gamer. Computers in Human Behavior, 104, 106164.
https://doi.org/10.1016/j.chb.2019.106164
Kelty, C. (2008). Two bits: The cultural significance of free software. Duke University Press.
Kirschner, P., Strijbos, J. W., Kreijns, K., & Beers, P. J. (2004). Designing electronic collaborative
learning environments. Educational Technology Research and Development, 52(3), 47-66.
https://doi.org/10.1007/BF02504675
Knekta, E., Runyon, C., & Eddy, S. (2019). One size doesn’t fit all: Using factor analysis to gather
validity evidence when using surveys in your research. CBE—Life Sciences Education, 18(1), 1-19.
Kuem, J., Khansa, L., & Kim, S. S. (2020). Prominence and engagement: Different mechanisms
regulating continuance and contribution in online communities. Journal of Management Information
Systems, 37(1), 162-190. https://doi.org/10.1080/07421222.2019.1705510
Lei, C., & Chan, C. K. (2018). Developing metadiscourse through reflective assessment in knowledge
building environments. Computers & Education, 126, 153-169.
https://doi.org/10.1016/j.compedu.2018.07.006
Miller, L. M., & Prior, D. D. (2010). Online social networks and friending behaviour: A self-
determination theory perspective. Proceedings of the Australia and New Zealand Marketing Academy
Annual Conference, Christchurch, New Zealand, 1-9.
Morenoff, J. D., Sampson, R. J., & Raudenbush, S. W. (2001). Neighborhood inequality, collective
efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517-558.
https://doi.org/10.1111/j.1745-9125.2001.tb00932.x
Petter, S., Barber, C. S., & Barber, D. (2020). Gaming the system: The effects of social capital as a
resource for virtual team members. Information & Management, 57(6), 1-11.
https://doi.org/10.1016/j.im.2019.103239
Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A
self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604.
https://doi.org/10.1016/j.chb.2007.06.001
Rovai, A. P. (2002). Development of an instrument to measure classroom community. The Internet and
Higher Education, 5(3), 197-211. https://doi.org/10.1016/S1096-7516(02)00102-1
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory
perspective: Definitions, theory, practices, and future directions. Contemporary Educational
Psychology, 61, 1-11. https://doi.org/10.1016/j.cedpsych.2020.101860
Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-
determination theory approach. Motivation and Emotion, 30(4), 344-
360. https://doi.org/10.1007/s11031-006-9051-8
Sampson, R. J., Morenoff, J. D., & Earls, F. (1999). Beyond social capital: Spatial dynamics of collective
efficacy for children. American Sociological Review, 64(5), 633-
660. https://www.jstor.org/stable/2657367
Scardamalia, M. (2004). CSILE/Knowledge forum®. Education and Technology: An encyclopedia, 183,
1-13.
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation
models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological
Research Online, 8(2), 23-74.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of
Technology and Distance Learning, 2(1), 1-9.
https://jotamac.typepad.com/jotamacs_weblog/files/Connectivism.pdf
Sörbom, D. (1989). Model modification. Psychometrika, 54(3), 371-384.
Sun, B., Mao, H., & Yin, C. (2020). Male and female users’ differences in online technology community
Australasian Journal of Educational Technology, 2022, 38(6).
90
based on text mining. Frontiers in Psychology, 11, 806. https://doi.org/10.3389/fpsyg.2020.00806
Tilak, S., & Glassman, M. (2020). Alternative lifeworlds on the Internet: Habermas and democratic
distance education. Distance Education, 41(3), 326-
344. https://doi.org/10.1080/01587919.2020.1763782
Tilak, S., Glassman, M., Kuznetcova, I., & Pelfrey, G. L. (2022). Applications of cybernetics to
psychological theory: Historical and conceptual explorations. Theory & Psychology, 32(2), 298–
325. https://doi.org/10.1177/09593543211053804
Urdan, T., & Kaplan, A. (2020). The origins, evolution, and future directions of achievement goal
theory. Contemporary Educational Psychology, 61,
101862. https://doi.org/10.1016/j.cedpsych.2020.101862
Vodanovich, S. J., Wallace, J. C., & Kass, S. J. (2005). A confirmatory approach to the factor structure of
the boredom proneness scale: Evidence for a two-factor short form. Journal of Personality
Assessment, 85(3), 295-303. https://doi.org/10.1207/s15327752jpa8503_05
Wang, X., & Li, Y. (2016). Users' satisfaction with social network sites: A self-determination
perspective. Journal of Computer Information Systems, 56(1), 48-
54. https://doi.org/10.1080/08874417.2015.11645800
Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and
recommendations for best practices. The Counseling Psychologist, 34(6), 806-
838. https://doi.org/10.1177/0011000006288127
Yoon, C., & Rolland, E. (2012). Knowledge-sharing in virtual communities: familiarity, anonymity and
self-determination theory. Behaviour & Information Technology, 31(11), 1133-1143.
Zhao, L., Lu, Y., Wang, B., & Huang, W. (2011). What makes them happy and curious online? An
empirical study on high school students’ Internet use from a self-determination theory
perspective. Computers & Education, 56(2), 346-356. https://doi.org/10.1016/j.compedu.2010.08.006
Corresponding author: Shantanu Tilak, tilak.6@buckeyemail.osu.edu
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Please cite as: Tilak, S., Glassman, M., Peri, J., Xu, M., Kuznetcova, I., & Gao, L. (2022). Need
satisfaction and collective efficacy in undergraduate blog-driven classes: A structural equation
modelling approach. Australasian Journal of Educational Technology, 38(6), 75-90.
https://doi.org/10.14742/ajet.7963
