Interactive Learning Environments
ISSN: 1049-4820 (Print) 1744-5191 (Online) Journal homepage: https://www.tandfonline.com/loi/nile20
Online learners’ interactions and social
anxiety: the social anxiety scale for e-learning
environments (SASE)
Sinan Keskin, Muhittin Şahin, Sait Uluç & Halil Yurdugul
To cite this article: Sinan Keskin, Muhittin Şahin, Sait Uluç & Halil Yurdugul (2020): Online
learners’ interactions and social anxiety: the social anxiety scale for e-learning environments
(SASE), Interactive Learning Environments, DOI: 10.1080/10494820.2020.1769681
To link to this article: https://doi.org/10.1080/10494820.2020.1769681
Published online: 01 Jun 2020.
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INTERACTIVE LEARNING ENVIRONMENTS
https://doi.org/10.1080/10494820.2020.1769681
Online learners’ interactions and social anxiety: the social anxiety
scale for e-learning environments (SASE)
Sinan Keskin
, Muhittin Şahin
a
b
, Sait Uluç
c
and Halil Yurdugul
d
a
Computer Education & Instructional Technology, Van Yuzuncu Yil University, Van, Turkey; bComputer Education &
Instructional Technology, Ege University, Izmir, Turkey; cDepartment of Psychology, Hacettepe University, Ankara,
Turkey; dComputer Education & Instructional Technology, Hacettepe University, Ankara, Turkey
ABSTRACT
ARTICLE HISTORY
Social sharing in virtual learning environments differs from real
environments, resulting in a need for a specialized data collection tool
related to social anxiety in these environments. This study seeks to
develop a scale to identify the levels of social anxiety experienced in elearning. The study group consists of 275 students who have previously
experienced an online or a blended learning environment. The data
were analyzed through exploratory factor analysis, confirmatory factor
analysis, convergent and divergent validity tests. The interpersonal
interactions of learners in online environments were considered in
developing the tool. Two subscale forms were created to identify the
levels of social anxiety in learner-learner interaction and learnerinstructor interaction. Each subscale of the Social Anxiety Scale for ELearning Environments (SASE) consists of 23 items and three subfactors.
These sub-factors are called negative evaluation, somatic symptoms and
avoidance of interaction. The analyses indicate that the SASE is a reliable
and valid measurement tool useful for assessing the social anxiety levels
of online learners.
Received 27 July 2019
Accepted 24 April 2020
KEYWORDS
Social anxiety; e-learning;
student-computer
interaction; online
interaction; social anxiety
scale
1. Introduction
According to the social constructivist learning theory, individuals construct knowledge through cultural and social activities. The structuring of knowledge is the process of creating a common meaning
with the views of other individuals in a social environment (Vygotsky, 1978; Woo & Reeves, 2007).
Individuals share their opinions in this social environment. They affect and are affected by other individuals in this environment. Thus, learners can do more and learn more easily in a social learning
environment with others (Kalina & Powell, 2009; Vygotsky, 1978). According to this learning paradigm, knowledge is constructed internally through experiences that result from the student’s
social interactions (Woo & Reeves, 2007). New generation e-learning technologies are designed
with this in mind. With the emerging e-learning technologies, social learning environments have
become online environments learners can access from anywhere and at any time without having
to physically coexist. With these new technologies, various barriers may prevent the social interactions of learners, although the possibilities of interaction have increased. In the context of e-learning, the barriers that limit interaction in the e-learning environment include social anxiety, technical
and academic skills, motivation, e-learning readiness, low technology literacy, communication skills,
self-regulation, self-efficacy, time constraints, and technophobia (Hill et al., 2009; Muilenburg & Berge,
2005; Song et al., 2004). Personality structures such as social anxiety, introversion, shyness, etc. are the
CONTACT Sinan Keskin
sinan.keskin@hacettepe.edu.tr
© 2020 Informa UK Limited, trading as Taylor & Francis Group
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S. KESKIN ET AL.
barriers to interaction between individuals, which is the most basic component of the process of constructing the knowledge expressed by the social constructivist learning theory (Schroeder & Ketrow,
1997; Yen et al., 2012). This study seeks to develop a scale for the identification of social anxieties
based on different types of interaction in the e-learning environments of learners. This article
begins with an explanation about the concept of interaction and then offers an in-depth discussion
of the concept of social anxiety. The concept of social anxiety is thoroughly discussed in relation to its
related constructs, its relationship with learning, and the dimensions identified in available studies.
1.1. Interaction in e-learning
Online learning is an important solution to meet the increasing demand for learning in today’s higher
education institutions. Today, many educational institutions organize online and blended courses in
an online context, and many learners participate in these courses independently of time and space.
The experiences that learners have had in these learning environments can provide important information for researchers about the quality of the teaching and how much the learners utilize online
platforms. These experiences can be explained by the concept of interaction which is one of the
most important concepts explaining the quality of online learning (Kuo, 2010; Miranda & Vegliante,
2019). An examination of the literature reveals that the types, duration, and number of interactions of
learners in learning environments are important predictors of their learning performance (Nandi
et al., 2011; Yu & Jo, 2014). One of the most important structures discussed in this context is the
types of interaction that show with what and whom the learners interact. In the context of distance
and online education, interaction types are divided into the following three subcategories: (a) interaction with content, (b) interaction with the instructor, and (c) interaction with the learners (Moore,
1989). With the widespread use of e-learning environments, researchers have proposed some new
classifications in relation to the types of interaction. For example, some researchers have mentioned
the interface, the course materials, feedback, the dashboard, the staff, and so on as interaction types
(Bağrıacık Yılmaz & Karataş, 2018; Muirhead & Juwah, 2004; Sherry, 1995; Thurmond, 2003; Wei et al.,
2015). This study hinges on the classification proposed by Moore for the types of interaction, but disregards learner-content interaction as this study focuses on interaction in the context of social
anxiety. Because, interpersonal communication is necessary for social anxiety, which is defined as
an individual’s avoidance and fear of performing and interacting in social groups (Heimberg et al.,
1999).
1.2. Social anxiety
Social anxiety is defined as a fear or anxiety of being judged negatively by others, of being humiliated,
or of making a negative impression and doing something wrong, leading to a feeling of discomfort
(American Psychiatric Association, 2000). In other words, social anxiety is an individual’s fear or avoidance of performance situations and interactions in social groups (Heimberg et al., 1999). An individual
with social anxiety is preoccupied with the idea that s/he is constantly watched and judged negatively by others. Individuals with social anxiety disorders behave differently than other individuals
in terms of physiological, cognitive, and behavioral aspects (Baltacı & Hamarta, 2013). The anxiety
that such individuals experience may prevent them from interacting and performing in groups.
Experts have identified a relationship between the preferred communication method (face-to-face
or online) and social anxiety (Behrens & Kret, 2019; Pierce, 2009; Yen et al., 2012). Chiu and Wang
(2008) have identified a negative correlation between web-based learning continuance intentions
and the levels of anxiety among learners regarding these environments. In other words, the individuals who frequently use web-based learning environments and are involved in online interactions
would probably experience low levels of anxiety. Regarding this phenomenon, Leary (1983) reported
that individuals with social anxiety avoid giving performances to reduce social risks and refrain from
interacting with others and exhibiting behaviors that may harm their personal image. It is known that
INTERACTIVE LEARNING ENVIRONMENTS
3
different anxiety types such as computer, language, and social anxiety effectively keep students away
from e-learning (Ajmal & Ahmad, 2019). Potential privacy risks regarding personal information or distinguishing characteristics cause social anxiety in online communication (Alkis et al., 2017). Accordingly, social anxiety has an important effect in determining how individual chooses to interact
with others, including interaction methods, duration, count. As this study develops a measurement
tool to evaluate levels of social anxiety, it also offers information on the cognitive model of social
anxiety to analyze this construct in greater depth.
1.3. The cognitive model of social anxiety (phobia)
For almost all phobias, such as a fear of heights, animals, or enclosed spaces, it is possible for an individual to achieve a complete avoidance of the fearful situation or object in a behavioral context. This
avoidance is the main factor that allows for the continuation of the phobic anxiety. The success of
avoidance is the most important factor that prevents the fear response from extinguishing (APA,
2000). The only exception for this scientific formulation is social anxiety disorder. In the case of
social anxiety, the phobic object is people or situations involving people, so complete avoidance is
not possible. Therefore, despite being important for the continuation of social anxiety, avoidance
is not a sufficient condition. Various studies on cognitive therapy have examined how anxiety can
persist in the case of social anxiety disorder notwithstanding continuous confrontations (Clark,
1997; Clark & Wells, 1995; Wells, 1997; Wells & Clark, 1997).
According to the cognitive model of social anxiety, three main components allow for the continuance of the phobia: (a) experiences of an individual with phobia when encountered with a social situation, (b) processing of the self as a social object, and (c) safety behaviors. Upon entering a social
situation, an individual with social anxiety has developed a series of assumptions about themselves
and their social world based on early experience (Clark & Wells, 1995). These assumptions are divided
into the following three main groups: (a) excessively high standards for social performance (e.g. I
must always sound intelligent and fluent), (b) conditional beliefs concerning the consequences of
performing in a certain way (e.g. If I am quiet, people will think I am boring), and (c) unconditional
negative beliefs about the self (e.g. I am boring; Clark & Beck, 2010). These assumptions lead the
phobic person to believe that s/he will be subject to social exclusion or neglect. Thus, the presence
of a social situation is perceived as a major threat. Further, one’s beliefs that s/he will never achieve
the desired social performance combines with the negative interpretation of uncertain social experiences to lead to vicious cycles.
The phobic person focuses entirely on observing himself/herself in extreme detail, resulting in the
processing of the person as a social object under a microscope. Thus, the phobic person shuts
himself/herself off from all kinds of external information. The individual’s decisions and interpretations are now based on the negative thoughts that emerge as a product of the self-monitoring
process. In this way, the phobic person starts to feel trapped in the fear s/he produces. An important
part of the processing of the self as a social object is the mental image in the mind of the phobic
person about himself/herself through which s/he sees himself/herself from the perspective of an
observer. This image represents itself as a very distinct and different entity (e.g. I look like an alien;
I look like a loser).
The last component that leads to the continuation of social anxiety is safety behaviors. As
with other types of phobia, social phobic individuals develop various safety and avoidance
behaviors to prevent anxiety. For instance, someone who is afraid of being seen sweating may
wear a jacket all the time. Another person who is anxious about being considered boring cannot
cope with silence and may talk constantly. These safety behaviors not only allow for the continuation
of the anxiety but also occasionally bring about the outcome the person wishes to avoid. In broad
terms, in the framework of the cognitive model, phobic anxiety in phobic people persists despite
their positive social experiences because they ignore objective feedback and act only upon their
own perceptions.
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S. KESKIN ET AL.
1.4. Literature review
This section presents the studies and findings in the literature. Firstly, the relation between social
anxiety and interaction environment is examined, secondly, studies about privacy concerns, which
is one of the causes of social anxiety, are discussed and finally the relation between social anxiety
and psycho-educational structures is examined. Social anxiety may vary depending on an individual’s
environment. Yen et al. (2012) compared the severity of social anxiety in real-life and online interactions and determined that social anxiety is decreased in online interactions. People with social
phobia often favor online interaction since they can interact anonymously by hiding their identity
or using nicknames in online environments (Shepherd & Edelmann, 2005; Weidman et al., 2012). Analyzing the relationship between social anxiety and the use of technology, Pierce (2009) reported that
socially anxious people who feel uncomfortable with face-to-face interactions tend to communicate
with others through text messaging and online environments. Research on fear of missing out, which
is one type of social anxiety, has identified a relationship between social anxiety and problematic
internet use and internet addiction (Vaidya et al., 2016; Weinstein et al., 2015). Conversely, online
communication opportunities affect individuals’ self-esteem and well-being positively through maintaining relationships and improving current relationships (Lee & Stapinski, 2012). Therefore, online
communication opportunities may have a positive aspect that eliminates negative situations such
as low self-esteem and well-being caused by social anxiety. Individuals prefer not to communicate
online when they think their interactions are likely to reveal their personal identities or allow their
performance to be observed by everyone, which will cause social anxiety (Alkis et al., 2017). Accordingly, a correlation exists between online interaction behaviors and social anxiety. Thus, we say that
social anxiety should be handled specifically in the context of online learning, which has with
different dynamics.
In e-learning environments, learners mostly use their personal identities instead of remaining
anonymous. Privacy concerns and certain potential privacy risks can cause individuals to experience
social anxiety in online communication (Alkis et al., 2017). These issues lead to the persistence of the
social anxiety they experience in face-to-face interactions in these environments and makes them
experience social anxiety due to the dynamics of these environments. Song (2005) stated that experienced students who are familiar with and can easily use e-learning tools have less anxiety and interact more in the learning environment. As social anxiety decreases, individuals become more likely to
make new friends in online environments (Pierce, 2009). Therefore, it can be argued that social
anxiety arguably adversely impacts individuals while making new friends.
Social anxiety is associated with many psycho-educational constructs. First, the relationship
between self-efficacy and social anxiety, which has a decisive role in online learning, was discussed.
It is known that social anxiety decreases when learners’ skills and competencies increase (Hill et al.,
2009; Saadé & Kira, 2009; Yang et al., 2010). For example, self-efficacy towards computer use and the
perception that it is easy to use a computer have a significant effect on anxiety experienced in elearning environments (Saadé & Kira, 2009). Thus, one’s knowledge that s/he will make fewer mistakes as s/he gains experience reduces the likelihood that s/he will be judged negatively by
others, which can also decrease social anxiety. Self-efficacy from the perspective of social learning
helps reduce social anxiety in web-based learning environments (Hill et al., 2009). As self-efficacy
increases, learners are more likely to interact with the task and other individuals in the environment,
thereby decreasing the levels of social anxiety of learners.
Secondly, we discussed the studies examining the effect of social anxiety on the behaviors and
learning performances of learners. In this context, Eryılmaz and Çigdemoğlu (2018) showed that learners have lower social anxiety levels in online teaching environments using cooperative learning
strategies. Social anxiety problems in the learning process negatively affect the performance of learners (Ajmal & Ahmad, 2019; Rapee & Heimberg, 1997). Bernstein et al. (2008) concluded that individuals with high levels of social phobia have trouble making friends and prefer being alone rather than
with their peers, and they further found that this preference adversely affects these individuals in
INTERACTIVE LEARNING ENVIRONMENTS
5
terms of academic achievement. Several studies in the relevant literature have linked high levels of
social anxiety with high school dropout (van Ameringen et al., 2003) and with poor employment performance and social isolation (Davidson et al., 1993). Individual differences in psychological structures
such as social anxiety and empathy, which are known to be influenced by preferred communication
methods (face-to-face or online), affect collaborative decision-making processes (Behrens & Kret,
2019). That being said, an effort to identify the social anxiety levels of individuals and the relationship
between social anxiety and other variables would potentially offer some important insights into how
to reduce social anxiety in online environments.
Various studies have been carried out in the literature on measuring social anxiety. Some of these
are given in Table 1 together with their sub-dimensions. The Liebowitz Social Anxiety Scale is one of
the most widely used scales in this field (Heimberg et al., 1999). The scale is composed of 24 items and
rates each item separately for “fear or anxiety” and “avoidance behavior”. The purpose of the scale is
to determine to what extent the situations presented in each item lead to fear and avoidance. It is
notable that many such studies presented similar constructs such as avoidance of social situations,
fear of negative evaluation, avoidance of new social environments, and fear of physical symptoms.
In research studies examining anxiety in online learning, the social anxiety scales used were initially
developed for social interactions in physical environments. For example, Eryılmaz and Çigdemoğlu
(2018) used the social anxiety scale for adults developed by Caballo et al. (2010) to monitor
changes in students’ unease, stress, or nervousness levels in different blended learning scenarios.
However, the factors included in such scales cannot address anxiety states directly in virtual
environments.
The backbone of social constructivism consists of collaboration and interaction with others in
the learning process (Kalina & Powell, 2009). When the social sharing in virtual environments is
taken into consideration, it differs from real environments, resulting in a need for a specialized
data collection tool related to social anxiety in these environments. This study seeks to develop a
scale to identify the levels of social anxiety experienced in e-learning based on the literature on elearning environments, interactions in e-learning, and social anxiety. The following sections offer
information on the method followed for developing the scale as well as the findings and the conclusion of the study.
2. Method
This study aimed to develop a measurement tool to determine the social anxiety levels of learners in
e-learning environments. We decided to develop a scale in order to determine the level of social
anxiety, which cannot be observed directly. Figure 1 summarizes the process steps in the scale development process, which were carried out in parallel with the existing studies in the literature.
2.1. Study group
The study group consisted of 275 students who had previously experienced an online or a blended
learning environment. The data of the study were obtained from third and fourth-level students
studying at four different universities in Turkey. Both online and face-to-face education programs
are carried out at these universities. The selection criterion for universities was that the curricula of
these universities included courses that enabled their students to gain experience in online and
blended learning. Thus, this study employs the criterion sampling method, which is a purposive
sampling method. Study group is composed of 47% (129) male and 53% (146) female students. All
the participants were students in the university’s department of computer education and instructional technology. The students in this department receive both theoretical education and learning
experience in distance learning and blended learning in the context of various courses. For example,
in the universities where research participants were selected, first-year students take compulsory
history or grammar courses through distance education.
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S. KESKIN ET AL.
Table 1. Overview of the existing social anxiety scales.
Scale
Liebowitz Social Anxiety Scale (LSAS) (Liebowitz, 1987)
Social Phobia Inventory (Campbell-Sills et al., 2015)
Social Anxiety Scale for Adolescents (La Greca & Lopez, 1998)
Social Anxiety Questionnaire for Adults (Caballo et al., 2010)
Social Anxiety Scale for Social Media Users (Alkis et al., 2017)
Dimensions
- fear
- avoidance
- fear of negative evaluation
- fear of physical symptoms
- fear of uncertainty
- fear of negative evaluation
- social avoidance and distress in general
- social avoidance specific to new situations
- awkward behavior in social embarrassing situations
- interactions with the opposite sex
- interactions with strangers
- criticism and embarrassment
- expression of annoyance, disgust or displeasure
- performing in public
- shared content anxiety
- privacy concern anxiety
- interaction anxiety
- self-evaluation anxiety
2.2. Item generation
This study seeks to develop a scale entitled the Social Anxiety Scale for E-Learning Environments
(SASE) to identify the levels of social anxiety experienced by learners in an e-learning environment.
For the development of this scale, the researchers followed the steps presented in Figure 1.
The first step in the formation of the scale involved a review of the literature in the following
fields: interaction in online learning, anxiety, social anxiety, and social anxiety in online
environments. There are basically three types of learner interaction in online learning environments: learner-learner, learner-instructor, and learner-content. Since this study deals with social
anxiety, the learner interactions discussed in the study are limited to learner-learner and learnerinstructor interactions. Following the first step, the study probed into the concepts of anxiety
and social anxiety and analyzed the items and the structures of the factors in the measurement
tools developed to assess social anxiety. Then, the researchers fed the items they had created
into an item pool consisting of 74 items. Half of the items in the item pool concerned learnerinstructor interactions (37 items), and the other half regarded learner-learner interactions (37
items). The researchers developed the items of the scale by considering the two-factor structure
of avoidance and anxiety, which has proved to be significant in studies on social anxiety
(Connor et al., 2000; La Greca & Lopez, 1998; Liebowitz, 1987). The researchers developed a form
to obtain expert opinions and used it to gather information from five experts, namely three
experts on instructional technologies, one on clinical psychology, and one on social psychology.
The form included the categories of purpose, target group, item pool, and anticipated dimensions.
The experts were asked to assess each item as “suitable, unsuitable, or requires improvement” and
to justify their assessments for unsuitable items. Based on their opinions, four items were eliminated from the scale, and some items were improved. The items were arranged for a 7-point
scale and administered to the participants in paper format.
3. Results
The data obtained from the administration of the assessment tool to the target group were analyzed
through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and tested for convergent and divergent validity. The researchers conducted analyses on factorial and construct validity
to examine the psychometric properties of the developed measurement tool. First, EFA was performed to identify the item-factor relations. Kaiser–Meyer–Olkin (KMO) and Bartlett Sphericity tests
INTERACTIVE LEARNING ENVIRONMENTS
7
Figure 1. Scale development process.
were carried out to determine whether the data were suitable for this analysis or not. Following EFA,
CFA was conducted to identify the item-factor relations and the relationships between the factors.
Lastly, convergent and divergent validity (Fornell & Larcker, 1981) tests were performed to assess
the construct validity. The reliability of the developed measurement tool was assessed based on
McDonald’s omega and Cronbach’s alpha coefficients.
3.1. Dimensions of social anxiety scale for e-learning environments
The data of the study were gathered by means of the measurement tool that consists of 70 items. This
measurement tool is composed of two sections that include 35 similar items for the assessment of
learner-learner and learner-instructor interactions based on the interactions in e-learning environments. The data obtained from the tool were analyzed separately for each interaction type.
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S. KESKIN ET AL.
Table 2. CFA of the alternative models.
Models
Single-factor
Three-factor (uncorrelated)
Three-factor (correlated)
Subscales
χ2/df
RMSEA
NFI
NNFI
CFI
Learner-instructor
Learner-learner
Learner-instructor
Learner-learner
Learner-instructor
Learner-learner
12.93
14.31
6.48
5.10
4.26
3.18
0.21
0.22
0.14
0.12
0.10
0.09
0.94
0.91
0.95
0.94
0.97
0.97
0.94
0.92
0.95
0.95
0.97
0.98
0.94
0.92
0.96
0.96
0.98
0.98
The study first employed EFA to identify empirical relations between items and factors. The results of
the KMO and Bartlett tests performed for both learner-learner interaction (X2=9362.52, df=595, p < .05)
and learner-instructor interaction (X2=11283.33, df=595, p < .05) indicated that the data of the study
were suitable for the analysis (Tabachnick & Fidell, 2019). According to the results of eigenvalues statistics
obtained in these analyses, learner-learner interaction explained 63.81% and learner-instructor interaction
explained 76.12% of the total variance of a three-factor structure. The rotated component matrix, which
presents the correlations between the items of the scale and the factors, was also examined. Based on the
results, the items with a factor weight value below 0.30 and those that correlate closely with multiple
factors were removed from the scale.
3.2. CFA and model comparison
The study carried out CFA and factorial validity analysis to test the validity of the scale. It is expected
in a scale development process that alternative models are put forward and the results from different
models are compared (Noar, 2003; Teo, 2010). To that end, the study formed a single-factor model as
well as a correlated and an uncorrelated three-factor model through CFA. Table 2 presents the values
of fit indices for the alternative models.
The results for alternative models in Table 2 demonstrated that the related three-factor model
yielded the most favorable results for both scales. The three-factor model, which was hypothetically
formed in the scale development process, was thus confirmed. Accordingly, the social anxiety
observed in e-learning environments can be identified through a correlated three-factor model.
These factors were called negative evaluation, somatic symptoms, and avoidance of interaction.
Factors and sample items related to the factors are given in Table 3.
Figure 2 illustrates the factor-item parameters of the SASE which consists of two subscales. Inspection of the standardized factor loads between the items in the scale and the factor structures revealed
that all factor loads were greater than 0.30. Further, the t-tests for the factor loads were statistically
significant. A total of 46 items in the subscales of learner-learner interaction (23 items) and learnerinstructor interaction (23 items) in the SASE successfully measured the hypothesized substructures,
which means that the factorial validity of the scale was confirmed.
3.3. Construct validity
Fornell and Larcker (1981) reported that construct validity can be tested through convergent and
divergent validity tests based on average variance explained (AVE) values. It is expected that
reliability coefficients will be greater than 0.70 (Nunnally & Bernstein, 1994) and AVE values will be
Table 3. Sample items.
Dimension
ID
Item
Negative evaluation
01
T3
T16
017
T19
023
In e-learning, I am afraid to be misunderstood when communicating with the instructor.
In e-learning, I’m afraid to be criticized by others on discussion pages.
I feel uneasy when communicating on discussion pages in e-learning.
I’m blushing when communicating with the instructor in e-learning.
In e-learning, I avoid asking questions on discussion pages.
I prefer to remain silent when I need to communicate with the instructor in e-learning.
Somatic symptoms
Avoidance of interaction
INTERACTIVE LEARNING ENVIRONMENTS
9
Figure 2. Factor structure of the SASE (standardized coefficients). (a) Factor structure of learner-learner interaction subscale. (b)
Factor structure of learner-instructor interaction subscale.
greater than 0.50. Moreover, the AVE value must be less than the coefficient regarding the construct
validity. The AVE values and reliability values of the assessment tool were calculated, and Table 4 presents the relevant findings on the subscale of learner-instructor interaction.
Inspection of the AVE values calculated for the subscale of learner-instructor interaction (see Table
4) revealed that the AVE value for all three factors was less than the structural reliability coefficients
and greater than 0.50. Further, the structural reliability (ω) and the Cronbach’s alpha (α) coefficients
were expected to be greater than 0.70. As seen in Table 4, the measurement tool met the requirements for convergent and divergent validity and structural reliability in the dimension of learnerinstructor interaction. Table 5 presents the findings on the AVE values and reliability coefficients of
the subscale of learner-learner interaction.
As observed in Table 5, the AVE value for all three factors was less than the structural reliability
coefficients and greater than 0.50. Thus, the subscale met the requirements for convergent validity.
The results of reliability measurements indicated that all values were greater than 0.70, which means
that the measurement tool can be considered reliable. Table 6 shows the inter-factor correlation
coefficients and the square root values of the AVE values regarding the subscales.
Table 4. AVE and reliability coefficients for the subscale of learner-instructor interaction.
Dimensions
AVE
Structural Reliability (ω)
Cronbach’s alpha (α)
Negative evaluation
Somatic symptoms
Avoidance of interaction
0.77
0.76
0.74
0.97
0.93
0.97
0.97
0.93
0.97
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S. KESKIN ET AL.
Table 5. AVE and reliability coefficients for the subscale of learner-learner interaction.
Dimensions
AVE
Structural Reliability (ω)
Cronbach’s alpha (α)
Negative evaluation
Somatic symptoms
Avoidance of interaction
0.67
0.74
0.66
0.95
0.92
0.95
0.95
0.92
0.95
Table 6. Discriminant validity measures.
Learner-learner int.
AI
0.81*
SS
0.86*
0.76
Learner-instructor int.
NE
0.82*
0.71
0.73
Dimensions
Negative evaluation (NE)
Somatic symptoms (SS)
Avoidance of interaction (AI)
NE
0.88*
0.79
0.84
SS
AI
0.87*
0.82
0.86*
*The diagonal elements of the matrices are the divergent validity coefficients, which were obtained through the calculation of the
square root values of the AVE.
According to Fornell and Larcker (1981), the discriminant validity of a scale is established when the
square root of each construct’s AVE is higher than its correlation with another construct. As seen in
Table 6, the values met this criterion; thus, the discriminant validity of the scale was established.
4. Discussion
This study yields a new measurement tool to identify the levels of social anxiety experienced by learners in e-learning environments. The interpersonal interactions of learners in online environments
were considered in developing the tool, resulting in the creation of two subscale forms to identify
the levels of social anxiety in learner-learner interaction and learner-instructor interaction. The
items of the scale were developed based on expert opinions and the review of the literature. The
data obtained from the administration of the tool to the target group were used in EFA, CFA, and
convergent and divergent validity tests.
As previously stated, two subscale forms were created to identify the levels of social anxiety in
learner-learner interaction and learner-instructor interaction. Each subscale of the SASE consists of
23 items and the following three subfactors: negative evaluation, somatic symptoms, and avoidance
of interaction. The analyses reveal that the SASE is a valid and reliable measurement tool. The dimension of negative evaluation measures the levels of negative feelings, thoughts, fear, and concerns
among learners about the probability that they might be misunderstood and judged by others
during their interactions in the e-learning environment. The dimension of somatic symptoms discusses the physical reactions (e.g. blushing, increased heart rate, discomfort) that an individual
may develop when s/he experiences social anxiety due to any interaction. The dimension of avoidance of interaction assesses one’s failure to perform an interaction, avoidance of interaction, and dissociation from the interaction environment (see Table 2 for sample items).
Although the exploratory nature (i.e. AVE values) of the dimensions are very close to each other,
the subdimension of negative evaluation has relatively the highest share (.77). This is congruent with
the following argument proposed in the scientific formulation of social phobia: the reason why
phobic anxiety in phobic people persists despite their positive social experiences is that the
phobic person ignores objective feedback and acts only on his/her own perceptions.
As a result of this research, a general social anxiety scale was developed for e-learning. The validity
and reliability examinations of the learner-learner and learner-instructor interaction subscales were
conducted separately, and their validity and reliability were proven. These findings allow researchers
to use the subscales together or separately in different e-learning contexts such as massive open
online courses (MOOC), distance education, and blended learning. To reflect the flexible nature of
the developed scale, which can be used in different e-learning contexts, the scale was named the
Social Anxiety Scale for E-Learning Environments (SASE).
INTERACTIVE LEARNING ENVIRONMENTS
11
5. Conclusion
The study proposes the SASE, which consists of 46 items in total. This measurement tool includes two
subscales for social anxiety in learner-learner interaction and learner-instructor interaction. Both subscales feature the dimensions of negative evaluation, somatic symptoms, and avoidance of interaction. A high score on this scale, which is designed as a 7-point scale tool, indicates high levels of
social anxiety in the relevant dimensions.
The reliability coefficients of the tool are also at the required level. Further, the tests performed for
the construct validity show that the items adequately represent the relevant constructs. In conclusion, it can be argued that the measurement tool is valid, and the items of the tool accurately
measure the constructs that the tool purports to measure.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Dr Sinan Keskin is a faculty member at Van Yüzüncü Yıl University, Faculty of Education, Department of Computer Education and Instructional Technology (CEIT). He completed his undergraduate education at Gazi University in 2010 and
worked as a teacher for one year. Sinan Keskin earned his MA and PhD degree in CEIT from Hacettepe University. His
research interests relate to e-assessment, feedback design, adaptive e-learning, learning analytics, and educational
data mining.
Dr Muhittin Şahin is an academic staff at Ege University, Turkey and postdoctoral researcher at Learning, Design and
Technology, University of Mannheim, Germany. He completed his undergraduate education in the CEIT at Ege University
in 2009. He gets his PhD degree from Hacettepe University, Institute of Educational Sciences. His research interests relate
to learning analytics, educational data mining, e-learning, decision theory, and statistics.
Dr Sait Uluç is a faculty member at Hacettepe University, Faculty of Letters, Department of Psychology. His research interests are clinical psychology, child development, child psychopathology, and Asperger syndrome.
Dr Halil Yurdugül has bachelor and master’s degrees from the Statistics Department at Hacettepe University. He completed his PhD study at Hacettepe University Educational Sciences/ Measurement and Evaluation Department. He works
as a Professor at Hacettepe University, CEIT Department. His research interests relate to e-assessment, learning analytics,
educational data mining, e-learning, decision theory, and statistics.
ORCID
Sinan Keskin
http://orcid.org/0000-0003-0483-3897
Sait Uluç
http://orcid.org/0000-0002-7048-8545
Halil Yurdugul
http://orcid.org/0000-0001-7856-4664
Muhittin Şahin
http://orcid.org/0000-0002-9462-1953
References
Ajmal, M., & Ahmad, S. (2019). Exploration of anxiety factors among students of distance learning: A case study of Allama
Iqbal Open University. Bulletin of Education and Research, 41(2), 67–78. https://files.eric.ed.gov/fulltext/EJ1229454.pdf
Alkis, Y., Kadirhan, Z., & Sat, M. (2017). Development and validation of social anxiety scale for social media users.
Computers in Human Behavior, 72, 296–303. https://doi.org/10.1016/j.chb.2017.03.011
APA (American Psychiatric Association). (2000). Task Force on DSM-IV. Diagnostic and statistical manual of mental disorders:
DSM-IV-TR (4th ed.). American Psychiatric Association.
Bağrıacık Yılmaz, A., & Karataş, S. (2018). Development and validation of perceptions of online interaction scale. Interactive
Learning Environments, 26(3), 337–354. https://doi.org/10.1080/10494820.2017.1333009
Baltacı, Ö., & Hamarta, E. (2013). Analyzing the relationship between social anxiety, social support and problem solving
approach of university students. Education and Science, 38(167), 226–240. http://egitimvebilim.ted.org.tr/index.php/
EB/article/view/1757/479
12
S. KESKIN ET AL.
Behrens, F., & Kret, M. E. (2019). The interplay between face-to-face contact and feedback on cooperation during real-life
interactions. Journal of Nonverbal Behavior, 43, 513–528. https://doi.org/10.1007/s10919-019-00314-1
Bernstein, G. A., Bernat, D. H., Davis, A. A., & Layne, A. E. (2008). Symptom presentation and classroom functioning in a
nonclinical sample of children with social phobia. Depression and Anxiety, 25(9), 752–760. https://doi.org/10.1002/
da.20315
Caballo, V. E., Salazar, I. C., Irurtia, M. J., Arias, B., & Hofmann, S. G. (2010). Measuring social anxiety in 11 countries.
European Journal of Psychological Assessment, 26(2), 95–107. https://doi.org/10.1027/1015-5759/a000014
Campbell-Sills, L., Espejo, E., Ayers, C. R., Roy-Byrne, P., & Stein, M. B. (2015). Latent dimensions of social anxiety disorder: A
re-evaluation of the Social Phobia Inventory (SPIN). Journal of Anxiety Disorders, 36, 84–91. https://doi.org/10.1016/j.
janxdis.2015.09.007
Chiu, C. M., & Wang, E. T. (2008). Understanding web-based learning continuance intention: The role of subjective task
value. Information & Management, 45(3), 194–201. https://doi.org/10.1016/j.im.2008.02.003
Clark, D. M. (1997). Cognitive therapy for social phobia: Some notes for therapists [Unpublished manuscript].
Clark, D. A., & Beck, A. T. (2010). Cognitive theory and therapy of anxiety and depression: Convergence with neurobiological findings. Trends in Cognitive Sciences, 14(9), 418–424. https://doi.org/10.1016/j.tics.2010.06.007
Clark, D. M., & Wells, A. (1995). A cognitive model of social phobia. In R. Heimberg, M. Liebowitz, D. A. Hope, & F. R.
Schneier (Eds.), Socialphobia: Diagnosis, assessment, and treatment (pp. 69–93). Guilford Press.
Connor, K. M., Davidson, J. R. T., Churchill, L. E., Sherwood, A., Weisler, R. H., & Foa, E. (2000). Psychometric properties of the
Social Phobia Inventory (SPIN). British Journal of Psychiatry, 176(4), 379–386. https://doi.org/10.1192/bjp.176.4.379
Davidson, J. R., Hughes, D. L., George, L. K., & Blazer, D. G. (1993). The epidemiology of social phobia: Findings from the
Duke Epidemiological Catchment Area Study. Psychological Medicine, 23(3), 709–718. https://doi.org/10.1017/
S0033291700025484
Eryılmaz, M., & Çigdemoğlu, C. (2018). Individual flipped learning and cooperative flipped learning: Their effects on students’ performance, social, and computer anxiety. Interactive Learning Environments, 27(4), 432–442. https://doi.org/
10.1080/10494820.2018.1522652
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement
error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Heimberg, R. G., Horner, K. J., Juster, H. R., Safren, S. A., Brown, E. J., Schneier, F. R., & Liebowitz, M. R. (1999). Psychometric
properties of the Liebowitz social anxiety scale. Psychological Medicine, 29(01), 199–212. https://doi.org/10.1017/
S0033291798007879
Hill, J. R., Song, L., & West, R. E. (2009). Social learning theory and web-based learning environments: A review of research
and discussion of implications. American Journal of Distance Education, 23(2), 88–103. https://doi.org/10.1080/
08923640902857713
Kalina, C., & Powell, K. C. (2009). Cognitive and social constructivism: Developing tools for an effective classroom.
Education, 130(2), 241–250. https://docdrop.org/static/drop-pdf/Powell-and-Kalina-U6g4p.pdf
Kuo, Y. C. (2010). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in distance
education courses [Unpublished doctoral dissertation]. Utah State University, Instructional Technology and Learning
Sciences.
La Greca, A. M., & Lopez, N. (1998). Social anxiety among adolescents: Linkages with peer relations and friendships.
Journal of Abnormal Child Psychology, 26(2), 83–94. https://doi.org/10.1023/A:1022684520514
Leary, M. R. (1983). A Brief version of the fear of negative evaluation scale. Personality and Social Psychology Bulletin, 9(3),
371–375. https://doi.org/10.1177/0146167283093007
Lee, B. W., & Stapinski, L. A. (2012). Seeking safety on the internet: Relationship between social anxiety and problematic
internet use. Journal of Anxiety Disorders, 26(1), 197–205. https://doi.org/10.1016/j.janxdis.2011.11.001
Liebowitz, M. R. (1987). Social phobia. Modern Problems of Pharmacopsychiatry, 22, 141–173. https://doi.org/10.1159/
000414022
Miranda, S., & Vegliante, R. (2019). Learning analytics to support learners and teachers: The navigation among contents as
a model to adopt. Journal of e-Learning and Knowledge Society, 15(3), 101–116. https://doi.org/10.20368/1971-8829/
1135065
Moore, M. G. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/
08923648909526659
Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26
(1), 29–48. https://doi.org/10.1080/01587910500081269
Muirhead, B., & Juwah, C. (2004). Interactivity in computer-mediated college and university education: A recent review of
the literature. Educational Technology & Society, 7(1), 12–20. www.jstor.org/stable/jeductechsoci.7.1.12
Nandi, D., Hamilton, M., Harland, J., & Warburton, G. (2011). How active are students in online discussion forums? In J.
Hamer & M. Raadt (Eds.), Proceedings of the thirteenth Australasian computing education conference (Vol. 114, pp.
125–134). Australian Computer Society, Inc. https://dl.acm.org/doi/abs/10.5555/2459936.2459952
Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling: A
Multidisciplinary Journal, 10(4), 622–647. https://doi.org/10.1207/S15328007SEM1004_8
Nunnally, J. C., & Bernstein, I. H. (1994). The assessment of reliability. Psychometric Theory, 3, 248–292.
INTERACTIVE LEARNING ENVIRONMENTS
13
Pierce, T. (2009). Social anxiety and technology: Face-to-face communication versus technological communication
among teens. Computers in Human Behavior, 25(6), 1367–1372. https://doi.org/10.1016/j.chb.2009.06.003
Rapee, R. M., & Heimberg, R. (1997). A cognitive-behavioral model of anxiety in social phobia. Behaviour Research and
Therapy, 35(8), 741–756. https://doi.org/10.1016/S0005-7967(97)00022-3
Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information
Technology Education: Research, 8(1), 177–191. https://doi.org/10.28945/166
Schroeder, J. E., & Ketrow, S. M. (1997). Social anxiety and performance in an interpersonal perception task. Psychological
Reports, 81(3), 991–996. https://doi.org/10.2466/pr0.1997.81.3.991
Shepherd, R. M., & Edelmann, R. J. (2005). Reasons for internet use and social anxiety. Personality and Individual Differences,
39(5), 949–958. https://doi.org/10.1016/j.paid.2005.04.001
Sherry, L. (1995). Issues in distance learning. International Journal of Educational Telecommunications, 1(4), 337–365.
Song, L. (2005). Self-directed learning in online environments process, personal attribute, and context [Unpublished doctoral
dissertation]. University of Georgia.
Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59–70. https://doi.org/10.1016/j.iheduc.2003.11.003
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Teo, T. (2010). Development and validation of the e-learning acceptance measure (ElAM). The Internet and Higher
Education, 13(3), 148–152. https://doi.org/10.1016/j.iheduc.2010.02.001
Thurmond, V. A. (2003). Examination of interaction variables as predictors of students’ satisfaction and willingness to enroll in
future web-based courses while controlling for student characteristics [Unpublished doctoral dissertation]. University of
Kansas.
Vaidya, N., Jaiganesh, S., & Krishnan, J. (2016). Prevalence of internet addiction and its impact on the physiological balance
of mental health. National Journal of Physiology, Pharmacy and Pharmacology, 6, 97–100. https://doi.org/10.5455/
njppp.2015.5.0511201588
van Ameringen, M., Mancini, C., & Farvolden, P. (2003). The impact of anxiety disorders on educational achievement.
Journal of Anxiety Disorders, 17(5), 561–571. https://doi.org/10.1016/S0887-6185(02)00228-1
Vygotsky, L. S. (1978). Mind and society: The development of higher mental processes. Harvard University Press.
Wei, H. C., Peng, H., & Chou, C. (2015). Can more interactivity improve learning achievement in an online course? Effects of
college students’ perception and actual use of a course-management system on their learning achievement.
Computers & Education, 83, 10–21. https://doi.org/10.1016/j.compedu.2014.12.013
Weidman, A. C., Fernandez, K. C., Levinson, C. A., Augustine, A. A., Larsen, R. J., & Rodebaugh, T. L. (2012). Compensatory
internet use among individuals higher in social anxiety and its implications for well-being. Personality and Individual
Differences, 53(3), 191–195. https://doi.org/10.1016/j.paid.2012.03.003
Weinstein, A., Dorani, D., Elhadif, R., Bukovza, Y., Yarmulnik, A., & Dannon, P. (2015). Internet addiction is associated with
social anxiety in young adults. Annals of Clinical Psychiatry : Official Journal of the American Academy of Clinical
Psychiatrists., 27(1), 4–9. https://pubmed.ncbi.nlm.nih.gov/25696775/
Wells, A. (1997). Cognitive therapy of anxiety disorders: A practice manual and conceptual guide. Wiley.
Wells, A., & Clark, D. M. (1997). Social phobia: A cognitive approach. In G. C. L. Davey (Ed.), Phobias: A handbook of description, treatment and theory (pp. 3–26). Wiley.
Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The
Internet and Higher Education, 10(1), 15–25. https://doi.org/10.1016/j.iheduc.2006.10.005
Yang, H. J., Lay, Y. L., Tsao, W. Y., & Lay, J. S. (2010). The impact of internet on social anxiety and learning adaptability for
young adult internet users. 2010 International Conference on e-Business (ICE-B), Athens, Greece (pp. 1–4). IEEE. https://
ieeexplore.ieee.org/document/5740446
Yen, J. Y., Yen, C. F., Chen, C. S., Wang, P. W., Chang, Y. H., & Ko, C. H. (2012). Social anxiety in online and real-life interaction
and their associated factors. Cyberpsychology, Behavior, and Social Networking, 15(1), 7–12. https://doi.org/10.1089/
cyber.2011.0015
Yu, T., & Jo, I. H. (2014). Educational technology approach toward learning analytics: Relationship between student online
behavior and learning performance in higher education. In M. Pistilli, J. Willis, D. Koch, & K. Arnold (Eds.), Proceedings of
the fourth international conference on learning analytics and knowledge (pp. 269–270). Indianapolis, IN: ACM.https://doi.
org/10.1145/2567574.2567594