陆续的解释:学生互动与社区感的关系研究

A study of the relationship between student communication interaction and sense of community

Shane Dawson ^{, a,}

^aCentre for Learning Innovation, Queensland University of Technology Kelvin Grove, Queensland, 4059, Australia

Accepted 21 June 2006. 

Available online 17 August 2006.

Abstract

This study developed a quantitative methodology to ascertain lead indicators of student sense of community whilst undertaking a course of study. Study participants (N = 464) were drawn from students enrolled in undergraduate and postgraduate Education units within a large Australian metropolitan university. Through juxtaposing student online behaviours with an online survey, the data demonstrates that students and study units with greater frequencies of communication interactions possess stronger levels of sense of community as determined by Rovai's [Rovai, A.P. (2002b). Development of an instrument to measure classroom community. Internet and Higher Education, 5(3), 197–211.] Classroom Community Scale (CCS). As a result of the identification of this relationship utilising a quantitative process, education practitioners and managers now possess the formative evaluative tools and indicators necessary to gauge student sense of community on an ongoing basis. Therefore, education managers and practitioners have the capacity to monitor and alter the learning and teaching practices designed and implemented to promote community among the student cohort in a just-in-time environment.

Keywords: Sense of community; Communication; Quantitative methodology; Higher education

Article Outline

1. Introduction

2. Background

2.1. Sense of community

2.2. Computer-mediated communication

3. Methodology

3.1. Study overview and sample size

3.2. Survey instrument

3.3. Validation of the survey instrument

3.4. Statistical analyses

4. Data analysis

4.1. Participants

4.2. Descriptive statistics

4.3. Communication frequency

5. Limitations of the analysis

6. Discussion

6.1. Increased communication interactions and SOC

6.2. Unit level communication frequency and SOC

7. Conclusion

References

1. Introduction

Contemporary educators are embracing socio-constructivist practices which emphasise learning as a social and interactive activity (Gabelnick et al., 1990 and Levine Laufgraben and Shapiro, 2004). As a result of this pedagogical philosophy there has been an increased importance placed on implementing educational practices that seek to foster the concept of community. In particular, the development and support of learning communities have been suggested as a means to facilitate and support student learning through the establishment of ongoing social networks (Cho et al., 2005 and Shapiro and Levine, 1999). However, as higher education institutions further promote the introduction of flexible delivery options, there is a corresponding decrease in the requirement for students to attend on-campus lectures. Consequently, the capacity for students to form social networks, and therefore, learning communities, is potentially inhibited as a result of the limited availability for face-to-face interactions (Palloff & Pratt, 1999). This limitation in the degree of potential interactions available among a group of learners can be largely overcome through the introduction of various information and communication technologies (ICTs). The adoption of these technologies (e.g. computer-mediated communication) among educators provides staff and students with an opportunity to communicate and collaborate regardless of spatial and temporal differences (Haythornthwaite, Kazmer, & Robins, 2000).

Given that the education literature suggests that communication amongst community participants is essential for the development of a sense of community (Palloff and Pratt, 1999 and Rovai, 2002a), the integration of computer-mediated communication (CMC) can be seen as an integral component for the establishment of an online community amongst previously disparate individuals (Barajas, 2002 and Hew and Cheung, 2003). However, the implementation of CMCs alone does not guarantee community among learners, nor are all attempts at fostering cohesive communities of learners effective (Brook and Oliver, 2003). Consequently, there is a need for the development of potential lead indicators of community to provide practitioners with an ongoing assessment of community development within the learning environment. While tools such as Rovai's (2002b) Classroom Community Scale (CCS) have provided educators with a quantitative measure of community among the student cohort, the complex logistics of administering the CCS instrument to the student population on a regular and large-scale basis is not practical in most situations. Similarly, educational research concerning community has often adopted non-scaleable methodologies that analyse the artefacts of student discourse, such as forum contributions and chat logs, in order to provide an indication of community formation (Tu and Corry, 2001 and Vonderwell, 2003). Although these qualitative analyses have provided valuable foundational information concerning community, the characteristics inherent in the methodologies impedes monitoring of developmental indicators and overall scalability.

While research focussing on communities in the educational context suggests that communication frequency (Wood & Smith, 2005) and learner-to-learner discussions (Palloff & Pratt, 1999) are necessary to create and sustain a learning community, there has been little empirical evidence provided to support this notion. This paper reports on an empirical study investigating the correlation between communication behaviours and student perceived classroom sense of community in a large Australian metropolitan university. The paper also proposes a quantitative approach of monitoring direct and observable indicators of community in order to inform educators of learning and teaching practices that facilitate the development of a sense of community amongst learners.

2. Background

The higher education sector is undergoing a shift from a teacher-centred to a learner-centred pedagogy. Feldman (2000, p. xiii) describes this transition from behaviourist to socio constructivist approaches as a move from the “age of the individual to the era of community”. Similarly, Gibson (2003, p. 36) in advocating the evolution towards community-based learning, maintains that the shift from a focus on the “interpersonal to intrapersonal” is central to the way in which individuals learn. Essentially Gibson argues that a focus on socially constructed networks and interactions is more aligned with current perceptions of effective approaches to learning. The development and integration of communities of practice and in particular the concept of learning communities in higher education can be seen as an artefact of this pedagogical transition (Kilpatrick, Barratt, & Jones, 2003). The focus on the concept of community is viewed as a strategy to cater to external (e.g. government accountability, reducing financial resources) and internal (e.g. student attrition and satisfaction) demands whilst advocating effective approaches to learning.

Communities such as learning communities or communities of practice are formed through the aggregation of individuals for the purposes of learning (Bielaczyc & Collins, 1999). The terms learning communities and communities of practice are often used interchangeably, as both concepts relate to the process of learning and the socialisation that serves to facilitate learning. The associated pedagogical benefits emerging from the introduction of communities have been well documented. Learning communities have been linked to reduced attrition (Tinto, 1998), the promotion of critical thinking skills (Fink, 2003), and facilitation of the achievement of learning outcomes (Gibbs, Angelides, & Michaelides, 2004).

2.1. Sense of community

Although the notion of community has been widely discussed within the literature, researchers are yet to establish an authoritative definition (Harrington, 1997, p. 17). Hillery (1955), in an attempt to ascertain consensus, codified 94 occurrences of the term in sociological studies. Emerging from the data is the notion that community can be characterised by two broad domains – locality and the sharing of common interests (Hillery, 1955). While these identified characteristics of community still appear largely amorphous, the domains refer to the notion that people aggregate and develop social relationships through common proximity or shared interest. This emphasis on community as a form of emotional connection fostered via social relationships is expressed by Sarason (1974) as the psychological sense of community (PSOC). McMillan and Chavis (1986) in building upon Sarason's (1974, p. 9) foundational work on community, define this concept as a “feeling that members have of belonging, a feeling that members matter to one another and to the group, and a shared faith that members' needs will be met through their commitment to be together”. Furman (1998) also adopts a similar definition in suggesting that community does not exist until members experience a sense of belonging, trust and safety.

The difficulty arising from the interpretation of community as a psychological dimension is how to apply appropriate scholarly methodologies to assess the level of community experienced. The sense of community index (SCI) is one approach that has been commonly adopted in recent community studies. The SCI developed by Chavis, Hogge, McMillan, and Wandersman (1986) provides a quantitative methodology to evaluate the sense of community experienced by an individual within a given community. However, the sense of community experienced by an individual is also influenced by the specific social context of investigation (Hill, 1996). Consequently, while the SCI has been readily adopted in community studies (Long & Perkins, 2003) the idiosyncrasies of the education milieu (e.g. assessment practices, instructor characteristics, learning activities) results in the establishment of a social environment with unique extrinsic and intrinsic pressures. Thus, Rovai (2002b) developed a scale to measure sense of community within the educational environment. While the Classroom Community Scale (CCS) is derived from the definition of sense of community proposed by McMillan and Chavis (1986), the instrument is designed to gauge the strength of community experienced amongst participants specifically within the educational sphere. The CCS has been validated and incorporated within education studies designed to evaluate the degree of community experienced among various student cohorts from primary to tertiary institutions. Hence the correlation of the data deriving from the CCS with data concerning various communication behaviours (frequency and mode) operating within the classroom environment may provide additional quantitative lead indicators of the degree of sense of community occurring among the student body.

2.2. Computer-mediated communication

As higher education institutions promote the development of learning communities and socio-constructivist teaching practices, the integration of online technologies has evolved into a central and essential resource. Practitioners have predominantly focussed on CMCs, such as asynchronous forum software, in order to support the development for learning communities. These socially-based CMC software have been widely integrated into educational practices in order to support the integration of blended, traditional and online learning communities (Brook and Oliver, 2003, Hew and Cheung, 2003 and Misanchuk and Dueber, 2001). CMC technologies afford the exchange of dialogue in the form of posted messages to facilitate the development of a sense of community regardless of temporal and spatial constraints.

Numerous authors have noted the importance of communication in the development of a sense of community in an online environment (Brook and Oliver, 2003, Palloff and Pratt, 1999 and Rafaeli et al., 2004). Palloff and Pratt (1999) discuss the importance of contributions and the role of the facilitator in evoking communication among participants in order to form a sense of community. Similarly, Wood and Smith (2005) maintain that for community to exist, there must be a minimum level of communicative exchanges amongst a number of contributors. According to these studies the mere act of accessing online content or reading forum postings does not assist in the establishment of a social identity or presence among fellow members, and therefore, does not contribute to community building. This raises the question of how much and what type of communication serves to enable community? This study aimed to address the following research questions:

• Do individuals with increased communication interactions possess a stronger sense of community then their less interactive peers?

• Do classes exhibiting a greater degree of communication interactions indicate a stronger sense of community?

Through investigation of the research questions, education practitioners and managers can develop an understanding of the relationship between communication interactions and sense of community. This comprehension of factors influencing the degree of community experienced provides an avenue for developing and implementing formative evaluative measures to guide practitioners in the design of learning and teaching activities and in the introduction of specific intervention techniques in order to create a stronger sense of community.

3. Methodology

3.1. Study overview and sample size

Students currently enrolled in undergraduate and postgraduate units within the Faculty of Education, Queensland University of Technology were invited to participate in the study. All education teaching units (N = 25) selected for the study contained an online component of supplementary learning and teaching resources with associated computer-mediated communication (CMC) software (discussion forum, listserv, email, synchronous chat). Units consisted of both undergraduate and postgraduate areas of study and were available for external or internal only modalities. External mode of delivery refers to study undertaken off campus – no traditional modes of education delivery such as face-to-face lectures are included. An internal mode of delivery refers to study undertaken on campus – traditional modes of education delivery such as face-to-face lectures are required. Additional supplementary online resources and activities are also integrated into the teaching curriculum.

Data was generated from student participation from the selected units' online CMC activity and student responses to an online survey. The survey was designed to ascertain the degree of sense of community experienced by students undertaking current studies and to gauge the level of communication frequency occurring among the cohort. The survey was promoted through both the online units of study home page and via email notification. Modes of CMC (discussion forum, chat) utilised within the University's information technology system, tracked and recorded the participants' communication frequency.

3.2. Survey instrument

Rovai's (2002b) Classroom Community Scale (CCS) was adopted to quantify the degree of community experienced among the student cohort. Additional demographic questions were incorporated to gauge the extent of student use of various modes of communication such as phone, face-to-face meetings and mobile phone text messaging with classroom peers. The CCS consisted of 20 question items designed to assess the perceived level of community within a classroom. The items were ranked according to a 1–5 Likert scale (strongly agree, agree, neutral, disagree and strongly disagree). These rankings were codified into a quantitative score from 0 to 4, with higher aggregated scores indicating a stronger sense of community. The cumulative scores potentially range from 0 to 80. The CCS instrument comprises two subscales that Rovai (2002b) has termed connectedness and learning community. The authors refer to connectedness as the social community or more specifically, the students' sense of belonging, trust and cohesion. The remaining sub-scale, learning community, is described as the degree to which students share similar learning values and goals.

3.3. Validation of the survey instrument

Validation of the survey instrument proceeded in three distinct phases. The initial phase involved a small focus group session incorporating student responses to an examination of the survey instrument. Specifically, students were requested to peruse the survey and note any items of ambiguity or confusion. As Rovai (2002b) had previously established expert validation of the survey questions, the focus group was incorporated to ensure adequate usability and understanding of the survey instrument for this study's specific context. Based upon the student focus group feedback the survey instrument was revised to address the usability and comprehension issues. These alterations centred on cultural misunderstandings of specific terminology. For example, Rovai (2002b) incorporates the term ‘course’ into various survey questions. In the Australian context this term is used to describe a series of units of study in contrast to a single entity.

The second phase involved the implementation of a pilot study (N = 160) to validate the survey using exploratory factor analysis. The results from the pilot study demonstrated factorial validity. The constructs observed in the exploratory factor analysis are analogous to those ascertained by Rovai's (2002b) study developing a measure of classroom community. Cronbach's α and Guttman split-half coefficients were incorporated to ascertain reliability and consistency of the survey. Cronbach's α and Guttman split-half for the survey instrument was 0.90 and 0.89, respectively, indicating excellent reliability and consistency. Similarly, analysis of the two subscales demonstrated excellent reliability and consistency with a 0.86, 0.85 (Cronbach's α, Guttman split-half, respectively) for connectedness and 0.84, 0.76 (Cronbach's α, Guttman split-half, respectively) for learning community. Given that the utilised survey instrument indicated a sound assessment of the CCS, the final stage involved the release of the instrument to the broader study participants (N = 464).

3.4. Statistical analyses

Data garnered from the sampled student cohort was examined using the software package SPSS for Windows© (Version 12.0.1) incorporating ordinary least squares regression analyses. Categorical variables such as gender and modality were effect-coded so that the direction of observed relationships could be readily determined. Additionally, a simple parametric correlation was employed to ascertain the degree of relationship between the overall communication frequency and reported CCS at both individual student and unit organisational levels.

4. Data analysis

4.1. Participants

The response rate for the study was 23% (N = 464) of students enrolled in the 25 Education teaching units. Females represented 83.84% and males 16.16% of the sampled population with 80% internal enrolments and 87% as full time students. The mean age of the participants was 26.20 years (S.D. = 8.0) and the sampled population had a mean employment per week of 16.19 h (S.D. = 11.9) and mean prescribed university contact hours of 14.38 (S.D. = 9.2). The survey participants demonstrated a comparable demographic profile to the broader faculty.

4.2. Descriptive statistics

Descriptive statistics were generated to gauge the degree of community experienced and the frequency of communication interactions among the sampled student population. Aggregate data from units sampled (N = 25) resulted in a sampled student population size of N = 464. Table 1 summarises the descriptive statistics obtained from the CCS (Rovai, 2002b) and the two constructs forming the CCS, namely, connectedness and learning community. Table 2 summarises the mean frequency of the reported modes of communication with email and face-to-face meetings as the most commonly utilised forms.

Table 1.

Means and standard deviations for community and the subscales measured using the CCS

Community^a

Connectedness

Learning community

Mean^b

47.5 (S.D. = 11.0)

22.1 (S.D. = 6.1)

25.5 (S.D. = 6.2)

Internal students

Mean^c

49.0 (S.D. = 10.2)

23.1 (S.D. = 5.4)

25.9 (S.D. = 5.9)

External students

Mean^d

41.8 (S.D. = 12.5)

17.8 (S.D. = 6.9)

24.0 (S.D. = 6.9)

Full-size table

^a Community is equal to the sum of the 2 constructs connectedness and learning. Community scores range from a minimum of 0 to a maximum 80.
^b N = 464.
^c n = 372.
^d n = 92.

View Within Article

Table 2.

Mean weekly frequency and standard deviation for individual modes of communication

All students^a

Internal^b

External^c

Email

2.9 (S.D. = 3.9)

2.8 (S.D. = 3.0)

3.1 (S.D. = 6.3)

Phone

1.6 (S.D. = 2.9)

1.8 (S.D. = 3.0)

0.9 (S.D. = 2.4)

Forum contributions

0.6 (S.D. = 1.4)

0.4 (S.D. = 1.0)

1.4 (S.D. = 2.2)

Chat sessions

0.1 (S.D. = 0.2)

0.1 (S.D. = 0.2)

0.1 (S.D. = 0.3)

Face-to-face meetings

2.0 (S.D. = 2.1)

2.4 (S.D. = 2.1)

0.4 (S.D. = 0.9)

Text messaging

1.9 (S.D. = 3.2)

2.3 (S.D. = 3.3)

0.5 (S.D. = 1.8)

Full-size table

^a N = 464.
^b n = 372.
^c n = 92.

View Within Article

4.3. Communication frequency

An ordinary least squares (OLS) regression procedure was conducted with frequency of communication per week as the predictor and CCS as the criterion variables. The adjusted R² value with all modes of communication incorporated was 0.242, indicating that a significant yet moderate proportion of the variance in community was accounted for by the measured variables (Table 3). The adjusted R² value for the connectedness and learning community subscales was 0.271 and 0.140, respectively (Table 3). Email, face-to-face meetings and discussion forum postings were individually significant predictors of the CCS criteria variables (at p < 0.05). In contrast, modes of communication such as phone, online chat, and text messaging were not significant predictors. When student demographic variables were included in the model, only modality of study was consistently significant; external mode of study was a significant negative predictor of community and connectedness (p < 0.01) and also learning community (p < 0.05). Part-time study was a significant positive predictor of the CCS sub-scale learning community (p < 0.05). The demographic variables, age and gender, did not obtain significant beta weights in the tested model (Table 3).

Table 3.

Regression of community as measured by the CCS, on all variables

Variables

Community

Connectedness

Learning community

R² (adjusted) = 0.242

R² (adjusted) = 0.271

R² (adjusted) = 0.140

F = 14.982

F = 17.301

F = 8.103

df₁ = 10, df₂ = 428

df₁ = 10, df₂ = 428

df₁ = 10, df₂ = 428

β

t

β

t

β

t

Email

0.207

4.464

0.194

4.271

0.176

3.560

Phone

–

–

–

–

–

–

Forum posts

0.216

4.812

1.99

4.504

0.188

3.930

Chat sessions

–

–

–

–

–

–

Face-to-face

0.230

4.703

0.203

4.242

0.208

3.998

Text messaging

–

–

–

–

–

–

Part time

–

–

–

–

0.144

2.525

(Full time)

–

–

–

–

− 0.144

− 2.525

External

− 0.279

− 4.898

− 0.317

− 5.674

− 0.182

− 2.998

(Internal)

0.279

4.898

0.317

5.674

1.82

2.998

Female

–

–

–

–

–

–

(Male)

–

–

–

–

–

–

Age

–

–

–

–

–

–

Full-size table

 Correlation is significant at the 0.05 level.
 Correlation is significant at the 0.01 level.
 Correlation is significant at the 0.001 level.

View Within Article

Table 4 presents the results of the regression predicting community at a unit level of analysis. The adjusted R² value with all modes of communication incorporated at a unit level was 0.825 (community), 0.823 (connectedness) and 0.552 (learning community). The results demonstrate that a high proportion of the observed variance in the reported levels of community was accounted for by the measured communication variables (Table 4).

Table 4.

Regression of community as measured by the CCS, on all variables at a unit level

Variables

Community

Connectedness

Learning community

R² (adjusted) = 0.825

R² (adjusted) = 0.823

R² (adjusted) = 0.552

F = 16.527

F = 16.238

F = 5.053

df₁ = 7, df₂ = 16

df₁ = 7, df₂ = 16

df₁ = 7, df₂ = 16

β

t

β

t

β

t

Email

0.537

5.158

0.342

3.259

0.627

3.763

Phone

–

–

–

–

–

–

Forum posts

0.442

3.527

–

–

0.650

3.237

Chat sessions

–

–

–

–

–

–

Face-to-face

0.832

5.280

0.609

3.834

0.866

3.429

Text messaging

–

–

–

–

− 0.787

− 2.758

Full-size table

 Correlation is significant at the 0.05 level.
 Correlation is significant at the 0.01 level.

View Within Article

Table 5 shows the aggregation of student data to a unit level. The data demonstrates a strong correlation with CCS and its underlying constructs when amalgamating the sampled population to a unit level of study using Pearson's correlation coefficient (r). Significant correlations were observed between sense of community and the mean aggregated communication frequency. The analyses indicate a strong relationship between the frequency of communication interactions and student perceived sense of community.

Table 5.

Correlation between communication frequency and CCS for a unit of study

Unit^a

Community

Connectedness

Learning community

Mean

45.5 (S.D. = 6.7)

19.9 (S.D. = 4.4)

25.7 (S.D. = 3.3)

Communication frequency

r = 0.840

r = 0.828

r = 0.586

Full-size table

^a n = 24.
 Correlation is significant at the 0.01 level (2-tailed).
 Correlation is significant at the 0.001 level (2-tailed).

View Within Article

5. Limitations of the analysis

The research design and the subsequent analyses have several potential limitations for broader generalisations. Firstly, the research was undertaken within a single institution which limits the degree of generalisations to the specific organisation. However, this study is founded on developing a benchmark of student sense of community within the local environment in lieu of direct institutional comparisons. It is envisaged that the current data will provide a benchmark for future comparative analyses between international and national higher education institutions as the study is further refined and expanded.

An additional limitation to the study is the relatively low response rate. However, as the demographic profile of the participants is analogous to the faculty profile, it is reasonable to suggest that the non-respondents would report similar responses to the survey and demonstrate comparable communication trends (Dey, 1997).

6. Discussion

To date, the assessment of a students' sense of community has predominantly relied on qualitative methodologies (Hew & Cheung, 2003). While these approaches have served to provide a foundation level of understanding of student sense of community, the inherent characteristics of the methodologies (e.g. analysis of textual artefacts) limit scalability and lack the capacity to guide practitioners in a ‘just-in-time’ environment. This study aimed to address this evaluative inadequacy by developing a quantitative methodology designed to gauge and monitor lead indicators of community development. Furthermore, the study sought to address the research questions:

• Do individuals with increased communication interactions possess a stronger sense of community then their less interactive peers?

• Do classes exhibiting a greater degree of communication interactions indicate a stronger sense of community?

6.1. Increased communication interactions and SOC

This study demonstrates the existence of a significant relationship between student frequency of communication and sense of community (SOC) as measured by Rovai's (2002b) Classroom Community Scale (CCS). Fulford and Zhang (1993) and Beaudoin (2002) note similar observations maintaining that increased levels of student interaction are associated with levels of student satisfaction in both distance and internal learning environments. Essentially, the authors suggest that students interacting more with peers and teaching staff indicate a higher level of satisfaction with the course of study. Analogous conclusions relating to sense of community can be surmised from the present study. Students communicating more with peers and staff indicate a higher degree of SOC experienced then their less interactive peers.

McInnerney and Roberts (2004) argue that the fostering of online communication interactions leads to the formation of an online identity and therefore, the development of increased social ties among participants. Results from this study are supportive of this notion. The data suggests that the frequency of communicative interactions undertaken by the student body positively impacts on the development of the social community experienced among the cohort. It is suggested that an enhanced social community has direct repercussions on the development of a learning community. The development of a classroom social presence (online and offline) stimulates the fostering of a functional ‘learning community’ among the student cohort.

The formation of a learning community may be influenced by the time required to establish close social relationships among the student cohort. The development of a social presence can be seen as a pre-cursor to the establishment of a learning community. Vonderwell (2003) suggests that online students often engage in social discussions in order to develop interpersonal relationships in lieu of a more learning-oriented discourse. Given the short duration of time available in a course of study, the development of a social presence may consume the greater proportion of this available time. Consequently, the time required to form a social presence influences the subsequent level of learning community developed and therefore, the degree of learning-oriented discourses undertaken.

Examination of the impact of communication frequency on community by mode of enrolment (internal or external), revealed a greater emphasis on communication interactions for the external cohort. Palloff and Pratt (1999) suggested that the creation of a learning community is reliant upon the degree of student participation and interaction within the online environment. As the external student cohort has reduced opportunity to liaise with teaching staff and peers in an offline setting, there is a greater reliance on the online modality and its communicative affordances. Consequently, as a ratio of overall interaction with teaching staff and peers in traditional versus online settings, the external cohort understandably favour a more flexible modality and therefore, utilise more asynchronous modes of communication. It is anticipated that examination of online behaviours (e.g. time spent online, CMC audits) and various psychological dimensions such as satisfaction and community would be more pronounced amongst the external student body. This finding has implications for pedagogical design as the promotion of communication interactions via asynchronous CMC promotes a stronger sense of community among the external cohort.

The specific mode of communication adopted appears to impact on the level of participation evoked from the external cohort and therefore, influences the degree of community developed within the student cohort. Possibly, the absence of additional social experiences, such as those obtained in a lecture format (e.g. teaching staff personalities, cognitive engagement), may also account for the lower overall SOC experienced by the external students in comparison to the internal cohort. Further study is required to ascertain both the quality of interaction and timeline of discussion conducted by the cohort and how this impacts on community development. Similarly, examination of the specific modes of communication (Table 3 and Table 4) will serve to promote understanding of how the specific communicative affordances facilitate community development. While the data in this study revealed relatively weak correlations with the specific communicative affordances there is a differentiation in the manner in which internal and external students interact with their peers.

6.2. Unit level communication frequency and SOC

This study illustrates a strong correlation exists between the frequency of communication undertaken at a unit level and sense of community (Table 5). The aggregation of data to a unit level acts to remove outliers and therefore, to reduce the degree of variation observed in the communication frequency. Unit level data provides education managers and practitioners with an excellent indicator of the progress of implemented practices deigned to promote community. Dawson, Burnett, and O'Donohue (2006) discuss the notion of monitoring the integration of specific online resources as a means of evaluating progress towards achievement of stated teaching and learning strategic goals. This concept can be further enhanced through the monitoring of student communication interactions. This presents both practitioners and education managers with a non-invasive, just-in-time evaluative measure to guide the development and implementation of teaching and learning activities designed to foster community.

More recently Morris, Finnegan, and Wu (2005) has demonstrated a linkage between student online behaviours and assessment outcomes. Essentially, students interacting more online achieved higher assessment grades. It would be of interest to further this study and ascertain the relationship between community, and assessment and student online behaviours. A greater understanding of how student online behaviours impact on the learning environment will aid in the development of enhanced learning and teaching activities.

7. Conclusion

This study has developed a quantitative methodology as a supplementary formative evaluative measure to guide practitioners in the progress of implemented learning and teaching activities designed to promote community within the learning environment. The scalability of this methodology affords the introduction of a series of lead indicators to gauge the assessment of implemented practices and policies at a managerial level, thus allowing educators time to introduce appropriate intervention activities and practices designed to enhance student sense of community. The study also demonstrated a clear linkage between the degree of communication interactions students undertake and their level of sense of community experienced.

As education policy is increasingly emphasising the demonstration of ‘quality’ practices and relying on student satisfaction indices the integration of this quantitative approach affords practitioners and managers with a just-in-time guide to ascertain overall achievement of designated lead indicators. Researchers have indicated a relationship exists between levels of student satisfaction and sense of community experienced within educational settings (Brown, 2001, McInnerney and Roberts, 2004, Osterman, 2000 and Rovai and Wighting, 2005). As government funding for education institutions is increasingly determined through analysis of the student satisfaction of the learning experience the establishment of formative evaluative measures will serve to provide the institutions with an ongoing quantitative scalable and organic monitor of student satisfaction. Further research is required to investigate the relationship between student online behaviours and other dimensions influencing student satisfaction such as student experiences of assessment practices and level of appropriate cognitive engagement.

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