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AN EXPLORATION OF THE RELATIONSHIP BETWEEN INDICATORS OF THE COMMUNITY OF INQUIRY FRAMEWORK AND RETENTION IN ONLINE PROGRAMS

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  • Colorado State University - Global Campus

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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.
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
AN EXPLORATION OF THE RELATIONSHIP
BETWEEN INDICATORS OF THE COMMUNITY
OF INQUIRY FRAMEWORK AND RETENTION IN
ONLINE PROGRAMS
Wally Boston
Phil Ice
American Public University System
American Public University System
Sebastián R
.
Díaz
Jennifer Richardson
West Virginia University
Purdue University
Angela M
.
Gibson
Karen Swan
American Public University System
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
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
[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.
4
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
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.
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
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 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:
Standard
Mean
Deviation
N
4.46
0.806
28877
4.48
0.785
28877
1.The instructor clearly communicated important course topics.
2.
The instructor clearly communicated important course goals.
6
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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.
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.
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.
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.
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.
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.
23.
Problems posed increased my interest in course issues.
24.
Course activities piqued my curiosity.
25.
I felt motivated to explore content related questions.
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.
29
.
Combining new information helped me answer questions
raised in course activities.
30. Learning activities helped me construct
explanations/solutions.
31
.
Reflection on course content and discussions helped me
understand fundamental concepts in this class.
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
4.45
0.830
28877
4.54
0.749
28877
4.30
0.927
28877
4.31
0.941
28877
4.30
0.952
28877
4.30
0.931
28877
4.36
0.888
28877
4.27
0.955
28877
4.32
0.921
28877
4.27
1.036
28877
4.30
1.032
28877
3.94
0.958
28877
4.01
0.934
28877
4.03
0.942
28877
4.37
0.741
28877
4.40
0.743
28877
4.37
0.755
28877
4.30
0.786
28877
4.30
0.793
28877
4.18
0.887
28877
4.13
0.911
28877
4.21
0.903
28877
4.25
0.905
28877
4.37
0.768
28877
4.28
0.803
28877
4.11
0.927
28877
4.28
0.785
28877
4.27
0.815
28877
4.30
0.815
28877
7
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
1. The instructor clearly
communicated important course
topics.
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.
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.
11.
The instructor helped to focus
discussion on relevant issues in a
way that helped me to learn.
8
4.30
4.26
4.33
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
0.806
28877
0.824
28877
0.820
28877
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 know
ledge created in this course to my work
or other non-class related activities.
Table 1. Descriptive Statistics
The means show a generally high level of satisfaction, with relatively large standard deviations
a significant clustering of replies around the mean
.
The three lowest means are clustered on the
of affective expression (questions 14, 15, and 16).
The following table depicts the results of the principal axis factor analysis:
Factor 1
Teaching
Presence
0.881
0.877
0.867
0.767
0.900
0.926
0.904
0.904
0.843
0.871
0.833
Factor 2
Factor 3
Social
Cognitive
Presence
Presence
Eignevalue
% Variance
-0.019
-0.016
-0.008
-0.003
0.015
0.034
0.038
0.021
-0.012
-0.018
-0.021
-0.020
0.044
0.031
20.920
0.015
-0.020
0.006
-0.058
0.084
0.023
0.004
-0.094
61.530
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
0.844
-0.039
-0.052
0.831
-0.022
0.032
0.043
0.626
-0.154
0.029
0.593
-0.169
-0.063
0.678
-0.128
0.039
0.846
0.013
0.084
0.870
0.051
0.027
0.974
0.107 3.277
9.638
0.010
0.895
0.049
0.047
0.859
0.022
-0.026
0.827
-0.077
0.041
0.052
-0.736
0.079
-0.019
-0.801
0.069
-0.024
-0.821
-0.027
0.048
-0.770
-0.044
0.047
-0.822
1.649
4.849
-0.043
0.406
-0.483
-0.031
0.097
-0.833
0.078
0.002
-0.834
0.089
0.026
-0.804
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
12. The instructor provided feedback
that helped me understand my
strengths and weaknesses.
13. The instructor provided feedback
in a timely fashion.
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.
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.
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.
23.
Problems posed increased my
interest in course issues.
24. Course activities piqued my
curiosity.
25. I felt motivated to explore content
related questions.
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.
29.
Combining new information
helped me answer questions raised
in course activities.
30
.
Learning activities helped me
construct explanations/solutions.
31. Reflection on course content and
discussions helped me understand
fundamental concepts in this class.
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
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
Beta
.
290
.
223
.
051
-.089
.
061
-.049
.
074
-.035
Std. Error
.
008
.
002
.
002
.
002
.
002
.
002
.
003
.
004
.
003
(Constant)
Q16: Online or web-based
communication is an excellent
medium for social interaction.
Q15: I was able to form distinct
impressions of some course
participants.
Q28: Online discussions were
valuable in helping me appreciate
different perspectives.
Q14: Getting to know other course
participants gave me a sense of
belonging in the course.
Q22: Online discussions help me
to develop a sense of
collaboration.
Q21: I felt that my point of view
was acknowledged by other course
participants.
Q19: I felt comfortable interacting
with other course participants.
Q20: I felt comfortable disagreeing
with other course participants while
still maintaining a sense of trust.
10
t
67.040
35.518
23.993
5.581
-8.710
5.810
-4.685
5.411
-3.317
B
.
509
.
064
.
049
.
011
-.019
.
014
-.013
.
020
-.009
Type of
Sig
.
Presence
.
000
n/a
.
000
Social
.
000
Social
.
000
Cognitive
.
000
Social
.
000
Social
.
000
Social
.
000
Social
.
001
Social
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
-.012
.
002
-.053
-5.708
.
000
Cognitive
.
009
.
002
.
040
3.918
.
000
Cognitive
.
011
.
002
.
052
4.535
.
000
Teaching
-.008
.
002
-.041
-5.151
.
000
Teaching
-.013
.
003
-.049
-4.135
.
000
Cognitive
.
012
.
003
.
049
4.586
.
000
Cognitive
-.009
.
003
-.038
-3.135
.
002
Cognitive
.
008
.
003
.
033
2.993
.
003
Cognitive
.
007
.
002
.
026
3.150
.
002
Cognitive
-.008
.
003
-.029
-2.342
.
019
Social
-.007
.
003
-.032
-2.903
.
004
Teaching
.
008
.
003
.
034
2.925
.
003
Teaching
-.006
.
003
-.023
-2.150
.
032
Cognitive
Table 3.
Forward Regression Results
Q23: Problems posed increased
my interest in course issues.
Q25: I felt motivated to explore
content related questions.
Q7: The instructor helped to keep
course participants engaged and
participating in productive
dialogue.
Q13: The instructor provided
feedback in a timely fashion.
Q32: I can describe ways to test
and apply the knowledge created
in this course.
Q34: I can apply the knowledge
created in this course to my work
or other non-class related
activities.
Q33: I have developed solutions to
course problems that can be
applied in practice.
Q31: Reflection on course content
and discussions helped me
understand fundamental concepts
in this class.
Q26: I utilized a variety of
information sources to explore
problems posed in this course
Q18: I felt comfortable participating
in the course discussions.
Q9: The instructor encouraged
course participants to explore new
concepts in this course.
Q11: The instructor helped to focus
discussion on relevant issues in a
way that helped me to learn.
Q29: Combining new information
helped me answer questions
raised in course activities.
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 R
2
as each variable is
entered:
Std
.
Error
R
Adjusted R Square of the
Model
R
Square
R Square
Change
Estimate
1
.
424
a
0.180
0.180
0.180
0.187
2
.
450
b
0.202
0.202
0.022
0.185
3
.
451
c
0.203
0.203
0.001
0.185
4
.
453
d
0.205
0.205
0.002
0.184
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
5
.
453
e
0.205
0.205
0.001
0.184
6
.
454
f
0.206
0.206
0.001
0.184
7
.
454
g
0.206
0.206
0.000
0.184
8
.
455
h
0.207
0.207
0.000
0.184
9
.
455
i
0.207
0.207
0.000
0.184
10
.
456
j
0.208
0.208
0.001
0.184
11
.
456
k
0.208
0.208
0.000
0.184
12
.
457
l
0.209
0.209
0.001
0.184
13
.
458
m
0.209
0.209
0.000
0.184
14
.
458
n
0.210
0.210
0.000
0.184
15
.
458
o
0.210
0.210
0.000
0.184
16
.
459
p
0.210
0.210
0.000
0.184
17
.
459
q
0.210
0.210
0.000
0.184
18
.
459
r
0.211
0.210
0.000
0.184
19
.
459
s
0.211
0.210
0.000
0.184
20
.
459
t
0.211
0.210
0.000
0.184
21
.
460
u
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
12
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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
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
13
An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
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
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.
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.
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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
16
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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Community of Inquiry Framework and Retention in Online Programs
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.
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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An Exploration of the Relationship Between Indicators of the
Community of Inquiry Framework and Retention in Online Programs
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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Journal of Asynchronous Learning Networks, Volume 14: Issue 1
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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
Journal of Asynchronous Learning Networks, Volume 14: Issue 1
19
... In the CoI framework, social presence reflects interpersonal interactions that provide students with a sense of connection to their instructor (Garrison et al., 2010). Social presence (for example, instructor responsiveness) has been linked to improved outcomes for online students (Boston et al., 2009;Richardson & Swan, 2003). Additionally, teaching presence, methods used to design and facilitate supportive instruction and guide online learning (Garrison et al., 2010), and instructor presence, actions taken by instructors to be viewed as real people (Richardson et al., 2015(Richardson et al., , 2016, are important for success in online courses (Boston et al., 2009). ...
... Social presence (for example, instructor responsiveness) has been linked to improved outcomes for online students (Boston et al., 2009;Richardson & Swan, 2003). Additionally, teaching presence, methods used to design and facilitate supportive instruction and guide online learning (Garrison et al., 2010), and instructor presence, actions taken by instructors to be viewed as real people (Richardson et al., 2015(Richardson et al., , 2016, are important for success in online courses (Boston et al., 2009). ...
... However, more research is needed into students' percep-tions of interactions with online instructors (Mullen & Tallent-Runnels, 2006;Sitzman, 2010). The predominant literature examining instructor caring and support in online settings comes from the field of nursing (Mastel-Smith et al., 2015;Plante & Asselin, 2014;Sitzman, 2010), while the predominant literature examining social, teaching, and instructor presence has been conducted with fully online courses (Boston et al., 2009;Richardson & Swan, 2003). While these studies provide an indication of students' perceptions of instructor connections, research is needed to understand students' perceptions of instructor caring and support in online and face-to-face courses across the larger undergraduate population. ...
Article
Professors in a university setting questioned if requiring students to take in-class notes for points towards final grade would affect student quiz scores post-lecture. Students were assigned one of two conditions, no-notes-required control and required note-taking experimental. A Mixed-design ANOVA was used to test mean differences among groups in quiz scores over time. Across the 106 student participants, those in the experimental condition scored better on post-lecture quizzes than those in the control condition. Students not required to take notes indicated that they may voluntarily take notes regardless of the expectation in class and “somewhat agreed” that they would take better notes if they were being graded.
... Instructor-student and student-student interactions have been shown to critically influence student engagement [12][14] [24][25], and interpersonal interactions more generally to influence course satisfaction, instructor satisfaction, students' participation, learning, and persistence rates [13] [26][27][28]. Student motivation and cognitive processes are impacted by both instructor-student and student-student interactions [29]. Conversely, lack of satisfactory interpersonal interactions -including interactions that are too mandated and too frequent --have also been shown to generate dissatisfaction and reduced student motivation in online courses [28] [30]. ...
... A significant relationship between course-level persistence intentions and the perceptions of course LMS dialog, perceptions of instructor practices, and perceptions of peer support was found through the multi-level modeling analysis. These findings make sense given that both instructor-student and student-student interactions have been shown to critically influence student engagement [12] , which is linked to persistence [26] [52][53][54]. ...
Conference Paper
Full-text available
This research paper examines the influence of interpersonal interactions on the course-level persistence intentions of online undergraduate engineering students. Online learning is increasing in enrollment and importance in engineering education. Online courses also continue to confront issues with comparatively higher course dropout levels than face-to-face courses. This study correspondingly explores relevant student perceptions of their online course experiences to better understand the factors that contribute to students' choices to remain in or drop out of their online undergraduate engineering courses. Data presented in this study were collected during fall 2019 and spring 2020 from three ABET-accredited online undergraduate engineering courses at a large southwestern public university: electrical engineering, engineering management, and software engineering. The data was collected during the pre-COVID time. Participants were asked to respond to surveys at 12-time points during their 7.5-week online course. Each survey measured students' perceptions of course LMS dialog, perceptions of instructor practices, and peer support for completing the course. Participants also reported their intentions to persist in the course during each survey administration. A multi-level modeling analysis revealed that the Perceptions of course LMS dialog, Perceptions of Instructor Practices, and Perceptions of Peer Support are related to Perceptions of course-level Persistence Intentions. Time was also a significant predictor of persistence intentions and indicated that the course persistence intentions decrease towards the end of the course. A multi-level modeling analysis revealed that LMS dialog, perceptions of instructor practices, and peer support are related to course persistence intentions. Time was also a significant predictor of persistence intentions and indicated that the course persistence intentions decrease towards the end of the course. Additionally, interactions between demographic variables and other predictors (Perceptions of course LMS dialogue, Perceptions of Instructor Practices, and Perceptions of Peer Support) were significant. With the increase in perceptions of course LMS dialog, perceptions of instructor practices, and perceptions of peer support, there was a relatively smaller increase in the persistence intentions of veterans than non-veterans. There is relatively more increase in the persistence intentions of females than males as their perceptions of instructor practices increase. Finally, increasing perceptions of peer support led to a relatively larger increase in the persistence intentions of non-transfer students than transfer students and a relatively smaller increase in persistence intentions of students working full-time than other students.
... The results also showed that, among the delivery methods, the most preferred course delivery method was fully online (n = 60, [58.82%]). In this context, it can be said that these findings are consistent with the literature, as CoI is a framework for both blended and online courses (Akyol, Garrison, & Ozden, 2009;Wicks, Craft, Mason, Gritter, & Bolding, 2015;Zhang, 2020), and one of the most frequently used models for online learning in higher education (Boston et al., 2009;Harrell & Wendt, 2019). It was revealed that a large number of the participants were higher education students. ...
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... Because successful online courses create a CoI where students interact with one another, the instructor and the learning materials and develop new knowledge and skills (Rubin et al., 2013). When learners feel a higher degree of interaction in online settings, generally a strong CoI is constructed so that students can mostly participate in discussions, have a learning experience and higher perceived learning outcomes (Boston et al., 2009;Eom et al., 2006;Garrison & Cleveland-Innes, 2005;So & Brush, 2008). Thus, considering the level of interactions among learners, teachers and content; instructional designers can create appropriate learning settings (Agudo-Peregrina et al., 2014;Beldarrain, 2006). ...
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This study aims to use LMS log data to suggest a way to understand CoI constructs. Students’ interactions in Moodle components were weighted for indicators of cognitive, teaching and social presences. Traces reflecting students’ online interactions were obtained from the Moodle LMS and analyzed through learning analytics techniques. The data is examined with the Euclidean Distance Model, and Correspondence Analysis methods to evaluate the levels of interactions and presences. The results indicated that, cognitive presence is at the center of the CoI constructs, and student-content interaction, is found is more prominent than other interactions in terms of its relation to cognitive presence. Social presence scores were mostly related with student-student and student-teacher interaction scores. In addition, teaching presence scores were found in parallel with student-system interaction scores.
... In face-to-face courses, learners can physically see and immediately receive feedback from instructors, whereas in online courses, communication lacks the vocal tones, nuances, and immediacy of responses (Hailey et al., 2001). These issues have led students to report areas of concerns such as feelings of alienation or disconnectedness with others (Boston et al., 2010;Hart, 2012;Phirangee & Malec, 2017). ...
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Using three interdependent constructs: social, cognitive, and teaching presence, the Community of Inquiry framework is a theoretical process model of online learning. Specifically, teaching presence contains three sub-elements—(a) facilitation of discourse, (b) direct instruction, and (c) instructional design and organization—that work together to create a collaborative-constructivist learning environment. Data from the Community of Inquiry survey from 160 learners in 11 course sections were analyzed using a one-way analysis of variance (ANOVA) to determine whether statistically significant differences existed in teaching presence scores between sections of two online courses with identical course design taught by different instructors. Results showed significant differences between individual instructors’ teaching presence scores for each of the two courses. Specifically, significant differences were found in each sub-element of teaching presence except for one course’s instructional design and organization. Conceptual and methodological explanations of the findings are provided, and implications and suggestions for future research are discussed.
... Although a majority of studies on social presence have focused on student satisfaction, research has shown that social presence can affect both actual (e.g., course grades, assignment grades) and perceived learning (Hostetter & Busch, 2013;Joksimović, Gašević, Kovanović, Riecke, & Hatala, 2015;Kang & Im, 2013;Picciano, 2002;Richardson & Swan, 2003;Russo & Benson, 2005;Wise, Chang, Duffy, & del Valle, 2004). Moreover, social presence has also been linked to student retention and intention to reenroll in online course rates (Boston et al., 2009;Liu et al., 2009;Reio & Crim, 2013). In fact, Boston and colleagues (2009) found that two affective expression indicators of social presence-"Online or web-based communication is an excellent medium for social interaction" and "I was able to form distinct impressions of some course participants"-as measured by the CoI survey accounted for more than 20% of the variance in student retention. ...
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Social presence theory was the term first proposed in 1976 to explain how telecommunications influence how people communicate (Short, Williams, & Christie, 1976). Short and colleagues (1976) defined social presence as the degree of salience (i.e., quality or state of being there) between two communicators using a communication medium. This theory became particularly important for online educators trying to understand how people communicated in primarily text-based online courses during the 1990s (Lowenthal, 2009). In fact, social presence was identified as one of the core elements of the Community of Inquiry (CoI) framework, a widely used guide for planning, developing, evaluating, and researching online learning (Boston et al., 2011; Kumar & Ritzhaupt, 2014; Swan, Day, Bogle, & Matthews, 2014). The CoI framework is a dynamic process model of online learning based on the theory that effective learning requires a community based on inquiry (Garrison, 2011,2015). At the heart of the model are the interdependent constructs of cognitive, social, and teaching presence (Swan, Garrison, & Richardson, 2009). Social presence, the first element, is the ability of participants "to project their personal characteristics into the community, thereby presenting themselves to other participants as 'real people'" (Garrison, Anderson, & Archer, 2000, p. 89). The second element, teaching presence, involves instructional management, building understanding, and direct instruction. And the third element, cognitive presence, is "the extent to which the participants in...a community of inquiry are able to construct meaning through sustained communication" (Garrison et al., 2000, p. 89).
... Over the last ten years, the CoI survey (Arbaugh et al., 2008) has proved a reliable measurement of the three constructs in the CoI framework, which researchers consistently report are positively correlated (Kozan & Richardson, 2014). Much of the existing CoI Survey research focuses on the relationship between the three presences (Lambert & Fisher, 2013), or on the relationship between the survey responses and other variables such as course grades (Rockinson-Szapkiw et al., 2016), student satisfaction and perceived learning (Akyol & Garrison, 2008), and retention (Boston et al., 2009). While some researchers have proposed modifications to the framework (e.g., Shea et al.'s (2012) "learning presence" and Cleveland-Innes and Campbell's (2012) "emotional presence"), the original three-presence framework is more commonly used in empirical studies (Caskurlu, 2018;Stenbom, 2018). ...
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This article applies the Community of Inquiry (CoI) framework to a particular disciplinary context: first-year writing (FYW). Students enrolled in online FYW courses across three institutions (n = 272) completed a version of the CoI survey that was slightly modified to fit the disciplinary context of writing studies. A factor analysis was conducted to determine how well the CoI in Writing Studies data aligned with typical CoI survey research; teaching presence and cognitive presence loaded onto single factors, but the social presence items divided into multiple factors. The authors put their findings in conversation with other scholarship about social presence, especially Carlon et al. (2012) and Kreijns et al. (2014), and advocate for differentiating between survey items that relate to “social presence,” “social comfort,” “attitude,” and “social learning.” They also recommend that future disciplinary uses of the CoI Survey include survey items that ask students to report on the extent to which they engaged in the types of social learning that the discipline values.
... Three indicators define social presence: Affective Expression (i.e., sharing personal details and emotions), Open Communication (i.e., students openly share ideas) and Group Cohesion (i.e., sense of community; Shea et al., 2010). Affective expression refers to how online students project their personalities through sharing personal details, jokes, opinions and other emotions (Boston et al., 2010). Open communication is the extent to which students feel like the classroom climate allows for trust, open sharing of ideas, and comradery. ...
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Cyberbullying affects the majority of undergraduate women, contributing to withdrawal from social media and chilling their participation in the growing world of collaborative online discussions. This pilot mixed-methods study integrates surveys, observations, and interviews of nine undergraduate women at a Mid-Atlantic research university to investigate how the chilling effect of cyberbullying may extend into peer interactions within an increasingly common online instructional practice: online discussion boards. It is observed that, in comparison to their non-victimized peers, participating women with prior cyberbullying experiences enact lower social presence and adopt self-silencing and conflict avoidant coping strategies. In particular, these women avoid ever disagreeing with peers out of fear of starting “drama.” Findings challenge educators to consider the potential unintended consequences of instructional design choices and contributes to our understanding of how to design more equitable online learning environments for today’s socially networked learners.
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This chapter will address results of LE@D's project “Teaching in Times of Emergency: Digital Transition,” which focused on the experience of rapid digital transition to an “emergency teaching,” a scenario quite different from distance education. Through a mixed methods approach, data was collected through an online questionnaire applied to students and videoconference interviews conducted with both higher education faculty and students. Participants in this research are students and faculty from eight Portuguese higher education institutions, four from universities (three public and one private) and four from polytechnic institutes (three public and one private), covering the regions of Lisbon and Tagus Valley, Alentejo and Algarve (Central and Southern Portugal). In this chapter, the authors present a preliminary analysis of the results obtained related to the psychological aspects experienced during this period, aiming at understanding the impact this shift has had on students' cognitive adaptation and social and emotional processes.
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The purpose of this study was to explore the dynamics of an online educational experience through the lens of the Community of Inquiry framework. Transcript analysis of online discussion postings and the Community of Inquiry survey were applied in order to understand the progression and integration of each of the Community of Inquiry presences. The results indicated significant change in teaching and social presence categories over time. Moreover, survey results yielded significant relationships among teaching presence, cognitive presence and social presence, and students’ perceived learning and satisfaction in the course.
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“Social presence,” the degree to which participants in computer-mediated communication feel affectively connected one to another, has been shown to be an important factor in student satisfaction and success in online courses. This mixed methods study built on previous research to explore in greater depth the nature of social presence and how it develops in online course discussions. The study combined quantitative analyses of survey results from students enrolled in four online graduate courses, and qualitative comparisons of students with the highest and lowest perceptions of social presence. Quantitative results revealed significant correlations between perceived social presence and satisfaction with online discussions, and teased apart the respective influences of the perceived presence of instructors and peers. The findings indicate that the perceived presence of instructors may be a more influential factor in determining student satisfaction than the perceived presence of peers. Correlations with other course and learner characteristics suggest that course design may also significantly affect the development of social presence. Qualitative findings support the quantitative results. In addition, they provide evidence that students perceiving the highest social presence also projected themselves more into online discussions,and reveal meaningful differences in perceptions of the usefulness and purpose of online discussion between students perceiving high and low social presence.
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This article examines work related to the development and validation of a measurement tool for the Community of Inquiry (CoI) framework in online settings. The framework consists of three elements: social presence, teaching presence and cognitive presence, each of which is integral to the instrument. The 34 item instrument, and thus framework, was tested after being administered at four institutions in the Summer of 2007. The article also includes a discussion of implications for the future use of the CoI survey and the CoI framework itself. Introduction Online learning models are increasingly present in higher education. In 2006, 3.5 million, or almost 20%, of US higher education students were taking at least one online course (Allen & Seaman, 2007). While researchers have been relatively successful in identifying the properties of successful online learning environments (Aragon, 2003; Cleveland-Innes, Garrison & Kinsel, 2007), a more in-depth analysis requires a theoretical framework that illuminates the complexities of online learning. One model that has gained a good deal of attention is the Community of Inquiry (CoI) framework developed by Garrison, Anderson and Archer (2000). The CoI framework is a process model that provides a comprehensive theoretical model that can inform both research on online learning and the practice of online instruction. It assumes that effective online learning requires the development of a community (Rovai, 2002; Thompson & MacDonald, 2005; Shea, 2006) that supports meaningful inquiry and deep learning. Such development is not a trivial challenge in the online environment.
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This paper builds on the model we have developed for creating quality online learning environments for higher education. In that model we argue that college-level online learning needs to reflect what we know about learning in general, what we understand about learning in higher-education contexts, and our emerging knowledge of learning in largely asynchronous online environments. Components of the model include a focus on learner roles, knowledge building, assessment, community, and various forms of "presence." In this paper we focus on two components—teaching presence and community—and review the rationale and benefits for an emphasis on community in online learning environments. We argue that learning is social in nature and that online learning environments can be designed to reflect and leverage the social nature of learning. We suggest that previous research points to the critical role that community can play in building and sustaining productive learning and that teaching presence, defined as the core roles of the online instructor, is among the most promising mechanism for developing online learning community. We present a multi-institutional study of 2,036 students across thirty-two different colleges that supports this claim and provides insight into the relationship between online learning community and teaching presence. Factor and regression analysis indicate a significant link between students' sense of learning community and their recognition of effective instructional design and directed facilitation on the part of their course instructors—and that student gender plays a small role in sense of learning community. We conclude with recommendations for online course design, pedagogy, and future research.
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