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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 67
AN EXPLORATION OF THE RELATIONSHIP
BETWEEN INDICATORS OF THE COMMUNITY
OF INQUIRY FRAMEWORK AND RETENTION IN
ONLINE PROGRAMS
Wally Boston
American Public University System
Sebastián R. Díaz
West Virginia University
Angela M. Gibson
American Public University System
Phil Ice
American Public University System
Jennifer Richardson
Purdue University
Karen Swan
University of Illinois Springfield
*Authors listed in alphabetical order to denote equal contributions.
ABSTRACT
As the growth of online programs continues to rapidly accelerate, concern over retention is increasing.
Models for understanding student persistence in the face-to-face environment are well established,
however, the many of the variables in these constructs are not present in the online environment or they
manifest in significantly different ways. With attrition rates significantly higher than in face-to-face
programs, the development of models to explain online retention is considered imperative. This study
moves in that direction by exploring the relationship between indicators of the Community of Inquiry
Framework and student persistence. Analysis of over 28,000 student records and survey data
demonstrates a significant amount of variance in re-enrollment can be accounted for by indicators of
Social Presence.
KEYWORDS
Community of Inquiry, Retention, Online Programs
I. INTRODUCTION
With almost four million students enrolled in online courses in the United States alone, and a 12.9%
growth rate in online enrollments, program growth is considered a priority at over 80% of major US
institutions of higher education [1]. While compelling, this accelerated growth has raised significant
questions related to the quality of online instruction in terms of outcomes. One measure of outcomes is
student learning and perceived efficacy. In their 2009 study, the US Department of education isolated 51
common factors across thousands of studies and concluded that, in general, online learning is more
effective than face-to-face learning [2]. However, despite this highly positive finding, the question of
retention remains problematic for online programs, with several studies and anecdotal evidence indicating
attrition rates for online courses frequently being much higher than for their campus-based counterparts
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
68 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
[3, 4, 5, 6]. In more recent work, Patterson and McFadden [7] found dropout rates to be six to seven times
higher in online programs.
In the traditional campus setting, student persistence and retention have been a documented issue in
higher education in the United States since the late 1800’s [8]. Formal research studies on the topic of
retention began as early as 1926 [9] but publications of research on retention escalated in the 1970’s with
academics such as Spady [10], Astin [11], Tinto [12, 13], Pascarella [14], and Braxton [15] publishing
influential research on the topic of student retention.
A number of researchers have found that the higher the high school GPA and the higher the SAT or ACT
score of a college student, the stronger the chance that the student will persist in college and graduate
[11]. However, this is only one set of positive indicators of retention. Researchers have also identified the
importance of social integration in the student retention rates of colleges [10, 12, 11]. Tinto’s model of
student persistence theorized that the greater the level of academic and social integration, the greater the
student’s chances at persisting until graduation [12].
While the social integration process is well documented in traditional higher education settings, similar
research in the online environment is in its infancy. However, the Community of Inquiry Framework
provides a widely recognized model for understanding interactions in the online environment and insight
into how social integration may occur in online environments.
Developed by Garrison, Anderson, and Archer [16], the Community of Inquiry (CoI) model is a
theoretical framework that explains the online learning experience in terms of interactions between three
overlapping presences: Teaching, Social and Cognitive. Since its inception, the CoI framework has been
the most frequently cited model for explaining the online learning experience, with extensive research
undertaken on each of the individual presences [17, 18]. In 2007, the framework was operationalized as
survey instrument and validated through multi-institutional data collection and analysis [19].
The first of the three presences, social presence, is the basis of collaborative learning and the foundation
for meaningful, constructivist learning online [20]. In the context of online learning, social presence is
described as the ability of learners to project themselves socially and emotionally as well as their ability
to perceive other learners as “real people” [21]. The three main factors that allow for the effective
projection and establishment of social presence are affective expression, open communication and group
cohesion [22, 21].
Affective expression is the ability of online learners to project themselves through such text-based verbal
behaviors as the use of para-language, self-disclosure, humor, and other expressions of emotion and
values. Open communication refers to the provision of a risk-free learning climate in which participants
trust one another enough to reveal themselves. Group cohesion refers to the development of a group
identity and the ability of participants in the learning community to collaborate meaningfully. Research
has shown a link between perceived social presence and perceived learning and satisfaction in online
courses [22, 21]. There is also some indication that social presence has a direct [23] and/or mediating
[24] effect on learning and learning processes. However, it has also been shown that there are differences
in the effects of the social presence of instructors and peers on learning and interactions online [21] and it
may be that it is hard to tease apart the social presence of instructors from teaching presence.
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 69
Cognitive presence is the extent to which learners are able to construct and confirm meaning through
reflection and discourse and is defined as a four stage process of practical inquiry. First is a triggering
event, where an issue or problem is identified for further inquiry. Next is exploration, where students
explore the issue both individually and as a community, through reflection and discourse. The third stage
is integration, where learners construct meaning from ideas developed during exploration. Finally, the
process culminates in resolution, where learners apply the new knowledge [16, 18].
Teaching presence, the third component of the CoI framework, is described by Garrison and colleagues
(2001) as having a three-part structure consisting of: instructional design and organization, facilitation of
discourse, and direct instruction.
Instructional design and organization involves the planning and design of the structure, processes,
interaction and evaluation aspects of an online course [16]. Some activities within this category might
include building curriculum materials, such as creating presentations and lecture notes on the course site,
and providing audio/video mini-lectures, offering a mix of individual and group activities along with a
clear schedule for their completion, and providing guidelines on how to use the medium effectively,
including netiquette [16, 18].
Facilitation of discourse is described as the means by which students engage in interacting about and
building upon the information provided in the instructional materials [16]. In order to facilitate discourse,
the instructor may review and comment upon student posts, raise questions and make observations to
direct discussions as desired, keep discussions moving efficiently, draw out inactive students and limit the
activities of dominant students if detrimental to the group [25, 26].
Direct instruction is described as providing intellectual and scholarly leadership from a subject matter
expert in order to diagnose comments for accurate understanding, inject sources of information, direct
useful discussions, and scaffold learner knowledge to a higher level [27]. Within this role, the instructor
uses various means of assessment and feedback that should be delivered in a timely fashion.
II. METHOD
The problem addressed in this study is whether CoI survey indicators can be used to predict students’
likelihood to remain enrolled in an online educational program of study. The following research question
is used to examine this problem:
RQ 1: Is there a statistically significant predictive relationship between CoI survey indicators and
a students’ likelihood to remain enrolled in an online educational program of study?
Linear regression was utilized to analyze the relationship between a linear combination of the 34
independent variables (i.e. Likert scale responses to each of the 34 CoI survey items) and the binary
dependent variable measuring whether or not a student enrolled in the subsequent semester. A binary
dependent variable typically demands logistic, as opposed to linear regression. This study’s use of a
binary dependent variable with linear regression is supported in the literature even though it compromises
the assumption that residuals are normally distributed about the predicted DV scores (Cohen, Cohen,
West & Aiken, 2002). The number of subjects included in this study (n = 28,877) ensures adequate
statistical power by far exceeding the minimally adequate sample sizes suggested by Green (1991).
Multicollinearity is a limitation inherent in this study given the instances of high correlations among the
predictor variables.
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
70 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
A. Instructional Setting
American Public University System (APUS) is an online, for-profit university. Founded in 1991, it was
originally known as American Military University (AMU) and offered graduate degrees for officers in the
United States Armed Forces. In 2002, AMU reorganized as APUS and created two virtual universities
operating under APUS’ accreditation, American Military University and American Public University.
Shortly after reorganizing, APUS applied for accreditation with the Higher Learning Commission of the
North Central Association and achieved candidacy status in 2004 and initial accreditation in 2006.
Founded as an institution devoted to serving the needs of military students, APUS’ top priority has always
been engaging dispersed learners in high quality, collaborative learning experiences; a philosophy that
extends to the civilian market served by APU. Since 2000, APUS has experienced a compound annual
growth rate in student enrollment of 66.9% and expanded to 51 certificates, 19 Associates degrees, 32
Bachelor degrees and 23 Masters degrees. As of June 30, 2009, APUS served 53,600 students in all 50
states and 109 countries. Courses are offered every month, with a semester being either eight or 16 weeks
in duration. Over 90% of courses are currently offered in the eight week semester format.
B. Participants
Students (n = 28,877) who completed the CoI survey were all enrolled in bachelors or associates level
courses. The survey was administered to all students, taking classes, at the end of each semester; this
sample constitutes a response rate of 38.91%. Age of participants ranged from 18 to 62 years old, with a
mean of 28.2 years. Males comprised 68% of the sample and females comprised 32%.
C. Design
CoI survey (Appendix A) is administered to students at APUS at the end of every semester as part of a
large-scale institutional, continuous quality improvement initiative [28]. Data used in this study were
collected over a period of six semesters. Descriptive statistics were used to assess the means and standard
deviations for each item. Principal axis factor analysis, with direct oblimin rotation, was used to insure the
conceptual integrity of the data by inspection of alignment with the findings of Swan et al. [27].
Following confirmation of the expected factor pattern, linear regression was applied to the data. The
dependent variable was established as students’ enrollment status in the semester following the
completion of the CoI survey. As enrollment status is a categorical variable, a dummy variable was
created to represent the criterion variable using suggestions by Cohen, Cohen, West and Aiken [29]. The
predictor variables were student responses to each of the 34 CoI survey items, measured on a 5 point
Likert scale, with Strongly Disagree = 1 and Strongly Disagree = 5. For this linear regression, the
Forward method was used in the SPSS version 17. This means that the order in which variables are
listed in this table indicates their relative statistical significance in the predictive model.
III. RESULTS OF THE STUDY AND DISCUSSION
The following table depicts the means and standard deviations for each the 34 indicators:
Mean Standard
Deviation N
1.The instructor clearly communicated important course topics. 4.46 0.806 28877
2. The instructor clearly communicated important course goals. 4.48 0.785 28877
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 71
3. The instructor provided clear instructions on how to
participate in course learning activities. 4.45 0.830 28877
4. The instructor clearly communicated important due
dates/time frames for learning activities. 4.54 0.749 28877
5. The instructor was helpful in identifying areas of agreement
and disagreement on course topics that helped me to learn. 4.30 0.927 28877
6. The instructor was helpful in guiding the class towards
understanding course topics in a way that helped me clarify my
thinking. 4.31 0.941 28877
7. The instructor helped to keep course participants engaged
and participating in productive dialogue. 4.30 0.952 28877
8. The instructor helped keep the course participants on task in
a way that helped me to learn. 4.30 0.931 28877
9. The instructor encouraged course participants to explore new
concepts in this course. 4.36 0.888 28877
10. Instructor actions reinforced the development of a sense of
community among course participants. 4.27 0.955 28877
11. The instructor helped to focus discussion on relevant issues
in a way that helped me to learn. 4.32 0.921 28877
12. The instructor provided feedback that helped me
understand my strengths and weaknesses. 4.27 1.036 28877
13. The instructor provided feedback in a timely fashion. 4.30 1.032 28877
14. Getting to know other course participants gave me a sense
of belonging in the course. 3.94 0.958 28877
15. I was able to form distinct impressions of some course
participants. 4.01 0.934 28877
16. Online or web-based communication is an excellent
medium for social interaction. 4.03 0.942 28877
17. I felt comfortable conversing through the online medium. 4.37 0.741 28877
18. I felt comfortable participating in the course discussions. 4.40 0.743 28877
19. I felt comfortable interacting with other course participants. 4.37 0.755 28877
20. I felt comfortable disagreeing with other course participants
while still maintaining a sense of trust. 4.30 0.786 28877
21. I felt that my point of view was acknowledged by other
course participants. 4.30 0.793 28877
22. Online discussions help me to develop a sense of
collaboration. 4.18 0.887 28877
23. Problems posed increased my interest in course issues. 4.13 0.911 28877
24. Course activities piqued my curiosity. 4.21 0.903 28877
25. I felt motivated to explore content related questions. 4.25 0.905 28877
26. I utilized a variety of information sources to explore
problems posed in this course. 4.37 0.768 28877
27. Brainstorming and finding relevant information helped me
resolve content related questions. 4.28 0.803 28877
28. Discussing course content with my classmates was
valuable in helping me appreciate different perspectives. 4.11 0.927 28877
29. Combining new information helped me answer questions
raised in course activities. 4.28 0.785 28877
30. Learning activities helped me construct
explanations/solutions. 4.27 0.815 28877
31. Reflection on course content and discussions helped me
understand fundamental concepts in this class. 4.30 0.815 28877
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
72 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
32. I can describe ways to test and apply the knowledge
created in this course. 4.30 0.806 28877
33. I have developed solutions to course problems that can be
applied in practice. 4.26 0.824 28877
34. I can apply the knowledge created in this course to my work
or other non-class related activities. 4.33 0.820 28877
Table 1. Descriptive Statistics
The means show a generally high level of satisfaction, with relatively large standard deviations indicating
a significant clustering of replies around the mean. The three lowest means are clustered on the indicators
of affective expression (questions 14, 15, and 16).
The following table depicts the results of the principal axis factor analysis:
Factor 1
Teaching
Presence
Factor 2
Social
Presence
Factor 3
Cognitive
Presence Eignevalue % Variance
1. The instructor clearly
communicated important course
topics. 0.881 -0.019 -0.016
20.920
61.530
2. The instructor clearly
communicated important course
goals. 0.877 -0.008 -0.003
3. The instructor provided clear
instructions on how to participate in
course learning activities. 0.867 0.015 0.034
4. The instructor clearly
communicated important due
dates/time frames for learning
activities. 0.767 0.038 0.021
5. The instructor was helpful in
identifying areas of agreement and
disagreement on course topics that
helped me to learn. 0.900 -0.012 -0.018
6. The instructor was helpful in
guiding the class towards
understanding course topics in a way
that helped me clarify my thinking. 0.926 -0.021 -0.020
7. The instructor helped to keep
course participants engaged and
participating in productive dialogue. 0.904 0.044 0.031
8. The instructor helped keep the
course participants on task in a way
that helped me to learn. 0.904 0.015 -0.020
9. The instructor encouraged course
participants to explore new concepts
in this course. 0.843 0.006 -0.058
10. Instructor actions reinforced the
development of a sense of
community among course
participants. 0.871 0.084 0.023
11. The instructor helped to focus
discussion on relevant issues in a
way that helped me to learn. 0.833 0.004 -0.094
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 73
12. The instructor provided feedback
that helped me understand my
strengths and weaknesses. 0.844 -0.039 -0.052
13. The instructor provided feedback
in a timely fashion. 0.831 -0.022 0.032
14. Getting to know other course
participants gave me a sense of
belonging in the course. 0.043 0.626 -0.154
3.277
9.638
15. I was able to form distinct
impressions of some course
participants. 0.029 0.593 -0.169
16. Online or web-based
communication is an excellent
medium for social interaction. -0.063 0.678 -0.128
17. I felt comfortable conversing
through the online medium. 0.039 0.846 0.013
18. I felt comfortable participating in
the course discussions. 0.084 0.870 0.051
19. I felt comfortable interacting with
other course participants. 0.027 0.974 0.107
20. I felt comfortable disagreeing with
other course participants while still
maintaining a sense of trust. 0.010 0.895 0.049
21. I felt that my point of view was
acknowledged by other course
participants. 0.047 0.859 0.022
22. Online discussions help me to
develop a sense of collaboration. -0.026 0.827 -0.077
23. Problems posed increased my
interest in course issues. 0.041 0.052 -0.736
1.649
4.849
24. Course activities piqued my
curiosity. 0.079 -0.019 -0.801
25. I felt motivated to explore content
related questions. 0.069 -0.024 -0.821
26. I utilized a variety of information
sources to explore problems posed in
this course. -0.027 0.048 -0.770
27. Brainstorming and finding
relevant information helped me
resolve content related questions. -0.044 0.047 -0.822
28. Discussing course content with
my classmates was valuable in
helping me appreciate different
perspectives. -0.043 0.406 -0.483
29. Combining new information
helped me answer questions raised
in course activities. -0.031 0.097 -0.833
30. Learning activities helped me
construct explanations/solutions. 0.078 0.002 -0.834
31. Reflection on course content and
discussions helped me understand
fundamental concepts in this class. 0.089 0.026 -0.804
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
74 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
32. I can describe ways to test and
apply the knowledge created in this
course. 0.026 -0.035 -0.889
33. I have developed solutions to
course problems that can be applied
in practice. -0.014 -0.033 -0.914
34. I can apply the knowledge
created in this course to my work or
other non-class related activities. 0.032 -0.048 -0.867
Cumulative
Variance
Accounted
for = 76.017
Table 2. Principal Axis Factor Analysis
Visual inspection confirms the expected three factor solution, with 76% of the cumulative variance
accounted for. These findings validated the conceptual alignment of the survey data, allowing for linear
regression analysis to proceed with a high degree of confidence in the validity of the construct measured
by predictor variables.
Forward method linear regression, illustrated in the following table, resulted in 21 of the 34 CoI items
serving as statistically significant predictors. In addition to denoting the particular item number (Q1 =
Item 1 of the CoI), the table indicates the respective type of presence the item measures.
Unstandardized
Coefficients Standardized
Coefficients
t Sig. Type of
Presence
B Std. Error Beta
(Constant) .509 .008 67.040 .000 n/a
Q16: Online or web-based
communication is an excellent
medium for social interaction. .064 .002 .290 35.518 .000 Social
Q15: I was able to form distinct
impressions of some course
participants. .049 .002 .223 23.993 .000 Social
Q28: Online discussions were
valuable in helping me appreciate
different perspectives. .011 .002 .051 5.581 .000 Cognitive
Q14: Getting to know other course
participants gave me a sense of
belonging in the course. -.019 .002 -.089 -8.710 .000 Social
Q22: Online discussions help me
to develop a sense of
collaboration. .014 .002 .061 5.810 .000 Social
Q21: I felt that my point of view
was acknowledged by other course
participants. -.013 .003 -.049 -4.685 .000 Social
Q19: I felt comfortable interacting
with other course participants. .020 .004 .074 5.411 .000 Social
Q20: I felt comfortable disagreeing
with other course participants while
still maintaining a sense of trust. -.009 .003 -.035 -3.317 .001 Social
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 75
Q23: Problems posed increased
my interest in course issues. -.012 .002 -.053 -5.708 .000 Cognitive
Q25: I felt motivated to explore
content related questions. .009 .002 .040 3.918 .000 Cognitive
Q7: The instructor helped to keep
course participants engaged and
participating in productive
dialogue.
.011 .002 .052 4.535 .000 Teaching
Q13: The instructor provided
feedback in a timely fashion. -.008 .002 -.041 -5.151 .000 Teaching
Q32: I can describe ways to test
and apply the knowledge created
in this course. -.013 .003 -.049 -4.135 .000 Cognitive
Q34: I can apply the knowledge
created in this course to my work
or other non-class related
activities.
.012 .003 .049 4.586 .000 Cognitive
Q33: I have developed solutions to
course problems that can be
applied in practice. -.009 .003 -.038 -3.135 .002 Cognitive
Q31: Reflection on course content
and discussions helped me
understand fundamental concepts
in this class.
.008 .003 .033 2.993 .003 Cognitive
Q26: I utilized a variety of
information sources to explore
problems posed in this course .007 .002 .026 3.150 .002 Cognitive
Q18: I felt comfortable participating
in the course discussions. -.008 .003 -.029 -2.342 .019 Social
Q9: The instructor encouraged
course participants to explore new
concepts in this course. -.007 .003 -.032 -2.903 .004 Teaching
Q11: The instructor helped to focus
discussion on relevant issues in a
way that helped me to learn. .008 .003 .034 2.925 .003 Teaching
Q29: Combining new information
helped me answer questions
raised in course activities. -.006 .003 -.023 -2.150 .032 Cognitive
Table 3. Forward Regression Results
The following table illustrates the relative contributions of each of the predictor variables to the
significant predictive model. The Forward method in SPSS enters predictor variables one by one in order
of decreasing significance. This table, therefore, illustrates the changes in Adjusted R2 as each variable is
entered:
Model R R
Square Adjusted
R Square R Square
Change
Std. Error
of the
Estimate
1 .424a 0.180 0.180 0.180 0.187
2 .450b 0.202 0.202 0.022 0.185
3 .451c 0.203 0.203 0.001 0.185
4 .453d 0.205 0.205 0.002 0.184
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
76 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
5 .453e 0.205 0.205 0.001 0.184
6 .454f 0.206 0.206 0.001 0.184
7 .454g 0.206 0.206 0.000 0.184
8 .455h 0.207 0.207 0.000 0.184
9 .455i 0.207 0.207 0.000 0.184
10 .456j 0.208 0.208 0.001 0.184
11 .456k 0.208 0.208 0.000 0.184
12 .457l 0.209 0.209 0.001 0.184
13 .458m 0.209 0.209 0.000 0.184
14 .458n 0.210 0.210 0.000 0.184
15 .458o 0.210 0.210 0.000 0.184
16 .459p 0.210 0.210 0.000 0.184
17 .459q 0.210 0.210 0.000 0.184
18 .459r 0.211 0.210 0.000 0.184
19 .459s 0.211 0.210 0.000 0.184
20 .459t 0.211 0.210 0.000 0.184
21 .460u 0.211 0.211 0.000 0.184
Table 4 Relative Contributions to the Predictor Variables
The analysis shows that a total of 21.1% of the variance in student re-enrollment is accounted for by 19 of
the CoI indicators. However, all but 0.9% of that variance can be accounted for by two indicators:
SP 16. Online or web-based communication is an excellent medium for social interaction.
And
SP 15. I was able to form distinct impressions of some course participants.
These two items are two of the three affective expression indicators. The former item accounts for 18%
(i.e. almost all) of the total variance and the latter accounts for 2.2%. This suggests that projections of
social presence in general and affective expression in particular are important determinants for persistence
in online education. Social presence, the degree to which a person is perceived as a ‘real person’ in
mediated communication” [30] has been found in research studies to have an impact on students’
satisfaction with a course [30, 31, 32, 22, 33, 21] perceived learning [22, 34] and actual learning [23, 33].
In addition, a recent study by Liu, Gomez, and Yen [35] suggests that social presence as a construct is a
significant predictor of course retention and final grade in the community college online environment.
Perhaps more to the point for these findings, Tu & McIssac found that “students who feel more like
insiders in the learning community were more likely to achieve success. In a computer-mediated
environment, feelings of community and social presence may be considered to be strongly connected to
each other and to online interaction” [32].
Rodriguez, Plax, and Kearney (1996) claimed that teacher immediacy behaviors influenced students’
affective learning, which in turn influenced students’ cognitive learning and similarly, the CoI “posits that
the ability to construct knowledge in online environments is contingent on the capacity of teachers and
learners to move beyond direct instruction to establish forms of ‘‘presence”. The implication is that
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
Journal of Asynchronous Learning Networks, Volume 13: Issue 3 77
teaching and social presence represent the processes needed to create paths to and cognitive presence for
online learners” [24]. In other words, students who positively perceive online learning environments,
which is potentially increased by their perception that they are part of a larger (social) learning
community are more likely to have increased retention.
Of the remaining 17 significant indicators, it is notable that six are from the social presence category. As
such, all but one of the social presence indicators was a significant predictor of re-enrollment; or 88% of
all social presence indicators were significant predictors of student re-enrollment. Of the remaining
significant indicators, four were from teaching presence (33% of all teaching presence indicators) and
nine were from cognitive presence (75% of all cognitive presence indicators).
IV. CONCLUSIONS
Although statistical results in social science should never be deemed definitively causal, the sample size
in this study warrants further and closer inspection of the impact of two Social Presence items on
retention. Responses to CoI item # 16 (Online or web-based communication is an excellent medium for
social interaction.) account for over 18% of the variance associated with whether a student returned to
studies in the semester subsequent to completing the survey. This is, simply stated, a remarkable finding,
especially in light of the sample size obtained.
One may reason that students attending fully online universities seek social interaction primarily online.
However, future research can also examine whether similar results would be obtained in a blended
setting. The extent to which students at any university seek social interaction via the Web has profound
implications for both academic and student affairs. In the academic realm, faculty may need to redesign
their curriculum to allow students opportunities to engage with one another online, even in traditional
face-to-face courses. In the student affairs realm, programming designed to enhance student engagement
(and in turn retention) may need to provide today’s students opportunities for such interaction online.
Although residential campuses are designed to promote face-to-face interaction, students on these
campuses are often seen texting friends while walking to and from class, and their participation on social
networking sites such as Facebook continues to grow.
Caution is needed when attempting to generalize the results of this study, conducted at a fully online
university, to more conventional postsecondary settings. Regardless, the results of this study may help
explain why the retention models of Astin [11] and others, developed almost 20 years ago, do not fill well
with current enrollment trends. Social interaction remains a crucial factor for student retention. How
college students interact with one another, has changed dramatically in a relatively short time. As
educators continue to develop interventions to promote retention, they should pay particular attention to
how the institution encourages interaction among its students. In our current wired world, traditional
residential postsecondary institutions may need to look to the online institution to better understand how
to promote student interaction and increase college retention.
V. LIMITATIONS AND DIRECTION FOR FUTURE RESEARCH
As with all research conducted at a single institution, the results may not be generalizable to other
institutions. As such, this study should be duplicated to assess potential difference between various
student populations. Similarly, this study only examined the relationship between the CoI indicators and
retention patterns for undergraduate students. In a student of the value students place on the importance of
teaching presence indicators, Kupczynski, Ice, Weisenmayer and McCluskey [30], found significant
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78 Journal of Asynchronous Learning Networks, Volume 13: Issue 3
differences between learners at the associates, undergraduate and graduate levels. It is possible that
similar differences could apply to social presence indicators and, in turn, impact retention in a fashion
other than was detected in this study.
Though this study demonstrates the significance of social presence indicators on retention, other studies
[37, 24] demonstrate the importance of the teaching presence construct on student success, vis-à-vis the
establishment of both social and cognitive presence. Research exploring potential indirect influences of
teaching presence on retention should be considered to form a better understanding these complex
interactions.
In work exploring the impact of technology on student satisfaction, the impact of rich media on student
perceptions of increased social presence have been noted [38, 39]. Future research should also explore the
influence of media rich programs on retention. From a methodological perspective, there are three
limitations that should be considered when reviewing this study. First, though high for online surveys, the
response rate for this study (38.91%) may not be representative of all students. Future research should
examine whether any inherent self-selection bias occurs based on the type of student who chooses to
complete the CoI survey.
Second, this study only examined the influence of CoI indicators on retention. Future studies that include
other variables such as age, gender, ethnicity, economic indicators, etc. should be conducted to create
more exhaustive models, such as those that exist for face-to-face courses. As the use of dummy variables
in regression analysis can produce an exaggerated effect, such research would be important in reinforcing
or contextualizing the findings of this study.
VI. REFERENCES
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10. Spady, W. Dropouts from higher education: An interdisciplinary review and synthesis. Interchange
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11. Astin, A. What matters in college: Four critical years revisited. San Francisco: Jossey Bass, 1993.
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13. Tinto, V. Leaving college: Rethinking the causes and cures of student attrition, 2nd ed. Chicago: The
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15. Braxton, J. M., A. S. Hirschy and S. A. McClendon. Understanding and reducing college student
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16. Garrison, D. R., T. Anderson and W. Archer. Critical thinking, cognitive presence, and computer
conferencing in distance education. American Journal of Distance Education 15(1): 7–23, 2001.
17. Arbaugh, J. B. An empirical verification of the community of inquiry framework. Journal of
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18. Garrison, D. R., and J. B. Arbaugh. Researching the community of inquiry framework: Review,
issues, and future directions. The Internet and Higher Education 10(3): 157–172, 2007.
19. Arbaugh, J. B., M. Cleveland-Innes, S. R. Diaz, D. R. Garrison, P. Ice, J. C. Richardson and K.
P. Swan. Developing a community of inquiry instrument: Testing a measure of the community of
inquiry framework using a multi-institutional sample. Internet and Higher Education 11(3–4): 133–
136, 2008.
20. Akyol, Z., B. Arbaugh, M. Cleveland-Innes, R. Garrison, P. Ice, J. Richardson and K. Swan. A
response to the review of the Community of Inquiry Framework. Journal of Distance Education
23(2): 123–136, 2009. http://www.jofde.ca/index.php/jde/article/view/630/885.
21. Swan, K. and L. F. Shih. On the nature and development of social presence in online course
discussions. Journal of Asynchronous Learning Networks 9(3): 115–136, 2005.
22. Richardson, J. C. and K. Swan. Examining social presence in online courses in relation to students’
perceived learning and satisfaction. Journal of Asynchronous Learning Networks 7(1): 68–88, 2003.
23. Picciano, A. G. Beyond student perceptions: Issues of interaction, presence and performance in an
online course. Journal of Asynchronous Learning Networks 6(1): 2002.
http://www.aln.org/publications/jaln/v6n1/pdf/v6n1_picciano.pdf.
24. Shea, P. and T. Bidjerano. Community of inquiry as a theoretical framework to foster “epistemic
engagement” and “cognitive presence” in online education. Computers and Education 52(3): 543–
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25. Coppola, N. W., S. R. Hiltz and N. G. Rotter. Becoming a virtual professor: Pedagogical roles and
asynchronous learning networks. Journal of Management Information Systems 18(4): 169–189, 2002.
26. Shea, P., K. Swan, C. S. Li and A. Pickett. Developing learning community in online asynchronous
college courses: The role of teaching presence. Journal of Asynchronous Learning Networks 9(4):
59–82, 2005.
27. Swan, K., J. C. Richardson, P. Ice, D. R. Garrison, M. Cleveland-Innes and J. B. Arbaugh.
Validating a measurement tool of presence in online communities of inquiry. eMentor 24I(2): 2008.
http://www.e-mentor.edu.pl/artykul_v2.php?numer=24&id=543.
28. Ice, P. and J. Richardson. Using the Community of Inquiry Framework survey for multi-level
institutional evaluation and continuous quality improvement. http://www.sloan-c.org/node/1931.
29. Cohen, J., P. Cohen, S. West and L. Aiken. Applied multiple regression/correlation analysis for the
behavioral sciences 3rd ed. Mahwah, NJ: Lawrence Erlbaum, 2002.
30. Gunawardena, C. N. Social presence theory and implications for interaction and
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Telecommunications 1(2/3): 147–166, 1995.
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31. Gunawardena, C. N. and F. J. Zittle. Social presence as a predictor of satisfaction within a
computer-mediated conferencing environment. The American Journal of Distance Education
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32. Hostetter, C. and M. Busch. Measuring up online: The relationship between social presence and
student learning satisfaction. Journal of Scholarship of Teaching and Learning 6(4): 1–12, 2006.
33. Russo, T. and S. Benson. Learning with invisible others: Perceptions of online presence and
their relationship to cognitive and affective learning. Educational Technology & Society,
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34. Christophel, D. The relationships among teacher immediacy behaviors, student motivation,
and learning. Communication Education 39:323–340, 1990.
35. Liu, S., J. Gomez and C. Yen. Community college online course retention and final grade:
Predictability of social presence. Journal of Interactive Online Learning 8(2): 165–182, 2009.
36. Kupczynski, L., P. Ice, R. Weisenmayer and F. McCluskey. Student perceptions of the
relationship between indicators of teaching presence and success in online courses. (In Review)
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37. Akyol, Z. and D. R. Garrison. The development of a community of inquiry over time in an online
course: Understanding the progression and integration of social, cognitive and teaching presence.
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38. Ice. P. The impact of asynchronous audio feedback on teaching, social and cognitive presence. Paper
presented at the first international conference of the Canadian Network for Innovation in Education,
April 27–30, in Banff, Alberta, Canada, 2008.
39. Ice., P., R. Curtis, P. Phillips and J. Wells. Using asynchronous audio feedback to enhance teaching
presence and student sense of community. Journal of Asynchronous Learning Networks 11(2): 3–25,
2007.
VII. AUTHOR BIOGRAPHIES
Wallace Boston, Jr. was appointed as President and Chief Executive Officer of American Public
University System (APUS) and its parent company, American Public Education, Inc. (APEI) in July
2004. He joined APUS in 2002 as its Executive Vice President and Chief Financial Officer. Mr. Boston
guided APUS through its successful accreditation with the Higher Learning Commission of the North
Central Association in 2006. In 2006, he initiated the institution’s application to be the first totally
distance learning university to receive Federal Student Aid after the repeal of the 50/50 rule. In
November 2007, Mr. Boston led APEI to an initial public offering on the NASDAQ Exchange and led
successful secondary offerings in February and December of 2008. In 2008, the Board of Trustees of
APUS awarded him a Doctorate in Business Administration, honoris causa. In his spare time, he is
enrolled in a doctoral program in Higher Education Management at the University of Pennsylvania’s
Graduate School of Education.
Sebastián R. Díaz, Ph.D., J.D. serves as Assistant Professor in the Department of Technology, Learning
& Culture at West Virginia University's College of Human Resources and Education. His research
interests include developing measures germane to Intellectual Capital and applying Knowledge
Management perspectives to program evaluation. He also serves as President of Diaz Consulting, LLC, a
firm that assists organizations in healthcare and education to utilize data-driven methods to guide
decision-making, strategic planning, and curriculum development. Sebastián also serves as Mayor of
Brandonville, WV.
Angela M. Gibson, Ed.D. is the Instructional Design Project Leader at American Public University
System. Her research interests focus on student engagement and retention, the role of technology in
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course design and instruction, online learning environments, and adult education. Specific areas of
research include instructional and collaborative methods to enhance online transparency and learning,
effective characteristics of K-12 teachers, Hispanic student performance on Advanced Placement tests,
engagement of first-year and at-risk populations at community colleges and universities, and student
success at online institutions.
Phil Ice, Ed.D. is the Director of Course Design, Research and Development at American Public
University System. His research is focused on the impact of new and emerging technologies on cognition
in online learning environments. Work in this area has brought Dr. Ice international recognition in the
form of two Sloan-C effective practices, a Sloan-C Effective Practice of the Year Award – 2007,
application of his work at over 50 institutions of higher education in 5 countries, membership in Adobe's
Higher Education Leader's Advisory Committee and multiple invited presentations, workshops and book
chapters related to the integration of emerging technologies in online courses and the impact on teaching,
social and cognitive presence. Examples of his research include the use of embedded asynchronous audio
feedback mechanisms, using web 2.0 tools for collaborative construction of knowledge through
integration of RIA’s and remote observation of student teaching experiences using asynchronous, flash-
based environments.
Jennifer Richardson, Ph.D. is an Associate Professor in the College of Education at Purdue University.
Jennifer’s research, which focuses on distance learning, includes social presence, developing a
professional development framework for mentoring graduate students to teach online, instructional
strategies, and the Community of Inquiry. She has been teaching and doing research in distance education
for the past nine years and was a recipient of the Excellence in Distance Teaching Award at Purdue
University. Jennifer is the past Chair for the AERA SIG Instructional Technology and past Program
Chair for the SIG Media, Culture & Curriculum. Dr. Richardson is currently the PI on a USDOE FIPSE
grant that is examining how peer feedback can be utilized to maintain quality online discussions while
increasing students' higher-order thinking.
Karen Swan, Ed.D. is the Stukel Distinguished Professor of Educational Leadership at the University of
Illinois Springfield. Her research is in the area of media and learning on which she has published and
presented extensively. She has authored over 100 publications as well as several hypermedia programs,
and co-edited two books on educational technology topics. Her current interests include online learning,
ubiquitous computing and data literacy. Dr. Swan has been involved with online teaching and learning for
over a decade, both as an instructor and as a researcher. She helped develop one of the first fully online
Masters degrees while she was at the University of Albany, is active in the online learning community,
and is well known for her research on learning effectiveness in online environments. In 2006, Dr. Swan
received the Sloan-C award for Outstanding Achievement in Online Learning by an Individual for her
work in this area.
VIII. APPENDIX A
Community of Inquiry Survey Instrument (draft v15)
Developed by Ben Arbaugh, Marti Cleveland-Innes, Sebastian Diaz, Randy Garrison, Phil Ice,
Jennifer Richardson, Peter Shea & Karen Swan
Teaching Presence
Design & Organization
1. The instructor clearly communicated important course topics.
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2. The instructor clearly communicated important course goals.
3. The instructor provided clear instructions on how to participate in course learning activities.
4. The instructor clearly communicated important due dates/time frames for learning activities.
Facilitation
5. The instructor was helpful in identifying areas of agreement and disagreement on course
topics that helped me to learn.
6. The instructor was helpful in guiding the class towards understanding course topics in a way
that helped me clarify my thinking.
7. The instructor helped to keep course participants engaged and participating in productive
dialogue.
8. The instructor helped keep the course participants on task in a way that helped me to learn.
9. The instructor encouraged course participants to explore new concepts in this course.
10. Instructor actions reinforced the development of a sense of community among course
participants.
Direct Instruction
11. The instructor helped to focus discussion on relevant issues in a way that helped me to learn.
12. The instructor provided feedback that helped me understand my strengths and weaknesses.
13. The instructor provided feedback in a timely fashion.
Social Presence
Affective expression
14. Getting to know other course participants gave me a sense of belonging in the course.
15. I was able to form distinct impressions of some course participants.
16. Online or web-based communication is an excellent medium for social interaction.
Open communication
17. I felt comfortable conversing through the online medium.
18. I felt comfortable participating in the course discussions.
19. I felt comfortable interacting with other course participants.
Group cohesion
20. I felt comfortable disagreeing with other course participants while still maintaining a sense of
trust.
21. I felt that my point of view was acknowledged by other course participants.
22. Online discussions help me to develop a sense of collaboration.
Cognitive Presence
Triggering event
23. Problems posed increased my interest in course issues.
24. Course activities piqued my curiosity.
25. I felt motivated to explore content related questions.
Exploration
26. I utilized a variety of information sources to explore problems posed in this course.
27. Brainstorming and finding relevant information helped me resolve content related questions.
28. Discussing course content with my classmates was valuable in helping me appreciate
different perspectives.
Integration
29. Combining new information helped me answer questions raised in course activities.
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30. Learning activities helped me construct explanations/solutions.
31. Reflection on course content and discussions helped me understand fundamental concepts in
this class.
Resolution
32. I can describe ways to test and apply the knowledge created in this course.
33. I have developed solutions to course problems that can be applied in practice.
34. I can apply the knowledge created in this course to my work or other non-class related
activities.
5 point Likert-type scale
1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree
