Computers & Education 59 (2012) 222–235
Contents lists available at SciVerse ScienceDirect
Computers & Education
journal homepage: www.elsevier.com/locate/compedu
Characterizing communication networks in a web-based classroom: Cognitive
styles and linguistic behavior of self-organizing groups in online discussions
Pamela Vercellone-Smith a, *, Kathryn Jablokow a,1, Curtis Friedel b, 2
a
b
Engineering Division, School of Graduate Professional Studies, Penn State University – Great Valley, 30 E. Swedesford Rd., Malvern, PA 19355, USA
Department of Agricultural and Extension Education, 288 Litton-Reaves Hall (0343), Virginia Tech, Blacksburg, VA 24061, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 29 July 2011
Received in revised form
9 January 2012
Accepted 10 January 2012
In this study, we explore the cognitive style profiles and linguistic patterns of self-organizing groups
within a web-based graduate education course to determine how cognitive preferences and individual
behaviors influence the patterns of information exchange and the formation of communication hierarchies in an online classroom. Network analysis was performed on communication data collected from
1131 student messages posted in 19 asynchronous online discussion forums to determine centrality,
clique membership, and core-periphery structure in the communication networks. The social network
data were examined in relation to the students’ cognitive style profiles, which were assessed using the
Kirton Adaption-Innovation Inventory (KAI) (Kirton, 1976, 2011). The cognitive style composition of small
cliques (dyadic and triadic) was found to be highly heterogeneous, often with large cognitive gaps
between clique members, which suggests that web-based environments may mask cognitive style
differences that have been shown to create conflict in face-to-face interactions. In addition, the cognitive
style mean of the students in the core of the network was found to be significantly more adaptive than
that of the periphery group. To further characterize the nature of the communicative interactions,
automated linguistic analysis was used to analyze the students’ writing samples. Interestingly, students
in the core of the social network demonstrated a significantly higher usage of several language features
associated with individuals who actively promote enhanced group performance and cohesion. For our
sample, the linguistic behaviors of students in the core of the social network, coupled with their more
adaptive cognitive style preferences, suggest that these students may inherently place greater value on
fostering group cohesion than those in the periphery.
Ó 2012 Elsevier Ltd. All rights reserved.
Keywords:
Computer mediated communication
Distance education
Learning communities
1. Introduction
Given the expanding role of online education today, understanding how students engage with each other in web-based courses has
become increasingly important. By examining the patterns of participation that emerge in students’ communication networks, a better
understanding of the relationship between participation and performance can be discerned (Marshall & Stohl, 1993). Within online learning
environments, information exchange is often facilitated through the use of discussion-based activities, which are believed to play a crucial
role in the pedagogical experience. In particular, the use of asynchronous online discussion forums is thought to be essential for the
negotiation and exchange of ideas, as well as the development of critical thinking skills, all of which are important components of the
collaborative learning process (Garrison, Anderson, & Archer, 2001; Garrison & Cleveland-Innes, 2005; Pena-Shaff & Nicholls,
2004).Moreover, several studies have demonstrated a positive association between the levels of participation in online discussion
forums and positive learning outcomes (Schellens, Van Keer, Valcke, & De Wever, 2007; Webb, Jones, Barker, & van Schaik, 2004), as well as
higher levels of knowledge construction (Schellens & Valcke, 2005, 2006; Zhu, 2006).
* Corresponding author. Tel.: þ1 610 648 3337; fax: þ1 610 648 3377.
E-mail addresses: pav115@psu.edu (P. Vercellone-Smith), KWL3@psu.edu (K. Jablokow), cfriedel@vt.edu (C. Friedel).
1
Tel.: þ1 610 648 3372; fax: þ1 610 648 3377.
2
Tel.: þ1 540 231 8177; fax: þ1 540 231 3824.
0360-1315/$ – see front matter Ó 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compedu.2012.01.006
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
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As highlighted by several researchers (Beck, Fitzgerald, & Pauksztat, 2003; Kaptelinin & Cole, 2001; Lipponen, 2002; Sundararajan, 2009),
more insight is needed to clarify how individual differences impact student participation, patterns of information exchange, and the
formation of communication hierarchies in online classrooms. In this exploratory study, we contribute to this search for understanding by
characterizing the individual behaviors associated with the communicative exchanges in asynchronous online discussion forums using two
complementary analytical approaches. In particular, we explore the cognitive style profiles and linguistic behaviors of students in selforganizing groups that formed within the communication networks in a web-based graduate education course.
1.1. Cognitive style and students’ online interactions
Cognitive style, which is defined as “consistent individual differences found in ways of organizing and processing information and
experience” (Messick, 1984), or as an individual’s “stable, characteristic preferred way of managing structure when responding to or seeking
to bring about change – including the solution of problems” (Kirton, 2011), is a key influence on human behavior. Recently, researchers have
begun to address the impact that cognitive style has on student collaboration and team performance (Felder & Brent, 2005; Huang, Sheng, &
Huang, 2011; Jablokow, 2008; Lopez-Mesa & Thompson, 2006). While most of this research has focused (not unexpectedly) on the resident
classroom, some valuable early contributions have been made toward understanding the links between cognitive style and online student
behavior and performance as well (Graff, 2003; Jablokow & Vercellone-Smith, 2011; Tallent-Runnels et al., 2006).
Given the wide array of cognitive style dimensions that have been reported (Coffield, Moseley, Hall, & Ecclestone, 2004), it is important to
consider how these differences influence student participation and communication in a web-based course. For example: since an individual’s cognitive style influences his or her preferred way of managing structure when problem solving (Kirton, 2011), we might expect
students to respond and learn differently depending on the amount/type of structure present within a course. This view has already been
supported in part through Graff’s work (Graff, 2003), in which he found significant differences in academic performance among students
who were identified as having either an “imager” or a “verbaliser” cognitive style (Riding, 1991) and argued for web-based instruction to be
designed for both. Tallent-Runnels et al. (2006) reported that specific online tools and functions appeal differently to students depending on
their cognitive styles, and echoed Graff’s conclusions by emphasizing the importance of gaining a better understanding of the relationships
between the delivery environment, specific instructional tools, the learner, and the instructor.
Most importantly for our study, we might also expect cognitive style to impact the ways in which students interact with each other (and
their instructors) in a web-based environment. Recent studies have demonstrated that learning style, which is regarded by many as a subset
of cognitive style (Kirton, 2011; Zhang & Sternberg, 2009), can impact a student’s level of participation in an online class. Huang et al. (2011)
reported that “sensory” learners exhibit a higher level of online participation in terms of both the frequency and duration of their
communications as compared with “intuitive” learners. Samms and Friedel (in press) recently reported that undergraduate students who
had dissimilar cognitive styles from their instructors (in a face-to-face setting) employed different learning strategies to cope with the
instructor-to-student cognitive style gaps that existed; one might expect to see similar variations in an online course as well. Based on recent
studies that demonstrate the wide cognitive style diversity of students enrolled in online courses (Graff, 2003; Jablokow & Vercellone-Smith,
2011; Jablokow, Vercellone-Smith, & Richmond, 2009), and the fact that differences in cognitive style have been shown to create conflict
within face-to-face teams (Buffinton, Jablokow, & Martin, 2002; Creed, 2010; Hammerschmidt, 1996), the need to understand more about
how cognitive style impacts the dynamics of online student interactions and participation seems clear.
1.2. Linguistic behaviors and status structures within social networks
To gain further insight into the nature of online student interactions, social network analysis (SNA) is increasingly being used to evaluate
the communication patterns and status structures that emerge during students’ web-based discussions (Aviv, Erlich, Gilad, & Geva, 2003;
Erlin, Yusof, & Rahman, 2009; Haythornthwaite, 2001; Russo & Koesten, 2005; Willging, 2005; Zhu, 2006). Social network analysis can
readily reveal the most prolific and the most influential students, as well as those students who are isolated or those who assume roles as
mediators between their classmates. A status/influence hierarchy will characteristically emerge within any small interacting group; this
hierarchy impacts not only an individual’s position within the group, but also their level of participation and commitment to group activities
(Reynolds & Fisek, 1972). As described by Gould (2002): “Small groups regularly differentiate into a few core members who contribute
actively to conversation and task performance and receive a great deal of attention, and a larger number of peripheral members who
participate rarely and receive little attention”. This emergence of social differentiation was also observed in an educational setting by Shelley
and Troyer (2001), where group members of higher status were found to engage in more frequent conversation and for a longer duration.
In the online classroom, Beck et al. (2003) proposed that status hierarchies form because students are able to first view and evaluate
the contributions of their peers, then select to whom they will respond; this is a particular benefit of asynchronous online discussions
(Pena-Shaff & Nicholls, 2004), which enable students to modulate the frequency, length, and content of their discourse in response to their
peers. As Gould (2002) noted, status hierarchies include highly engaged students who assume central, influential roles in the social structure
of the class (i.e., the “core”), as well as other students who are less engaged and have fewer social connections (i.e., the “periphery”). Given
the self-selecting nature of these communicative exchanges, it will be important to consider how these differential contributions impact the
social dynamics and emergence of influence within the online classroom.
Recently, researchers have begun to apply social language processing in conjunction with social network analysis to help understand the
nature of the socially situated relationships between individuals in a communication network (Scholand, Tausczik, & Pennebaker, 2010) –
i.e., not only “who communicates with whom” but the content and tone of those communications as well. The analysis of differential
language patterns to characterize the personality attributes of individuals is a growing area of research (Pennebaker, 2002; Pennebaker &
King, 1999; Pennebaker, Mehl, & Niederhoffer, 2003; Tausczik & Pennebaker, 2010), with differences in cognitive complexity and emotional
stance among the variations known to be reflected in an individual’s language constructs. Gender has also been reported to impact the
stylistic aspects of students’ linguistic behavior in online discussion groups (Guiller & Durndell, 2007). Combined with the emergence of
status structures as discussed above, these investigations raise the question of whether (and how) core and periphery groups of online
students behave differently in terms of their linguistic patterns.
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1.3. Research questions and structure of this paper
To address the needs identified above, we explored the cognitive style profiles and linguistic patterns of self-organizing groups within
a web-based graduate education course to determine how cognitive preferences and individual behaviors influence participation (e.g.,
frequency and duration of communication), patterns of information exchange (e.g., ‘who’ students choose as communication partners), and
the formation of communication hierarchies (e.g., a student’s relative positioning and influence in the classroom social network) in an online
classroom. With an increased awareness of how individual behaviors contribute to the formation of communication hierarchies and social
roles in self-organizing groups, instructors can develop more effective strategies for designing online activities that will facilitate increased
student engagement. The central research questions for this study are these:
Q1: Do students preferentially communicate with other students who have similar cognitive styles?
Q2: Does cognitive style impact a student’s level of online participation (e.g., frequency and/or length of communications)?
Q3: Do the cognitive style preferences of the most highly engaged students in a communication network (i.e., the core) differ from those
who are more loosely connected to the network (i.e., the periphery)?
Q4: Do students in the core of the network exhibit different linguistic behaviors from those in the periphery?
The structure of this paper is as follows: in Section 2, we present a summary of key concepts from the literature that support our
theoretical frameworks and empirical approach. These include a brief review of cognitive style theory, key principles of social network
analysis, and some background on the analysis of social and psychological processes using linguistic markers. In Section 3, we describe our
research methods, including details about our sample, data collection, processing methods, and the analysis of linguistic patterns. Section 4
describes the findings of our analyses, including the cognitive style diversity found in our sample, the social structures that emerged, and the
linguistic markers associated with various groupings. Finally, in Section 5, we discuss the implications of our findings for the online
classroom, as well as limitations of this work and future research directions.
2. Theoretical frameworks and review of relevant literature
We begin this section with a short summary of the cognitive style framework used here (Kirton’s Adaption-Innovation Theory (Kirton,
2011)), along with a description of the psychometric instrument used to assess the cognitive styles of our student sample (i.e., KAIÒ). We
move then to a brief review of relevant research in the domain of social network analysis, including recent efforts that have begun to address
the impact of cognitive style in online environments and the formation of core-periphery structures within online communities. Finally, we
provide a short description of linguistic analysis as it relates to the psychological and social meaning of word usage patterns.
2.1. Cognitive style diversity: Adaption-Innovation (A-I) theory and KAI
Cognitive level and cognitive style are two fundamental variables commonly used to characterize the cognitive diversity of individuals
(Graff, 2003; Kirton, 2011; Messick, 1984; Torrance, Reynolds, Ball, & Riegel, 1978; Zhang & Sternberg, 2009). In the context of problem
solving, an individual’s cognitive level refers both to their potential capacity (e.g., intelligence or aptitude) for solving problems, as well as
their manifest level (e.g., skills, knowledge, and/or expertise in a given domain), while cognitive style refers to “the preferred way” in which
a person solves problems (i.e., one’s preference for managing cognitive structure) (Kirton, 2011). Numerous studies have demonstrated the
independence of cognitive style and cognitive level, as well as the stability of cognitive style over one’s lifetime (Clapp, 1993; Kirton, 2011).3
The importance of using sound theory and reliable, well-validated, psychometric instruments in educational research is repeatedly
stressed (Felder & Brent, 2005; Tiedemann, 1989). Accordingly, we rely on Kirton’s Adaption-Innovation (A-I) theory (Kirton, 2011) and the
corresponding KAIÒ (Kirton, 1976; Occupational Research Centre, 2011) to understand and assess an individual’s cognitive style. KAI is
a highly respected, rigorously validated, and efficient instrument with reported internal reliabilities between 0.84 and 0.89 (Kirton, 2011).
From a practical standpoint, the key distinction between “more adaptive” and “more innovative” individuals (remembering that style is
a continuum, not a dichotomy) is linked to their preferred manner of managing structure (of all kinds). In general, individuals who are more
adaptive prefer to operate with more structure and with more of that structure consensually agreed, while individuals who are more
innovative prefer to operate using less structure and are less concerned with achieving consensus around the structure they use (Jablokow &
Kirton, 2009; Kirton, 2011). The impact of cognitively diverse styles in the context of group collaboration will be discussed in greater depth
along with the data analysis in Section 4.
On the Adaption-Innovation continuum, a person’s KAI score will fall within the range of 32–160 (theoretical mean: 96), with a score of
32 representing the theoretical limit of highest Adaption, and a score of 160 representing the theoretical limit of highest Innovation. In
practice, scores typically fall between 45 and 145 (Kirton, 2011). For large general populations, KAI scores follow a normal distribution with
an observed mean close to 95 (0.5) and a standard deviation around 17 for all samples (Kirton, 2011). Smaller groups (e.g., occupational
samples) often exhibit skewed distributions about different means, depending upon the nature of the problems they typically encounter.
With respect to gender differences, women tend to be (on average) slightly more adaptive than men (female mean: 91; male mean: 98); thus
far, no culture differences have been found in the large sample studies (Kirton, 2011).
Within KAI’s wide range, the “just noticeable difference” (JND) between two individuals operating face-to-face is only 10 points (Kirton, 2011).
When collaborating with others, wide differences in cognitive style (greater than 20 points) have been shown to adversely affect communication,
trust, and performance in teams if they are not managed well. Such challenges can surface whether the team is engaged in a collaborative activity
designed to facilitate information exchange and knowledge construction within the group (Creed, 2010; Hammerschmidt, 1996; Kirton, 2011)
3
Cognitive style is not the same as behavior. While behavior is flexible, cognitive style has been shown to be fixed early in life and is highly resistant to change (Jablokow &
Kirton, 2009; Kirton, 2011).
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or working toward the development of a specific, shared deliverable (Buffinton et al., 2002; Kirton, 2011). These differences in cognitive style are
examples of cognitive gap, which is defined as any cognitive difference that has an impact on problem solving performance. Conversely, teams
that are homogeneous in style (comprised of individuals whose styles differ by 10 points or less) tend to experience fewer interpersonal difficulties, but they also tend to be less effective at carrying out a wider diversity of tasks (Jablokow & Kirton, 2009). Therefore, it is important to
realize that a wide range of cognitive diversity both enables and limits any collaboration, whether that collaboration is carried out face-to-face or
at a distance. While dissimilar cognitive styles can create tension and conflict within a group, these cognitively diverse approaches are also
essential for managing the complexity and magnitude of the problems we face today.
2.2. Social network analysis in higher education
Social network analysis (SNA) provides a powerful mechanism for understanding how human relationships form and develop over time,
as well as revealing patterns of communication and power structures that emerge as a result of these interactions. SNA has been used widely
to study social structures and emergent behaviors across numerous domains, including the social sciences, politics, business, communication, and information science (Hanneman & Riddle, 2005; Scott, 2000; Wasserman & Faust, 1994). With the steep growth in online
education, SNA is being used increasingly to gain insights into student interactions, social roles, and knowledge exchange within web-based
learning environments (Erlin et al., 2009; Haythornthwaite, 2001; Russo & Koesten, 2005; Willging, 2005; Zhu, 2006). SNA offers online
educators a perspective on classroom activity that extends beyond simply monitoring the number of postings a student makes in an online
discussion forum. It is also being used to assess the quality of student engagement in online discussions, as well as to provide insight into the
manner in which knowledge is constructed in these educational communities (Aviv et al., 2003; Schellens & Valcke, 2005, 2006).
Furthermore, the centrality and prestige of students in an online graduate class (i.e., those with higher out-degree and in-degree centrality
metrics, respectively) were found to be strongly associated with comprehension and retention of knowledge (Russo & Koesten, 2005).
Using social network analysis, a wide range of metrics can be computed to quantify the interactions between individuals (actors) in
a communication network. Centrality metrics are used to determine an individual’s role, position, and relative influence within a given
network. These metrics include out-degree centrality, which serves as a measure of how influential an actor is in terms of his/her
“expansiveness”, and in-degree centrality, which has been used as an indicator of a person’s prestige or popularity within the group. Other
centrality metrics include closeness centrality (related to how quickly a person can interact with others) and betweenness centrality (the
extent to which an actor serves as an information “broker” within the group). SNA also serves as a powerful tool for identifying subgroups,
such as clusters and cliques, which form within the social structure of the network. For a comprehensive overview of social network analysis
metrics, see (Hanneman & Riddle, 2005; Scott, 2000; Wasserman & Faust, 1994).
Social network analysis has also proven to be a valuable resource for evaluating the overall patterns of social dynamics and information
exchange that occur in a class as a whole. Network density metrics, which reflect the overall level of engagement in a social network, help
clarify the kinds of ties that exist between actors within a particular social structure, as well as the speed at which information diffuses
among the actors and the degrees of cohesion, trust, and social capital within a group. Recent studies of asynchronous online discussions
have also begun to employ the use of core-periphery analysis for evaluating the structure of student social networks (Beck et al., 2003; Wang,
2010). Core-periphery analysis is a modeling technique developed by Borgatti and Everett (1999) to facilitate the identification of actors in
a social network who have a high density of reciprocal interactions (the “core” of the social network), as compared to actors who are more
loosely connected (those in the “periphery”). Several studies have demonstrated that students who possess a high degree of centrality and
also assume a position in the core (i.e., attain a higher status) learn more effectively and demonstrate improved academic performance
(Baldwin, Bedell, & Johnson, 1997). In contrast to peripheral actors, these core actors are reported to have a greater proclivity for early
submission in discussion forums, as well as a high frequency of repeated reciprocal exchanges (Beck et al., 2003). In the present study, we
will build on all these efforts by investigating the emergent social structures within an online class (including cliques and core-periphery
subgroups) and examining the links with cognitive style in each case.
2.3. Analysis of social and psychological processes using linguistic markers
In recent years, the investigation of how individuals use words in their daily lives has gained increasing attention. Differences in cognitive
complexity, focal point(s) of attention, propensity for word proliferation, and emotional stance are just some of the variations known to be
reflected in an individual’s language constructs. Numerous studies have demonstrated that these unique variations in word usage patterns
can be beneficial in revealing the psychological and social processes that serve as the foundation for interpersonal interactions (Aragamon,
Dhawle, Koppel, & Pennebaker, 2005; Aragamon, Koppel, Pennebaker, & Schler, 2009; Chung & Pennebaker, 2007; Kramer & Rodden, 2008;
Pennebaker, 2002; Pennebaker & King, 1999; Pennebaker et al., 2003; Scholand et al., 2010; Sexton & Helmreich, 1999; Tausczik &
Pennebaker, 2010). Computerized linguistic analysis tools have been developed to identify and categorize not only the content words
used by individuals, but also to analyze the distinctive variations that occur in the use of function words (i.e., style words – those that
primarily serve a grammatical function) (Chung & Pennebaker, 2007; Pennebaker et al., 2003; Tausczik & Pennebaker, 2010). The Linguistic
Inquiry and Word Count tool (LIWC) (Pennebaker, Chung, Ireland, Gonzales, & Booth, 2007) is a widely used, highly validated, text analysis
program designed to measure 79 dimensions of word usage, including: standard linguistic dimensions (e.g., word count, words per sentence, percentage of words containing more than 6 letters, punctuation); function words (e.g., pronouns, prepositions, articles, auxiliary
verbs); relativity-related words (e.g., verb tense, time); and 32 psychometrically validated content word categories (e.g., positive emotion,
negative emotion, affect, cognition). The program analyzes each word in a text file by comparing it to an extensive dictionary that was
validated by independent judges (Pennebaker et al., 2007).
Tausczik and Pennebaker (2010) provide a comprehensive review of the literature pertaining to the psychological and social meaning of
word usage patterns. Brief overviews of the social and psychological correlates of some key word categories within the LIWC application that
are relevant for this study are summarized here. To begin: within a social network, word count reflects the relative degree of engagement of
actors in a social network and can identify the dominant members in these relationships. Individuals with higher social status are expected
to converse more, while lower status individuals would converse less, have a greater propensity for using words that are more tentative, and
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may ask more questions (Sexton & Helmreich, 1999; Tausczik & Pennebaker, 2010). The greater use of first person plural pronouns (e.g., we),
when coupled with higher word counts, can serve as good indicator of group cohesion, while the use of second person pronouns (e.g., you)
has been shown to be negatively related to the quality of a relationship (Simmons, Chambless, & Gordon, 2008; Tausczik & Pennebaker,
2010). The nature of these relationships can be further clarified by examining the incidence of assents, negation, and positive or negative emotion words. Verb tense and pronoun usage can provide insight into an individual’s attentional focus; for example, self-focused
individuals tend to use more first person personal pronouns (Tausczik & Pennebaker, 2010).
As described by Tausczik and Pennebaker (2010), there are several categories of words that can provide insight into the complexity of an
individual’s language constructs. These include words with more than six letters, exclusive words (e.g., but, except, without), and prepositions (e.g., to, with, above, in, by). Individuals employ exclusive words when they seek to emphasize a distinction between concepts that lie
within or outside of a given domain. In addition, the precision of an individual’s language is enhanced via the use of prepositions, which
serve as indicators that more precise information about a topic will follow. Two sub-categories associated with cognitive mechanisms
within the LIWC dictionary, namely, causation words (e.g., because, effect, hence) and insight words (e.g., think, know, consider), may be
associated with individuals who are actively engaged in re-evaluating past events.
Several recent studies have revealed that patterns of function/style word usage may serve as good indicators of an individual’s social and
psychological processes (Chung & Pennebaker, 2007; Tausczik & Pennebaker, 2010). As Tausczik and Pennebaker (2010, p. 29) note: “From
a psychological perspective, style words reflect how people are communicating, whereas content words convey what they are saying”. The
subtle differences in function word usage are so distinctive that researchers are beginning to couple the application of machine learning
methodologies to text categorization to automatically profile the authors of anonymous text (Aragamon et al., 2005, 2009). Social language
processing has also been used in conjunction with social network analysis to identify socially situated relationships between actors in
a social network (Scholand et al., 2010). In our study, we utilize this social language network analysis (SLNA) approach (Scholand et al., 2010)
to analyze the linguistic content of the communication exchanges within the students’ social network to provide further insight into the
nature of the relationships among them.
3. Research methods
3.1. Research sample
Our sample consisted of 21 education professionals (15 female, 6 male) enrolled in a web-based, graduate-level education course;
although this sample size might be considered small, the amount of data we had available enabled us to explore these students’ interactions
in depth. Specifically, the students’ patterns of communication were examined using communication data collected from 1131 student
messages that were posted in 19 asynchronous online threaded discussion forums. Nine forums were based on readings from the text, while
ten forums posed open-ended questions utilizing case studies from the literature and/or thought-provoking questions (e.g., “Given your
increased knowledge of the GEFT and LSI instruments from the readings, what can be done to improve your interactions with people of
dissimilar learning styles?”).
The forums were designed to provide students with a venue to discuss and debate the relationships between cognitive style, teaching
and learning, leadership, and managing organizational change in the field of education. For each discussion forum, student discussions were
graded utilizing a rubric with respect to four equally weighted criteria: content of the discussion, organization of content, accuracy of
content, and number of contributions to the discussion. To earn full credit for participating in the forums, students were required to respond
at least twice to other students’ posts, and the twelve doctoral students were required to initiate a new discussion in the ten open-ended
forums. Participation in 84.2% of the 19 forums exceeded the minimum required level of engagement.
3.2. Data collection and processing for cognitive style analysis
To measure cognitive style, the KAI was administered by a KAI-certificated practitioner to each student at the beginning of the course as
part of the course curriculum. Confidential feedback was provided to each student independently, and the students were encouraged to
share and discuss their cognitive styles with their classmates. As described by Jablokow (2008) and Jablokow and Vercellone-Smith (2011),
students (both face-to-face and online) are generally eager to share their scores and corresponding insights with their classmates once
a supportive environment has been established and the students clearly understand the value found in all cognitive styles across the
Adaptation-Innovation (A-I) spectrum. KAI total scores were calculated for each student; these results will be reported in Section 4.1.
3.3. Data collection and processing for social network analysis
Social network analysis (SNA) data were collected from transcripts of the threaded discussion forums, which were posted asynchronously through an online course management system (MoodleÒ, 2007). For each forum transcript, the directed patterns of communication
were manually collected and recorded in a forum adjacency matrix to show “who replied to whom”, as well as the number of replies made by
each student to each classmate’s post/thread. For the posts with multiple threaded replies (e.g., Student A’s thread received replies from
Students B and C), the content of the message bodies were examined to specifically determine to whom the reply was being made (i.e.,
whether Student C responded to Student A or B or both). Next, the interaction data were compiled for all discussion forums and aggregated
in a course adjacency matrix. The individual forum adjacency matrices, as well as the course adjacency matrix containing the aggregated
class data, served as the input for the SNA metric analysis, which was performed using the UCINET social network software (Borgatti, Everett,
& Freeman, 2002).
The adjacency matrix corresponding to the data collected from Discussion Forum 1 is shown in Fig. 1 as an example. In this matrix, the
row data correspond to students who sent replies to class members, while the column data represent students who were the recipients of
replies. For example, Student S(114) (i.e., a student with a KAI score ¼ 114) posted two replies to student S(81) (i.e., a student with a KAI
score ¼ 81), as shown by the circled entry. Thus, each column of an adjacency matrix contains the in-degree data for the individual at the
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
227
Fig. 1. Example adjacency matrix for a single discussion forum (Forum 1).
head of that column; each row contains the out-degree data for the individual at the head of that row. A description of the specific SNA
techniques used in this study, including the methodology used for identifying the core-periphery structure for the class, will be discussed
along with the corresponding results in Section 4.2.
3.4. Data collection and processing for linguistic pattern analysis
The Linguistic Inquiry and Word Count (LIWC 2007) program (Pennebaker et al., 2007) is a validated text analysis tool that counts and
parses the words in a given text file into 79 meaningful dimensions. The program analyzes each word by comparing it to an extensive,
validated dictionary that includes categories that correspond to psychological and social processes. To prepare the student writing samples
for linguistic analysis, separate text files were first created for each student. Next, the bodies of all messages posted by each student were
manually extracted from the 19 forum transcripts and aggregated in that student’s designated writing file. The student text files were then
analyzed using the LIWC 2007 program, which subsequently returned output data corresponding to each of the 79 LIWC language
dimensions. With the exception of the word count and words per sentence categories, the values for all linguistic variables in the LIWC output
reflect a percentage of the total words in a given sample (Pennebaker et al., 2007).
In this study, the linguistic output data were specifically used to determine whether there were differential patterns of language usage
between the core and periphery groups of the class (as determined via social network analysis). To accomplish this task, the mean values for
the 79 language variables were calculated for the core and periphery groups, respectively. Next, two-sample (independent) t-tests, assuming
unequal variances, were performed to determine whether there were statistically significant differences (p < 0.05) between the core and
periphery group means for each of the 79 linguistic variables; these results will be presented in Section 4.3.
4. Results and discussion
4.1. Cognitive style diversity within the sample
The cognitive style diversity of the sample was examined with respect to the students’ KAI scores; the KAI score distribution for our
sample is shown in Fig. 2. A wide range of cognitive style diversity was found within the sample, with a KAI total score range of 65–141 (76
points) and a mean of 97 points. While this mean is slightly more innovative than the general population mean of 95 (0.5), these results
align well with previous studies of educators across the US and UK, where the KAI total score means typically ranged from 95 to 97 (Kirton,
2011). Additionally, the cognitive style statistics of our sample were examined based on gender. Although wide ranges of cognitive style
were present for both men and women in the sample, with the female KAI scores (N ¼ 15) ranging from 65 to 141 and the male KAI scores
(N ¼ 6) ranging from 73 to 114, the means of the two groups were similar (97 20.82 for females; 97 16.04 for males).
4.2. Social network analysis
4.2.1. Cumulative degree centrality data
Degree centrality metrics are important for gaining insight into the social roles and relative influence of students in an online learning
environment. In asynchronous threaded discussions, the communication between two students in the direction A-to-B is not necessarily the
same as B-to-A. Therefore, it is important to calculate both in-degree and out-degree centrality metrics to effectively assess the patterns of
directed communication in the forums. Students with high out-degree values send many messages to others; as a result, they may be viewed
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P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
Fig. 2. KAI total score distribution for student sample (N ¼ 21).
as being more expansive “data sources”, with a greater degree of influence within the network (Hanneman & Riddle, 2005; Scott, 2000). In
contrast, students with high in-degree values receive many messages from other students and may be viewed as “data sinks”; these
individuals are often seen as having greater popularity or prestige within the social network (Hanneman & Riddle, 2005; Scott, 2000).
In this study, both the in-degree and out-degree centrality values were found to vary widely within the class. The lowest and highest
cumulative out-degree values observed were 22 and 106, respectively; the lowest and highest cumulative in-degree values were 2 and 116,
respectively (see Table 1). Note that a low in-degree value indicates that an individual received few messages from the remaining students –
i.e., the individual was socially isolated from the rest of the class. For this class, only one student fell into this category, with an in-degree
value of 2 (see Table 1). Interestingly, this same student had a low out-degree value (34) in this course. [Note: For comparative purposes,
observe that student S(96) had a similar out-degree value (36), but had a much higher in-degree value (50).] It would be beneficial for an
instructor to monitor communication patterns to identify such socially isolated students and strive to facilitate their engagement within the
discussions.
To examine the relationships between communication behavior, cognitive style, and gender, the in-degree and out-degree centrality
metrics were examined to determine whether particular patterns of communication could be linked to either variable. Table 1 contains the
cumulative total in-degree (recipient of reply) and out-degree (sender of reply) metrics across all 19 discussion forums, sorted high to low, in
relation to the gender and KAI score of each student. Using regression analysis, no correlation was found between cognitive style and either
out-degree centrality (R2 ¼ 0.0027) or in-degree centrality (R2 ¼ 0.1024). Interestingly, although numerous studies suggest that males are
more actively engaged in online interactions (i.e., make more frequent and longer postings) than females in asynchronous computermediated communication (CMC) environments (Herring, 1993, 2003; Sierpe, 2000), no statistically significant patterns could be discerned based on gender in our sample. Given that the education field is dominated by female educators (National Center for Education
Statistics, 2011), and coupled with the fact that this was a graduate-level education course in which 60% of the students were female, it
is possible that the social factors that have previously been reported to impact CMC interactions in other educational settings were offset
Table 1
Cumulative centrality values per student across 19 forums; sorted on value (high-to-low). (A) In-degree centrality and (B) Out-degree
centrality.
(A)
(B)
In-degree value
Gender
KAI
Out-degree value
Gender
KAI
116
95
89
83
75
65
63
61
54
52
50
44
44
42
38
38
37
35
25
23
2
M
M
F
F
F
F
F
F
M
M
F
M
F
F
F
M
F
F
F
F
F
114
82
81
92
65
69
90
101
101
103
96
73
110
113
77
109
94
141
90
129
106
106
88
85
75
74
69
59
57
52
49
49
45
44
44
42
40
39
36
34
22
22
F
M
F
F
F
F
M
F
M
M
M
F
M
F
F
F
F
F
F
F
F
92
114
65
113
90
141
101
69
109
82
103
90
73
77
101
110
81
96
106
94
129
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
229
here. Our results support Miller and Durndell’s (2004) findings that gender does not significantly influence the frequency or length of
postings in asynchronous online discussions when females represent the majority group.
4.2.2. Clique analysis: subgroup formation and composition
Within a social network, cliques are formed among actors who are more closely and integrally linked with one another relative to the
other members of the network. In social network theory, a clique is defined as a group of 2 or more individuals (in many analyses, 3 or more)
who are connected to each other by strong ties (i.e., reciprocal relationships) (Hanneman, 2005; Scott, 2000). In our work, dyads (2-person
cliques) and triads (3-person cliques) will both be relevant, since we want to explore the impact of cognitive style on the selection of all
communication partners, independent of clique size. Because cliques represent maximally complete sub-graphs within a directed network,
it is the most rigorous method for identifying subgroups in a network. In this study, clique analysis was performed using UCINET as a means
to explore whether cognitive style impacts the number of cliques to which a student belongs, as well as their selection of communication
partners (i.e., the composition of those cliques).
Across the 19 forums for our sample, nine three-member cliques (triads) and 159 two-member cliques (dyads) were identified. The mean
number of dyads formed per forum was quite high (M ¼ 15.83, SD ¼ 9.64, median ¼ 15), especially given a class of 21 students. Using
regression analysis, no correlation was found between cognitive style and the number of dyads to which a student belonged (R2 ¼ 0.0114);
however, cognitive gap analysis did reveal interesting results regarding the heterogeneity of cognitive style within the cliques, as we will
discuss next.
4.2.3. Analysis of cognitive gaps in cliques (dyads and triads)
Cognitive gap analysis of the cliques that formed in our sample (across the 19 online forums) revealed that the majority were highly
heterogeneous with respect to Adaption-Innovation (A-I) cognitive style. As shown in Table 2, the KAI cognitive style gaps between the most
adaptive and most innovative member of each triadic clique ranged from 10 to 60 points (M ¼ 34.22; SD ¼ 14.05). Likewise, the number of
dyadic cliques that formed with cognitive gaps of various sizes is shown in Fig. 3. Because the number of possible dyadic cliques for a given
gap size varied in relation to the gap size (e.g., there were fewer dyads possible with gaps greater than 50 points than gaps of 20 points), the
percentage of cliques that formed relative to the total cliques possible for the specific cognitive gap span is also specified in Fig. 3.
As noted earlier, groups of individuals with similar cognitive styles (i.e., KAI scores within a range of 10 points) tend to experience fewer
disagreements and less tension due to their inherent similarities in problem solving approach (Kirton, 2011). Hence, we might expect
students engaged in online learning to seek out classmates with similar styles, but this was not the case in this class. Instead, we see that the
vast majority of both the dyadic and triadic cliques were highly heterogeneous with respect to cognitive style. Specifically, only one of the
nine triadic cliques (11%) and 28 of the 159 dyadic cliques (17.6%) were homogeneous (10 point gap); the remaining cliques were
moderately to highly heterogeneous, with a mean KAI intra-clique cognitive gap of 34.22 points for the triads and 24.14 points for the dyads.
These results support the findings of Jablokow and Vercellone-Smith (2011), who reported that the cliques formed between engineering
students engaged in asynchronous online discussions were also highly heterogeneous in terms of Adaption-Innovation cognitive style.
These results suggest that the online learning environment may mask cognitive differences in ways that enable more diverse networks to
form more readily than in face-to-face operations, whatever the content domain may be.
4.2.4. Influence within the communication hierarchy
According to the SNA literature, influence and prestige within a social network are frequently associated with an individual’s degree of
connectivity within that network, as well as their membership in many subgroups (Hanneman, 2005; Hoppe & Reinelt, 2010; Scott, 2000).
Highly connected individuals will have greater access to more information and, consequently, may be able to summon resources more
readily. Because these individuals are often seen as more powerful sources of information for the group, their advice is often sought after by
other individuals in the network. From a cognitive style perspective, scholars generally claim that leadership and influence can come from
anywhere along the style continuum, depending on who is in best cognitive alignment with the current problem or task at the present time
(Jablokow & Vercellone-Smith, 2011; Kirton, 2011).
In this study, a student’s influence within the class communication hierarchy was evaluated using a combination of summed centrality
metrics (both in-degree and out-degree) and subgroup participation. First, following the example of Jablokow and Vercellone-Smith (2011),
we designated students as “most influential” if they fell within the top 33% of the class with respect to the highest summed degree centrality
metrics (i.e., 136 in total degree centrality) and membership in the most cliques (see Table 3, shaded rows). It is interesting to note the
substantially increased network connectivity exhibited by the top three students in the class in terms of both summed degree centrality and
clique membership, particularly in light of their diverse cognitive style preferences (KAI ¼ 65, 92, and 114). In line with the findings of
Table 2
Cognitive styles of students in triadic cliques.
KAI scores in 3-member cliques
KAI gap span (points)
Member 1
Member 2
Member 3
69
65
90
81
82
82
65
65
90
113
92
113
101
96
101
90
82
92
129
113
129
114
114
114
92
92
110
60
48
39
33
32
32
27
27
10
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P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
Fig. 3. The number of dyadic cliques within a given KAI cognitive gap range. The percentage of cliques that formed per cognitive gap interval, relative to the total number of cliques
possible for that gap span, is specified above each bar.
Jablokow and Vercellone-Smith (2011), and as expected from Adaption-Innovation theory (Kirton, 2011), these results confirm that influential individuals who assume positions of leadership can possess cognitive styles from across the A-I continuum.
4.2.5. Core-periphery structure
While centrality measures such as in-degree and out-degree metrics are useful for examining the frequency and direction of information
exchange between actors in a social network, core-periphery analysis is beneficial for modeling the structural hierarchy that exists within
that network (Borgatti & Everett, 1999); that is, core-periphery analysis can be used to determine an actor’s selection of communication
partners. In addition, core-periphery analysis has been identified as a powerful tool for evaluating the structure of leadership networks
(Hoppe & Reinelt, 2010). UCINET employs a genetic algorithm developed by Borgatti and Everett (1999) to analyze a network’s coreperiphery structure. This algorithm is designed to fit empirical data through matrix permutation to an idealized block model – i.e., the core/
core region is maximally connected to itself (a perfect 1-block), the core/periphery regions are imperfect 1-blocks (having some connection
to the core), and the periphery/periphery region is a 0-block, having no connections between actors within this space. Thus, an “idealized”
core-periphery structure in a student social network would contain a core comprised of students who engage in a high degree of reciprocated exchanges (i.e., are maximally connected with each other), while peripheral students are loosely connected to the core and also do
not communicate with each other (Borgatti & Everett, 1999; Beck et al., 2003).
In the core-periphery analysis of an actual sample, the fit function determines the positive correlation (i.e., goodness of fit) between the
empirical data and the idealized core-periphery structure, as described above; specifically, a fitness value of 0 indicates that there was no
core, whereas a fitness of 1 means that the observed data was a perfect fit to the ideal model. In practice, fitness values greater than 0.5
suggest that a core-periphery structure has developed, and such a structural model merits further consideration (Hanneman & Riddle,
2005). In this study, core-periphery analysis of the aggregate discussion forum data was performed to determine first: (a) whether
a core-periphery structure did develop, and (b) whether any correlations existed between cognitive style and core (or periphery)
membership within this sample.
Using UCINET, a distinct core-periphery structure was identified within the social network for this class. The model exhibited
a moderate-to-strong core formation, with a fitness of 0.609 (see Fig. 4). The central core consisted of eight students, while the periphery
contained 13 students. Interestingly, the eight students who comprised the core were also identified as being among the top nine students
who had the greatest influence in the class as identified by degree centrality and clique membership (see Table 3). There was one student in
Table 3
Influence within the class communication network. The KAI scores of the most influential members of the class (i.e., those in the top third
of the class based on their summed degree centrality and clique membership) are highlighted in bold italics.
Student KAI (* ¼ core member)
Summed degree centrality (in-degree þ out-degree)
Total no. cliques
114*
92*
65*
82*
90B*
81*
69*
113
101B*
141
101
103
109
73
96
110
77
90
94
129
106
204
189
160
144
137
128
122
117
113
104
103
101
90
88
86
84
82
70
59
45
36
43
32
28
17
19
16
16
17
12
15
14
16
18
14
12
5
10
6
6
6
1
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
231
Fig. 4. Blocked adjacency matrix showing core–periphery structure of student sample.
our sample (S113) who ranked 8th in the leadership rankings, but who was not a member of the core; this suggests that while he/she
communicated widely with many members of the class, his/her ties to the core members in particular were not strong. The core-periphery
analysis adds an additional dimension to the views on students’ influence and self-organizing behavior in online discussions as well: not
only do the top students communicate extensively with the entire class, as evidenced by their high total degree centrality metrics and
membership in a large number of cliques, but these students also preferentially select other top-performing students as communication
partners.
Upon examining the cognitive style profiles of the core and periphery students, it is interesting to note that the mean KAI score for the core
group was significantly more adaptive than that of the periphery group (core: M ¼ 86.75, SD ¼ 16.16, N ¼ 8 vs. periphery: M ¼ 103.23,
SD ¼ 18.69, N ¼ 13). The difference in the means was found to be statistically significant (p < 0.05) using a two-sample t-test assuming unequal
variances [t(17) ¼ 2.14, p ¼ 0.024]. According to A-I theory, more adaptive individuals tend to place greater value on group conformity in order
to maintain group cohesion; from that perspective, our result is not surprising, as the core is (by definition) more tightly coupled than the
periphery group. In addition, more adaptive individuals prefer to operate more methodically, paying closer attention to detail while searching
for relevant information in an organized fashion (Kirton, 2011). Recalling Huang et al.’s (2011) findings that “sensory” learners exhibit a higher
level of online participation in terms of both the frequency and duration of their communications than “intuitive” learners, we note that many
of the characteristics attributed to sensory learners align well with the preferred cognitive strategies used by more adaptive individuals.
Therefore, it is possible that the greater operational efficiency and more careful attention to detail preferred by more adaptive individuals may
facilitate a higher degree of connectivity as they seek to collect and support information exchange among their peers.
4.3. Linguistic analysis using LIWC
Continuing our characterization of student communication networks via SNA core-periphery analysis, we used LIWC (Pennebaker et al.,
2007) to determine whether (and how) patterns of word usage differed between students in the core of the social network(s) and those in
the periphery group. As will be discussed in detail below, seven language categories were identified that showed statistically significant
differences in their means (p < 0.05) between the core and periphery groups for this sample.
4.3.1. Linguistic features of core and periphery members
The significant results of the LIWC analysis of the core and periphery groups of our sample are summarized in Table 4, with the relevant
LIWC linguistic categories listed in the first column. In particular, members of the core were found to exhibit significantly higher word counts,
as well as a higher percentage of positive emotion words. The core members also made more references to time, relativity, and home in their
postings. In contrast, members of the periphery group were found to use more dashes and numbers in their postings. We will discuss the
interpretation of these results in the paragraphs below.
In interpreting our results, we note first that students with a high degree of centrality in online classrooms have been shown to learn
more effectively and demonstrate better academic performance (Baldwin et al., 1997; Wang, 2010). In particular, proximity to the core has
also been shown to have a positive impact on the “information sharing” component of knowledge building (Wang, 2010). In online
educational environments, students in the core are also reported to have a greater proclivity for early submission in discussion forums, and
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P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
Table 4
Linguistic markers associated with core and periphery members of the sample.
LIWC categories
a
Word count
Positive emotion
Time
Relativity
Home
Dash
Number
a
b
Core mean valuesa
11634.38
4.01
3.37
10.47
0.11
0.93
0.92
3367.61
0.28
0.31
0.84
0.05
0.23
0.16
Periphery mean valuesa
7741.5
3.61
2.93
9.74
0.06
1.26
1.12
2776.6
0.40
0.53
0.85
0.06
0.36
0.30
T-test resultb
t(13)
t(18)
t(18)
t(15)
t(16)
t(18)
t(17)
¼
¼
¼
¼
¼
¼
¼
2.71,
2.61,
2.35,
1.90,
1.78,
2.47,
1.92,
p
p
p
p
p
p
p
¼
¼
¼
¼
¼
¼
¼
0.009
0.009
0.015
0.039
0.047
0.012
0.036
With the exception of the word count category, all mean values represent the mean percentage of words in a given category relative to the total number of words.
Results are based on two-sample (independent) t-tests, assuming unequal variances. [t(df) ¼ t-value, p ¼ p-value].
they exhibit a higher frequency of repeat reciprocal exchanges than students in the periphery of the same social network (Beck et al., 2003).
Our results clearly reflect the higher level of communication expected from the core; specifically, the core members in our sample
contributed an average of 33% more text in their postings as compared to those in the periphery (see Table 4, word count).These findings also
complement the results of Shelley and Troyer (2001), who determined that the speeches of high status students (who had emerged from
initially unstructured groups) were of longer duration than their lower status peers.
In addition to a significantly higher word count, core members also used more positive emotion words (e.g., like, glad, support, trust) in
their writing samples, much of which was related to positive feedback aimed at each other and/or the instructor. The use of positive
feedback in conjunction with more extensive communication has been reported to promote better group performance, and the greater use
of positive emotion words has also been shown to indicate higher levels of agreement (Tausczik & Pennebaker, 2010). In our sample, the core
members showed a significantly higher frequency of words pertaining to relativity (e.g., above, below, past, present, prior) and time (e.g.,
hour, day, today, tomorrow). Pennebaker and Persaud (2010) suggest that individuals who use analytical thinking processes for problem
solving (i.e., breaking down a problem into its component parts) will often use linguistic devices such as relativity words to specify concepts
and objects. The increased references to time suggest that the core students pay greater attention to temporal detail, which would also add
specificity to their analysis.
These linguistic results are particularly interesting in light of the fact that the cognitive climate of the core was found to be significantly
more adaptive (as reflected by the group’s more adaptive mean) relative to the periphery. As previously noted, more adaptive individuals
tend to place greater value on gaining group consensus and maintaining group conformity; it is possible that the higher usage of positive
emotion words in conjunction with more extensive communication reflects these cognitive preferences. In addition, the increased usage of
relativity and time references in the language demonstrated by the core students may be a reflection of the more adaptive preference for
a detailed problem solving approach. Lastly, the core members also made more references to home (e.g., family, families, home). This finding
was somewhat surprising and required further investigation of the transcripts to explain adequately. In the discussions that took place
within this graduate education class, words such as “family” or “families” were commonly used in the context of “families of learning style
indices or instruments”. Also, words like “home” were often used in expressions such as “that point hits home”. The contextual issues
surrounding the identification of words in the home category highlight a key limitation of automated linguistic analysis – that is, their
inability to detect irony, sarcasm and idioms, or to discern the specific context in which a word is being used (Tausczik & Pennebaker, 2010).
In contrast to the core of the sample, students who were members of the periphery group used more dashes and number references (e.g.,
one, two, once, twice, first, etc.) in their postings. Dashes are commonly used to connect thoughts, compare or illustrate the relationship
between two entities, separate parenthetical expressions in a sentence, or to indicate a range of values. Whereas the core members used
more words overall, the use of more dashes and number references by the periphery group may reflect a more abridged strategy for
conveying their thoughts in writing – possibly due to a lack of interest, a lack of time, or both. This difference may align with membership in
the periphery group overall, as they appeared to devote less energy to communicating with each other and the core. These are suppositions,
however, that will need to be tested more thoroughly in future studies.
As a final part of this core-periphery linguistic analysis, the impact of age demographics was also considered. Pennebaker et al. (2003)
reported that individuals use more positive emotion words with increasing age. In this study, however, age was not a factor in the usage of
positive emotion words. In fact, the average age of the core members in the social network (who used more positive emotion words) was
actually slightly lower than the average age of the peripheral members. Specifically, the mean age for the core members was 31.5 years
(SD ¼ 8.3) as compared with a mean age of 32.2 years (SD ¼ 8.5) for the peripheral members.
In summary, social language network analysis (SLNA) yielded some interesting insights into the nature of the relationships and interactions that emerged within the hierarchical structure of the social networks for our sample. The linguistic behavior of the core members
was consistent with individuals who have more influential positions (e.g., higher status) in a social network. The substantially higher word
count for the core members supports their centralized and significant role in the information exchange within the network, while their
higher usage of time and relativity references illustrates that these individuals enhanced the specificity of the information they conveyed
within the online forum. Furthermore, their use of positive feedback, which is supported by their increased use of positive emotion words,
along with high interaction frequencies, are among the important characteristics associated with student leadership (Reed, 2001).
5. Implications, limitations, and future work
In this exploratory study, we used a novel combination of complementary research methods to investigate whether and how cognitive
preferences and individual behaviors influence the patterns of information exchange and the selection of communication partners within
the social networks in an online classroom. In considering the implications of this work, we will briefly revisit our key findings and discuss
the potential impact of each of them, along with some limitations of our study and recommended future research.
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
233
5.1. Our key findings and their implications
First, our investigation revealed that the students’ selection of communication partners in the asynchronous online discussions did not
correlate with cognitive style. Within our very diverse sample (cognitive style range: 76 points), the style profiles of the dyadic and triadic
cliques identified through social network analysis were highly heterogeneous, often with large cognitive gaps (>20 KAI points) between
clique members. Although cognitive style differences have been shown to create conflict in face-to-face settings (Buffinton et al., 2002;
Kirton, 2011), this did not happen here; students of diverse styles repeatedly selected each other as communication partners, with no
apparent conflict observed. In accord with the findings of Jablokow and Vercellone-Smith (2011), these results also suggest that web-based
learning environments may mask or dilute some of the cognitive style differences that can cause difficulties in face-to-face collaborations,
and/or they may provide an enabling structure for working with peers of very different styles in ways that cause less tension. This result is
particularly encouraging for instructors who make extensive use of asynchronous discussions in their online courses and who are concerned
about the impact of individual differences on student interactions. These findings bode well for both students and instructors in terms of the
diversity of thinking that is likely to develop and be shared within a web-based class.
Second, not only was a distinct core-periphery structure identified within the student social network, but the students who comprised
the core and the periphery, respectively, exhibited distinctly different social and linguistic behaviors. Core individuals displayed substantially higher degree centrality and clique membership, as well as the higher usage of several language features that have been associated
with individuals who actively promote enhanced group performance (e.g., higher word counts, more positive emotion words). In contrast,
students in the periphery group used more dashes and number references in their postings, which may reflect more direct but less complex
language, although other explanations might certainly apply. In any case, it is clear that influence hierarchies can and do develop in webbased courses, which has implications for online class management. If potentially at-risk students can be identified early on, for example
(say, through “periphery-linked” behaviors), then instructors might be able to intervene sooner in order to help those students become
better integrated into the classroom community.
Third, a comparison of the mean KAI scores for the core and periphery groups also revealed that the cognitive style preferences of the
core members (on average, within a wide range) were significantly more adaptive than their peripheral peers. The linguistic results discussed above are particularly interesting in light of this more adaptive cognitive climate of the core. According to A-I theory, more adaptive
individuals tend to place greater value on group consensus and conformity; for our sample, the linguistic behaviors exhibited by the core
appear to support these preferences. Also, the increased references to relativity and time displayed by the core members seem to reflect the
more detail-oriented problem solving approaches that are generally preferred by more adaptive individuals. This particular result will
require more investigation before it can be generalized, but it begs the question of whether the cognitive climates of the core and periphery
groups (as reflected by their cognitive style profiles) will generally tend to be different. The validity of this question is bolstered by the fact
that sensory learners, who tend to be more detail-oriented and prefer to solve problems using well established methods, have been found to
engage in a higher level of online participation in terms of both the frequency and duration of their communications when compared with
intuitive learners (Huang et al., 2011).
Going further, we are lead to wonder whether the core will typically be more adaptive (on average) than the periphery, even when a large
range of styles is present in both cases. If this proves to be the case, it will open up a broad range of questions related not only to differences
in linguistic and social behaviors, but also to the types of ideas being discussed and developed within the core and periphery groups,
respectively. Another interesting aspect to consider is how cognitive style will impact students’ responses to a given task structure. Schellens
et al. (2007) highlight the importance of the task structure for knowledge construction in asynchronous discussion groups. Since cognitive
style characterizes an individual’s preferred way of managing structure when responding to or seeking the solution of problems (Kirton,
2011), will cognitive processing be inhibited when a given task structure is not well-aligned (e.g., too rigid or too loose) with a student’s
preferred cognitive style?
5.2. Limitations and future work
The impact of cognitive style diversity on the dynamic interactions of students in online educational settings is a relatively new domain
of research. Our findings have provided some intriguing insights and raised several interesting questions, but a number of limitations must
be considered when interpreting the results of an exploratory study such as this. First and foremost, it is important to note that although this
study was performed using a large number of communicative exchanges within an online classroom (i.e., 1131 messages), the sample size for
this class was small, consisting of 21 students. Considerably larger sample sizes will be needed before the findings can be generalized and
the subtleties of the students’ interactions fully understood. Since multiple variables are likely to influence a student’s level of engagement
in an online discussion forum (e.g., motivation, time constraints, interest in the topic), as well as students’ overall preferences for working
individually or in groups (Oliver & Omari, 2001), future work with larger sample sizes will be also be needed to discern the impact that these
additional variables may have on the patterns of communication in an online classroom.
In addition, the participants were both graduate students and educators. While we do not expect a student’s previous pedagogical
training or educational level to influence the fundamental communicative behaviors we were observing here (e.g., preference for
communication partners based on style) (Kirton, 2011), this study should be repeated with different populations in the future – including
both graduates and undergraduates in other disciplines and with diverse levels of experience.
Finally, the study of individual differences in language usage is a field that is still in its infancy, although the body of research is growing at
a rapid pace. The contextual issues that arose surrounding the identification of words in the home category of LIWC highlight a key limitation
of automated linguistic analysis, as noted previously. The approaches used for linguistic analysis will need greater refinement to fully clarify
how individual differences in linguistic behavior manifest themselves during online communications.
In conclusion, our work here has demonstrated that a combination of cognitive style and linguistic pattern analyses in conjunction with
social network analysis is a promising strategy for investigating the communication networks of students in an online setting – but there is
much more work to be done. In particular, links between language usage and cognitive style need to be examined much more closely to see
if individuals of different styles exhibit distinct linguistic patterns. If this proves to be the case, such results could be extremely useful in
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P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
mentoring students toward improved performance in their writing, as well as helping both instructors and students bridge gaps in
understanding that come as a result of using different preferred ways of expression (both online and face-to-face). We also hope to expand
our use of social network analysis to explore the formation of core-periphery structures (or other structural hierarchies) across a broad range
of online environments, as well as the impact that such status hierarchies may have on student interactions and learning experiences.
References
Aragamon, S., Dhawle, S., Koppel, M., & Pennebaker, J. W. (2005). Lexical predictors of personality type. In Proceedings of classification society of North America, St. Louis, MI.
Aragamon, S., Koppel, M., Pennebaker, J. W., & Schler, J. (2009). Automatically profiling the author of an anonymous text. Communications of the ACM, 53(2), 119–123.
Aviv, R., Erlich, Z., Gilad, R., & Geva, A. (2003). Network analysis of knowledge construction in asynchronous learning networks. Journal of Asynchronous Learning Networks,
7(3), 1–23.
Baldwin, T. T., Bedell, M. D., & Johnson, J. L. (1997). The social fabric of a team-based M.B.A. program: network effects on student satisfaction and performance. Academy of
Management Journal, 40(6), 1369–1397.
Beck, R., Fitzgerald, W. J., & Pauksztat, B. (2003). Individual behaviors and social structure in the development of communication networks of self-organizing online discussion
groups. In B. Wasson, S. Ludvigson, & U. Hoppe (Eds.), Designing for change in networked learning environments (pp. 313–322). Dordrecht: Kluwer. www.lawrence.edu/fast/
beckr/pdfs/Norway_final_March.pdf Retrieved: June 29, 2011.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows: Software for social network analysis. Harvard: Analytic Technologies. http://www.analytictech.com/
ucinet/ Retrieved: April 8, 2011.
Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structure. Social Networks, 21, 375–395.
Buffinton, K. W., Jablokow, K. W., & Martin, K. A. (2002). Project team dynamics and cognitive style. Engineering Management Journal, 14(3), 25–33.
Chung, C., & Pennebaker, J. (2007). The psychological functions of function words. In K. Fiedler (Ed.), Social communication (pp. 343–359). New York: Psychology Press.
Clapp, R. G. (1993). The stability of cognitive style in adults: a longitudinal study of the KAI. Psychological Reports, 73, 1235–1245.
Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London: Learning and Skills Research
Centre.
Creed, M. (2010). Planning, problem solving, and Kirton’s A-I theory within an organizational framework. Ph.D. Dissertation, Chapel Hill: Univ. of North Carolina. http://dc.lib.unc.
edu/cdm4/item_viewer.php?CISOROOT¼/etd&CISOPTR¼3373&CISOBOX¼1&REC¼12 Retrieved: July 26, 2011.
Erlin, B., Yusof, N., & Rahman, A. A. (2009). Students’ interactions in online asynchronous discussion forum: a social network analysis. In Proceedings of the 2009 IEEE
international conference on education technology and computers (pp. 25–29).
Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57–72.
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance
Education, 15(1), 7–23.
Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presences in online learning: interaction is not enough. American Journal of Distance Education, 19(3),
133–148.
Gould, R. V. (2002). The origins of status hierarchies: a formal theory and empirical test. American Journal of Sociology, 107(5), 1143–1178.
Graff, M. (2003). Learning from web-based instructional systems and cognitive style. British Journal of Educational Technology, 34(4), 407–418.
Guiller, J., & Durndell, A. (2007). Students’ linguistic behavior in online discussion groups: does gender matter? Computers in Human Behavior, 23, 2240–2255.
Hammerschmidt, P. (1996). The Kirton Adaption-Innovation Inventory and group problem solving success rates. Journal of Creative Behavior, 30, 61–75.
Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods (Online Textbook). Riverside: University of California. published in digital form at. http://faculty.
ucr.edu/whanneman/nettext/ Retrieved: July 14, 2011.
Haythornthwaite, C. (2001). Exploring multiplexity: social network structures in a computer supported distance learning class. The Information Society, 17, 211–226.
Herring, S. C. (1993). Gender and democracy in computer-mediated communication. Electronic Journal of Communication, 3(2). http://ella.slis.indiana.edu/wherring/ejc.txt
Retrieved: July 8, 2011.
Herring, S. C. (2003). Gender and power in online communication. In J. Holmes, & M. Meyerhoff (Eds.), The handbook of language and gender (pp. 202–228). Oxford: Blackwell
Publishers. http://ella.slis.indiana.edu/wherring/gender.power.pdf Retrieved: July 8, 2011.
Hoppe, B., & Reinelt, C. (2010). Social network analysis and the evaluation of leadership networks. The Leadership Quarterly, 21(4), 600–619.
Huang, E. Y., Sheng, W. L., & Huang, T. K. (2011). What type of learning style leads to online participation in the mixed mode e-learning environment? A study of software
usage instruction. Computers & Education, 58, 338–349.
Jablokow, K. W. (2008). Developing problem solving leadership: a cognitive approach. International Journal of Engineering Education, 24(5), 936–954.
Jablokow, K. W., & Kirton, M. J. (2009). Problem solving, creativity, and the level-style distinction. In L.-F. Zhang, & R. J. Sternberg (Eds.), Perspectives on the nature of intellectual
styles (pp. 137–168). New York: Springer.
Jablokow, K. W., & Vercellone-Smith, P. (2011). The impact of cognitive style on social networks in online discussions. Advances in Engineering Education, 2(3), 1–29.
Jablokow, K., Vercellone-Smith, P., & Richmond, S. S. (2009). Exploring cognitive diversity and the level-style distinction. In Proceedings of the 2009 ASEE Conference on
Engineering Education, Austin, TX. http://soa.asee.org/paper/conference/paper-view.cfm?id¼10504 Retrieved: 2/17/10.
Kaptelinin, V., & Cole, M. (2001). Individual and collective activities in educational computer game playing. In T. Koschmann, R. Hall, & N. Miyake (Eds.), CSCSL 2 carrying
forward the conversation (pp. 303–316). Mahwah, NJ: Lawrence Erlbaum Associates.
Kirton, M. J. (1976). Adaptors and innovators: a description and measure. Journal of Applied Psychology, 61(5), 622–629.
Kirton, M. J. (2011). Adaption-innovation in the context of diversity and change. London: Routledge.
Kramer, A. D. I., & Rodden, K. (2008). Word usage and posting behaviors: modeling blogs with unobtrusive data collection methods. In CHI 2008 proceedings, April 5–10,
Florence, Italy.
Lipponen, L. (2002). Exploring foundations for computer-supported collaborative learning. In Proceedings of CSCL (pp. 72–80). Mahwah, NJ: Lawrence Erlbaum Associates.
Lopez-Mesa, B., & Thompson, G. (2006). On the significance of cognitive style and the selection of appropriate design methods. Journal of Engineering Design, 17(4), 371–386.
Marshall, A. A., & Stohl, C. (1993). Participating as participation: a network approach. Communication Monographs, 60(2), 137–157.
Messick, S. (1984). The nature of cognitive styles: problems and promise in education practice. Educational Psychologist, 19(2), 59–74.
Miller, J., & Durndell, A. (2004). Gender, language and computer-mediated communication. In K. Morgan, C. A. Brebbia, J. Sanchez, & A. Voiskounsky (Eds.), Human perspectives
in the Internet society: Culture, psychology and gender (pp. 235–244). Southampton: WIT Press.
Moodle Course Management System. (2007). http://moodle.org.
National Center for Education Statistics. (2011). Fast facts. http://nces.ed.gov/fastfacts/display.asp?id¼28.
Occupational Research Centre web site. (2011). www.kaicentre.com.
Oliver, R., & Omari, A. (2001). Exploring student responses to collaboration and learning in a web-based environment. Journal of Computer Assisted Learning, 17(1), 34–47.
Pena-Shaff, J. B., & Nicholls, C. (2004). Analyzing student interactions and meaning construction in computer bulletin board discussions. Computers & Education, 42(3),
243–265.
Pennebaker, J. W. (2002). What our words can say about us: toward a broader language psychology. Psychological Science Agenda, January/February 2002. http://homepage.
psy.utexas.edu/./Pennebaker/Reprints/PsychSciAgenda.pdf Retrieved: 2/16/2011.
Pennebaker, J. W., Chung, C. K., Ireland, M., Gonzales, A., & Booth, R. J. (2007). The development and psychometric properties of LIWC 2007. Austin, Texas: LIWC.net. www.liwc.
net/LIWC2007LanguageManual.pdf Retrieved: 10/20/2011.
Pennebaker, J. W., & King, L. (1999). Linguistic styles: language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296–1312.
Pennebaker, J. W., Mehl, M., & Niederhoffer, K. (2003). Psychological aspects of natural language use: our words, our selves. Annual Review of Psychology, 54, 547–577.
Pennebaker, J. W., & Persaud, R. (2010). The third UK debate: Assessing optimism, honesty, and thinking styles. Wordwatchers blog posted on April 29, 2010. http://wordwatchers.
wordpress.com/ Retrieved: July 20, 2011.
Reed, T. A. (2001). Student leaders in the classroom: A study of Virginia Tech student leaders and their accounts of curricular and co-curricular leadership. Doctoral Dissertation.
Virginia Tech, Blacksburg, VA. http://scholar.lib.vt.edu/theses/available/etd-02012002-171701 Retrieved July 7, 2011.
Reynolds, P. D., & Fisek, M. H. (1972). Development of influence hierarchies in small discussion groups: alternatives for measuring temporal change. Behavioral Science, 17,
542–548.
P. Vercellone-Smith et al. / Computers & Education 59 (2012) 222–235
235
Riding, R. J. (1991). Cognitive styles analysis user manual. Birmingham: Learning and Training Technology.
Russo, T. C., & Koesten, J. (2005). Prestige, centrality, and learning: a social network analysis of an online class. Communication Education, 54(3), 254–261.
Samms, C., & Friedel, C. R. Relationship between dissimilar cognitive styles and use of learning strategies. Academy of Educational Leadership Journal, in press.
Schellens, T., & Valcke, M. (2005). Collaborative learning in asynchronous discussion groups: what about the impact of cognitive processing. Computers in Behavior, 21,
957–975.
Schellens, T., & Valcke, M. (2006). Fostering knowledge construction in university students through asynchronous discussion groups. Computers & Education, 46, 349–370.
Schellens, T., Van Keer, H., Valcke, M., & De Wever, B. (2007). Learning in asynchronous discussion groups: a multilevel approach to study the influence of student, group and
task characteristics. Behaviour & Information Technology, 26(1), 55–71.
Scholand, A. J., Tausczik, Y. R., & Pennebaker, J. W. (2010). Social language network analysis. In CSCW 2010, February 6–10, 2010, Savannah, GA.
Scott, J. (2000). Social network analysis: A handbook (2nd ed.). Los Angeles: Sage.
Sexton, J. B., & Helmreich, R. L. (1999). Analyzing cockpit communication: the links between language, performance, error, and workload. In Proceedings of the tenth international symposium on aviation psychology (pp. 689–695). Columbus: The Ohio State University.
Shelley, R. K., & Troyer, L. (2001). Speech duration and dependencies in initially structured and unstructured task groups. Sociological Perspectives, 44(4), 419–444.
Sierpe, E. (2000). Gender and technological practice in electronic discussion lists: an examination of JESSE, the library/information science education forum. Library and
Information Science Research, 22, 273–289.
Simmons, R. A., Chambless, D. L., & Gordon, P. C. (2008). How do hostile and emotionally overinvolved relatives view relationships? What relatives’ pronoun use tells us.
Family Process, 47, 405–419.
Sundararajan, B. (2009). Impact of communication patterns, network positions and social dynamics factors on learning among students in a CSCL environment. Electronic
Journal of e-Learning, 7(1), 71–84.
Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: a review of the research. Review of Educational Research,
76(1), 93–135.
Tausczik, Y., & Pennebaker, J. W. (2010). The psychological meaning of words, LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1),
24–54.
Tiedemann, J. (1989). Measures of cognitive styles: a critical review. Educational Psychologist, 24(3), 261–275.
Torrance, E. P., Reynolds, C. R., Ball, O. E., & Riegel, T. R. (1978). Revised norms – Technical manual for your style of thinking and learning. Athens, GA: University of Georgia.
Wang, L. (2010). How social network position relates to knowledge building in online learning communities? Frontiers of Education in China, 5(1), 4–25.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.
Webb, E., Jones, A., Barker, P., & van Schaik, P. (2004). Using e-learning dialogues in higher education. Innovations in Education and Teaching International, 41(1), 93–103.
Willging, P. A. (2005). Using social network analysis techniques to examine online interactions. US-China Education Review, 2(9), 46–56.
Zhang, L.-F., & Sternberg, R. J. (2009). Perspectives on the nature of intellectual styles. New York: Springer.
Zhu, E. (2006). Interaction and cognitive engagement: an analysis of four asynchronous online discussions. Instructional Science, 34, 451–480.