Relationships among Sense of Classroom Community, Perceived Cognitive Learning and Satisfaction of Students at an E-learning Course

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). 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