Editor’s Note: One early criticism of distance learning was loss of eye contact and sense of community. Guy Bensusan and others showed that peer interaction and peer learning could play a significant role in building a sense of community and support for learning. This article s explores the relative significance of various factors that build student motivation and participation and in turn facilitate learning and retention.

An Empirical Examination of Sense of Community
and its Effect on Students’ Satisfaction, Perceived Learning Outcome,
and Learning Engagement in Online MBA Courses

Xiaojing Liu, Richard J. Magjuka, Seung-hee Lee

United States

Abstract

This article aims to examine the relationships between self-reported sense of community and the level of teaching facilitation, social presence, and technology use levels in online courses. This study concluded that all three aspects are predictors of sense of community, with teaching facilitation being the strongest predictor. The study also revealed that sense of community is significantly associated with course satisfaction, perceived learning outcome, and learning engagement.

Introduction

The past several years have witnessed the exponential growth of courses offered online, which is increasingly becoming a significant portion mainstream higher education. Sixty-five percent of schools offering graduate face-to-face courses also offer graduate courses online (Allen & Seaman, 2005). Along with this mainstream, online MBA programs have seen a steady growth in student enrollment during recent years while enrollment in traditional in-residence MBA programs have experienced a decline (Magjuka, Shi, & Bonk, 2005).

However, such dramatic change in the higher education landscape does not come without concerns. Two major issues related to web-based education—retention and quality—have been of central concern for online educators (Rovai, 2002a, 2002b, 2002c). Driven by such concerns, it is not surprising that there has been growing enthusiasm toward building sense of community in online learning. It is believed that great benefits of an online community are its potential to facilitate greater information flow and knowledge sharing among participants, and stimulate innovation and creativity by cross-pollinating diversified perspectives and expertise in a shared space (Blunt, 2001; Bonk, Wisher, & Nigrelli, 2004). The mutual understanding and shared values developed from such communication and interaction in a community will eventually benefit online learners through the availability of greater support and socio-emotional well being (Wellman & Gulia, 1999; Rovai, 2001).

While it seems that the value of virtual learning communities is readily accepted by many practitioners, empirical studies seemed unable to produce consistent results regarding the role of sense of community in teaching and learning (Misanchuk, 2003). Based on the current status of research, a few scholars point out that there is a lack of clear directions on how to build learning communities based on empirical studies (Lock, 2002). Bonk et al. (2004) claim that few research studies have examined the formation of online communities, and many factors remain unknown with regard to their impact on sense of community.

The purpose of this study was to add this empirical piece to the existing knowledge of building learning communities in online courses though examining the relationship between sense of community and other pedagogical, social, and technical factors in online courses. The study will focus on the following research questions:

1. What was the relationship between sense of community and technology use, online facilitation, and social presence?

2. Did sense of community contribute significantly to course satisfaction, perceived learning, and learning engagement?

Literature

Although online learning communities have been defined in different ways, the majority of the definitions share common elements such as shared goals, connectedness, belonging, mutuality, collaboration, and community boundaries (Shea, Swan, & Pickett, 2002). In this paper, an online class learning community is defined as the participants of an online course have “a feeling that members belong to each other, a feeling that members matter to one another and to the group, and a shared faith that member’s needs will be met through their commitment to be together”(McMillan, & Chavis, 1986. p. 9). This definition reflects two major dimensions of a community. One aspect is socio-emotional ties that hold the community members together. Another aspect is the instrumental purpose for a community to exist to satisfy the needs of individual development and growth in that community. In this article, online class community, virtual learning community, and online classroom community will be used interchangeably.

Although educators may not agree on the role of technology in building learning communities in a traditional setting, there seems to be a consensus regarding the critical role of technology in the growth of a virtual learning community. Existing literature highlights several important roles of technology. First, an effective technology infrastructure provides a gateway to a virtual community because it offers a basic gathering and communication space for members. For a healthy growth of a virtual community, a failure proof, widely accessible, and easy to use technology is also required. (Hill, 2001; Kearsley, 2000; Lock, 2002).

Second, the role of technology will be in its full play only when pedagogies are designed with the aims of fully utilizing its media features to promote sense of community (Schwier, 2002). Research suggests that carefully designing a technology-enriched environment through enhancing sociability and usability of technical systems fosters community development (Moller, 1998, p.120; Preece, 2000, as cited in Lock, 2002). For example, empirical evidence suggests the unique technical attributes and sociability of each technology can be used to foster virtual community building in different ways. Synchronous communications, such as text-based chat discussions and video conferencing, have the merits of providing real-time feedback and enabling highly interactive, spontaneous dialogue that will contribute significantly to sense of belonging to an online class (Schwier, & Balbar, 2002). On the other hand, asynchronous communications allow time for students to reflect on their learning and community experience. Sense of community may be enhanced with deeper dialogue and continuous discourse without time or geographical limitations (Schwier, 2002; Duffy, Dueber, & Hawley, 1998).

According to Berge (1995), one of the important roles of online instructors is to use a variety of strategies to foster students’ understanding of critical concepts and principles and develop skills. Such tasks include offering timely and effective feedback, encouraging students’ knowledge construction through facilitating interactive discussion, designing a variety of learning experiences, and referring to external resources or experts in the field. Instructor mentoring and support have proven to be one of the critical predictors for effective online learning (Peltier, Drago, & Schibrowsky, 2003). Research also found a close relationship between teacher behaviors and the development of virtual learning communities in online courses (Shea at al., 2002). The students' sense of their instructors' teaching presence, the effective instructional design and organization, and directed facilitation of discourse is strongly associated with students’ sense of community.

A critical aspect of online facilitation is to foster interaction and collaborative learning in an online community. In a study that examined the community building process in online graduate school courses, Brown (2001) identified that allocating sufficient time and placing high priority in course interaction and dialogue are critical conditions for community building. Both quality and quantity of online interaction should be emphasized in the process of community building (Rovai, 2002c). An online discourse that is constructed on shallow interaction or lacks in-depth dialogue is unlikely to foster a sense of community in online courses (Liu, 2006). Facilitating small group activities or collaborations in an online course enables an interactive environment through engaging students in meaningful team-based learning activities (Rovai, 200b). Thus, intentionally building collaborative assignments and electronic sharing activities will help to foster a sense of belonging together with a shared learning experience (Barab, Thomas, & Merrill, 1999; Anderson, Rourke, Archer, & Garrison, 2001). Studies have found that simple tasks—such as requiring students to make a regular post to interact with a newsgroup or take part in decision making on community rules—may also assist in facilitating a sense of community (Rice-Lively, 1994).

Social presence is defined as the degree of the feelings of the salience of the other person in the interaction (Rourke, Anderson, Garrison, & Archer, 2001). It is believed that social presence is a precondition for developing social bonding, impression formation, and interpersonal relationships for meaningful interaction, group cohesion and collaboration (Kirschner & Van Bruggen, 2004). In an online environment, the diminished socio-contextual cues present an obstacle for community building and carefully planning a support structure is needed to heighten the level of social awareness to enhance a sense of community (Rovai, 2002). However, empirical studies indicate that inexperienced online instructors usually lack skills to enhance social awareness and neglect the development of social presence in online courses (Conrad, 2004). Wegerif (1998) asserts, “Many evaluations of asynchronous learning networks (ALNs) understandably focus upon the educational dimension, either learning outcomes or the educational quality of interactions, overlooking the social dimension which underlies this.” (p.1).

Method

Research Setting

The study was conducted in an accredited online MBA program at a top-ranked business school at a large Midwestern university. The program was designed for professionals who wished to earn their MBA degree while continuing their employment. The faculty pool was drawn from full-time, tenured faculty members from various departments of the business school. The program has grown to include more than 1000 students in just a few years.

Instrument Development

A program survey related to students’ perceptions of the online learning experience was used to assess students’ satisfaction with online learning experiences and their sense of online community. The 23-item survey questionnaire contained a five-point scale with Likert type questions about student perceptions and attitudes toward pedagogical, technical, and social aspects of learning online. The internal reliability of the survey, Cronbach’s alpha, was reported at .89.

Students’ sense of community (SoC) (Appendix I) was measured through six items selected from Rovai’s (2002) SoC scale that measured the connectedness dimension of SoC. The rationale we chose to focus on—affective components (e.g., emotional attachment, sense of belonging) rather than instrumental dimensions of SOC (e.g., influence, fulfillment of personal needs)—is similar to Zeldin’s (2002) argument that research has shown that the former is highly predictive of the later. Cronbach’s alpha for SoC was .723.

The effectiveness of instructors’ online facilitation was measured through five items. The survey items focus on measuring the perceived immediacy and quality of instructor feedback and the effectiveness of facilitation strategies to facilitate a meaningful learning experience. Cronbach’s alpha for teaching facilitation was .802

Social presence instrument was modified based on Kreijns, Kirschner, Jochems, and Van Buuren (2004); and Towell and Towell (as cited in Kreijns et al., 2004). Towell and Towell’s scale used one single five=point Likert scale item (e.g., "I feel a sense of actually being in the same room with others when I am connected to a MOO.”) to measure social presence in computer-mediated communication. Kreijins et al. used separate items to measure asynchronous and synchronous communication CSCL environment. We designed two items to measure the social presence in terms of the degree of the presence of socio-emotional cues in the communication and interaction process in online courses (Appendix I). Cronbach’s alpha for technology scale was .69.

The perceived technology effectiveness (Appendix I) was measured through five items. Among those items, the effectiveness of using technology to support learning, the ease of use of technology, and the availability of technical support were measured. Cronbach’s alpha for technology scale was .671.

Finally, the survey used three items respectively to measure the perceived satisfaction (“Overall, I am satisfied with the quality of KD courses.”), perceived learning outcome (“I feel that I have learned a lot from KD courses.”), and perceived learning engagement (“In general, I think I am deeply engaged in learning in my online courses.”).

Data Collection

The questionnaire was given to second-year MBA students in this program. One hundred and six students filled out the survey during the week when students came to have a one-week on-campus program. The return rate for the survey is 100%. Eighteen percent of participants were females, 47% of the participants are in their twenties, and 10.8% are above forty. The majority of the participants (79.4%) have taken 7 to 10 courses in the program. Ten percent of the participants took fewer than seven courses.

Analysis Method

Several statistical procedures were conduct for data analysis. First, the zero-order correlations were computed among all variables. The aim of this operation is to have an initial test of whether there were relationships among the variables. Secondly, we conducted standard multiple-regression procedures with SoC as the dependant variable, whereas gender, age, courses taken, social presence, teaching, and technology were treated as independent variables. The interaction of technology with teaching or social presence was considered if including those items would increase the power of the regression model substantially. Thirdly, three standard multiple-regression procedures were conducted with course satisfaction, perceived learning outcome, and learning engagement as dependent variables, and SoC as one of the independent variables. All assumptions of normality, linearity, and homoscedasticity of residuals were checked in those regression analyses.

Findings

Table 1 displays the means and standard deviations of six variables.

Table 1.

Descriptive statistics

Correlation analyses were conducted between SoC with other study variables. Positive correlations (Table 2) were found between SoC with all the study variables except social presence. This result indicates high correlations between SoC and teaching facilitation and course satisfaction.

Table 2.

Correlations among the study variables

** p<.01, * p<.05

Standard multiple regression analyses were applied to examine the relationship between the SoC and independent variables (demographic variables, instructor facilitation, social presence, and technology). The demographic data included gender, age, and courses taken. No violations were found in the assumptions of normality, linearity, and homoscedasticity of residuals.

Table 3 shows the results of this regression analysis. The results of the regression model were found to be significant: F (8, 92) = 16.899, p < .001. The multiple correlation coefficient is .771 with adjusted R² as .595, indicating that 59.5% of total variance of the learning community could be accounted for by independent variables. The regression coefficients demonstrate a significant relationship between SoC and courses taken, teaching facilitation, social presence, and technology. The partial correlations suggest that teaching facilitation accounted for 6.3% of unique variance in SoC whereas the contribution of social presence (2.5%) or technology effectiveness (1.9%) was relatively smaller.

Table 3

Standardized regression coefficients
for regression results and partial correlations

** p<.01, * p<.05

As evidenced by Table 3, there is a significant interaction effect of technology with both teaching and social presence. This effect indicates that technology moderates the relationship between teaching facilitation and SoC as well as the relationship between social presence and SoC. A follow-up plotting of the interaction between teaching and technology found that teaching facilitation will show a stronger effect on SoC when the reported technology integration level is lower (Figure 1). The plotting of the interaction effect between social presence and technology suggests that the SoC will benefit more from social presence when the technology integration level is higher (Figure2).

Figure 1. The interaction effect of technology in the association between
teaching facilitation and sense of community

Figure 2. The interaction effect of technology in the association
between social presence and sense of community

Table 4 shows the results of a regression analysis with course satisfaction as the independent variable. The regression model were found to be significant, F (9, 91) = 12.113, p< .001. The adjusted R² (R = .738) indicates that 50% of total variance of course satisfaction could be accounted for by the regression model. The regression coefficients demonstrate a significant relationship between course satisfaction, courses taken, teaching facilitation, technology, and SoC. The partial correlations indicate that teaching facilitation and SoC have greater unique contributions in predicting course satisfaction than other significant variables. The results also show the significant interaction effect of technology with teaching. This effect indicates that technology moderates the relationship between teaching facilitation and course satisfaction. A follow-up plotting of this interaction found that the course satisfaction would benefit more from teaching facilitation when the reported technology integration level is lower.

Table 4

Standardized regression coefficients
for regression results and partial correlations

** p<.01, * p<.05

Table 5

Standardized regression coefficients
for regression results and partial correlations

** p<.01, * p<.05

Table 5 shows the results of the regression analysis with perceived learning as the independent variable. The results of the regression model were also significant ( F (7, 93) = 9.681, p < .001). The multiple correlation coefficient (R = .649) and adjusted R², indicate that 37.8% of the total variance of course satisfaction could be accounted for by independent variables. The regression coefficients demonstrate perceived learning outcome has significant relationship with teaching facilitation and SoC. The interaction of variables was not included in the regression because the test of including those variables in the equations resulted lower multiple correlation coefficient.

Table 6 shows the results of the regression analysis with learning engagement as the independent variable. The results of the regression model were found to be significant, F (9, 91) = 7.402, p < .001. The adjusted R² indicates that 33.6% of the total variance of course satisfaction can be explained by independent variables. The regression coefficients show a significant relationship between learning engagement and SoC. Age, teaching facilitation, and technology all show an appreciable amount of unique contribution to learning engagement. The regression results also show the significant interaction effect of technology with teaching, indicating that technology moderates the relationship between teaching facilitation and learning engagement.

Table 6

Standardized regression coefficients
for regression results and partial correlations

** p<.01, * p<.05

Discussion

The goals of the present study were twofold. The primary goal was to examine whether three factors of online courses—instructor facilitation, social presence, and effective technology use—had significant relationships with SoC. The second objective was to examine whether SoC can predict students’ satisfaction, learning engagement, and perceived learning.

This study concludes that all three variables (social presence, instructor facilitation, and technology) have significant contributions to SoC in online courses—among which, teaching facilitation has the strongest contribution according to the partial correlation coefficients. This result is consistent with the results of a number of studies that suggest that frequent interaction with students through giving prompts and informative feedback and using a variety of learning activities to foster an in-depth understanding of concepts may be important to establish SoC in online courses (Shea, Li, Swan, & Pickett 2002; Rovai, 2001). This result can be further supported through transactional distance theory. While the dialogue between students and instructor is increased through the online instructor’s active facilitation in the ways of providing timely and regular feedback and engaging students in active learning experiences through a variety of learning activities, students will feel less distance from online instructors and students, and more connected within a learning community (Moore, 1980).

The significant moderation effect of technology for teaching facilitation and social presence on SoC is worth noting. This finding suggests that SoC will benefit more from incremental teaching facilitation when perceived technology integration level is lower. This result implies that when the level of technology use in online courses was constrained by available resources, to fully explore the potential of pedagogical facilitation strategies will be especially beneficial for creating SoC. For example, the asynchronous text-based discussion forum has moderate interactivity in terms of its technology attribute. However, through carefully designed facilitation strategies, such as role assignments, SOC may be significantly enhanced.

Although there is significant main effect of social presence on SoC, the partial correlations indicate that the effect size of social presence is relatively low as compared with the effect of teaching or technology, accounting for only 1.5% of the unique contribution of the total variance whereas teaching or technology both make about 4% or 5% of unique contribution to total variance. The notable interaction effect (accounting for 5.7 % of total variance) between social presence and technology suggests that the SoC will benefit more from social presence when the technology integration level is higher. This finding implies that when highly interactive technology is used in online courses, utilizing the sociability of technology to heighten the level of social presence in online courses may be beneficial for establishing a community of learners. Interestingly, the social presence did not present any significant effect on course satisfaction, perceived learning, and learning engagement. This suggests that social presence may not be directly related to learning engagement or learning outcomes, but may indirectly affect learning outcomes through enhanced SoC or other variables.

For the second purpose, the results show that SoC had significant relationships with students’ satisfaction, learning engagement, and perceived cognitive learning. In each case, SoC makes about a 3% to 4% unique contribution to the total variaance based on partial correlation coefficients. The students who had higher SoC were more satisfied with online courses. They also have proven to be more engaged in learning and feeling, having learned more when they felt a sense of belonging to online courses. The results of this study added to the evidence that SoC is related to a meaningful learning experience (Rovai, 2002; Chao, 1999 ).

This study also revealed a significant relationship between the amount of courses taken and SoC. When students had taken more courses, they had a more positive feeling of belonging to a learning community. This result provided evidence to support Brown’s (2001) assertion that the relationship among students may be amplified through taking multiple courses and thus SoC will be enhanced.

Another interesting finding regarding the demographic factors is the positive relationship between age and learning engagement. The older students tended to have higher learning engagement. This may be due to the reason that older students also had increased social and professional experiences. They may be willing to share and converse with online learning environments than younger students, and consequently this may foster a deeper learning engagement and SoC.

Limitations and Future Research

This study has several limitations. First, the participants of this study were limited to one online MBA program. Generalizations of the findings from this study to other online programs or disciplines may be limited. Future studies are warranted to extend the study to a larger scale and to online students in different disciplines and examine whether the results would be different across disciplines. Secondly, this study only examined the relationships between SoC with other factors. It cannot explain the causal relationship from this study. Controlled experiment studies that examine the effect of SoC would strengthen the findings of the present study. Thirdly, the literature suggests developmental stages of SoC. A longitude study could be conducted to determine whether SoC is associated with change in time and whether three predictors—teaching facilitation, social presence, and technology use—remain stable over a period of time.

References

Allen, I.E., & Seaman, J. (2005). Growing by degrees: Online education in the United States. Needham, MA: Sloan-C.

Anderson, T., Rourke, L., Archer, W., & Garrison, R. (2001). Assessing teaching presence in computer conferencing context. Journal of Asynchronous Learning Networks, 5(2), 1–17. Retrieved October 20, 2005, from
http://www.sloan-c.org/publications/jaln/v5n2/v5n2_anderson.asp.

Barab, S. A., Thomas, M. K., & Merill, H. (1999). Online learning: From information dissemination to building a shared sense of community. Unpublished manuscript. Indiana University Bloomington.

Berge, Z. L. (1995). Facilitating computer conferencing: Recommendations from the field. Educational Technology, 15(1), 22–30. Retrieved October 20, 2005 from http://www.emoderators.com/moderators/teach_online.html

Blunt, R. (2001). How to build an e-learning community. E-learning, 2(11), 18-20.

Bonk, C. J., Wisher, R. A., & Nigrelli, M. L. (2004). Learning communities, communities of practice: Principles, technologies, and examples. In K. Littleton, D. Miell, & D. Faulkner (Eds.), Learning to collaborate, collaborating to learn (pp. 199-219). Hauppauge, NY: NOVA Science.

Brown, R. (2001). The process of community building in distance learning classes. Journal of Asynchronous Learning Networks, 5(2), 18–35.

Conrad, D. (2004). University instructors' reflections on their first online teaching experiences. Journal of Asynchronous Learning Networks 8(2), 31–44. Retrieved October 20, 2005, from http://www.sloanc.org/publications/jaln/v8n2/v8n2_conrad.asp.

Duffy, T. M., Dueber, B., & Hawley, C. L. (1998). Critical thinking in a distributed environment: A pedagogical base for the design of conferencing systems. In C. J. Bonk (Ed.), Electronic collaborators: Learner-centered technologies for literacy, apprenticeship, and discourse (pp. 51-78). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Hill, J. R. (2001). Building community in web-based learning environments: Strategies and techniques. Retrieved November, 23, 2005, from http://ausweb.scu.edu.au/aw01/papers/refereed/hill/paper.html.

Kearsley, G. (2000). Online education: Learning and teaching in cyberspace. Belmont, CA: Wadsworth.

Kirschner, P., & Van Bruggen, J. (2004). Learning and understanding in online collaborative groups. Cyberpsychology & Behavior, 7(2), 135-139.

Kreijns, K., Kirschner, P.A., Jochems, W., & Van Buuren, H. (2004). Cyberpsychology & Behavior, 7(2), 155-172.

Liu, X. (2006). The socio-cultural factors affecting the success of an online MBA course: A case study viewed from activity theory perspective. Unpublished dissertation transcripts. Indiana University Bloomington.

Lock, J.V. (2002). Laying the groundwork for the development of learning communities within online courses. The Quarterly Review of Distance Education, 3(4), 395-408.

Magjuka, R., Shi, M., & Bonk, C. J. (2005). Critical design and administration issues in online education. Online Journal of Distance Learning Administration, 8(4). Retrieved online on January 27, 2006, from: http://www.westga.edu/%7Edistance/ojdla/winter84/magjuka84.htm.

McMillan, D. W., & Chavis, D. M. (1986). SoC: A definition and theory. Journal of Community Psychology, 14(1), 6-23.

Misanchuk, M. (2003). SoC, satisfaction, and performance in a distance education program. Unpublished dissertation transcripts. Indiana University Bloomington.

Moller, L. (1998). Designing communities of learners for asynchronous distance education. Education technology research and development, 46(4), 115-122.

Moore, M. G. (1980). Independent study. In R. Boyd, J. W. Apps and Associates (Ed.), Redefining the discipline of adult education (pp. 16-31). San Francisco: Jossey-Bass.

Palloff, R., & Pratt, K. (1999). Building learning communities in cyberspace: Effective strategies for the online classroom. San Francisco: Jossey-Bass.

Peltier, J. W., Drago, W., & Schibrowsky, J. A. (2003). Virtual communities and the assessment of online marketing education. Journal of Marketing Education, 25(3), 260-276.

Rice-Lively, M.L. (1994). Wired warp and woof: An ethnographic study of a networking class. Internet Research, 4(4), 20-35.

Rovai, A.P. (2001). Building classroom community at a distance: A case study. Education Technology Research and Development, 49(4), 33-48.

Rovai, A. P. (2002a). A preliminary look at structural differences in sense of classroom community between higher education traditional and ALN courses. Journal of Asynchronous Learning Networks, 6(1), 41–56.

Rovai, A. (2002b). Building SoC at a distance. International Review of Research in Open and Distance Learning, 3(1). Retrieved September 21, 2005 from: http://www.irrodl.org/content/v3.1/rovai.html.

Rovai, A. P. (2002c). Development of an instrument to measure classroom community. Internet and Higher Education, 5(3), 197–211.

Schwier, R. (2002). Shaping the metaphor of community in online learning environments. Retrieved online on October 20, 2006 from http://cde.athabascau.ca/ISEC2002/papers/schwier.pdf.

Schwier, R.A., & Balbar, S. (2002). The interplay of content and community in synchronous and asynchronous communication: Virtual communication in a graduate seminar. Canadian Journal of Learning and Technology, 28(2). Retrieved April 27, 2006, from http://www.cjlt.ca/content/vol28.2/schwier_balbar.html.

Shea, P., Li, C. S., Swan, K., & Pickett, A. (2002). Developing learning community in online asynchronous college courses: The role of teaching presence. Journal of Asynchronous Learning Networks, 9(4), 59-82.

Wellman, B., & Gulia, M. (1999). The network basis of social support: A network is more than the sum of its ties. In B. Wellman (Ed.), Networks in the global village (pp. 83-118). Boulder, CO: Westview Press.

Zeldin, S. (2002). Sense of community and positive adult beliefs toward adolescents and youth policy in urban neighborhoods and small cities. Journal of Youth and Adolescences, 31(5), 331-342.