This article was downloaded by: [TÜBİTAK EKUAL]
On: 26 March 2010
Access details: Access Details: [subscription number 772815468]
Publisher Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 3741 Mortimer Street, London W1T 3JH, UK
Interactive Learning Environments
Publication details, including instructions for authors and subscription information:
http://www.informaworld.com/smpp/title~content=t716100701
Relationships among sense of classroom community, perceived cognitive
learning and satisfaction of students at an e-learning course
M. H. Baturay a
a
Institute of Informatics, Gazi University, Ankara, Turkey
First published on: 19 March 2010
To cite this Article Baturay, M. H.(2010) 'Relationships among sense of classroom community, perceived cognitive learning
and satisfaction of students at an e-learning course', Interactive Learning Environments,, First published on: 19 March
2010 (iFirst)
To link to this Article: DOI: 10.1080/10494821003644029
URL: http://dx.doi.org/10.1080/10494821003644029
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial or
systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or
distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contents
will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses
should be independently verified with primary sources. The publisher shall not be liable for any loss,
actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly
or indirectly in connection with or arising out of the use of this material.
Interactive Learning Environments
2010, 1–13, iFirst article
Relationships among sense of classroom community, perceived cognitive
learning and satisfaction of students at an e-learning course
M.H. Baturay*
Institute of Informatics, Gazi University, Ankara, Turkey
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
(Received 22 June 2009; final version received 12 December 2009)
This study aims to determine whether there is a relationship between students’ sense
of community, perceived cognitive learning, and satisfaction in an e-learning course.
Additionally, the relationship of these variables with Internet self-efficacy and final
examination scores is investigated. The participants were 88 students enrolled in
elementary level English as a Foreign Language course of the distance education
program at a higher education institution in Turkey. The results of the study suggest
that sense of community and course satisfactions are strongly related to each other.
Moreover, students’ course satisfaction is highly related to their perceived cognitive
learning. Students’ perceived cognitive learning was observed to have a very strong
relationship with learner-to-content interaction, while learner-to-learner interaction
was at medium level and learner-to-instructor interaction was weak.
Keywords: e-learning; sense of community; cognitive learning; satisfaction;
achievement
Introduction
The place and time flexibility, cost effectiveness, multimedia-rich and customized
learning advantages of e-learning attract many learners, suggesting continued
growth. Nonetheless, two issues remain: the higher dropout rates and low quality of
learning attainment some learners and educators perceive (Rovai, 2002a). There is a
clear agreement in the literature that the higher dropout rate is a difficult and
perplexing phenomenon (Levy, 2007). There are many reasons hypothesized to
explain the lower degree of perceived learning and the higher dropout rates in some
online programs, which are in fact not different from the problems encountered in
traditional learning environments. Some of these reasons include insufficient
feedback from the teacher (Morgan & Tam, 1999), limited interaction among
learners and the teacher (Saba, 2002), lack of social integration (King, 2002),
underestimated effort necessary for courses (Arsham, 2002), lower quality of
learning materials (Rossett & Schafer, 2003), inexperienced instructors (Terry, 2001),
lack of time management (Saba, 2002), lack of motivation (Morris & Finnegan,
2005), lack of technology proficiency and technical support (McVay-Lynch, 2002),
poor learning responsibility (Saba, 2002) and increased learner responsibilities
_
(Yükseltürk & Inan,
2006).
*Email: baturay@gazi.edu.tr
ISSN 1049-4820 print/ISSN 1744-5191 online
Ó 2010 Taylor & Francis
DOI: 10.1080/10494821003644029
http://www.informaworld.com
2
M.H. Baturay
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Persistence in distance education as well as perceived quality of learning
attainment might be enhanced by increasing student satisfaction. Moreover, Rovai
(2002a) claims that research provides evidence that strong feeling of community may
increase both persistence in courses and motivation to learn; therefore, students
should be provided with increased affective support by promoting a strong sense of
community.
The present study investigates the relationships of sense of community, student
satisfaction, perceived cognitive learning, Internet self-efficacy, and achievement scores
in an e-learning environment. The author hypothesized that these variables are strongly
inter-related and represent potential causes for lower e-learning persistence rates. Thus,
by examining these variables, one might better understand the factors that affect
students’ satisfaction and persistence in an e-learning course. The author believes that
such studies are needed to assess e-learning environments and create research-based
guidelines for students’ satisfaction and effective e-learning.
Sense of community in an e-learning environment
The social context that impacts communication, learning, satisfaction and sense of
online community in an e-learning environment has been analyzed in the distance
education literature. The perception of ‘‘online participation’’ (Hrastinski, 2009) and
presence have been designated in various studies and models. ‘‘Transactional
Presence’’ is defined as the degree to which a student perceives the availability of, and
connectedness with, other parties involved. It is briefly the distance students’
perceptions of teachers, peers and institutions (Shin, 2002). ‘‘Social Presence’’ is
defined by Short, Williams and Christie (1976) as ‘‘the degree of salience of the other
person in the interaction and the consequent salience of the interpersonal
relationships’’ (p. 65). It is the degree to which a person is perceived as ‘‘real’’ in
mediated communication (Richardson & Swan, 2003, p. 70). The model of
‘‘Community of Inquiry’’ assumes that learning occurs within the community
through the interaction of three core elements: cognitive (construction of meaning
through sustained communication); social (ability of participants to project their
personal characteristics into the community); and teaching presence (the design and
facilitation of educational experience). Social presence is the support for cognitive
presence, indirectly facilitating the process of critical thinking (Garrison, Anderson,
& Archer, 2000).
Picciano (2002) states that the term ‘‘community’’ is related to presence and
refers to a group of people who belong to a social unit similar to students in a class.
Rovai (2002a) claims that there is not a common definition of the term ‘‘sense of
community.’’ The classroom community can be defined in terms of two components:
(a) social community or the feelings of connectedness among community members
and (b) learning community or their common expectations of learning and goals.
Connectedness means the feelings of friendship, cohesion and satisfaction that
develop among students, which, in turn, develops the feelings of safety and trust and
that facilitates exposure of learning gaps by community members. To Shin’s (2002)
definition, connectedness solely ‘‘refers to the belief or feeling that a reciprocal
relationship exists between two or more parties’’. The second component is the
feeling that knowledge and meaning are actively constructed within the community
and that the community enhances the acquisition of learning and acquisition (Rovai,
2002a).
Interactive Learning Environments
3
Social community is often lower than in face-to-face classroom environments
because of the fact that learners feel disconnected from each other and from their
teachers. However, social community can be nurtured by instructor efforts to
increase the amount and quality of social interaction through effective use of
discussion boards, chat sessions, e-mail correspondence, and video or audio
conferencing. In an interrelated way, the development of social presence and a
sense of online community becomes a key for the promotion of collaborative
learning and active knowledge building (Gunawardena, 1995).
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Perceived cognitive learning of students in an e-learning environment
Tallent-Runnels, Thomas, Lan and Cooper (2006) note that many online studies
use single-item measures of key variables and that there should be more
systematic studies specifically designed to measure learning effectiveness of online
educational practices. Rovai, Wighting, Baker and Grooms (2009) claim that
grades do not always indicate the amount of student learning since grades may be
more related to pre-existing knowledge and not what was learned in the course.
Additionally, grades might be much more influenced by class participation or
timely assignment submissions. More importantly, as pointed out by Rovai
(2002a), the reliability of the grades is not always high since different teachers or
even the same teacher at different times may be likely to assign inconsistent
grades. As suggested by Bloom (1956), an instrument that consisted of all
measurements of cognitive, affective and psychomotor domains would be
beneficial.
Satisfaction in an e-learning environment
Considering the rapid growth and interest in online education, a key concern of
educators and researchers is the quality and effectiveness of online education
(Nachmias, 2002) and enhancement of learners’ satisfaction in this environment.
Student satisfaction is an important factor in measuring the effectiveness of elearning (Levy, 2003) since higher satisfaction related to higher levels of learning
(Fredericksen, Pickett, Shea, Pelz, & Swan, 2000) and satisfaction was reported to be
a major factor related to students’ decision of dropping out from distance education
courses (Chyung, Winiecki, & Fenner, 1998).
However, there are a great many variables such as sense of classroom
community, technical problems, level of the Internet or computer self-efficacy,
instructor’s quality of interaction and feedback, the content, the e-learning
material, etc. that might affect students’ satisfaction, particularly in an elearning environment. It is stated that both quality and quantity of interaction
with the instructor and peers are much more crucial to the success of online
courses and student satisfaction than that are in traditional courses (Woods,
2002). Similarly, Fulford and Zhang (1993) found the students’ perception of
interaction as the critical predictor of satisfaction in a distance-learning course.
Tu (2002), on the other hand, explains that social presence is a strong predictor
of satisfaction within computer-mediated communication environment. By
focusing on some of these variables, this study aims to demonstrate some
relationships that might affect an e-learner’s satisfaction through an empirical
analysis.
4
M.H. Baturay
Methodology
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
The goal of this research study is to measure the relationships between students’
sense of classroom community, satisfaction, perceived cognitive learning, Internet
self-efficacy and final exam scores. Further, the differences among these variables
regarding students’ demographics are examined. As emphasized by Picciano (2002),
the interaction and presence might affect student performance independently.
Therefore, the concept community which is ‘‘related to presence’’ and the
satisfaction based on three interactions were measured independently in the study.
Three research questions that guided this study are as follows:
(1) Are there any differences between students’ sense of classroom community,
satisfaction, perceived cognitive learning, the Internet self-efficacy and final
exam scores by students’ demographics?
(2) Is there a relationship between students’ sense of classroom community,
satisfaction, perceived cognitive learning, the Internet self-efficacy and final
exam scores?
(3) Can classroom community and course satisfaction predict perceived
cognitive learning?
Setting and the participants
Participants were enrolled in an elementary-level English language course taught
entirely via the Internet using a learning management system (LMS). Students only
came to school at the end of the semester for the proctored final exam, which was
taken on-campus. The participants in the study were enrolled in the knowledge
management (KM) and accountancy (AC) departments of the distance education
program at a higher education institution in Turkey. One hundred seventy-eight
students participated voluntarily in the study. The researcher only compared
students who submitted completely filled-in surveys for the correlational analysis
since there were missing values in some of the surveys. Therefore, the final number of
participants for the correlational analysis was 88. Males represented 23% (n ¼ 20) of
the sample, and females represented 77% (n ¼ 68). Eight percent (n ¼ 7) of the
participants were below the age of 18; 67% (n ¼ 59) were between the ages of 19 and
25; 22% (n ¼ 19) were between the ages of 26 and 35; and 3% (n ¼ 3) were above
the age of 36.
The characteristics of the online learning environment
The characteristics of the learning environment that could affect students’ sense of
classroom community, course satisfaction, cognitive learning, and final exam scores
were as follows. The distance English language course was entirely given via the
Internet through an LMS. Students did not meet each other or with the course
instructor except for the weekly net meeting that was text-based. The online course
was embedded in an LMS that consisted of an integrated set of tools in the following
categories: (a) content management tools that allowed the course instructor to
present multimedia content, supplementary course materials, and course weekly
schedule; (b) assessment tools such as online test/exam preparation, online testing
and test/exam question pool; (c) student tools such as student lists, students’ reports
Interactive Learning Environments
5
and student grade book; (d) communication and collaboration tools, that consisted
of e-mail, net meeting, announcements, discussion boards and an agenda to take
personal notes.
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
. Net meeting: This was the session where students met with the course
instructor and their peers in real time. Students had the opportunity to ask
questions about issues that hadn’t been understood well or the instructor
identified as problematic. They addressed issues of grammar and syntax with
the instructor in this hour. If students did not have questions, the instructor
conducted a language drill and practice activity with the students.
. Discussion board: Students interacted with their peers using the text-based
discussion board. It was not obligatory for the students to participate. The
instructor monitored the students’ postings.
The English language course included sections on vocabulary, grammar, reading
and writing, listening, and speaking. The grammar was supported with video
recordings in which the instructor taught grammatical structures in the students’
native language, Turkish.
Instrumentation
There were three scales and two surveys used for gathering the participants’
perceptions, their Internet self-efficacy and demographics. Apart from the scale for
cognitive learning, students’ face-to-face final exam scores were added to the analysis
as a learning achievement indicator.
Data regarding students’ sense of classroom community were collected through
the Classroom Community Scale (CCS) by Rovai (2001, 2002b). This scale was
presented to and rated by a panel of experts of professors by Rovai for content
validity and the scale’s construct validity was supported. Its internal validity was
additionally calculated by using Cronbach’s coefficient a and it was found to be 0.93.
The scale measures sense of classroom community in a learning environment and
consists of 20 items, such as: ‘‘I feel that students in this course care about each
other,’’ ‘‘I feel that I receive timely feedback,’’ and ‘‘I feel that my educational needs
are not being met.’’ The instrument includes a five-point Likert-type scale of
potential responses: strongly agree, agree, neutral, disagree, and strongly disagree,
with the assigned values ranging from 4 to 0. The students’ scores are assigned the
value of 4 for the most favorable answer and the value 0 for the least favorable
response. The scale measures both social community and learning community.
Data regarding students’ perceived cognitive learning were collected through CAP
Perceived Learning Scale developed by Rovai et al. (2009) who provide evidence of
content and construct validity. They report 0.79 as the internal consistency reliability
of the scale using Cronbach’s a. This scale includes cognitive, affective and
psychomotor subscales. This scale is particularly useful for measuring perceived
student learning within an online virtual classroom environment (Rovai et al., 2009).
The Internet Self-efficacy Scale was adapted from Joo, Bong and Choi (2000) and
used to determine the perceived capability of students to use the Internet. The scale
has high internal consistency reliability as demonstrated by Cronbach’s a of 0.95.
Potential responses for the five-point Likert-type scale are: very true, mostly true,
somewhat true, mostly not true, and not true at all, with assigned values between 5
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
6
M.H. Baturay
and 1. Items were scored as 5 for the answer ‘‘very true’’ and the value 1 for the
answer ‘‘not true at all.’’
The Course Evaluation Survey was used to evaluate students’ perceptions of
satisfaction with the online course. It was prepared and administered using Web
Builder developed by North Carolina State University’s College of Agriculture and
Life Sciences (CALS) to be administered to students enrolled in university courses
(Lucas, 2007). It consists of three sub-parts for evaluating learner-to-learner
interaction within the course with 10 items, learner-to-content interaction within the
course with 11 items, and learner-to-instructor interaction within the course with 10
items. Cronbach’s coefficient a was 0.94 for the learner-to-learner interaction
subscale, 0.90 for the learner-to-content interaction, and 0.96 for learner-toinstructor interaction. There was a five-point Likert-type scale of potential
responses: strongly agree, somewhat agree, agree, somewhat disagree, and strongly
disagree. The assigned values for each item ranged between 5 and 1, with 5 for the
answer ‘‘strongly agree’’ and the value 1 for the answer ‘‘strongly disagree’’.
The demographics survey includes items that address the age, gender, school
graduated, department enrolled, and years of computer use. Finally, the final exam is
a teacher-produced proctored test that consists of 25 multiple choice questions and
measures student learning. Each correctly answered question is scored as 4 points
with 100 possible points.
Results
The reliability of the instruments was checked. Cronbach’s coefficient a values for
the CAP Perceived Learning Scale was 0.78, the Internet self-efficacy scale was 0.86,
the CCS was 0.83, and the Course Evaluation Survey was 0.92. These coefficients
provide evidence that all instruments are reliable.
A total of 88 student participants’ data were analyzed using the Internet Selfefficacy Scale, CCS, the course evaluation survey, CAP Perceived Learning Scale,
and their final exam scores with student demographics. There was no difference
between the scales regarding students’ demographics of gender, age range, their
working time (not working/full time/part time), where the course was taken (at
home, at work). However, there were significant differences found regarding school
graduated, department enrolled, years of computer use. These differences are
described in order as follows.
A one-way analysis of variance (ANOVA) was conducted to evaluate the effect of
school graduated on the scales and final exam scores. ANOVA results presented in
Table 1 reveal that the test is significant for the final exam scores, F(5, 82) ¼ 4.92,
p 5 0.01, and reveal a difference in the mean final exam scores of the commercial high
school (M ¼ 39.83, SD ¼ 18.39) and of the Super Lycee (M ¼ 80.00, SD ¼ 24.33).
As the test was significant, Scheffe multiple comparison tests were carried out to
determine pair wise differences as shown in Table 2. Post hoc comparisons indicated
that there was not a significant difference among the other graduated schools
regarding the final exam scores but only between the commercial high school and
Super Lycee p ¼ 0.04.
An independent-samples t-test was conducted to investigate if there was a
significant difference among the scales and final exam scores of students from KM
and AC departments. The independent samples t-test was significant for CCS, t
(86) ¼ 2.24, p ¼ 0.03 and for final exam scores t (87) ¼ 2.14, p ¼ 0.04. Students of
7
Interactive Learning Environments
the KM department were more successful on the average CCS scores (M ¼ 57.16,
SD ¼ 11.55) than the students of the AC department (M ¼ 51.09, SD ¼ 13.72) with
a 6.07 mean difference; similarly, students of the KM department were more
successful on the average final exam scores (M ¼ 51.63, SD ¼ 20.85) than the
students of the AC department (M ¼ 42.84, SD ¼ 17.49) with a 25.35 mean
difference. The classroom community and final exam scores of the students seemed
to change regarding their departments (Table 3).
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Table 1. The Results of one-way analysis of variance by scales and by students’ schools
graduated.
Internet self-efficacy
Classroom community
Course evaluation
Cognitive learning
Final exam
Table 2.
F(5,82)
Sig.
0.89
1.37
0.19
0.23
4.92
0.51
0.24
0.98
0.97
50.01
Multiple comparisons.
Dependent variable: final exam
School graduated
Super Lycee
School graduated
Mean difference
Lycee
Industrial vocational high school
Commercial high school
Anatolian high school
Other
32.78
41.33
40.17*
32.00
24.00
*The mean difference is significant at the 0.05 level.
Table 3.
The differences among the scales regarding students’ departments enrolled.
Internet self-efficacy
Classroom community
Course evaluation
Cognitive learning
Final exam scores
Independent samples test
Internet self-efficacy
Classroom community
Course evaluation
Cognitive learning
Final exam scores
Department
N
Mean
SD
KM
AC
KM
AC
KM
AC
KM
AC
KM
AC
t
43
45
43
45
43
45
43
45
43
45
t-test for equality of
means Sig. (2-tailed)
60.00
58.91
57.16
51.09
122.16
117.84
32.65
30.82
51.63
42.84
5.09
7.75
11.55
13.72
14.83
18.75
6.17
6.96
20.85
17.49
0.78
2.24
1.19
1.30
2.14
0.44
0.03
0.24
0.20
0.04
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
8
M.H. Baturay
A one-way ANOVA was conducted to evaluate the effect of years of computer
use on the scales and final exam scores. According to the results indicated in Table 4,
the test was significant for the Internet Self-Efficacy Scale, F(3, 84) ¼ 3.09, p 5 0.05.
Multiple comparisons were carried out because this test was significant and in order
to determine pairwise differences. Post hoc comparisons (Scheffe) indicated that there
was only a significant difference between 1 and 3 years and 4 and 7 years of students’
years of computer use, p ¼ 0.04. The mean score of 4–7 years (M ¼ 61.36,
SD ¼ 3.86) was higher than 1–3 years (M ¼ 56.50, SD ¼ 9.71).
Pearson product–moment correlations were computed to determine the bivariate
correlations among the Internet Self-Efficacy Scale, CCS, the course evaluation
survey, CAP Perceived Learning Scale, and students’ final exam scores. Additionally,
the subscales of the course evaluation survey measuring learner-to-learner, learnerto-content, and learner-to-instructor interactions were analyzed. The results are
displayed in Table 5.
According to the correlation analysis results, a relationship exists between
students’ sense of classroom community and their perceived cognitive learning.
There was a medium level positive correlation between the two variables, r ¼ 0.37,
p 5 0.01. The analysis of the relationship between students’ classroom community
and their satisfaction of the course (course evaluation) indicated a strong, positive
correlation between the two variables, r ¼ 0.51, p 5 0.01. The analysis of the
relationship between students’ perceived cognitive learning and their satisfaction of
the course indicated a strong, positive correlation between the two variables,
r ¼ 0.53, p 5 0.01. The analysis of the relationship between students’ perceived
cognitive learning and their final exam scores indicated a weak, positive correlation
between the two variables, r ¼ 0.24, p 5 0.05.
Table 4. The results of one-way analysis of variance by scales and by students’ years of
computer use.
Variables
Internet self-efficacy
Classroom community
Course evaluation
Cognitive learning
Final exam scores
Table 5.
1.
2.
3.
4.
5.
6.
7.
8.
F(3,84)
Sig.
3.09
0.22
1.54
0.50
2.37
0.03
0.89
0.21
0.69
0.08
Intercorrelation matrix (N ¼ 88).
Internet self-efficacy
Classroom community
Course evaluation
Cognitive learning
Final exam scores
Learner to learner
Learner to content
Learner to instructor
1
2
3
4
5
6
7
8
1
0.28**
1
0.04
0.51**
1
0.04
0.37**
0.53**
1
0.05
0.08
0.20
0.24*
1
0.06
0.48**
0.88**
0.45**
0.20
1
0.01
0.38**
0.80**
0.62**
0.067
0.62**
1
0.07
0.39**
0.76**
0.22*
0.17
0.49**
0.46**
1
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Interactive Learning Environments
9
The detailed analysis of the relationship between learner-to-learner interaction
and classroom community indicated a medium level positive correlation between the
two variables, r ¼ 0.48, p 5 0.01. Similarly, there was a medium level positive
correlation between learner-to-learner interaction and students’ perceived cognitive
learning, r ¼ 0.45, p 5 0.01. The analysis of the relationship between learner-tocontent interaction and classroom community indicated a medium level positive
correlation between the two variables, r ¼ 0.38, p 5 0.01. Similarly, there was a
strong, positive correlation between learner-to-content interaction and students’
perceived cognitive learning, r ¼ 0.62, p 5 0.01. The analysis of the relationship
between learner-to-instructor interaction and classroom community indicated a
medium level positive correlation between the two variables, r ¼ 0.39, p 5 0.01.
Similarly, there was a weak positive correlation between learner-to-instructor
interaction and students’ perceived cognitive learning, r ¼ 0.22, p 5 0.05.
The analysis of the relationship between students’ Internet self-efficacy scores and
their final exam scores did not indicate any significant relationship between the two
variables. Similarly, there was no significant relationship between students’ Internet
self-efficacy scores and their satisfaction.
Next, a standard multiple regression was also conducted to evaluate how well the
classroom community and course satisfaction (course evaluation) predict perceived
cognitive learning of the students. The Durbin–Watson statistic of 2.03 suggests the
absence of serial correlation of terms for adjacent cases. Additionally, there was no
autocorrelation and collinearity statistics suggest that there was no multicollinearity.
The multiple regression analysis indicated the regression model was significant, F(2,
86) ¼ 1,399.52, p 5 0.001 (see Table 6). According to the results, 97% cognitive
learning of the students was related to their sense of classroom community and
satisfaction.
The analysis revealed that the relationship between students’ cognitive learning
and their course satisfaction was statistically significant t(88) ¼ 9.19, p 5 0.01;
whereas, there was no statistical significant between students’ cognitive learning and
their sense of community, t(88) ¼ 1.46, p ¼ 0.15.
Discussion
The present study investigated how sense of classroom community, cognitive
learning, satisfaction, the level of the Internet self-efficacy, and achievement scores of
students were related to each other. The findings indicated that the sense of
classroom community was highly related to students’ satisfaction of the course,
suggesting that students feel there was friendship and cohesion in the classroom,
Table 6. Summary of standard regression analysis for variables predicting perceived
cognitive learning (N ¼ 88).
Unstandardized
coefficient
Variable
Course evaluation
Classroom community
R2 ¼ 0.97 (p 5 0.05).
Standardized coefficient
b
SE B
b
t
0.23
0.08
0.03
0.05
0.85
0.14
9.2
1.6
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
10
M.H. Baturay
which, in turn, should develop their feelings of safety, trust, and increase their
learning (Rovai, 2002a). The relationships between the students’ sense of classroom
community and satisfaction based on learner-to-learner, learner-to-content, learnerto-instructor interactions were medium. Next, it was found that students’ cognitive
learning was very much related to their satisfaction with the course. Sense of
classroom community was moderately related to students’ perceived cognitive
learning. In fact, the variables of perceived cognitive learning, sense of classroom
community, and satisfaction were found to be strongly inter-related to each other.
Similar to the findings, Richardson and Swan (2003) in their correlational design
study found that students with high overall perceptions of social presence also scored
high in terms of perceived learning and satisfaction with the instructor. Social
presence is a strong predictor of distance student satisfaction (Shin, 2002; Tu, 2002)
and has a positive relationship with the degree of perceived learning outcome
(Hackman & Walker, 1990). The students’ perceived cognitive learning was,
particularly, observed to have a very strong relationship with learner-to-content
interaction; while learner-to-learner interaction was at medium level and learner-toinstructor interaction was weak. This finding could be interpreted as the students
were satisfied with the content of e-course more than with their interactions with
their peers and the instructor. Shin (2002) confirms that interaction is a very
significant element that can affect various aspects of distance learning. Regarding
this Saba (2002) further implies that the level of interaction among learners and
teacher might affect students’ persistence or withdrawal from a course besides all
aforementioned reasons.
It was notable that perceived cognitive learning and final exam scores were
moderately related to each other. This result might have been due to the fact that the
self-report instrument, CAP Perceived Learning Scale, measured cognitive learning
within the cognitive, affective and psychomotor domains; whereas, final exam test
only measured and reflected students’ superior achievement. Another reason might
have been due to the fact that the students might have had difficulty in judging how
much they learned and in the comprehension of the items of the CAP Perceived
Learning Scale regarding their cognitive learning and affective perceptions (Rovai
et al., 2009).
On the other hand, the Internet self-efficacy and final exam scores of students
were found to be expectedly unrelated. This was most probably due to the fact that
final exams were taken face-to-face on-campus, which did not have any relation to
the students’ level of Internet literacy and/or experience. Similarly, students’ Internet
self-efficacy levels were not related to their satisfaction with the course. Although the
learning environment was completely Internet based, this did not have any relation
to students’ satisfaction which might have been due to the fact that the e-learning
environment required minimum level of Internet literacy and/or experience. This
finding stands against what Arif (2001) stated, as participation in e-learning courses
was seriously affected by IT experience deficiencies of students, which was a
significant contributor to their withdrawal from the course.
Data analysis revealed difference in the final exam scores based on students’ prior
schools. This difference probably occurred due to the fact that Super Lycee students
were accepted to school according to their academic achievement levels and exposed
to an intensive English language education at school when compared to commercial
high school students who mostly focused on commercial and vocational courses
during their high-school years and exposed to medium level English language
Interactive Learning Environments
11
education in limited hours. Another difference in data was on the classroom
community and final exam scores of the students based on their departments.
Students from KM department attained higher scores in the final exam than AC
department students. These results might have occurred due to the fact that KM
students were more aware, interested in or exposed to the management earned higher
final exam scores in an English course than AC students, which might have been related
to these students’ entry level skills of English prior to beginning their school. Finally,
years of computer use had an effect on the Internet self-efficacy levels of the students,
which was not unexpected. The analysis indicated that 4–7 years of computer use
affected students’ Internet experience and literacy levels more than those with 1–3 years
use. This difference probably occurred due to the fact that as students’ computer use
experience in years increased, their Internet self-efficacy levels also increased.
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Conclusion
The sense of classroom community, cognitive learning, satisfaction, and achievement
scores of students are related as indicated in the findings of the present study. This
study emphasizes that: e-learning students can feel connected to their virtual
classroom community, which is highly related to their satisfaction of the e-course.
Students’ satisfaction of one e-course is in turn very much related to their cognitive
learning and that satisfaction of the students has more effect on cognitive learning of
them when compared to their senses of classroom community.
The results do not suggest that there is a causal relationship among the
aforementioned variables. There is the possibility of a third variable affecting the
relationship. For example, the use of video and net meetings in the present study
might have lessened psychological distance and revealed a sense of classroom
community feeling and increased the students’ satisfaction of the course. Moreover,
the participants of the study were thought to be typical undergraduate students
learning on a typical e-learning environment (LMS), which was mostly preferred at
universities in Turkey. However, since the study was implemented at one university,
the generalization of the findings is still limited. There might be differences in the
results of other studies due to the pedagogy applied, the content used, the
instructor’s communication styles with the students and instructor’s immediacy.
There is future research needed to investigate these relationships on the classroom
community, cognitive learning and satisfaction of the students and their subscales. It
is suggested that current study might further be expanded with the analysis of
messages’ content, number of postings to the course discussion board, and the
number of e-mails sent to the instructor by students. Moreover, the relationship
between sense of classroom community and instructor communication, characteristics of the content, applied pedagogy, students’ social strata, and cultural
communication patterns might be examined in future research studies. The same
study might be carried out at other cultural settings to identify culture specific
patterns affecting satisfaction, sense of community and cognitive learning constructs
and their relationships.
Notes on contributor
Meltem Huri Baturay is an instructor at the Institute of Informatics at Gazi University in
Ankara, Turkey. She received her doctorate degree in Computer Education and Instructional
Technology from Middle East Technical University. Dr. Baturay’s areas of professional
12
M.H. Baturay
interest include e-learning, online social learning environments and web-based foreign
language teaching.
Acknowledgement
I would like to express my deepest gratitude to Professor Alfred P. Rovai in providing me with
help and support throughout the study.
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
References
Arif, A. (2001). Learning from the Web: Are students ready or not? Educational Technology &
Society, 4(4), 32–38.
Arsham, H. (2002). Interactive education: Impact of the Internet on learning & teaching.
Retrieved May 8, 2009, from http://home.ubalt.edu/ntsbarsh/interactive.htm
Bloom, B.S. (1956). Taxonomy of educational objectives. Handbook 1: Cognitive domain. New
York: David McKay.
Chyung, S.Y., Winiecki, D.J., & Fenner, J.A. (1998). A case study: Increase enrollment by
reducing dropout rates in adult distance education (Vol. 98). In Proceedings of the annual
conference on distance teaching and learning (pp. 97–102). Madison, WI: University of
Wisconsin-Madison.
Fredericksen, E., Pickett, A., Shea, P., Pelz, W., & Swan, K. (2000). Student satisfaction and
perceived learning with on-line courses: Principles and examples from the SUNY learning
network. Journal of Asynchronous Learning Networks, 4(2), 7–41.
Fulford, C.P., & Zhang, S. (1993). Perceptions of interaction: The critical predictor in distance
education. The American Journal of Distance Education, 7(3), 8–21.
Garrison, D.R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based
environment. Computer conferencing in higher education. Internet in Higher Education,
2(2), 87–105.
Gunawardena, C.N. (1995). Social presence theory and implications for interaction and
collaborative learning in computer conferences. International Journal of Educational
Telecommunications, 1(2), 147–166.
Hackman, M.Z., & Walker, K. (1990). Instructional communication in the televised classroom: The effects of system design and teacher immediacy on student learning and
satisfaction. Communication Education, 39, 196–206.
Hrastinski, S. (2009). A theory of online learning as online participation. Computers and
Education, 52, 78–82.
Joo, Y.J., Bong, M., & Choi, H.J. (2000). Self-efficacy for self-regulated learning, academic
self-efficacy, and Internet self-efficacy in Web-based instruction. Educational Technology
Research and Development, 48(2), 5–17.
King, F.B. (2002). A virtual student not an ordinary Joe. Internet and Higher Education, 5(2),
157–166.
Levy, Y. (2003). A study of learners’ perceived value and satisfaction for implied effectiveness
of online learning systems. Dissertation Abstracts International 65(03), 1014A. (UMI No.
AAT 3126765).
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers and
Education, 48, 185–204.
Lucas, J.W. (2007). Personality type (MBTI) relationship to performance and satisfaction in
web-based instruction (WBI). A dissertation, Graduate Faculty of North Carolina State
University, US.
McVay-Lynch, M. (2002). The online educator: A guide to creating the virtual classroom.
London: Routledge.
Morgan, C.K., & Tam, M. (1999). Unraveling the complexities of distance education student
attrition. Distance Education, 20(1), 96–108.
Morris, L., Wu, S., & Finnegan, C. (2005). Predicting retention in online general education
courses. The American Journal of Distance Education, 19, 23–36.
Nachmias, R. (2002). Research framework for the study of a campus-wide web-based
academic instruction project. Internet and Higher Education, 5, 213–229.
Downloaded By: [TÜBTAK EKUAL] At: 09:51 26 March 2010
Interactive Learning Environments
13
Picciano, A. (2002). Beyond student perceptions: Issues of interaction, presence and
performance in an online course. Journal of Asynchronous Learning Networks, 6(1), 21–40.
Richardson, J.C., & Swan, K. (2003). Examining social presence in online courses in relation
to students’ perceived learning and satisfaction. Journal of Asynchronous Learning
Networks, 7(1), 68–88.
Rossett, A., Schafer, L. (2003). What to do about e-dropouts. Training and Development, 57,
40–46.
Rovai, A.P. (2001). Building classroom community at a distance: A case study. Educational
Technology Research and Development Journal, 49(4), 35–50.
Rovai A.P. (2002a). Sense of community, perceived cognitive learning, and persistence in
asynchronous learning networks. The Internet and Higher Education, 5(4), 319–332.
Rovai, A.P. (2002b). Development of an instrument to measure classroom community.
Internet and Higher Education, 5(3), 197–211.
Rovai, A.P., Wighting, M.J., Baker, J.D., & Grooms, L.D. (2009). Development of an
instrument to measure perceived cognitive, affective, and psychomotor learning in
traditional and virtual classroom higher education settings. The Internet and Higher
Education, 12(1), 7–13.
Saba, F. (2002). Student attritions: How to keep your online learner focused. Distance
Education Report, 14, 1–2.
Shin, N. (2002). Beyond interaction: the relational construct of ‘Transactional Presence’. Open
Learning: The Journal of Open and Distance Learning, 17(2), 121–137.
Short, J., Williams, E., & Christie, B. (1976). The Social Psychology of Telecommunication.
New York: John Wiley.
Tallent-Runnels, M.K., Thomas, J.A., Lan, W.Y., & Cooper (2006). Teaching courses online:
A review of the research. Review of Educational Research, 76(1), 93–135.
Terry, N. (2001). Assessing enrollment and attrition rates for the online MBA. T.H.E. Journal,
28(7), 64–68.
Tu, C.H. (2002). The measurement of social presence in an online learning environment.
International Journal on E-Learning, 1(2), 34–45.
Woods, R.H., Jr. (2002). How much communication is enough in online courses? Exploring
the relationship between frequency of instructor-initiated personal email and learners’
perceptions of and participation in online learning. International Journal of Instructional
Media, 29(4), 377–394.
_
Yükseltürk, E., & Inan,
F.A. (2006). Examining the factors affecting student dropout in an
online learning environment. Retrieved April 24, 2009, from http://www.eric.ed.gov/
ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/27/f6/ae.pdf