International Review of Research in Open and Distributed Learning

Volume 18, Number 6

September - 2017

Verifying Causal Relationships Among the Presences of the Community of Inquiry Framework in the Chinese Context

Zhiqiang Ma¹, Jing Wang¹, Qiyun Wang², Lili Kong¹, Yajie Wu³, and Hao Yang^4
1Research Center for Educational Informatization, Jiangnan University, Wuxi, China; ²National Institute of Education, Nanyang Technological University, Singapore; ³Beijing Open University, Beijing, China; ⁴School of Foreign Studies, Jiangnan University, Wuxi, China

Abstract

The purpose of this study was to verify a Chinese version of Community of Inquiry (CoI) instrument with learning presence and explore the causal relationships of the factors in the instrument. This study first examined the reliability and validity of the instrument. All four presences had acceptable levels of reliability (all Cronbach's α>.765 or higher). The confirmatory factor modeling approach was used to assess its validity. Then, the study used path analysis and regression analysis to explore the causal relationships of the presences. The key findings showed that teaching and social presences directly influenced the perceptions of learning presence. Learning presence was a partial mediating variable of interactional relationship within CoI constructs.

Keywords: community of inquiry, learning presence, social presence, teaching presence

Introduction

China has the world's largest higher education population, with more than 36 million (Ministry of Education, 2016). In recent years, Chinese universities have devoted great effort to the development of online courses such as Massive Open Online Courses (MOOCs) to serve university students and adult learners (Zhang, Perris, Zheng, & Chen, 2015). A large number of online courses have been developed, but the learning experience of online course needed to be examined and improved (Songhe & Xuan, 2014). It is well known that creating and sustaining a learning community is valuable to enhance the online learning experience (Akyol, Garrison, & Ozden, 2009). Therefore, generating a reliable instrument to measure learners' perception of online learning community in China becomes essential.

The Community of Inquiry Framework

The internationally recognized Community of Inquiry (CoI) framework seems to be a valid theoretical framework to understand learners' perceptions of online learning experience (Shea & Bidjerano, 2010; Traver, Volchok, Bidjerano, & Shea, 2014). It has been adopted and adapted by a lot of researchers worldwide (Garrison, Anderson, & Archer, 2010; Swan & Ice, 2010). It provides collaborative-constructivist perspective and methodology for studying online learning experiences through the development of three core presences (Arbaugh et al., 2008; Garrison & Arbaugh, 2007): teaching presence, social presence, and cognitive presence (Garrison, Anderson, & Archer, 2010).

The CoI framework contains three conceptual elements: teaching presence, cognitive presence, and social presence (Garrison, Anderson, & Archer, 2000). Teaching presence refers to the online instructor's "design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes" (Anderson, Rourke, Garrison, & Archer, 2001, P. 5). Teaching presence depends on instructional design and organization of the curriculum and activities, as well as the maintenance of online discussion and organization of instruction (Ice, Gibson, Boston, & Becher, 2011). Cognitive presence is defined as the degree to which learners are able to construct knowledge through sustained self-reflection and dialogue (Swan, Garrison, & Richardson, 2009). It consists of four interconnected and cyclical phases: triggering event, exploration, integration, and resolution (Garrison, Anderson, & Archer, 2001). Social presence is defined as the ability to which participants demonstrate "real people" socially and emotionally through computer-mediated communication (Garrison & Arbaugh, 2007). It depends on the expressions of personal emotion, the sense of group identity and the group cohesion of learners (Ice et al., 2011). The CoI framework with the three core presences offers a theoretical perspective and methodology for studying the potential and effectiveness of online and blended learning (Garrison et al., 2010).

Recently, some researchers suggested the CoI framework can be further extended (Traver et al., 2014), as it lacks attention to behaviors and attitudes as learners adjust to metacognitive, motivational, and behavioral activities in online learning (Shea et al., 2013). For example, learners' discourse about individual and collaborative efforts to regulate their learning process could not be accounted for by the existing three presences in the CoI framework (Hayes, Uzuner-Smith, & Shea, 2015).

Shea and Bidjerano (2010) proposed that learning presence could be a new form of presence in the CoI framework. Learning presence is described as "the proactive stance adopted by students who marshal thoughts, emotions, motivation, behaviors, and strategies in the service of successful online learning" (Shea & Bidjerano, 2012, p. 90). Learning presence has been used to explain self-regulated learning, which includes the planning of learning tasks, monitoring of learning process, and the application of strategy in online learning (Shea & Bidjerano, 2012). The addition of learning presence into the CoI framework helps to enhance the understanding of successful online learning experiences through individual differences of self-regulation (Shea & Bidjerano, 2010).

The viewpoint of whether learning presence should be added into the CoI framework is contested among researchers. Several researchers suggested adding learning presence as a missing dimension into the CoI framework (Hayes et al., 2015; Traver et al., 2014). However, Garrison (2016) believed that the re-conceptualization of the CoI framework must be strictly validated. There seem to be a common acceptable view that regulation is an important factor in the CoI framework (Garrison, 2016; Garrison & Akyol, 2015; Shea et al., 2013). Therefore, whether to incorporate the regulation into the CoI framework through learning presence needs more research.

The Reliability and Validity of the CoI Instrument

Much existing research has attempted to develop instruments to examine the CoI framework and reported its reliability and validity. For instance, the 34-item survey instrument developed by Arbaugh et al. (2008) to measure students' perception of the three presences, has been proved to be a valid and reliable tool for the CoI framework (Garrison, Cleveland-Innes, & Fung, 2004; Shea & Bidjerano, 2009; Swan & Ice, 2010). This instrument is a valid and reliable survey measure of social presence, teaching presence, and cognitive presence (Arbaugh et al., 2008). Swan et al. (2008) reported that the internal consistency reliability of the survey instrument was: a Cronbach's Alpha of 0.91 for social presence, 0.95 for cognitive presence, and 0.9 for teaching presence. Kozan and Richardson (2014a) examined the validity of the CoI instrument conducting exploratory factor analysis (EFA) and found that the final three-factor structure explained 64.83% of the variances in the pattern of relationships among the items (e.g., teaching presence 48.21%, cognitive presence 10.64%, and social presence 5.98%). Confirmatory factor analysis (CFA) found that the hypothesized model of the three factors was verified and exhibited a reasonably good fit for the data (Kozan & Richardson, 2014a). After adding learning presence to the CoI model, the reliability of learning presence was examined. The internal consistencies of self-efficacy and effort regulation scales (called the learning presence) were.95 and.75 (Shea & Bidjerano, 2010). The CFA examined factorial validity and found that the new model has reasonable factor structure (Shea & Bidjerano, 2010).

Some research studies attempted to translate and validate the CoI instrument into other languages and use it in non-English speaking countries. For example, Portuguese researchers translated and adapted the CoI instrument into Portuguese, and validated the CoI instrument in a blended learning setting (Moreira, Ferreira, & Almeida, 2013). Yu and Richardson (2015) examined the reliability and validity of a Korean version of the CoI instrument. Through reliability analysis, Cronbach's α of teaching, social, and cognitive presences were.954,.913, and.956 respectively. The CFA results showed that the hypothesized model of the CoI instrument was verified as an excellent fit for the data in Korean context (Yu & Richardson, 2015). It is worth noting that the reliability and validity were different between Korean and English versions of the CoI instrument. So, it is necessary to examine the reliability and validity of different language versions of the CoI instrument before using it in another different context.

The Causal Relationships Among the CoI Presences

Much existing research focused on examining the direct and indirect causal relationships of the CoI presences (Garrison, Cleveland-Innes, & Fung, 2010; Shea & Bidjerano, 2008, 2009). Shea and Bidjerano (2009) found that teaching presence and social presence have direct effects on cognitive presence. Garrison, Cleveland-Innes, and Fung (2010) revealed significant direct effects of teaching presence and social presence on cognitive presence, and a direct effect of social presence on cognitive presence. The above two studies confirmed the direct relationships of the three presences. The indirect relationships focus on the mediating effect of social presence and cognitive presence. Shea and Bidjerano (2009) confirmed that social presence had a partial mediating effect between teaching presence and cognitive presence. The follow-up studies further confirmed that cognitive presence is a full mediator and social presence is a partial mediator in the CoI presences (Kozan, 2016; Kozan & Richardson, 2014b).

After the addition of learning presence, there is a lack of research revealing the relationship between learning presence and the other factors of the CoI framework. Shea and Bidjerano (2010) confirmed a correlation existed between self-efficacy (one component of the learning presence) and other presences in the CoI framework. They further suggested that learning presence should be connected with the CoI constructs (see Figure 1) (Shea & Bidjerano, 2010). Based on correlation between factors, researchers may further look for mediators and explore the interdependence behind the relationships (Frazier, Tix, & Barron, 2004). So, the mediators and causal relationships among the presences need to be further validated. Meanwhile, the relationships between the presences may change depending on the learning context (Shea et al., 2014). It is therefore necessary to examine the relationships among the presences in a different learning context.

Figure 1. A revised CoI model (Source: Shea & Biderjano, 2010).

In conclusion, the CoI instrument with learning presence has been popularly used in English speaking countries (Shea & Bidjerano, 2010, 2012; Shea et al., 2012). It is important to develop validation and refinement of the CoI instrument using different learner groups and learning contexts (Kozan & Richardson, 2014b). Meanwhile, the relationships between the presences need to be further validated and explored in different learning contexts. In China, there is a distinct lack of research to examine the reliability and validity of the Chinese version of the CoI instrument and verify the causal relationships of the CoI framework consisting of learning presence.

Therefore, the purpose of this study is to examine the reliability and validity of the CoI instrument with learning presence and explore the causal relationships between the four presences in the Chinese context. Hopefully, this instrument can provide a valid tool for measuring learners' perception of online and blended learning in the Chinese context. It could also further verify the applicable scope of the CoI instrument and causal relationships of presences in different learner groups and learning contexts.

Accordingly, in this study we aimed to answer three interrelated questions. The first question attempted to examine the reliability and validity of the Chinese version of the CoI instrument. The second question explored the causal relationships between learning presence and CoI. The goal of the third question is to provide deeper understanding of the relationship between learning presence and CoI constructs. The proposed hypothetical relationships of the variables are stated below and shown in Figure 2:

RQ1: Is the Chinese version of the CoI survey instrument reliable and valid?

RQ2: Are teaching and social presences perceived to positively influence learning presence?

RQ3: Does learning presence mediate the relationships between cognitive and teaching presences and between cognitive and social presences?

Figure 2. Hypothesized model of the four presences.

Methods

Context

The research was conducted in the School of Humanities, is an institute of Jiangnan University in Southern China. This school began to implement blended learning in 2010. At the time of this study, there were 375 courses on the online platform, including about 50 active courses and 2,725 registered students. The blended learning mode used in this study includes asynchronous online learning and face-to-face teaching with the use of the online learning platform.

The courses involved in this study covered three academic disciplines: the first discipline included three Educational Technology courses: Accounting, Design and Development of E-learning Platform, Educational Video Edition and Studio, and Multimedia Courseware Design; the second discipline included two Primary School Education courses: Primary Computer-assisted Instruction, and Foreign History of Education; and the third one included two Chinese Linguistic Literature courses: Chinese Contemporary Literature (I) and Chinese Contemporary Literature (II). The data collection process lasted for over five weeks (March-April 2015)

Participants

The participants of this study were 350 Chinese undergraduate students, chosen from three majors in the School of Humanities, including 122 Chinese Linguistic Literature students, 111 Primary School Education students, and 117 Educational Technology students. Among them, 184 were sophomores (52.6%) and 166 were juniors (47.4%). The participant had at least a year of blended learning experience and studied two to three blended courses. They were asked to respond to a survey during class time. A total of 325 students completed the survey questionnaire, and the response rate was 92.9%.

Instrument

The CoI survey instrument was originally created by Arbaugh et al.(2008), with 34 items used for data collection. It was provided on a 5-point Likert scale, ranging from 0-Strongly Disagree to 4-Strongly Agree. The scale employed in this study ranged from 1 (strongly disagree) to 5 (strongly agree). The numbers of items are 13 (for teaching presence), 12 (for cognitive presence), and 9 (for social presence). The learning presence (self-efficacy and effort regulation) was measured by 14 items (Shea & Bidjerano, 2010; Traver et al., 2014).

Two translators who had prior knowledge of the CoI framework and a teacher of English linguistics participated in the translation of the instrument. All three translators were native speakers of Chinese and fluent in both Chinese and English. Four stage translations in Chinese were undertaken from the original English language version of the CoI Survey instrument. Firstly, the translation from English into Chinese was undertaken by one translator. Secondly, another translator translated the reconciled Chinese language version back into English independently. Thirdly, the teacher of English linguistics reviewed the back-translation in meaning and identied problematic items. In the last step, the problematic items were discussed by the three translators until the discrepancies were resolved. Throughout the translation process, the three translators ensured the validity of the Chinese version of the COI instrument. The 34-item of the English and Chinese CoI instruments were shown in the Appendix.

Procedure

Confirmatory factor analysis (CFA) and path analysis (PA). LISREL v. 8.7 was used to run a full structural equation model. The full structural equation model included four measurement models and a structural model. Confirmatory factor analysis (CFA) was used to examine the four measurement models (relationships among the latent and manifest variables). The main purpose of running CFA was to verify the proposed structure of the CoI instrument along with learning presence. The goodness of fit indices, such as comparative fit index (CFI), Non-Normed Fit Index (NNFI), Root Mean Square Error of Approximation (RMSEA), and the ratio of Chi-Squared to the degree of freedom (χ²/df) were used to examine the predictive validity of the proposed structure of the CoI instrument. According to Schreiber, Nora, Stage, Barlow, and King (2006), for a good-enough fit, χ²/df value should be between 2 to 5. At the same time, above 0.9 of NNFI and CFI, below 0.08 of RMSEA, and all values of t in the key matrixes should be greater than 2.

Path analysis (PA) was used to assess the structural model (direct relationships among teaching, social, cognitive presences, and learning presence). Unstandardized path coefficients (B), Standard path coefficients (β), standard error and significance (p) were used to test the significance of the relationships.

Regression analysis. The mediating effect of learning presence was tested by regression analysis. According to Baron and Kenny (1986), in the equation Y=cX, M=aX, Y=c'X+bM, if c, a, b, c' were all significant, then M could be seen a significant partial mediating factor between X and Y. If c, a, b were all significant but c' was not, then M could be seen a complete mediating factor between X and Y. In this research, we regarded cognitive presences (Cg) as Y, learning presences (Lp) as M, teaching presences (Tp) and social presences (Sp) as X1 and X2 respectively. Regression equation, standard error, and t value were calculated.

Reliability analysis. SPSS 17.0 was used to validate the reliability of each dimension as well as the total instrument. We used the parameter of Cronbach's α. Since the number of items in each dimension is small, Cronbach's α value higher than 0.7 was accepted. For the whole scale, the Cronbach's α higher than 0.9 could be considered excellent (George & Mallery, 2003).

Results

This part will report the validity of the Chinese version instrument (CFA results) and the relationships of the factors (PA results) respectively. The results of descriptive statistical analysis, the correlation between the four presences, reliability, and mediating effect will also be presented.

Descriptive Statistical Analysis

Table 1 shows descriptive statistics including means, standard deviations, skewness, kurtosis, minimum value, and maximal value of the four presences. For the sample used in this research, no abnormal value was found. In addition, the results presented in the data in this study were normally distributed based on the degrees of Skewness and Kurtosis, because both were less than the absolute value 1.

Table 1

Descriptive Statistics of Four Dimensions

*Note. N=325, Min=1.00, Max=5.00.

It seems that learners' perceptions of presences were generally lower than that of international studies. For example, Kozan and Richardson (2014a) reported the average individual item ratings of three presences ranged from 4.17 to 4.57. In this study, the average individual item ratings of the three presences were below 3.61. We noticed that similar results existed in the context of South Korea as well. The South Korean students' perceptions of the three presences were below 3.87.

Three possible reasons may explain this discrepancy. The first is the context differences between North America and China. Previous studies compared the perceptions of students across countries and confirmed the influence of group and cultural differences on perceptions of online learning (Ashong & Commander, 2012). We hypothesized that the inclination of inquiry learning influences students' perception of CoI. Specifically, Chinese learners were found to be less critical and questioning in online discussions than American learners (Thompson & Ku, 2005). They are more likely to get a definite answer from teachers, rather than getting answers from the interaction (Liang & McQueen, 1999). So, the significant differences of students' perceptions of the CoI presences in Chinese and western contexts may depend on the inclination of inquiry learning. The second may be the issue of translation. Although we adjusted the translation content for Chinese students, students may still have difficulties in understanding specific options. The third reason seems to be that the participants' perception of the online learning community could be a little bit lower than that of some previous studies (Swan, Matthews, Bogle, Boles, & Day, 2012).

The strength of the linear relationships among learning presence, teaching presence, social presence, and cognitive presences were examined by Spearman's rho. As Table 2 shows, the four constructs are moderately to strongly correlated. The largest correlations are between cognitive presence and social presence (ρ=.698, p<0.01), and between cognitive presence and teaching presence (ρ=.665, p<0.01). Learning presence shows strong correlation with cognitive presence (ρ=.648, p<0.01)

Table 2

Spearman's Rho Correlation Coefficients Between the Four Presences

** p<0.01 (2-tailed).

Validity of the Chinese version Instrument

In this research, the validity of the Chinese version instrument was tested during two rounds. Separate model analysis rounds were conducted on the first 48-item model and the second model.

The first round of model analysis. The first round of model analysis was conducted on the 48-item model. In this research, fit indices for the hypothesized structural equation model were as follows: χ²/df =2.44, NNFI=0.927，CFI=0.931，RMSEA =0.0723. All items loaded significantly on the four factors because all the values of t in the matrixes of LX, LY, GA, and BE were greater than 2 (the smallest values of t in LX, LY, GA were 5.752, 5.523, and 2.660 respectively, and the value of t in BE was 3.383). An obviously high modification index (393.21) was found in the matrix of TD, which indicated a high relationship between the item of Sc7 and Sc9. So we decided to delete the item Sc9. The description of Sc7 was more specific.

The second round of model analysis. To test whether we could come up with a better fit, the second round of model analysis was conducted on the modified data. The overall model fit statistics were as follows: χ²/df =2.29, NNFI=0.933，CFI=0.936，RMSEA =0.0674. The CFA results indicated the hypothesized model of the 47-item structure of the four-factor CoI instrument was verified as an excellent fit for the data. We accepted the 47-item model as our final version (See Fig.3). The results of the CFA confirmed that the model fit is excellent between the proposed model and the observed data.

Figure 3. Final model of the 47-item structure (TP = Teaching presence; SP = Social presence; Lp = Learning presence; Cg = Cognitive presence).

Reliability of the Chinese Version Instrument

Table 3 presents Cronbach's α values of the four presences. All four presences had acceptable reliabilities (all Cronbach's α>.765 or higher). The instrument overall has a Cronbach's α of 0.934, which indicated the instrument was overall highly reliable (Meyers, Gamst, & Guarino, 2013).

Table 3

Reliability of the Instrument

The Causal Relationships Among the Four Presences

For the structural equation in this model, teaching presence and social presence have significant direct effects on learning presence (β=0.251, p＜0.001; β=0.533, p＜0.001). Meanwhile, learning presence was perceived to positively influence cognitive presence (β=0.272, p＜0.001). Teaching and social presence have direct influence on cognitive presence (β=0.295, p＜0.001; β=0.504, p＜0.001) (See Table4).

Table 4

Results of the Path Analysis

To further test the mediating effect of learning presence, regression analysis was carried out in six steps. Regression equations and results are shown in Table 5. As Table 5 shows, in the first three steps, the regression coefficients c(0.697), a(0.518), c'(0.482), and b(0.417) were all significant, so learning presence (M) was verified as a significant partial mediating factor between teaching presence (X1) and cognitive presence (Y). In the last three steps, the regression coefficients c(0.716), a(0.565), c'(0.499), and b(0.384) were all significant, so learning presence (M) was verified as a significant partial mediating factor between social presence (X2) and cognitive presence (Y).

Table 5

Mediating Effects of Learning Presence

*** P<0.001.

Discussion and Conclusions

The first question of this study was to confirm the reliability and validity of a Chinese version of the CoI instrument with learning presence. It was verified that the internal consistency reliabilities of the Chinese version instrument were acceptable. Consistent with previous studies (e.g., Garrison et al., 2010; Shea & Bidjerano, 2010, 2012), the CFA results showed the CoI instrument with a learning presence structure was verified as highly valid. The results of the study indicated that the instrument is a reliable tool for researchers in China to measure students' perception of learning experience.

For the second research question, this study found importance about the causal relationships between the learning presence and the CoI constructs. Teaching and social presences directly influence perception of learning presence. Perception of learning presence also has a direct effect on cognitive presence. Meanwhile, teaching and social presence have direct influence on cognitive presence. Shea and Biderjano (2010) suggested the CoI framework should be expanded to reflect the influence of learner's characteristics on the three presences and learning presence should be associated with teaching and social presences. They proposed the revised CoI model with four factors and provided the correlative relationship between the four factors. However, the causal relationships between the four factors still need further validation. This study verified the direct influential relationship between teaching presence, social presence, and learning presence. This result confirmed that causal relationships exist between the CoI constructs and learning presence. Based on the result, we believe that the CoI framework may need additional emphasis on the role of learning presence.

The last question concerned the mediating effect of learning presence. We identified that learning presence had an important partial mediating effect on the teaching presence-cognitive presence and social presence-cognitive presence relationships. Kozan and Richardson (2014b) argued that previous research had shown conflicting results between the interdependence of the three presences. The interdependent relationship may change based on learners' characteristics and learning context, which means additional factors related to learners' characteristics and learning context are needed to explain conflicting results. In this study, learning presence as a partial mediator is defined. The mediator is used to explain "how" or "why" one variable causes an outcome variable (Frazier et al., 2004). The learning presence has emerged to be a possible effect approach between teaching presence, social presence, and cognitive presence. This means higher levels of teaching presence and social presence may increase the perception of learning presence, and in turn promote the level of cognitive presence. This might help to explain why the interdependence relationship within the CoI model may be influenced by learners' certain characteristics (Shea & Bidjerano, 2010, 2012).

Based on the understanding of the mediating effect of learning presence, we should pay more attention to the self-regulatory effect of learning presence in maintaining CoI. We suggest that the effort to increase learning presence may automatically result in increased cognitive presence. From a teaching presence perspective, this might suggest that teaching presence should focus not only on instructional management, building understanding, and direct instruction (Szeto, 2015), but also on adopting pedagogical strategies to help foster self-regulatory behaviors and strategies of online learners. These results also suggest that CoI should encourage students to take personal responsibility (self-regulation) for their learning.

This study expanded the CoI instrument to a different language and cultural context. Firstly, we verified a Chinese version of the CoI instrument. The result confirmed that the CoI instrument to evaluate social, teaching, cognitive, and learning presence was a reliable tool for measuring the perception of learning experience in China. The study confirmed that the causal relationships between learning presence and CoI constructs. The results extended the CoI framework's components with an additional focus on self-regulatory behaviors and strategies of learners in online and blended learning.

The present study engaged a mid-range sample size of 350 students in a university of China. Although this sample from China helped to diversify the research context of the CoI framework, comparatively a mid-range sample size may limit the generalizability of findings. In addition, some researchers suggested that it was necessary to examine the CoI framework across disciplines and institutions (Garrison & Arbaugh, 2007; Garrison et al., 2010). Future research should increase the number of participants and explore the dynamic relationships among the presences across disciplines and institutions. Lastly, exploratory factor analysis should be conducted to further identify the dimensionality of the Chinese version of the CoI instrument.

This study provided a reliable instrument for researchers and practitioners in China to measure their students' perception of online and blended learning. It will contribute to the development and application of the CoI framework in other contexts. We hope our validated survey will not only enable Chinese researchers and practitioners to assess the quality of the existing online courses, but also guide them to design and develop more high-quality online courses.

Acknowledgements

This work is supported by the National Natural Social Youth Foundation of China (CCA140151) and The Basic Scientific Research Special Foundation of Chinese Central University "Internet based environment of the understanding of learning and cognitive research" (2017JDZD07). Corresponding author: Dr. Jing Wang, tranquilgo@163.com.

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Appendix

Dimensions and Items of the Chinese CoI Survey Instrument

Translated by Zhi-qiang Ma, Ya-jie Wu, and Li-li Kong.

Dimensions and Items of the English CoI Survey Instrument

The Community of Inquiry Survey Instrument is from:

Arbaugh, B., Cleveland-Innes, M., Diaz, S., Garrison, D. R., Ice, P., Richardson, J. C., et al. (2008). Developing a community of inquiry instrument: Testing a measure of the Community of Inquiry Framework using a multi-institutional sample. The Internet and Higher Education, 11(3-4), 133-136.

Verifying Causal Relationships Among the Presences of the Community of Inquiry Framework in the Chinese Context by Zhiqiang Ma, Jing Wang, Qiyun Wang, Lili Kong, Yajie Wu, and Hao Yang is licensed under a Creative Commons Attribution 4.0 International License.