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Journal of Open, Flexible and Distance Learning, 26(2)

The Role of Nonverbal Communication in Asynchronous
Talk Channels

Josiah Koh, Open Polytechnic of New Zealand
Tara Hulbert, Open Polytechnic of New Zealand

Abstract

With the increased adoption of online learning (even greater as a result of the COVID-19
pandemic), online asynchronous discussions have become a mainstay of many online
learning platforms. As teachers struggle to communicate and connect with students due to
the forced transition online, we can better appreciate the differences between traditional
nonverbal communication in a face-to-face environment and that of online nonverbal
communication. Because digital literacy underpins the whole online learning experience, and
because nonverbal communication (NVC) cues such as body language and paralanguage are
not visible in asynchronous text-based online learning, this paper presents the relationship (if
any) between electronic nonverbal communication (eNVC) and teaching/social presences
and digital literacy, as well as its role in student motivation and engagement. A correlational
study was conducted using surveys to gather data from 88 Level 5 Business Area students.
The data was analysed using a Pearson’s correlation analysis. The study has found that there
is a correlation between eNVC and the social/teaching presence and digital literacy in the
asynchronous online discussions, and that eNVC is related to teaching and social presences,
but not to digital literacy.

Keywords: online learning; electronic nonverbal communication; eNVC; asynchronous talk
channels; online communication

Introduction
Since the start of the COVID-19 pandemic, online learning has become even more critical for
delivering education (Lederman, 2018; Khalil et al., 2020). This is reflected both in the
burgeoning size of the market (Technavio, 2021), and student preference for online delivery
remaining high (Bashir et al., 2021). Asynchronous online delivery still dominates online
learning as the main means of delivery (Schaffhauser, 2017), and talk channels are a key feature
of such systems. These are often designed as avenues for students to conduct meaningful
discourse with their teachers and fellow classmates, to demonstrate their grasp of relevant topics,
and to share their personal experience and reflections. Thus, they are often described as the
“heart of the virtual classroom”.

However, the sudden shift to online learning has also meant that some teachers struggle to
communicate and connect with students (Carrillo & Flores, 2020; Moorhouse, 2020). This is
partly because of the differences between traditional nonverbal communication in a face-to-face
environment and that of online nonverbal communication (Khalil et al., 2020).

Koh, J., Hulbert, T.

Background
The relationship between nonverbal communication cues and learning has been well documented
(Schneider et al., 2022; Wahyuni, 2018). Hence, it is not surprising that positive nonverbal
communication cues are associated with improved academic performance, student engagement,
and motivation (Schneider et al., 2022; Wahyuni, 2018). However, nonverbal communication in
the online learning environment is traditionally thought to happen insufficiently often in online
learning to be a factor of consideration (McBrien et al., 2009).

Because online activities continue to integrate with every aspect of life, Al Tawil (2019)
recognised that nonverbal communication cues exist significantly in online asynchronous
learning environments. A follow-up pilot study in 2021 (Koh, 2021) has indicated that these
nonverbal communication cues not only exist online, but there is significant correlation between
the teacher and social presences, and student engagement and motivation.

Given the recent developments, we ask the following research questions.

1. What is the correlation between nonverbal communication in the asynchronous online
talk channels and the presences (teacher/social/digital) of online learning?

2. What is the correlation of nonverbal communication in the asynchronous online talk
channels and student motivation and engagement?

Literature review
Given that nonverbal communication in online learning environments has recently been noted to
play a significant role in online learning outcomes, let’s look at an expanded view of the
literature on electronic nonverbal communication, pedagogical theories of learning in
technological environments, and discussions about the technology and asynchronous
discussion/talk channels.

Electronic nonverbal communication (eNVC)
Al Tawil (2019) coined the term “eNVC” for nonverbal online communication. She argues that
because it is distinctly different from face-to-face nonverbal communication (due to the absence
of body language and paralanguage), the distinction between the terms should be made clear.
eNVC can be viewed in the context of being text based and non-text based. Text-based eNVC is
based on the actual words and text used in the communication. This includes word choice,
sentence structure, and phrasing. Emoticons and emojis are also considered to be text-based
eNVC because they convey an emotion or feeling (Gajadhar & Green, 2005).

Non-text-based eNVC is viewed as being any information that is communicated outside that of
the words used. This includes the profile picture, the font choice and style, and the perceived
effort and tone of the communication. This is because additional meaning is implied and
assigned to the communication to psychologically paint a more complete picture in the reader’s
mind (Al Tawil, 2019). These eNVC cues are presented in four main aspects: tone, style, effort,
and timeliness (T.E.S.T).

Tone is the perceived manner of online communication. The interpretation of tone goes beyond
the choice and phrasing of words—it includes multiple layers of cultural and societal context
(Sheerman-Chase et al., 2011), in which words have different connotations and so communicate
different messages in context.

Journal of Open, Flexible and Distance Learning, 26(2)

Style refers to the stylistic choices of how discussions are presented. For instance, a sentence in
uppercase seemingly “shouts” at the reader and communicates aggression. Style also extends to
the length of responses and choice of font.

The amount of effort that students perceive their teachers to have invested in the communication
also matters. Long, standardised replies to a query are usually perceived as being inauthentic.
Conversely, single word responses communicate disinterest and a lack of genuineness on the part
of the teacher to engage with students. Genuineness looks at how “real” the teacher is perceived
to be. The level of genuineness perceived by students also communicates a picture of the teacher,
and communicates what they are trying to say with their words (Al Tawil, 2019).

Timeliness, or chronomatics, is another important factor in how eNVC cues are communicated. A
slow response, or no response, is often taken very negatively (e.g., a “read” receipt on a
WhatsApp message). Interestingly, an immediate response also communicates a lack of
authenticity or that the answers are likely to be from a databank of templated answers (Koh,
2021).

In traditional face-to-face teaching, nonverbal communication has been shown to have a
significant impact on the success of teaching (Bambaeeroo & Shokrpour, 2017), but in an online
e-learning environment, these nonverbal elements have even greater importance and impact on
the way students learn, adopt, and (by extension) predict student success (Al Tawil, 2019; Koh,
2021)

Community of inquiry (CoI) model
In the context of online learning, the CoI model is a framework designed specifically to better
understand learning through an online medium (Garrison, 2017). This constructivist framework
captures the dimensions of higher education in computer-based online education succinctly,
defining them as social, cognitive, and teacher presences. Each presence, and the interactions
between them, constitutes an aspect of the online educational experience (Fig. 1). The social
presence looks at the ability of students/participants to express a projection of themselves which
is “most authentic”. The cognitive presence looks at the ability to derive meaning and purpose.
The teaching presence looks at how the design and facilitation of the processes help students to
achieve a certain learning outcome(s).

Figure 1 Community of Inquiry model

Note: This model is adapted from Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based
environment: Computer conferencing in higher education model. The Internet and Higher Education, 2(2–3), 87–105.

Koh, J., Hulbert, T.

Figure 2 Technology acceptance model

Each dimension is complemented with a 4-phase model of practical inquiry; that is, triggering an
event, exploration, integration, and resolution. In most computer-based online education
environments, the primary medium of communication would be via a text-based, asynchronous
learning management system (LMS). In such a learning environment, it is not clear if the
communication will be as effective as more traditional media of oral and face-to-face
communication. Traditionally, teaching presence is thought to be more central to CoI, and
influences both social and cognitive presence.

Moore’s transactional distance theory
Moore’s transactional distance theory (Moore, 1991) takes a more humanistic approach to
viewing the educational experience by viewing it in terms of “transactional distance”, which is
defined as a psychological and communication gap that exists due to physical separation.

Moore viewed the distance as consisting of three sets of variables. The first set looks at the
“structure”—the design elements of what is to be learned. Structure is often seen as being “rigid”
or “flexible”, depending on the extent of goal prescription, model of delivery, nature of the
course, and the ability to adapt to learner needs.

The second set of variables looks at the interaction or communication among and between the
teachers and students. This includes teacher–student and student–student interactions. Key
considerations of this set of variables are the quantity (i.e., frequency) and quality of interactions.
The quality can be viewed via the lens of the ability to resolve a student’s questions and
problems.

The third set of variables is the “autonomy” of the student—the student’s ability and confidence
to learn. Learner autonomy is very closely affected by individual self-determination and self-
direction, which, in turn, is affected by the two other aspects of the theory.

Moore’s transactional distance theory posits that the three aspects have an inverse relationship,
meaning an increase in one aspect can lead to decreases in the other aspects.

Technology acceptance model (TAM)
The technology acceptance model (TAM) was developed by Davies (Davis, 1989) and has since
been used widely in many studies of the behaviour arising from the acceptance of technology.
The basic premise of the model is that the primary components of the TAM (perceived ease of
use; perceived usefulness) affect attitudes towards adoption which, in turn, affects the
behavioural intention and actual use of the system. It draws a causal relationship between the
perceptions and attitudes and behavioural outcomes.

TAM has been used extensively in the context of online learning (Mulwa et al., 2012; Sheng et
al., 2008). Causal links were drawn between the perceptions and behavioural outcomes.
Although TAM is still a technology-focused model, there have been some limited applications in
communication spaces (Maican et al., 2019). The perception and subsequent adoption of online

Journal of Open, Flexible and Distance Learning, 26(2)

communication and collaboration tools are one of the factors that help to predict academic
success (Maican et al., 2019)

Asynchronous discussions
As online learning developed, online discussion channels were created so that distance learners
who were learning asynchronously would have an avenue for reflection, debate, and critical
discourse of topics with another person (Hew & Cheung, 2013). In addition, the messages, or
“posts”, are visible to all participating students and give them the option of either actively
participating (i.e, replying or writing new posts) or participating passively (i.e., just reading the
exchanges). Students can engage with each other more “meaningfully” and at a deeper level
because they are not penalised for a delayed response and can take more time to structure and
compose better responses (Garrison, 2009; Putman et al., 2012).

Asynchronous talk channels intentionally encourage sharing of, and debate about, experiences
and knowledge by allowing students to proactively participate in self-enforcing collaborative
learning (Kozan & Richardson, 2014). Discussion posts also remain online, allowing students to
revisit them as often as necessary (Garrison, 2009). Due to the time-delayed nature of
asynchronous discussions, students can also do more research before posting their responses
online. This elevates the level of discussion and gives a greater sense of understanding of the
topics. The time delay also means that students can identify the critique on their responses as
critiques rather than personal attacks (Kemp & Grieve, 2014). This encourages more online
learner participation. Arguably, active participation has led to higher levels of student academic
achievement and student satisfaction (Romero et al., 2013).

Methodology
Before we discuss the methodology of the research, we will explore some context on the
theoretical paradigm underpinning this project. A pragmatist approach (e.g., Dewey, 1938) is
most suitable for this project. Pragmatism, as defined by the APA Dictionary of Psychology, is “a
philosophical position holding that the truth value of a proposition or a theory is to be found in its
practical consequences” (APA, n.d.). This project is designed primarily as an applied project, so
having a paradigm that allows for both theoretical rigour and flexibility will be suitable . Because
there is an expectation that the project will contribute in theory, practice, methodology, and
policy, it will be more useful if the findings can be generalised and broadly applied, rather than
relating to just a small focused sample group. This new knowledge would also be grounded in
practicality and be nuanced enough for the results to be interpreted accurately.

Research process
To meet the research goal and answer the research questions, this project will undertake the
research in three stages, as seen in Fig. 3.

Figure 3 Research process flow

Stage 1:
Development of

theoretical
framework

Stage 2: Conduct
of study and

analysis of result

Stage 3:
Conclusion

Koh, J., Hulbert, T.

Stage 1: Development of theoretical framework
The first step in Stage 1 is to build the theoretical framework. This guides the study and frames
how it will proceed. The theoretical framework will leverage the literature review and synthesise
the information and models into a working model for this study.

Stage 2: Conduct of survey and analysis of results
The next stage is the conduct of the study. In line with the pragmatist approach, a semi-structured
survey was used due to its speed, efficiency, and cost-effectiveness (Gürbüz, 2017). By adapting
the survey instrument by Arnaugh et al. (2008) to account for digital literacy, the 40-question
survey instrument aims to explore the impact of eNVC on student outcomes. This survey was
administered via an online cloud-based survey software over 14 days.

Students from a New Zealand-based fully online distance provider studying Level 5 business
courses were invited to participate in the project. In total, 850 invitations were sent out. Having
students from the same course lessens the possible variability of experience due to subject areas
and delivery methods. It also means that the experience of the students can be attributed to
similar events that have happened at the same time. To mitigate the concerns of power
differential between students and researchers/teachers, the survey was non-identifiable and
confidential. Students from the researchers’ courses were excluded.

Using the data collected, a correlational analysis was then conducted. The Pearson’s Product
Moment Correlation (Pearson r) was selected to compute the coefficient of correlation due to its
reliability and ease of use because it is based on the method of covariance (Creswell & Plano
Clark, 2017). It provides information about the magnitude and direction of the relationship. A
95% confidence interval was selected for this study as that is mostly commonly used.

Qualitative data was thematically synthesised with the analysis of the quantitative data collected.
The integration strategy used was the data linking strategy, where data was combined (or linked)
to each other via “association, comparative or relational analyses” (Bazeley, 2018). This allowed
for more nuance in the way both sets of data corroborate, elaborate and/or illustrate each other,
and allowed for easier detection of group patterns, relationships, and differences (Bazeley, 2018).

Stage 3: Conclusion
Once the analyses were completed, a final conclusion could be drawn on the relationship
between eNVC, online learning, and student outcomes. Although this stage also meant the
conclusion of the scope of this project, the final product can be disseminated and incorporated
into existing and new workshops, courses, and public scholarship. This will ideally help to
propagate the theoretical framework and provide a possible solution to online teaching.

Theoretical framework
The CoI, TAM, and eNVC models have led and illuminated the way forward for many studies,
although each model has its limitations. Used in the context of an online teaching framework,
they sit too narrowly within their own philosophical underpinnings and applications. Because the
theoretical framework had to reach across the approaches, a pragmatist approach was suitable
(Evans et al., 2011; Ryu, 2020).

The CoI model is invaluable as a pedagogical framework for online education because it
describes the educational experience. However, although it has been validated by many later
studies (Burgess et al., 2010; Fiock, 2020; Lin & Reigeluth, 2019), the CoI model does not, by
itself, indicate a causal link between one aspect of communication and the outcomes.
Additionally, the concept of educational experience is not well explored in the CoI model, and it
does not explore individual aspects of the educational experience. Moreover, not all real-world
applications of the CoI model have resulted in the predicted levels of success (Jézégou, 2010).

Journal of Open, Flexible and Distance Learning, 26(2)

This has however, been attributed to implementation issues, rather than weakness in the model
itself (Garrison et al., 1999).

The TAM model, on the other hand, draws a link between the technology and behaviour well.
Online learning is predicated on student’s digital literacy, because that determines the student’s
autonomy in their learning. This also harks back to the CoI model where the context, and the
medium of communication on which the communication happens ,is important to the educational
experience (Koh et al., 2022). The technology acceptance model has also been linked to positive
educational experiences (Mulwa et al., 2012). However, the obvious weakness of TAM is that it
is designed primarily as a model of technological acceptance behaviour, rather than as a model of
pedagogical outcomes. While some studies have shown the relationship between technology
acceptance and improved academic success (Maican et al., 2019), these studies tend to be limited
in scope, focusing too narrowly on system use as the outcome, and the attitudes that get to that
outcome.

However, the two models need to be underpinned by a pedagogical model for more pedagogical
coherence. Moore’s transactional distance theory provides a good underpinning as it allows the
models the space to interact. Each model has aspects that can map clearly and coherently onto
Moore’s theory.

And yet the three models do not account for the existence and presence of eNVC. Nonverbal
cues affect the overall student experience significantly (Koh, 2021) and should be included in
how communication, especially online, should be framed. A more nuanced and balanced manner
of communication can be conducted using the T.E.S.T model, but T.E.S.T is also not without its
weaknesses. It is a communication-based model but relies on the validity of the CoI model that
informs it.

In short, by using a pragmatist approach and synthesising the three models, a theoretical
framework can be drawn (Fig. 4). This approach has the distinct advantage of drawing from
several fields of study that can contribute greatly to students having better student outcomes and
an improved experience.

Figure 4: Proposed theoretical framework

Research findings

Koh, J., Hulbert, T.

After cleaning for duplicates and incomplete surveys, a total of 88 responses were deemed
suitable. This was a response rate of approximately 10.35%. There were more female (75%) than
male respondents (25%), and there was a roughly representative spread of respondents across the
age bands (Table 1). The data seems to be distributed normally, fitting the requirements of
having the z value for the variables within the range of -3.29 to +3.29 (Aryadoust & Raquel,
2019). The z values are obtained by dividing the skewness and kurtosis by the relevant standard
errors.

Table 1 Spread of the participants

Age band Number Male Female

18–24 9 (10.23%) 1 8

25–29 19 (21.59%) 2 17

30–34 24 (27.27%) 7 17

35–39 11 (12.5%) 2 9

40–49 16 (18.18%) 3 13

50–59 9 (10.23%) 3 6

On average, the number of courses that the students have taken range from 1 to 20, with an
outlier at 50. Most respondents (90.7%) have taken more than one course online, indicating a
level of familiarity with online learning.

A table of correlations was derived from SPSS and is presented in Table 2—eNVC and teaching
presence and social presence have a significant correlation. However, eNVC does not have any
significant relationship with digital literacy. This result is similar to the pilot study done in 2020
(Koh, 2021). Breaking it down further by age band (Table 2A), eNVC and teaching presence
were most significantly correlated at age 30–35. Other age bands, such as 18–24, are also highly
correlated but not statistically significant, mainly due to the number of participants. eNVC and
social presence was significantly correlated for the 40–49 age bands. The 35–39 band were also
highly correlated, but not statistically significant. Most interestingly, at the 35–39 band, there
seems to be an inverse relationship between digital literacy and eNVC. This suggests that as the
eNVC decreases, the level of digital literacy actually increases, which seems to be an anomaly.

Table 2 Table of correlations between eNVC and the dimensions

Teaching
presence

Social
presence

Digital literacy

eNVC Pearson’s correlation 0.45 0.27 <0.001

Sig. <0.001 0.01 0.990

Journal of Open, Flexible and Distance Learning, 26(2)

Table 2A Correlations between eNVC and the dimensions by age band

Age band Teaching
presence

Social
presence

Digital
literacy

eNVC: 18–24 Pearson’s correlation 0.65 -0.11 -0.22

Sig. 0.059 0.787 0.578

eNVC: 25–29 Pearson’s correlation 0.33 0.24 0.21

Sig. 0.167 0.326 0.385

eNVC: 30–34 Pearson’s correlation 0.43 0.17 0.03

Sig. 0.034 0.427 0.887

eNVC: 35–39 Pearson’s correlation 0.46 0.51 -0.69

Sig. 0.158 0.108 0.019

eNVC: 40–49 Pearson’s correlation 0.43 0.82 0.02

Sig. 0.099 <0.0001 0.955

eNVC: 50–59 Pearson’s correlation -0.07 0.29 0.49

Sig. 0.865 0.443 0.183

Generally speaking, eNVC from the teachers teaching the courses has a significant impact on the
students’ motivation and engagement. eNVC cues on other aspects have significantly less impact
(Table 3). Breaking it down further by age bands (Table 3A), it seems that eNVC in teaching and
social presences has a significant effect on student engagement in the 30–34 band. Other areas of
significance would be the eNVC in digital literacy for 25–29 year olds. For eNVC in the social
presence for the 40–49 age band, engagement is highly correlated, although not statistically
significant.

eNVC also has a significant relationship with motivation, especially in the teaching presence.
However, the strength of the relationship could be categorised as weak. Interestingly, digital
literacy for 25–29 year olds has a significant relationship with motivation (Table 3A). In fact, for
that age band, eNVC in digital literacy has a significant impact on motivation and engagement—
much more than teaching presence or social presence. Motivation seems to be inversely related
to eNVC in the social presence for 40–49 year olds, as the level of eNVC may not encourage
motivation.

Table 3 Table of correlation between eNVC and motivation and engagement

Motivation Engagement

eNVC: Teaching
presence

Pearson’s correlation 0.21 0.25

Sig. 0.05 0.019

eNVC: Social
presence

Pearson’s correlation 0.11 0.14

Sig. 0.304 0.196

eNVC: Digital
literacy

Pearson’s correlation 0.18 0.16

Sig. 0.101 0.138

Koh, J., Hulbert, T.

Table 3A: Table of correlations between eNVC and motivation and engagement by age band

Age bands Motivation Engagement

eNVC: Teaching presence (18–24) Pearson’s
correlation

0.19 0.58

Sig. 0.660 0.104

eNVC: Social presence (18–24) Pearson’s
correlation

0.09 -0.12

Sig. 0.810 0.759

eNVC: Digital literacy (18–24) Pearson’s
correlation

0.08 0.05

Sig. 0.843 0.894

eNVC: Teaching presence (25–29) Pearson’s
correlation

-0.37 <0.001

Sig. 0.121 0.986

eNVC: Social presence (25–29) Pearson’s
correlation

-0.02 0.02

Sig. 0.922 0.940

eNVC: Digital literacy (25–29) Pearson’s
correlation

0.47 0.48

Sig. 0.04 0.036

eNVC: Teaching presence (30–34) Pearson’s
correlation

0.28 0.41

Sig. 0.193 0.046

eNVC: Social presence (30–34) Pearson’s
correlation

0.19 0.44

Sig. 0.373 0.033

eNVC: Digital literacy (30–34) Pearson’s
correlation

0.08 -0.09

Sig. 0.721 0.673

eNVC: Teaching presence (35–39) Pearson’s
correlation

0.35 0.06

Sig. 0.285 0.853

eNVC: Social presence (35–39) Pearson’s
correlation

0.48 0.34

Sig. 0.136 0.305

eNVC: Digital literacy (35–39) Pearson’s
correlation

-0.21 -0.26

Sig. 0.526 0.464

Journal of Open, Flexible and Distance Learning, 26(2)

eNVC: Teaching presence (40–49) Pearson’s
correlation

0.38 0.48

Sig. 0.165 0.068

eNVC: Social presence (40–49) Pearson’s
correlation

-0.58 -0.42

Sig. 0.019 0.103

eNVC: Digital literacy (40–49) Pearson’s
correlation

0.27 0.24

Sig. 0.316 0.363

eNVC: Teaching presence (50–59) Pearson’s
correlation

0.25 0.09

Sig. 0.521 0.824

eNVC: Social presence (50–59) Pearson’s
correlation

0.09 0.13

Sig. 0.816 0.741

eNVC: Digital literacy (50–59) Pearson’s
correlation

0.01 -0.41

Sig. 0.986 0.278

Discussion
In answering Research Question 1, this study finds that there is a strong correlation between
eNVC and online learning, primarily in the areas of teaching and social presence (Table 2). This
discovery corroborates the role of teaching and social presences in online learning (Kilis &
Yildirim, 2019; Zilka et al., 2018) but, more importantly, it quantifies the correlation. The
findings have found that teacher presence and social presence have significant relationships with
eNVC, but not for digital literacy. In an online learning environment, the talk channels serve as a
more “informal” medium of communication and act as a pre-built “marketplace” where students
and teachers can communicate freely. The fact that digital literacy does not have a significant
relationship with eNVC is a further indicator of Moore’s transactional distance theory holding
true in this study. Translating it to Moore’s theory, asynchronous talk channels provide an
avenue for increased “interaction” between students and teachers, and then we would see
decreases in the “structure” and “autonomy”. This translates to less transactional distance
between teachers and students than in a purely self-driven online course. With the increased
interaction, reduced structure and transactional distance in an online asynchronous talk channel,
eNVC cues are also picked up and interpreted more acutely. Thus, this translates to a stronger
relationship between nonverbal communication in the asynchronous online talk channels and the
teacher and social presences of online learning.

When we explore the findings from Research Question 1 in more depth, we see that they also
indicate that eNVC has the strongest correlation when it comes to how it influences the teaching
presence in online education, indicating that students tend to be more perceptive of eNVC cues in
the instructional realm. There are three possible reasons for this phenomenon. The first is the
demographics of the student participants, almost all of whom were studying alongside other life
and work commitments. This meant that the learners tended to be more transactional in their

Koh, J., Hulbert, T.

approach towards learning (Kara et al., 2019). As they juggled their limited time between work,
family, and studying, social connections via talk channels were not a high priority.

Another reason indicated was the possibility of using other asynchronous communication
channels external to the school’s learning environment. These channels could include private
social media groups or forum discussions (e.g., Reddit, Discord) or even comments sections on
selected videos (e.g., YouTube). So, although online discussions are happening, they do not
include the tutor and might not be focused as sharply on learning.

The third reason for students being more perceptive to eNVC cues due to the teaching presence
could be because teachers are the main touchpoints in an isolated online learning experience
(Kotera et al., 2021; Menchaca & Bekele, 2008). Students tend to perceive online education as
being a more solitary style of study (Jensen et al., 2021). As the “only constant”, teachers thus
represent a large part of online interactions (Wang et al., 2021). As the transactional distance
between learner and teacher decreases, and with the structure of the material already determined,
there is more dialogue between teacher and student. As such, students may be better primed and
more sensitive to the eNVC cues that teachers can transmit digitally.

Interestingly, although digital literacy and awareness underpins the whole online learning
experience, there is no correlation between the level of digital literacy and the interpretation of
eNVC cues. This is especially interesting as it shows that eNVC is still primarily a
communication issue, and is not affected by the students’ level of digital familiarity. This is also
suggests that teachers need not be IT experts, or be the most IT savvy person, to be well
perceived or presented online. However, we also note that because most of these participants
have already studied online, the level of impact of digital literacy on their ability to interpret
eNVC cues may have been severely muted.

Research Question 2 looks at the correlation between eNVC and the effect on student motivation
and engagement. The results show that eNVC did not have a strong direct correlation with
student motivation and engagement. However, if broken down by the eNVC cues interpreted due
to each element of online learning, it becomes clear that eNVC cues from the teaching presence
play a statistically significant role in student engagement and motivation.

eNVC cues from teaching presences show a higher degree of correlation between engagement
than with motivation. This aligns with current literature on motivation and engagement,
especially in online learning environments (Chiu, 2022; Kang & Zhang, 2020). This correlation
could also be due to the fact that student motivation and student engagement differ greatly in
terms of their makeup and determinants.

Student engagement is viewed as the psychological investment manifested as participation in the
course activities and tasks. Engagement is affected by a multitude of factors, of which the most
prominent are motivation, teacher presence (Anghelache, 2013; Kraft & Dougherty, 2013),
community of learners (Domun & Bahadur, 2014; Gedik et al., 2013; Kim & Callahan, 2013;
Teräs & Herrington, 2014; Trévidy et al., 2017) and eNVC factors (Al Tawil, 2019; Koh, 2021).
However, engagement is a manifestation (an outward display) which means that it is not as
affected by intrinsic factors. Therefore, communication by teachers here can make a difference in
how students decide to engage, even if the engagement is not motivated by the desire to learn.

Another possible reason for higher levels of engagement in relation to eNVC is the fact that
engagement is easier to measure, and so teachers can gear their communication towards
encouraging engagement. Modern LMSs can show when the student last logged in, how many
hours they spent studying, the level of learning interactions, the number of mouse clicks, and so
on. Teachers can use these metrics as shorthand to see if students are engaged with the course

Journal of Open, Flexible and Distance Learning, 26(2)

material, and communicate accordingly. Students can interpret the eNVC cues on the need for
more engagement with course activities and talk channel postings, thus resulting in a higher
correlation. This also is in line with current literature showing that higher levels of teaching
presence lead to higher levels of engagement (Zhang et al., 2016). In lieu of an immediate way to
measure motivation levels, engagement levels are often used as a proxy for motivation levels
(Harrison et al., 2017).

Despite engagement and motivation being intertwined, it is also important to differentiate that
student motivation is fundamentally different. Motivation works multi-dimensionally, and is
often defined as “an impetus or inspiration to act toward an end” (Ryan & Deci, 2000). Unlike
engagement, motivation is both an art and a science because it can be very hard to
deterministically pinpoint what contributes to it, or what generates it. However, many studies
over the years have indicated factors that could affect motivation, including teacher presence
(Bullock & Fletcher, 2017; Radel et al., 2010) and the community of learners (Bullock &
Fletcher, 2017)

Motivation can be viewed through intrinsic and extrinsic lenses. Intrinsic motivation is self-
determined and hence not as affected by external influences (Lee et al., 2012). This could explain
why eNVC cues do not have such a significant impact on motivation (because it is intrinsically
driven). Extrinsic motivation is driven by external factors such as getting good grades, and so is
more affected by external factors such as communication from the teacher. As such, eNVC still
has a correlation with student motivation, but it is less due to the presence of the intrinsic portion
of motivation.

Interestingly, social presence did not show a statistically significant relationship with student
motivation and engagement. Like the relationship between eNVC and teaching presence, social
presence seems to be affected by the nature of delivery and the demographics of students. Talk
channel participation is often viewed as optional and these working adult students will choose to
participate only if necessary (Kehrwald, 2008). In addition, students usually enrol in online
studies for other than social reasons (Hew & Cheung, 2013). This shows that students do not
view the social discussions as critical to their learning in an online environment. More critically,
this suggests a lack of intrinsic motivation, which then leads to a lack of student engagement.
However, this seems to suggest that a community of learners in an online asynchronous talk-
channel environment does not have the same effect as a community of learners in a traditional
setting.

The same can also be said for digital literacy. Because of the level of digital literacy, eNVC does
not have any significant effect on student motivation and engagement. As online learning
students, they have a higher level of self-efficacy. This could explain why eNVC cues have very
little effect on the level of engagement (Winne, 2005). Because a student has to have a certain
level of confidence in their own digital skills before embarking on an online course, the eNVC
cues arising from a lack of digital familiarity may not apply as strongly here, which in turn also
explains the statistically insignificant correlation between digital literacy and student outcomes
(motivation and engagement). Despite that, most surprisingly, the results showed that digital
literacy correlated significantly with motivation and engagement among 20–29 year olds. In fact,
the correlation between eNVC arising from digital literacy and student outcomes is much
stronger than eNVC cues from teacher and social presences. More research would need to be
conducted to have a clearer view of this unusual departure from the norm. One possible reason
could be that this age group spends the most time online by a significant margin (Johnson, 2022)

Koh, J., Hulbert, T.

Conclusion
This study has shown that eNVC and the teaching presence are significantly related and that
eNVC teaching presence has a significant relationship with motivation and engagement. Hence,
it is imperative that teachers understand how their communication affects how students learn.

However, this study is limited by its reliance on self-reported data from the survey. More
research will be needed to develop a better understanding of the role of nonverbal
communication in a text-based asynchronous online learning environment. Conducting more in-
depth qualitative studies will give a better and clearer view of why respondents responded the
way they did.

This study will help inform how teachers can “teach” online and even influence how AI teachers
could improve their responses to students. Online learning and asynchronous talk channels are
here to stay, so understanding the role and importance of eNVC can shape how teachers are
trained, the delivery of online teaching, and the pedagogy of teaching education via talk
channels.

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Biographical notes
Josiah Koh
Josiah.koh@openpolytechnic.ac.nz

Josiah Koh is a lecturer in business management at Open Polytechnic of New Zealand. He holds a Masters
in Enterprise Resource Planning Management from Victoria University. Josiah has substantial teaching
experience, having taught courses in the areas of business management, statistics, business computing and
operations management. His research interests include teaching and learning, artificial intelligence in
education, online communication, and online learning.

Tara Hulbert
tarahulbert@gmail.com

Tara Hulbert has a Bachelor’s degree from the University of Wisconsin (Whitewater)
(Marketing/Communications) and a Master’s degree from the University of Wisconsin (Milwaukee)
(Communication). She has taught at the university level for over 20 years in the United States and New
Zealand in the area of Business, Marketing and Communication. Tara is passionate about helping others to
use effective applied communication practices across industries and settings. Most recently, Tara has
applied her knowledge to digital work in online learning, social media, and digital marketing. You can find
her online at @kiwiamericans.

Koh, J., Hulbert, T.

Appendix A: Survey questions

Project overview
The purpose of the project will assist us in determining the ways in which nonverbal
communication affects student motivation and engagement levels, and the role digital literacy
plays in online learning. We intend to present the findings of the research in a journal article and
as a presentation at conferences. Nonverbal communication is the way information is transmitted
and interpreted in addition to the written text. Specific cues include the response time taken, the
tone, style and length of response. Data collected from this survey will remain anonymous. The
researchers will maintain confidentiality by restricting access to the data. They will not be
collecting the information from the courses the researchers teach. All results will be stored in a
secure storage facility.

The survey consists of 40 questions and should take you around 45 minutes to complete.

This survey is broken down into 5 sections.

• Section 1: About you
• Section 2: Teaching Presence
• Section 3: Social Presence
• Section 4: Digital Literacy
• Section 5: Final Questions

By participating in this survey, you agree to the terms and conditions set out in the
information sheet.

Thank you in advance for taking the time to share your experience.

Section 1: About you
Q1: Which country are you taking this survey from?

Q2: What is your age range?

Q3: How many online text-based modules/course/subjects have you undertaken thus far (in your lifetime)?

Q4: How long (how many years) have you been an online learner in a primarily text-based environment?

Q5: What is your general impression towards e-learning in a text-based environment?

Q6: How is/was your experience with e-learning in a text-based environment?

Here are some key definitions that could help you in understanding some of the terms in the
questions below.

Nonverbal communication in an electronic context (NVC) refers to the communication of
messages that are outside that of the written texts. For instance, the use of emoticons and choice
of words could convey a message that is more than just the words of the text.

Motivation refers to the impetus to complete the course/module.

Engagement refers to the psychological investment in a course. This can be expressed by
participating in the course activities and tasks (e.g., participation in the discussion channels (such
as forums etc.) or taking part in the course activities that may or may not be graded.)

Journal of Open, Flexible and Distance Learning, 26(2)

Section 2: Teaching presence
Q7. Based on your most recently completed online text-based course/module/subject, to what extent do
you feel that the teachers helped you in your e-learning experience?

Q8. Based on your most recently completed online text-based course/module/subject, to what extent did
the teacher impact your motivation while learning online?

Q9: Based on your most recently completed online text-based course/module/subject, did you feel that the
teachers responded in a timely manner?

Q10: How long do you think should be acceptable for the teacher to take to respond to you?

Q11: Based on your most recently online text-based completed course/module/subject, how genuine did
you feel your teacher to be?

Q12: Based on your most recently completed online text-based course/module/subject, to what extent does
the teacher’s tone (e.g., how things are worded or emphasised with punctuation) impact you positively or
negatively?

Q13: Based on your most recently completed online text-based course/module/subject, to what extent does
the length of your teacher’s response impact your impression of the teacher positively or negatively?

Q14: Based on your most recently completed online text-based course/module/subject, to what extent does
the formality of your teacher’s style of writing impact your impression of the teacher positively or
negatively?

Q15: Based on your most recently completed online text-based course/module/subject, to what extent does
the frequency of your teacher’s response impact your impression of the teacher positively or negatively?

Q16: Based on your most recently completed online text-based course/module/subject, how comfortable
were you in communicating to your teacher online in online talk channels/forums?

Q17: How helpful was your teacher/kaiako in acknowledging you personally as an individual?

Q18: What impact did your teachers have on the way you engaged in learning online in a text-based
environment?

Q19: Overall, how did your teachers’ presence affect your experience in the course?

Section 3: Social presence
Q20: Based on your most recently completed online text-based course/module/subject, what impact did the
online talk channels/forums have on your motivation in online learning?

Q21: Based on your most recently completed online text-based course/module/subject, how frequently did
you actively participate in the social aspect of online learning? (e.g., post/reply/comment in the talk
channels etc.)

Q22: Did you passively participate in the social aspect of online learning in this online text-based
environment? (e.g., read posts)

Q23: What impact did actively engaging in the talk channels with other students have in making you feel
like part of the class?

Q24: Did passively engaging in the talk channels with other students make you feel like part of the class?

Q25: What impact did these interactions (both passive and active) have in engaging you to participate in
the studying activities (e.g., discussions over the forums/ doing tasks together etc.) of the course?

Koh, J., Hulbert, T.

Q26: What sense of connection/belonging to the course did you feel as you went through the course in this
online text-based environment?

Q27: To what extent do you think talk channel/forum interactions should be controlled/regulated by the
teacher? (e.g., should your social interactions be managed by the teacher)

Q28: If you knew that your next course would be with the same teacher and students, what impact would
that have on your decision to take that course?

Section 4: Digital literacy
Q29: Generally speaking, how digitally savvy do you consider yourself to be?

Q30: Generally speaking, how digitally savvy do you consider your classmates to be?

Q31: Based on your most recently completed online text-based course/module/subject, how digitally savvy
do you consider your teachers to be?

Q32: How familiar are you with online etiquette (manners and rules)?

Q33: How often do you misinterpret messages in your online text-based course? (e.g., find out later that
you misunderstood something between teachers/students/course material)

Q34: How does misinterpreting messages impact on your engagement to participate in the various
activities (e.g., forum discussions) in the same online course?

Q35: How does misinterpreting messages impact on your motivation to complete the course?

Q36: How much impact does your familiarity with digital tools have in making you more willing to engage
(in the various activities such as forum discussions etc) with the online course?

Q37: What impact does your familiarity with digital tools have in making you more motivated to complete
your course?

Q38: If you were told that that the same technology was going to be used for the next course, how much
would that impact your decision to continue online learning?

Section 5: Final questions
Q39: What impact does the online learning experience have on your decision to continue e-learning?

Q40: What impact would having the same combination of these factors (e.g. same teacher, same
classmates, same learning management system) have on you continuing to learn in this online e-learning
environment?

7-point Likert scale used: 1 = most negative: 7 = most positive)

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

Koh, J., Hulbert, T. (2022). The role of nonverbal communication in asynchronous talk
channels. Journal of Open, Flexible and Distance Learning, 26(2), [29–50.].