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Australasian Journal of Educational Technology, 2022, 38(5).
100
A model of factors influencing in-service teachers’ social
network prestige in online peer assessment
Ning Ma
School of Educational Technology, Faculty of Education, Beijing Normal University, People’s Republic
of China; Advanced Innovation Center for Future Education, Beijing Normal University, People’s
Republic of China
Lei Du
School of Educational Technology, Faculty of Education, Beijing Normal University, People’s Republic
of China; Shenzhen Longhua Songhe School, Shenzhen, Guangdong, People’s Republic of China
Yao Lu
School of Educational Technology, Faculty of Education, Beijing Normal University, People’s Republic
of China
The rise of teacher training in online interactive learning environments has contributed to
teachers’ professional development and brought new vitality to the informatisation of
education. Many researchers have reported that there is a participation gap in online
interactive learning environments. Research on the factors influencing this is very important.
Social network prestige, which measures the degree to which learners gain peer attention in
directed social networks, is one of the important metrics to characterise the participation gap.
In this study, we offered an online teacher training course, and 1438 in-service teachers from
primary and secondary schools attended. Among them, we selected 457 in-service teachers
who participated in the three peer assessment activities as the final participants. To analyse
the factors influencing learners’ social network prestige in online peer assessment, we first
conducted a partial least squares structural equation modelling analysis to construct a model
of factors influencing social network prestige. Then, we adopted several semi-structured
interviews to investigate learners' perspectives to provide an in-depth analysis of the factors
influencing social network prestige. The purpose of this study was to gain insight into the
participation gap in online interactions and make effective suggestions on how to improve
learning performance in online peer assessment.
Implications for practice or policy:
• Course designers could improve the design of the introduction to peer assessment to
motivate learners and enhance their acceptance of the activities.
• Course designers could reduce participation gap by assigning work from low-prestige
learners to high-prestige learners in a non-mandatory way later in the course.
Keywords: online peer assessment, participation gap, social network prestige, participation
behaviour, attitude towards participation, motivation to participate
Introduction
The rapid development of Internet technology has meant that teacher training is no longer limited to face-
to-face training. One important training mode is teacher training in an online interactive learning
environment, which promotes teacher professional development by significantly enhancing the training
effect and improving the quality of teachers (Ma et al., 2020). In an online interactive learning environment,
interactions between learners create complex social networks. Some learners in online learning
communities can easily attract the attention of other learners and thus gain more benefits, including learning
performance and learning emotions, during the process of interaction (Russo & Koesten, 2005). However,
some learners have difficulty attracting others’ attention, resulting in gaps in learning opportunities and
learning outcomes (Mehall, 2020).
The participation gap can describe the phenomenon mentioned above. It is a state of imbalance in the social
relations of learners in online learning, characterised by differences in participation opportunities and
interactions. For example, Vaquero and Cebrian (2013) have described the participation gap as high-
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performing students being more likely to socialise with each other, while low-performing students fail to
gain reciprocity in their interactions. The participation gap makes it possible for interactions in the same
learning environment to be valued at different levels for each learner, resulting in disparities in learning
opportunities and outcomes (Mehall, 2020).
The attention level of a participant in a social network is referred to as their social network prestige (Chen
& Huang, 2019). It is a measure of the interactive influence of an individual learner at the micro level and
an important factor in facilitating effective learning. Therefore, this study attempted to understand the
participation gap in online interactive learning by constructing a model of factors influencing social
network prestige, aiming to propose practical ways to reduce the participation gap.
Literature review
Teacher training in an online interactive environment
Teacher training in an online interactive environment plays a significant role in promoting teacher
professional development. On the one hand, teacher training in an online interactive environment breaks
through the constraints of time and space, providing learners with opportunities for independent learning
(Koukis & Jimoyiannis, 2019). Different types of training resources with different focuses can be
aggregated to maximise the use of learning resources (Boltz et al., 2021; Parsons et al., 2019). On the other
hand, an online interactive environment means that learners have more opportunities to communicate,
reducing the loneliness of online learning (Crane & Comley, 2021) and, at the same time, integrate
individual learning into the group knowledge construction of social networks (Laurillard, 2016).
When it comes to teacher training in online interactive learning environments, researchers have expressed
concerns about the participation gap. Although teacher-learners participating in online teacher training have
a high level of social interaction (Ma et al., 2022), pre-service teachers who participated in online peer
assessment exhibited different levels of participation in their behaviour (Vásquez-Colina et al., 2017).
Through a study of online peer-review logs and interview transcripts of in-service teachers engaged in
physical education, Sato and Haegele (2018) found that otherwise equivalent teachers had different levels
of gains following the interaction. Macia and Garcia (2016) confirmed that the participation gap in Internet-
based communities of learners has a significant impact on their emotional support and professional
development. Thus, more research focusing on the participation gap in online interactive teacher training
is warranted.
Social network prestige
In an online peer assessment network, the participation gap can be characterised in sociological terms and
related metrics, such as social network prestige, prominence (Yen et al., 2022) and the Mathew effect (Perc,
2014). Compared with other metrics, social network prestige exists in directed networks and describes the
participation gap on a micro level of social network analysis (Bond & Gaoue, 2020; Ruggiero, 2016).
Prestige reflects the characteristics of the number of responses a learner receives in a social network (Zou
et al., 2021). In empirical research, prestige could be observed through various network metrics such as
network density, in-degree/out-degree and centrality (Aerne, 2020; Barnett, et al., 2010).
Prestige bias is an effective mechanism for social learning; learners prefer to ask for advice from people
with high prestige because this is a guarantee of effective information in a new environment (Atkisson et
al., 2021; Brand et al., 2021). This means that some learners with high prestige have needed to make little
effort to gain the attention of the majority of learners while other learners cannot (Hâncean et al., 2021),
thus forming different levels of social interaction in the network. However, the quality and intensity of
social interactions affects learning performance (Kozuh et al., 2015). Learners with different levels of
prestige may meet inequality in learning. Ma et al. (2022) found that prestige has an impact on learners'
learning performance, learning behaviour and social network structure. Therefore, it is necessary to explore
what factors influence social network prestige to find ways to reduce the participation gap.
Considering the diverse definitions in the current literature and the characteristics of peer assessment, we
regard social network prestige in online peer assessment as the strength of a learner’s assignment that can
trigger peer commenting behaviour. In other words, the level of a learner’s prestige is positively related to
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the number of evaluations their assignments have received, and the specific manifestation in the network
is the number of directed links received. The analysis of social network prestige helps researchers to analyse
and discuss in depth the interaction characteristics and learning impact of social networks from the
perspective of individual nodes (Andrews, 2020; Chen & Huang, 2019). Exploring prestige and its related
indicators in online peer assessment helps to go beyond counting basic learning behaviours, to uncover the
mechanism by which the participation gap influences learning interactions and to suggest ways to reduce
the participation gap.
Factors influencing social network prestige
Participation behaviour
Previous research has attempted to uncover the potential factors that influence social network prestige. In
online interactive learning, the most intuitive influence on learners’ performance and social network
formation is participation behaviour. Therefore, participation behaviours in online interactive learning
activities, such as Internet-based discussions and peer assessments, have the potential to become a direct
factor influencing social network prestige. Machine learning has been used to identify different
participation behaviours and participation states from forum posts in order to examine the relationship
between learners’ social participation and their prestige in massive open online courses. A study applying
machine learning demonstrated that the level of learners’ prestige was affected by participating behaviours
(Zou et al., 2021). Using social network theory and game theory, Aerne (2020) analysed the causes of social
network prestige and concluded that social participation behaviours directly affected the participation gap.
Using regression analysis, Chen and Huang (2019) analysed data from discussion boards of online
undergraduate courses in the United States of America and found that learners with different levels of
prestige did not show differences in post length, post symbol use or post readability, but that there were
significant differences in the temporal characteristics of discussion behaviour, with the higher prestige
group posting earlier. Zingaro and Oztok (2012) constructed a statistical model based on a comprehensive
synthesis of the literature to predict the likelihood of a post receiving a response; their model was based on
six quantitative predictors: posting date, participant activity, reading ease, word count, post quality and
publisher identity characteristics. Their findings showed that content posted earlier, with higher quality,
was more likely to attract responses. This led to the hypothesis that two kinds of participation behaviours,
assignment uploading time and assignment quality, may influence learning in teachers’ online peer
activities.
Attitude towards participation
Owing to the generative nature of learning, learners need to put in ongoing mental efforts to achieve good
learning outcomes. A positive learning attitude towards peer assessment activities might motivate learners
to make a sustained effort (Wang et al., 2020). Because of this, many studies (e.g., Podsiad & Havard,
2020; Zou et al., 2017) have explored learners' attitudes towards participation in peer assessment activities.
Ng and Yu (2021) have suggested that attitude towards participation may have an impact on participation
behaviour, thereby indirectly affecting social network prestige. Learners who are more active in peer
assessment activities are more likely to have higher enthusiasm (Cheng et al., 2014), and are also likely to
show higher-quality participation behaviour (Saterbak, 2018). For example, high-prestige learners tend to
have a positive attitude towards participation and exhibit positive participation behaviours such as a
willingness to communicate (Li & Du, 2014). However, the positive attitude towards participation does not
always exist, and some learners may have a negative attitude towards peer assessment activities due to
various reasons, such as peer pressure (Panadero & Alqassab, 2019). In this case, a negative attitude towards
participation can easily lead to inappropriate participation behaviour, which further damages prestige (Zou
et al., 2021).
Based on the literature review above, we hypothesised that learners’ attitudes towards participation may
play a role in social network prestige by influencing participation behaviour.
Intrinsic and extrinsic motivation to participate
In addition to attitude towards participation, we also considered that motivation to participate may have a
possible impact on prestige. Motivation is considered as a key factor in enhancing learners’ engagement
and learning performance (Nguyen et al., 2020). In an online environment, motivation is an important factor
that may influence participation behaviour (Hoskins & van Hooff, 2005; Rabin et al., 2020). Learners with
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high motivation tend to be more actively involved in learning activities (Moore & Wang, 2021). Motivation
can be divided into intrinsic motivation and extrinsic motivation (Pinder, 2011). In online peer assessment
activities, intrinsic motivation means that learners find participation in online peer assessment activities fun
or enjoyable, and extrinsic motivation refers to learners participating in peer assessment activities for a
particular purpose, such as meeting course requirements, avoiding negative feedback (Tseng & Tsai, 2010).
Learners' orientations of motivation towards participation (intrinsic or extrinsic) have a significant impact
on learning outcomes (Peng & Fu, 2021). From the perspective of activity theory, Yu and Lee (2015) argued
that learners’ participation in peer assessment was driven by learners’ own motivation, which affected their
participation behaviour in activities, thus affecting their levels of interaction and causing a participation
gap. This suggests that learners’ motivation to participate may indirectly and positively influence learners’
prestige through participation behaviour.
Research questions
Learners’ participation in online interactive learning is multifaceted and related to many factors. Previous
studies on the factors influencing prestige have recognised the important role of participation behaviour
(including assignment uploading time and assignment quality) and also speculated about possible indirect
effects of motivation to participate and attitude towards participation on participation behaviour. However,
most previous studies were conducted with school students, and the context of research was mostly limited
to online forums or discussion boards; very few explored the factors that shape the social network prestige
of teacher-learners, who constitute an important and specific group of learners. In this study, we employed
partial least squares structural equation modelling (PLS-SEM) analysis to construct and evaluate the
structural relationship of factors influencing social network prestige in online peer assessment and
interviewed six participants to gain an in-depth understanding of the factors influencing social network
prestige.
Methodology
Model construction
Based on a literature review of social network research, we calculated prestige of each learner by using the
following formula (Knoke & Yang, 2008; Tsvetovat, 2011):
where n is the total number of nodes in the social network formed by teachers’ online peer assessment, and
j and i are the row and column values in the social network matrix respectively. With this formula, the
prestige value of each learner can be derived. The higher the calculated value, the higher level the prestige
of the learner. Learners with relatively high prestige values in the group are called high-prestige learners,
while those with low prestige values are called low-prestige learners.
Based on the literature and the characteristics of the variables, we selected participation behaviour as the
formative indicator, and attitude towards participation, intrinsic motivation to participate and extrinsic
motivation to participate as the reflective indicators. Among them, attitude towards participation refers to
learners’ acceptance of participation in online peer assessment activities, that is, whether they hold positive
or negative attitudes toward peer assessment activities. Motivation to participate refers to learners’
motivation to participate in online peer assessment activities and is divided into intrinsic and extrinsic
motivation to participate. Participation behaviour is learning behaviour that occurs when learners
participate in peer assessment; we used assignment uploading time and assignment quality to reflect
participation behaviour indicators in this study. The descriptions of each indicator are shown in Table 1.
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Table 1
Variables and their calculation methods
Latent variables
Observed variables
Abbreviation
Calculation method of the
variables in this study
Social network prestige
Social network prestige
Prestige
Prestige is calculated from the
formula.
Intrinsic motivation to
participate
Intrinsic motivation to
participate
InMot
Questions 1–7 (InMot1- InMot7) of
the Motivation of Online Peer
Assessment Questionnaire (Tseng
& Tsai, 2010).
Extrinsic motivation to
participate
Extrinsic motivation to
participate
ExMot
Questions 8–12 (ExMot1- ExMot5)
of the Motivation of Online Peer
Assessment Questionnaire (Tseng
& Tsai, 2010).
Attitude towards
participation
Attitude towards
participation
PreAtt
Questions 1–4 (PreAtt1- PreAtt4)
of the Attitude towards Online Peer
Assessment Questionnaire (Wen &
Tsai, 2006).
Participation behaviour
Assignment uploading
time
PostTime
The number of days between the
submission date and the deadline of
the assignment.
Assignment quality
PostQua
The average score of a learner’s
three assignments.
We constructed a theoretical model of factors influencing social network prestige in online peer assessment,
as shown in Figure 1. The research hypotheses in the model were:
• H1: Learners’ participation behaviour has a positive effect on their social network prestige.
• H2: Learners’ attitudes towards participation have a positive influence on their participation
behaviour.
• H3: Learners’ intrinsic motivation to participate has a positive effect on their participation
behaviour.
• H4: Learners’ extrinsic motivation to participate has a positive effect on their participation
behaviour.
Figure 1. Theoretical model of factors influencing social network prestige in online peer assessment
Participants
We designed and opened up a course titled Project-Based Learning Under Blended Concepts aimed at in-
service teachers of primary and secondary in various disciplines across China. A total of 1438 participants
attended through a voluntary online application process. They had clear learning goals and willingness to
learn. We then selected 457 teachers who participated in three peer assessment activities and had complete
data as participants. The basic information about the learners is shown in Table 2. Most of them were
general teachers in primary and secondary schools, and their ages were concentrated between 25 and 35.
Most of the participants had bachelor’s or master’s degrees. Furthermore, all participants had experience
with online learning and were able to use information technology skilfully.
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Table 2
Demographics
Variables
Value
Frequency
Percentage (%)
Gender
Male
52
11.38%
Female
405
88.62%
Age
20–25
70
15.32%
26–30
300
65.65%
31–35
82
17.94%
>35
5
1.09%
Identity in education
Elementary school teachers
234
51.20%
Secondary school teachers
191
41.79%
Middle management cadres and above
32
7.00%
Degree program
Less than bachelor
7
1.53%
Bachelor
277
60.61%
Master or doctoral
173
37.86%
Online learning
experiences
Inexperienced
0
.00%
Less than 1 year
16
3.50%
1–3 years
372
81.40%
Over 3 years
69
15.10%
Experimental procedure
We developed an online teacher training course titled Project-Based Learning Under Blended Concepts for
teacher professional development and offered it on an online interactive learning platform, the Learning
Cell System (http://lcell.cn/). It was based on exploring the integration of information technology and
subject teaching for teachers in the new era; designing, developing and implementing project-based
learning based on the blended learning concept to improve teachers’ professionalism and skills. The content
structure of the course drew on social constructivism theory and adult learning theory and aimed to address
the needs of learners on how to undertake project-based learning. Starting from practice, this course focused
on five aspects of project-based learning: selection of learning topic; scenario setting and learning plan;
information retrieval and application; determination of results; evaluation of work presentation. The whole
course lasted for 5 weeks. To ensure the effective implementation of peer assessment, the first 2 weeks of
the course were mainly dedicated to teaching basic knowledge, and the peer assessment was carried out
over the last 3 weeks. The experimental procedure of this study is shown in Figure 2. Figure 3 shows a
combination of some course screenshots.
Figure 2. Experimental procedure
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Figure 3. Screenshot of course interface
Peer assessment
We designed three peer assessment activities to address the learning objectives: PBL front-end analysis,
PBL outcome and PBL evaluation scheme; the design of each peer assessment activity is shown in Figure
4.
Figure 4. Design of peer assessment activities
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In this course, learners could first study the learning materials in the form of video and text in the platform,
complete the corresponding assignments and submit them according to the requirements (as shown in
Figure 5). Then, in the peer assessment, they could select other learners' work from the assignment display,
score it according to the evaluation scale and give comments (as shown in Figure 6). Each learner was free
to choose the work they wanted to evaluate, and all participants were advised in the course guide to evaluate
approximately 10 assignments. The evaluated learner could view the learning feedback, including scores
and comments given by other learners.
Figure 5. Interface for submitting assignments
Figure 6. Interface for assignment display
Instruments
PLS-SEM
PLS-SEM enables the modelling and estimation of complex causal models. Compared with the general
structural equation model, PLS-SEM is more suitable for dealing with non-normally distributed data while
allowing the measures to be either formative or reflective indicators, which facilitates the acquisition of
more explanatory results. Among them, formative indicators means that all question items are one-way
directional indicators and deleting a certain item will not exert a helping influence on the indicators;
reflective indicators mean that individual items constitute indicators,and deleting an item will change the
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definition of the indicator. In addition, PLS-SEM can effectively address the covariance among the
observed variables, eliminating the interference in the regression and giving the model better robustness
(Manfrin et al., 2019). Therefore, PLS-SEM is highly predictive and is an empirical research method
suitable for theoretical and causal model verification. Based on the fact that this study used data from a
sample of 457 learners, some of the indicators did not conform to a normal distribution; these indicators
consisted of three reflective indicators (intrinsic motivation to participate, extrinsic motivation to participate
and attitude towards participation) and a formative indicator (participation behaviour). PLS-SEM was used
to investigate the causes of learners’ prestige in online peer assessment.
Motivation of Online Peer Assessment Questionnaire
In order to assess learners’ motivation to participate in online peer assessment, the Motivation of Online
Peer Assessment Questionnaire, a 5-point Likert scale that divides learners’ motivation into intrinsic
motivation (seven items) and extrinsic motivation (five items) developed by Tseng and Tsai (2010), was
used prior to the three peer assessment activities. The Cronbach’s alpha coefficient for the items was .78,
indicating a high degree of reliability.
Attitude towards Online Peer Assessment Questionnaire
In order to assess learners’ attitudes towards participation, the Attitude towards Online Peer Assessment
Questionnaire, developed by Wen and Tsai (2006), was used prior to the three peer assessment activities.
The questionnaire consists of five items that ask learners about their acceptance of online peer assessment
activities in terms of technology, fairness and interactivity. An average attitude score of more than 3.5 was
considered to represent a high degree of satisfaction with the course. The Cronbach’s alpha coefficient for
the items was .86, indicating a high degree of reliability.
Model measurement
All latent and measured variables passed the single dimensional tests. Confirmatory factor analysis allowed
those items with factor loadings greater than .05 to be retained; in this study, confirmatory factor analysis
was passed. Then, using the SmartPLS version 3.0 software, PLS-SEM was used to model the factors
influencing learners’ social network prestige in online peer assessment. The statistical significance of the
PLS-SEM results was calculated by combining the bootstrapping method with the estimated nonparametric
confidence interval set to the corrective acceleration (BCa) bootstrap, and the subsample size drawn was
2,000. All indicators were less than .001; thus, the indicators were retained, and the model was derived.
Measurement model assessment
Reliability and convergent validity test
PLS-SEM requires testing the reliability and validity of the constructed model by verifying Cronbach’s
alpha (α), composite reliability (CR) and average variance extracted (AVE). As shown in Table 3, α and
CR of all the reflective indicators were greater than .7, indicating good reliability of the model. The AVE
values were greater than .5, indicating good convergent validity of the measurement model (Thurasamy et
al., 2016; Urbach & Ahlemann, 2010).
Table 3
α, CR and AVE
Latent variables
α
CR
AVE
Intrinsic motivation to participate
.877
.904
.575
Extrinsic motivation to participate
.731
.731
.515
Attitude towards participation
.826
.877
.588
Social network prestige
1
1
1
Discriminant validity test
The Fornell-Larcker method was employed to calculate the correlation coefficients of the model, which
involves constructing a matrix of correlation coefficients among the latent variables. The diagonal of the
matrix is the square root value of the AVE of the latent variables, and the values below the diagonal are the
correlation coefficients among the latent variables respectively (Manfrin et al., 2019). As shown in Table
4, the correlation coefficients of all latent variables were smaller than the AVEs; thus, the discriminant
validity was appropriate (Barrett et al., 2021; Saeed & Al-Emran, 2018).
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Table 4
Fornell-Larcker discriminant validity test
Intrinsic
motivation to
participate
Extrinsic
motivation to
participate
Attitude
towards
participation
Participation
behaviour
Prestige
Intrinsic
motivation to
participate
.758
Extrinsic
motivation to
participate
.465
.704
Attitude towards
participation
.433
.475
.767
Participation
behaviour
.457
.614
.484
.855
Prestige
.412
.616
.434
.882
1
Recent studies (e.g., Alshurideh et al., 2020; Saeed & Al-Emran, 2018) have recommended the heterotrait-
monotrait (HTMT) criterion instead of the Fornell–Larcker traditional metric for correlation coefficient
tests. In this study, for the exact correlation coefficient test, we obtained the inferred HTMT results at the
95% confidence level by performing a complete bootstrapping procedure for all samples. The HTMT values
for all latent variables in the model were less than 1, which was acceptable (Barrett et al., 2021). In addition,
we measured the factor loadings and cross loadings of the model and found that all reflective indicators
were > .6, indicating that the validity of the model was good.
Predictive ability test
The coefficient of determination (R2) is a measure of a model’s in-sample predictive power, while the
Stone-Geisser’s Q2 is used to determine a model’s out-of-sample predictive relevance. As shown in Table
5, R2 of social network prestige was .778, indicating a high level of predictive ability for the social network
prestige model, and the R2 of participation behaviour was .440, indicating a moderate level of predictive
ability. The Stone-Geisser’s Q2 of social network prestige calculated by the Blindfolding algorithm was
.774, which indicated a high level of predictive accuracy for the social network prestige model, and
participation behaviour had a Q2 of .414, indicating a moderate level of predictive accuracy (Hair et al.,
2019).
Table 5
Predictive ability test of the model
R2
Q2
Social network prestige
.778
.774
Participation behaviour
.440
.414
Stability test
To test the stability of the model, we calculated the variance inflation factors (VIF) of the reflective and
formative indicators. The results are shown in Table 6. The values of all indicators were less than the critical
value of 5, indicating that there was no multicollinearity in the model; and therefore, the model results had
high stability (Thurasamy et al., 2016).
Table 6
Multicollinearity test for reflective and formative indicators
InMot1
InMot2
InMot3
InMot4
InMot5
InMot6
InMot7
VIF
2.004
1.878
1.786
2.019
1.843
1.946
2.521
ExMot1
ExMot2
ExMot3
ExMot4
PreAtt1
PreAtt2
PreAtt3
VIF
1.192
1.081
1.088
1.225
1.856
2.915
1.289
PreAtt4
PreAtt5
Prestige
AveScore
PostTime
VIF
2.095
1.836
1
1.286
1.218
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PLS-SEM model assessment
The statistics of the path coefficients for each latent variable are shown in Table 7; these coefficients
represented the relational effects within the model. The path coefficient of participation behaviour → social
network prestige showed a positive effect on a significant level with a high degree of influence (β = .882,
p < .001), confirming H1. The path coefficient of attitude towards participation → participation behaviour
had a significant level but a low degree of influence (β = .204, p < .001), verifying H2. The path coefficient
of intrinsic motivation to participate → participation behaviour had a moderate and significant effect (β =
.442, p < .001); therefore, H3 was accepted. The path coefficient of extrinsic motivation to participate →
participation behaviour had a low but statistically significant effect (β = .163, p < .01); therefore, H4 was
accepted.
Table 7
Descriptive statistics and t-test results of path coefficients
Path
β
M
SD
t
p
Participation behaviour →
social network prestige
.882
.882
.009
79.096
.000**
Attitude towards participation →
participation behaviour
.204
.204
.038
5.326
.000**
Intrinsic motivation to participate →
participation behaviour
.442
.443
.037
11.974
.000**
Extrinsic motivation to participate →
participation behaviour
.163
.164
.037
4.351
.002*
Note: *p < .05, **p < .001
As shown in Table 8, the external weights statistics of the formative indicators revealed that both
assignment quality and assignment uploading time showed significant levels of influence on participation
behaviour.
Table 8
Statistics of external weights of formative indicators
Path
β
M
SD
t
p
AveScore → participation behaviour
.484
.484
.013
35.907
.000**
PostTime → participation behaviour
.676
.676
.019
35.866
.000**
Note: *p < .05, **p < .001
To determine the type of mediating role of intrinsic motivation to participate, extrinsic motivation to
participate and attitude towards participation, we analysed the indirect effects of each indicator; the results
are shown in Table 9, showing that all indirect effects are significant. Combined with the path
coefficient statistics, intrinsic motivation to participate, extrinsic motivation to participate and attitude
towards participation had a partially mediating role on social network prestige via participation behaviour
(Thurasamy et al., 2016).
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Table 9
Results of indirect effect analysis
β
M
SD
t
p
Intrinsic motivation to participate →
participation behaviour → social network
prestige
.18
.18
.034
5.291
.000**
Extrinsic motivation to participate →
participation behaviour → social network
prestige
.144
.145
.033
4.348
.002*
Attitude towards participation →
participation behaviour → social network
prestige
.39
.391
.033
11.781
.000**
Note: *p < .05, **p < .001
Figure 7. The model of factors influencing learners’ social network prestige in online peer assessment
The final model is shown in Figure 7. Participation behaviour had a direct positive effect on learners’
prestige in online peer assessment; intrinsic motivation to participate, extrinsic motivation to participate
and attitude towards participation on social network prestige were partially mediated by participation
behaviour. In addition, extrinsic motivation to participate had a small effect on participation behaviour of
.163, while intrinsic motivation to participate and attitude towards participation had a moderate influence
on participation behaviour, with weights of .442 and .205 respectively. Participation behaviour had a
dominant influence on social network prestige, with a weight of .882. The weight coefficients of the two
indicators, assignment quality and assignment uploading time, were .484 and .676 respectively, indicating
that among the participation behaviours, assignment uploading time had the greatest influence on social
network prestige.
Results of interviews
To further understand the model and explore the learners’ perceptions, we conducted semi-structured
interviews through the Internet. This section describes the perceptions of the six participants who were
interviewed after the end of the course. In terms of gender, two of the six participants were male and four
were female; in terms of education, they all had a bachelor's degree or higher: four bachelor’s degrees and
two master’s degrees.
After explaining the meaning of the participation gap and prestige to the interviewees, we mainly asked the
following questions relevant to this study:
Australasian Journal of Educational Technology, 2022, 38(5).
112
(1) Do you think there is a participation gap in online peer assessment? That is, some learners can
easily get the attention of others with less effort, while some learners cannot. Do you think
participation gaps affect learning outcomes?
(2) Could you please describe your general behaviour when participating in peer assessment?
(3) What is your motivation to participate in online peer assessment?
(4) How do you feel about peer assessment activities?
In summary, first of all, all the interviewees expressed their concern about the participation gap, believing
that such unfair participation would affect their learning enthusiasm and learning outcomes.
With regard to participation behaviour, the data from the interviews further explained the importance of
participation behaviours to the model, such as assignment uploading time and assignment quality. All
interviewees thought they would pay attention to learners who published their work early and were more
likely to interact with them:
The assignments on the first page of the display area are easier to see.
Before writing homework, I will refer to the work that others have submitted.
Most interviewees indicated that they tended to interact with peers who wrote high-quality assignments:
I want to comment on assignments that look good, and I think it will lead to more reflection.
It's easier to interact with well-formed assignments.
Some homework is written in a mess and I don't know how to score them according to the
scale.
Regarding motivation to participate, some interviewees mentioned that:
I hope to get a certificate at the end of the course, so I will write high-quality homework
carefully and strive to make more people willing to score me.
I care what people think of me, so I want my homework to get a high score and be discussed
by more peers.
I want to understand how project-based learning unfolds and improves teachers’ skills, so I
will hand in my homework earlier, which I think will allow them to appear in the display
earlier and get suggestions from teachers and classmates.
This confirms that motivation to participate contributes to the emergence of high-prestige learners by
influencing participation behaviour. However, some interviewees also mentioned that:
I had hoped to master the method of project-based learning, but then work commitments put
the latter two assignments on hold and the quality of the assessments was not so high.
Therefore, the inconsistency of intrinsic motivation may be responsible for its lower weight in the model
than extrinsic motivation.
Regarding attitude towards participation, most of the interviewees mentioned that if they thought peer
assessment would be helpful to them, they would be more willing to participate in it:
I think that participating in peer assessment activities helps me understand the course content
better, so I am increasingly participating in peer assessment activities and find myself
receiving more and more responses.
Conversely, one of the interviewees who felt that participating in peer assessment activities is bit of waste
of time then exhibited negative participation behaviours, which in turn led to low prestige. This confirms
that attitude towards participation can affect prestige by influencing participation behaviour.
Australasian Journal of Educational Technology, 2022, 38(5).
113
Discussion and conclusions
Discussion of the model
Based on previous literature, we examined the factors influencing the level of learners’ prestige through a
PLS-SEM analysis of 457 learners who participated in online peer assessment. The results of the model
explicitly demonstrated the role of participation behaviours, attitude towards participation and motivation
on social network prestige and the interrelationship among each factor. This approach provided insight into
the participation gap in online interactive teacher training. Three main findings were drawn as follows:
• First, participation behaviour had the greatest positive influence on prestige. The results of the
PLS-SEM showed that the influence of participation behaviour on social network prestige was
dominant with a high weight of .882, which was consistent with Zou et al. (2021). Meanwhile, the
data further revealed that the weight of the two indicators of participation behaviour, assignment
quality and assignment uploading time, were .484 (t = 35.907, p = .00 < .001) and .676 (t = 35.866,
p = .00 < .001) respectively, indicating that among the participation behaviours, assignment
uploading time had a greater impact on social network prestige.
• Second, the weight of the influence of attitude towards participation on participation behaviour
was .204 (t=5.326, p=.00<.001), which had a moderate influence. The indirect effect of attitude
towards participation on prestige was .39 (t=11.781, p=.00<.001). Therefore, we believed that the
effect of attitude towards participation on prestige was partially mediated by participation
behaviour.
• Finally, as for the extrinsic and intrinsic motivation on participation behaviour, their effects on
participation behaviour were .163 (t=4.351, p=.002<.05) and .442 (t=11.974, p=.00<.001), and
their indirect effects on prestige were .144 (t=4.348, p=.002<.05) and .18 (t=5.291, p=.00<.01)
respectively. This implied that the effect of motivation to participate on social network prestige
was partially mediated by participation behaviour.
In summary, participation behaviour had the greatest weight of positive influence on prestige, and the
influences of attitude towards participation and motivation to participate on social network prestige were
partially mediated by participation behaviour. This indicated that attitude towards participation,
participation behaviour and motivation to participate were effective indicators for discovering high-prestige
learners. Those who were more positive towards peer assessment activities, submitted assignments earlier
and with higher assignment quality, and they also tended to gain high prestige in peer assessment.
Implications for research
Some studies have identified the potential influence of motivation, attitude towards participation and
learning behaviours on the participation gap. Building on these findings, we performed a PLS-SEM analysis
and semi-structured interviews.
First, participation behaviour had a significant positive effect on prestige. The interviews revealed that the
assignment quality made a significant contribution to prestige. High-quality assignments that received more
peer recognition generally followed a better-structured writing paradigm, and they were therefore more
likely to trigger evaluation behaviours from learners. This was consistent with Liu et al. (2018), who found
assignment quality was an important factor in the formation of high-prestige learners. Among the
participation behaviours, assignment uploading time contributed the most to prestige. We found that the
earlier the learners posted their assignments, the more likely they were to gain high prestige in peer
assessment activities. This validated the previous findings, using regression analysis (Chen & Huang, 2019;
Zingaro & Oztok, 2012), that assignment uploading time had a high predictive power on social network
prestige. The interviews also confirmed that uploading and presenting the assignments earlier in peer
assessment tended to obtain more views. Accordingly, they were more likely to be evaluated. This also
confirmed Koszalka et al.’s (2021) finding that the earlier the interaction begins, the more likely it is to
provoke longer and richer analysis and reflection.
Second, the measure selected for attitude towards participation was learners’ acceptance of peer assessment
activities. The recognition of peer assessment was an important prerequisite for learners to be able to
Australasian Journal of Educational Technology, 2022, 38(5).
114
complete peer assessments seriously (Liu & Li, 2014). Learners also indicated that they would develop a
deeper understanding of the activity and act accordingly if they recognised its value. Against this
background, these learners understood better how to achieve positive interaction.
Finally, learners’ motivation to learn drove them to be more willing to invest their energy and effort in
activities and affected learning behaviour during the process of participation (Simonova et al., 2021; Yu &
Lee, 2015). It should be noted that the participants in this study were teachers. According to adult learning
theory (McCray, 2016), teacher-learners, as adult-learners with heavy workloads as well as insufficient
time and energy (Zhao & Song, 2021), are more result-oriented in learning and adequate motivation
contributes to their better learning behaviours. The interview demonstrated that both extrinsic motivation
to learn and intrinsic motivation contributed to high-prestige learners by influencing participation
behaviours. Extrinsic motivations encouraged learners to emphasise the quality of their work while intrinsic
motivations allowed them to engage in peer assessment spontaneously and consciously and submit their
assignments earlier.
However, unlike Tseng et al.'s (2010) finding, the weight of the influence of extrinsic motivation was
relatively low, probably because extrinsic motivation did not awaken learners' enthusiasm for learning in
the long term and would fade in the absence of intervention to promote and reinforce it, narrowing the
influence on participation behaviours in peer assessment.
Implications for education and practice
In the design of the introduction to peer assessment activity, since motivation to participate and attitude
towards participation have significant positive effects on learners’ social network prestige and indirectly
affect their participation behaviour, the successful implementation of learning activities relies on learners’
expectations and understanding of the activities (Koszalka et al., 2021). Therefore, course designers can
improve the design of the introduction to peer assessment activities, as a way of motivating learners and
enhancing their understanding of peer assessment, thereby narrowing the participation gap. For example,
the introduction should explain the necessity and meaning of peer assessment as well as the proper use of
the assessment scale, so that the learners understand the specific requirements for assessment and know
how to evaluate the performance of their peers and themselves.
In the selection rules for the review of learners’ work, it is possible to consider adding a mandatory
assignment selection method, or to combine the mode of learners’ arbitrary choice of assignment and the
mode of the system to automatically push assignments. In the current online peer assessment, there are
mainly two selection rules for the review of learners’ work: one is that learners choose the work to review
according to their own preferences, and the other is assignment assigned by the system (Anaya et al., 2019).
Since participation behaviour positively and significantly affects learners’ social network prestige,
combined with directional research in social network characteristics, it can be inferred that learners who
submit assignments late and with poor quality have difficulty getting more feedback to help them reflect,
especially from a high-prestige group. Therefore, we propose a combination of the above two approaches,
allowing learners to freely choose the work to be assessed at the beginning of the peer assessment, and
pushing the work of low-prestige learners to high-prestige learners in a non-mandatory way later in the peer
assessment process. In this way, low prestige learners would be provided with additional opportunities to
gain interaction, and the damage caused by the participation gap may be reduced.
Limitations and recommendations
First, studies have extensively investigated how to improve the frequency of learners' online interactions
through the design of tools and strategies, but research on the participation gap of learners involved in the
interactions is relatively weak. Future researchers are advised to devote more attention to the participation
gap. Second, we conducted a 5-week online teacher training course and constructed a model of the factors
influencing in-service teachers' social network prestige. Future studies may consider extending the course
duration to explore how the model changes under the influence of time. Finally, in order to improve the
precision, a more typical online peer assessment interactive paradigm was selected as the background of
the research. Subsequent studies could consider expanding the selection scope of interactive activities on
the basis of this study.
Australasian Journal of Educational Technology, 2022, 38(5).
115
Acknowledgements
This research was funded by the Research on Time-Emotion-Cognition Analysis Model and Automatic
Feedback Mechanism of Online Asynchronous Interaction project (No. 62077007), supported by the
National Natural Science Foundation of China.
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Corresponding author: Ning Ma, horsening@bnu.edu.cn
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Please cite as: Ma, N., Du, L., & Lu, Y. (2022). A model of factors influencing in-service teachers’ social
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