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Relationships among sense of classroomcommunity, perceived cognitivelearning and satisfaction of students atan e-learning courseM.H. Baturay aa Institute of Informatics, Gazi University , Ankara, TurkeyPublished online: 19 Mar 2010.

To cite this article: M.H. Baturay (2011) Relationships among sense of classroom community,perceived cognitive learning and satisfaction of students at an e-learning course, InteractiveLearning Environments, 19:5, 563-575, DOI: 10.1080/10494821003644029

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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

(Received 22 June 2009; final version received 12 December 2009)

This study aims to determine whether there is a relationship between students’ senseof community, perceived cognitive learning, and satisfaction in an e-learning course.Additionally, the relationship of these variables with Internet self-efficacy and finalexamination scores is investigated. The participants were 88 students enrolled inelementary level English as a Foreign Language course of the distance educationprogram at a higher education institution in Turkey. The results of the study suggestthat sense of community and course satisfactions are strongly related to each other.Moreover, students’ course satisfaction is highly related to their perceived cognitivelearning. Students’ perceived cognitive learning was observed to have a very strongrelationship with learner-to-content interaction, while learner-to-learner interactionwas 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 customizedlearning advantages of e-learning attract many learners, suggesting continuedgrowth. Nonetheless, two issues remain: the higher dropout rates and low quality oflearning attainment some learners and educators perceive (Rovai, 2002a). There is aclear agreement in the literature that the higher dropout rate is a difficult andperplexing phenomenon (Levy, 2007). There are many reasons hypothesized toexplain the lower degree of perceived learning and the higher dropout rates in someonline programs, which are in fact not different from the problems encountered intraditional learning environments. Some of these reasons include insufficientfeedback from the teacher (Morgan & Tam, 1999), limited interaction amonglearners and the teacher (Saba, 2002), lack of social integration (King, 2002),underestimated effort necessary for courses (Arsham, 2002), lower quality oflearning 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(Yukselturk & _Inan, 2006).

*Email: baturay@gazi.edu.tr

Interactive Learning Environments

Vol. 19, No. 5, December 2011, 563–575

ISSN 1049-4820 print/ISSN 1744-5191 online

� 2011 Taylor & Francis

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Persistence in distance education as well as perceived quality of learningattainment might be enhanced by increasing student satisfaction. Moreover, Rovai(2002a) claims that research provides evidence that strong feeling of community mayincrease both persistence in courses and motivation to learn; therefore, studentsshould be provided with increased affective support by promoting a strong sense ofcommunity.

The present study investigates the relationships of sense of community, studentsatisfaction, perceived cognitive learning, Internet self-efficacy, and achievement scoresin an e-learning environment. The author hypothesized that these variables are stronglyinter-related and represent potential causes for lower e-learning persistence rates. Thus,by examining these variables, one might better understand the factors that affectstudents’ satisfaction and persistence in an e-learning course. The author believes thatsuch studies are needed to assess e-learning environments and create research-basedguidelines 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 ofonline community in an e-learning environment has been analyzed in the distanceeducation literature. The perception of ‘‘online participation’’ (Hrastinski, 2009) andpresence have been designated in various studies and models. ‘‘TransactionalPresence’’ is defined as the degree to which a student perceives the availability of, andconnectedness with, other parties involved. It is briefly the distance students’perceptions of teachers, peers and institutions (Shin, 2002). ‘‘Social Presence’’ isdefined by Short, Williams and Christie (1976) as ‘‘the degree of salience of the otherperson in the interaction and the consequent salience of the interpersonalrelationships’’ (p. 65). It is the degree to which a person is perceived as ‘‘real’’ inmediated communication (Richardson & Swan, 2003, p. 70). The model of‘‘Community of Inquiry’’ assumes that learning occurs within the communitythrough the interaction of three core elements: cognitive (construction of meaningthrough sustained communication); social (ability of participants to project theirpersonal characteristics into the community); and teaching presence (the design andfacilitation of educational experience). Social presence is the support for cognitivepresence, indirectly facilitating the process of critical thinking (Garrison, Anderson,& Archer, 2000).

Picciano (2002) states that the term ‘‘community’’ is related to presence andrefers 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 ofcommunity.’’ The classroom community can be defined in terms of two components:(a) social community or the feelings of connectedness among community membersand (b) learning community or their common expectations of learning and goals.Connectedness means the feelings of friendship, cohesion and satisfaction thatdevelop among students, which, in turn, develops the feelings of safety and trust andthat facilitates exposure of learning gaps by community members. To Shin’s (2002)definition, connectedness solely ‘‘refers to the belief or feeling that a reciprocalrelationship exists between two or more parties’’. The second component is thefeeling that knowledge and meaning are actively constructed within the communityand that the community enhances the acquisition of learning and acquisition (Rovai,2002a).

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Social community is often lower than in face-to-face classroom environmentsbecause of the fact that learners feel disconnected from each other and from theirteachers. However, social community can be nurtured by instructor efforts toincrease the amount and quality of social interaction through effective use ofdiscussion boards, chat sessions, e-mail correspondence, and video or audioconferencing. In an interrelated way, the development of social presence and asense of online community becomes a key for the promotion of collaborativelearning and active knowledge building (Gunawardena, 1995).

Perceived cognitive learning of students in an e-learning environment

Tallent-Runnels, Thomas, Lan and Cooper (2006) note that many online studiesuse single-item measures of key variables and that there should be moresystematic studies specifically designed to measure learning effectiveness of onlineeducational practices. Rovai, Wighting, Baker and Grooms (2009) claim thatgrades do not always indicate the amount of student learning since grades may bemore related to pre-existing knowledge and not what was learned in the course.Additionally, grades might be much more influenced by class participation ortimely assignment submissions. More importantly, as pointed out by Rovai(2002a), the reliability of the grades is not always high since different teachers oreven the same teacher at different times may be likely to assign inconsistentgrades. As suggested by Bloom (1956), an instrument that consisted of allmeasurements of cognitive, affective and psychomotor domains would bebeneficial.

Satisfaction in an e-learning environment

Considering the rapid growth and interest in online education, a key concern ofeducators 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 e-learning (Levy, 2003) since higher satisfaction related to higher levels of learning(Fredericksen, Pickett, Shea, Pelz, & Swan, 2000) and satisfaction was reported to bea major factor related to students’ decision of dropping out from distance educationcourses (Chyung, Winiecki, & Fenner, 1998).

However, there are a great many variables such as sense of classroomcommunity, technical problems, level of the Internet or computer self-efficacy,instructor’s quality of interaction and feedback, the content, the e-learningmaterial, etc. that might affect students’ satisfaction, particularly in an e-learning environment. It is stated that both quality and quantity of interactionwith the instructor and peers are much more crucial to the success of onlinecourses and student satisfaction than that are in traditional courses (Woods,2002). Similarly, Fulford and Zhang (1993) found the students’ perception ofinteraction as the critical predictor of satisfaction in a distance-learning course.Tu (2002), on the other hand, explains that social presence is a strong predictorof satisfaction within computer-mediated communication environment. Byfocusing on some of these variables, this study aims to demonstrate somerelationships that might affect an e-learner’s satisfaction through an empiricalanalysis.

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Methodology

The goal of this research study is to measure the relationships between students’sense of classroom community, satisfaction, perceived cognitive learning, Internetself-efficacy and final exam scores. Further, the differences among these variablesregarding 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 thesatisfaction 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 finalexam scores by students’ demographics?

(2) Is there a relationship between students’ sense of classroom community,satisfaction, perceived cognitive learning, the Internet self-efficacy and finalexam scores?

(3) Can classroom community and course satisfaction predict perceivedcognitive learning?

Setting and the participants

Participants were enrolled in an elementary-level English language course taughtentirely via the Internet using a learning management system (LMS). Students onlycame to school at the end of the semester for the proctored final exam, which wastaken on-campus. The participants in the study were enrolled in the knowledgemanagement (KM) and accountancy (AC) departments of the distance educationprogram at a higher education institution in Turkey. One hundred seventy-eightstudents participated voluntarily in the study. The researcher only comparedstudents who submitted completely filled-in surveys for the correlational analysissince there were missing values in some of the surveys. Therefore, the final number ofparticipants for the correlational analysis was 88. Males represented 23% (n ¼ 20) ofthe sample, and females represented 77% (n ¼ 68). Eight percent (n ¼ 7) of theparticipants were below the age of 18; 67% (n ¼ 59) were between the ages of 19 and25; 22% (n ¼ 19) were between the ages of 26 and 35; and 3% (n ¼ 3) were abovethe age of 36.

The characteristics of the online learning environment

The characteristics of the learning environment that could affect students’ sense ofclassroom community, course satisfaction, cognitive learning, and final exam scoreswere as follows. The distance English language course was entirely given via theInternet through an LMS. Students did not meet each other or with the courseinstructor except for the weekly net meeting that was text-based. The online coursewas embedded in an LMS that consisted of an integrated set of tools in the followingcategories: (a) content management tools that allowed the course instructor topresent multimedia content, supplementary course materials, and course weeklyschedule; (b) assessment tools such as online test/exam preparation, online testingand test/exam question pool; (c) student tools such as student lists, students’ reports

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and student grade book; (d) communication and collaboration tools, that consistedof e-mail, net meeting, announcements, discussion boards and an agenda to takepersonal notes.

. Net meeting: This was the session where students met with the courseinstructor and their peers in real time. Students had the opportunity to askquestions about issues that hadn’t been understood well or the instructoridentified as problematic. They addressed issues of grammar and syntax withthe instructor in this hour. If students did not have questions, the instructorconducted a language drill and practice activity with the students.

. Discussion board: Students interacted with their peers using the text-baseddiscussion board. It was not obligatory for the students to participate. Theinstructor monitored the students’ postings.

The English language course included sections on vocabulary, grammar, readingand writing, listening, and speaking. The grammar was supported with videorecordings 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 forcognitive learning, students’ face-to-face final exam scores were added to the analysisas a learning achievement indicator.

Data regarding students’ sense of classroom community were collected throughthe Classroom Community Scale (CCS) by Rovai (2001, 2002b). This scale waspresented to and rated by a panel of experts of professors by Rovai for contentvalidity and the scale’s construct validity was supported. Its internal validity wasadditionally 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 andconsists of 20 items, such as: ‘‘I feel that students in this course care about eachother,’’ ‘‘I feel that I receive timely feedback,’’ and ‘‘I feel that my educational needsare not being met.’’ The instrument includes a five-point Likert-type scale ofpotential responses: strongly agree, agree, neutral, disagree, and strongly disagree,with the assigned values ranging from 4 to 0. The students’ scores are assigned thevalue of 4 for the most favorable answer and the value 0 for the least favorableresponse. The scale measures both social community and learning community.

Data regarding students’ perceived cognitive learning were collected through CAPPerceived Learning Scale developed by Rovai et al. (2009) who provide evidence ofcontent and construct validity. They report 0.79 as the internal consistency reliabilityof the scale using Cronbach’s a. This scale includes cognitive, affective andpsychomotor subscales. This scale is particularly useful for measuring perceivedstudent learning within an online virtual classroom environment (Rovai et al., 2009).

The Internet Self-efficacy Scale was adapted from Joo, Bong and Choi (2000) andused to determine the perceived capability of students to use the Internet. The scalehas 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

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and 1. Items were scored as 5 for the answer ‘‘very true’’ and the value 1 for theanswer ‘‘not true at all.’’

The Course Evaluation Survey was used to evaluate students’ perceptions ofsatisfaction with the online course. It was prepared and administered using WebBuilder developed by North Carolina State University’s College of Agriculture andLife Sciences (CALS) to be administered to students enrolled in university courses(Lucas, 2007). It consists of three sub-parts for evaluating learner-to-learnerinteraction within the course with 10 items, learner-to-content interaction within thecourse with 11 items, and learner-to-instructor interaction within the course with 10items. Cronbach’s coefficient a was 0.94 for the learner-to-learner interactionsubscale, 0.90 for the learner-to-content interaction, and 0.96 for learner-to-instructor interaction. There was a five-point Likert-type scale of potentialresponses: strongly agree, somewhat agree, agree, somewhat disagree, and stronglydisagree. The assigned values for each item ranged between 5 and 1, with 5 for theanswer ‘‘strongly agree’’ and the value 1 for the answer ‘‘strongly disagree’’.

The demographics survey includes items that address the age, gender, schoolgraduated, department enrolled, and years of computer use. Finally, the final exam isa teacher-produced proctored test that consists of 25 multiple choice questions andmeasures student learning. Each correctly answered question is scored as 4 pointswith 100 possible points.

Results

The reliability of the instruments was checked. Cronbach’s coefficient a values forthe 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 coefficientsprovide evidence that all instruments are reliable.

A total of 88 student participants’ data were analyzed using the Internet Self-efficacy Scale, CCS, the course evaluation survey, CAP Perceived Learning Scale,and their final exam scores with student demographics. There was no differencebetween the scales regarding students’ demographics of gender, age range, theirworking time (not working/full time/part time), where the course was taken (athome, at work). However, there were significant differences found regarding schoolgraduated, department enrolled, years of computer use. These differences aredescribed in order as follows.

A one-way analysis of variance (ANOVA) was conducted to evaluate the effect ofschool graduated on the scales and final exam scores. ANOVA results presented inTable 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 highschool (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 todetermine pair wise differences as shown in Table 2. Post hoc comparisons indicatedthat there was not a significant difference among the other graduated schoolsregarding the final exam scores but only between the commercial high school andSuper Lycee p ¼ 0.04.

An independent-samples t-test was conducted to investigate if there was asignificant difference among the scales and final exam scores of students from KMand 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

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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) witha 6.07 mean difference; similarly, students of the KM department were moresuccessful on the average final exam scores (M ¼ 51.63, SD ¼ 20.85) than thestudents of the AC department (M ¼ 42.84, SD ¼ 17.49) with a 25.35 meandifference. The classroom community and final exam scores of the students seemedto change regarding their departments (Table 3).

Table 1. The Results of one-way analysis of variance by scales and by students’ schoolsgraduated.

F(5,82) Sig.

Internet self-efficacy 0.89 0.51Classroom community 1.37 0.24Course evaluation 0.19 0.98Cognitive learning 0.23 0.97Final exam 4.92 50.01

Table 2. Multiple comparisons.

Dependent variable: final exam

School graduated School graduated Mean difference

Super Lycee Lycee 32.78Industrial vocational high school 41.33Commercial high school 40.17*Anatolian high school 32.00Other 24.00

*The mean difference is significant at the 0.05 level.

Table 3. The differences among the scales regarding students’ departments enrolled.

Department N Mean SD

Internet self-efficacy KM 43 60.00 5.09AC 45 58.91 7.75

Classroom community KM 43 57.16 11.55AC 45 51.09 13.72

Course evaluation KM 43 122.16 14.83AC 45 117.84 18.75

Cognitive learning KM 43 32.65 6.17AC 45 30.82 6.96

Final exam scores KM 43 51.63 20.85AC 45 42.84 17.49t t-test for equality of

means Sig. (2-tailed)Independent samples testInternet self-efficacy 0.78 0.44Classroom community 2.24 0.03Course evaluation 1.19 0.24Cognitive learning 1.30 0.20Final exam scores 2.14 0.04

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A one-way ANOVA was conducted to evaluate the effect of years of computeruse 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 orderto determine pairwise differences. Post hoc comparisons (Scheffe) indicated that therewas 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 bivariatecorrelations among the Internet Self-Efficacy Scale, CCS, the course evaluationsurvey, CAP Perceived Learning Scale, and students’ final exam scores. Additionally,the subscales of the course evaluation survey measuring learner-to-learner, learner-to-content, and learner-to-instructor interactions were analyzed. The results aredisplayed in Table 5.

According to the correlation analysis results, a relationship exists betweenstudents’ 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 communityand their satisfaction of the course (course evaluation) indicated a strong, positivecorrelation between the two variables, r ¼ 0.51, p 5 0.01. The analysis of therelationship between students’ perceived cognitive learning and their satisfaction ofthe 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’ perceivedcognitive learning and their final exam scores indicated a weak, positive correlationbetween 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 ofcomputer use.

Variables F(3,84) Sig.

Internet self-efficacy 3.09 0.03Classroom community 0.22 0.89Course evaluation 1.54 0.21Cognitive learning 0.50 0.69Final exam scores 2.37 0.08

Table 5. Intercorrelation matrix (N ¼ 88).

1 2 3 4 5 6 7 8

1. Internet self-efficacy 1 0.28** 0.04 0.04 0.05 0.06 0.01 0.072. Classroom community 1 0.51** 0.37** 0.08 0.48** 0.38** 0.39**3. Course evaluation 1 0.53** 0.20 0.88** 0.80** 0.76**4. Cognitive learning 1 0.24* 0.45** 0.62** 0.22*5. Final exam scores 1 0.20 0.067 0.176. Learner to learner 1 0.62** 0.49**7. Learner to content 1 0.46**8. Learner to instructor 1

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

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The detailed analysis of the relationship between learner-to-learner interactionand classroom community indicated a medium level positive correlation between thetwo variables, r ¼ 0.48, p 5 0.01. Similarly, there was a medium level positivecorrelation between learner-to-learner interaction and students’ perceived cognitivelearning, r ¼ 0.45, p 5 0.01. The analysis of the relationship between learner-to-content interaction and classroom community indicated a medium level positivecorrelation between the two variables, r ¼ 0.38, p 5 0.01. Similarly, there was astrong, positive correlation between learner-to-content interaction and students’perceived cognitive learning, r ¼ 0.62, p 5 0.01. The analysis of the relationshipbetween learner-to-instructor interaction and classroom community indicated amedium 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-instructorinteraction and students’ perceived cognitive learning, r ¼ 0.22, p 5 0.05.

The analysis of the relationship between students’ Internet self-efficacy scores andtheir final exam scores did not indicate any significant relationship between the twovariables. Similarly, there was no significant relationship between students’ Internetself-efficacy scores and their satisfaction.

Next, a standard multiple regression was also conducted to evaluate how well theclassroom community and course satisfaction (course evaluation) predict perceivedcognitive learning of the students. The Durbin–Watson statistic of 2.03 suggests theabsence of serial correlation of terms for adjacent cases. Additionally, there was noautocorrelation 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% cognitivelearning of the students was related to their sense of classroom community andsatisfaction.

The analysis revealed that the relationship between students’ cognitive learningand 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 andtheir sense of community, t(88) ¼ 1.46, p ¼ 0.15.

Discussion

The present study investigated how sense of classroom community, cognitivelearning, satisfaction, the level of the Internet self-efficacy, and achievement scores ofstudents were related to each other. The findings indicated that the sense ofclassroom 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 perceivedcognitive learning (N ¼ 88).

Variable

Unstandardizedcoefficient Standardized coefficient

tb SE B b

Course evaluation 0.23 0.03 0.85 9.2Classroom community 0.08 0.05 0.14 1.6

R2 ¼ 0.97 (p 5 0.05).

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which, in turn, should develop their feelings of safety, trust, and increase theirlearning (Rovai, 2002a). The relationships between the students’ sense of classroomcommunity and satisfaction based on learner-to-learner, learner-to-content, learner-to-instructor interactions were medium. Next, it was found that students’ cognitivelearning was very much related to their satisfaction with the course. Sense ofclassroom community was moderately related to students’ perceived cognitivelearning. In fact, the variables of perceived cognitive learning, sense of classroomcommunity, and satisfaction were found to be strongly inter-related to each other.Similar to the findings, Richardson and Swan (2003) in their correlational designstudy found that students with high overall perceptions of social presence also scoredhigh in terms of perceived learning and satisfaction with the instructor. Socialpresence 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-contentinteraction; while learner-to-learner interaction was at medium level and learner-to-instructor interaction was weak. This finding could be interpreted as the studentswere satisfied with the content of e-course more than with their interactions withtheir peers and the instructor. Shin (2002) confirms that interaction is a verysignificant element that can affect various aspects of distance learning. Regardingthis Saba (2002) further implies that the level of interaction among learners andteacher might affect students’ persistence or withdrawal from a course besides allaforementioned reasons.

It was notable that perceived cognitive learning and final exam scores weremoderately related to each other. This result might have been due to the fact that theself-report instrument, CAP Perceived Learning Scale, measured cognitive learningwithin the cognitive, affective and psychomotor domains; whereas, final exam testonly measured and reflected students’ superior achievement. Another reason mighthave been due to the fact that the students might have had difficulty in judging howmuch they learned and in the comprehension of the items of the CAP PerceivedLearning Scale regarding their cognitive learning and affective perceptions (Rovaiet al., 2009).

On the other hand, the Internet self-efficacy and final exam scores of studentswere found to be expectedly unrelated. This was most probably due to the fact thatfinal exams were taken face-to-face on-campus, which did not have any relation tothe students’ level of Internet literacy and/or experience. Similarly, students’ Internetself-efficacy levels were not related to their satisfaction with the course. Although thelearning environment was completely Internet based, this did not have any relationto students’ satisfaction which might have been due to the fact that the e-learningenvironment required minimum level of Internet literacy and/or experience. Thisfinding stands against what Arif (2001) stated, as participation in e-learning courseswas seriously affected by IT experience deficiencies of students, which was asignificant contributor to their withdrawal from the course.

Data analysis revealed difference in the final exam scores based on students’ priorschools. This difference probably occurred due to the fact that Super Lycee studentswere accepted to school according to their academic achievement levels and exposedto an intensive English language education at school when compared to commercialhigh school students who mostly focused on commercial and vocational coursesduring their high-school years and exposed to medium level English language

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education in limited hours. Another difference in data was on the classroomcommunity and final exam scores of the students based on their departments.

Students from KM department attained higher scores in the final exam than ACdepartment students. These results might have occurred due to the fact that KMstudents were more aware, interested in or exposed to the management earned higherfinal exam scores in an English course than AC students, which might have been relatedto 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 useaffected students’ Internet experience and literacy levels more than those with 1–3 yearsuse. This difference probably occurred due to the fact that as students’ computer useexperience in years increased, their Internet self-efficacy levels also increased.

Conclusion

The sense of classroom community, cognitive learning, satisfaction, and achievementscores of students are related as indicated in the findings of the present study. Thisstudy emphasizes that: e-learning students can feel connected to their virtualclassroom 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 cognitivelearning and that satisfaction of the students has more effect on cognitive learning ofthem when compared to their senses of classroom community.

The results do not suggest that there is a causal relationship among theaforementioned variables. There is the possibility of a third variable affecting therelationship. For example, the use of video and net meetings in the present studymight have lessened psychological distance and revealed a sense of classroomcommunity feeling and increased the students’ satisfaction of the course. Moreover,the participants of the study were thought to be typical undergraduate studentslearning on a typical e-learning environment (LMS), which was mostly preferred atuniversities in Turkey. However, since the study was implemented at one university,the generalization of the findings is still limited. There might be differences in theresults of other studies due to the pedagogy applied, the content used, theinstructor’s communication styles with the students and instructor’s immediacy.There is future research needed to investigate these relationships on the classroomcommunity, cognitive learning and satisfaction of the students and their subscales. Itis suggested that current study might further be expanded with the analysis ofmessages’ content, number of postings to the course discussion board, and thenumber of e-mails sent to the instructor by students. Moreover, the relationshipbetween sense of classroom community and instructor communication, character-istics of the content, applied pedagogy, students’ social strata, and culturalcommunication patterns might be examined in future research studies. The samestudy might be carried out at other cultural settings to identify culture specificpatterns affecting satisfaction, sense of community and cognitive learning constructsand their relationships.

Notes on contributor

Meltem Huri Baturay is an instructor at the Institute of Informatics at Gazi University inAnkara, Turkey. She received her doctorate degree in Computer Education and InstructionalTechnology from Middle East Technical University. Dr. Baturay’s areas of professional

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interest include e-learning, online social learning environments and web-based foreignlanguage teaching.

Acknowledgement

I would like to express my deepest gratitude to Professor Alfred P. Rovai in providing me withhelp and support throughout the study.

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