as measured by the teaching presence, social presence, and cognitive presence subscales, and transformative learning, as measured by the critical reflection subscale, in online, graduate business courses.
Descriptive Statistics
The sample size for this study was (N = 242). Data was obtained for the predictor variables: teaching presence (X1), social presence (X2), and cognitive presence (X3), and the
criterion variables: reflection (Y1), and critical reflection (Y2). Mean (M) and standard deviation
(S. D.) for each group were calculated using SPSS, where (X1 = M = 54.12, S. D. = 11.06), (X2 =
M = 36.88, S.D. = 6.61), (X3 = M = 49.95, S. D. = 9.89), and (Y1 = M = 17.12, S. D. = 2.60), and
(Y2 = M = 13.56, S. D. = 4.18). Descriptive statistics can be found in Table 1.
Table 1
Descriptive Statistics
Teaching Social Cognitive Reflection Critical
Valid 242 242 242 242 242
Mean 54.12 36.88 49.95 17.12 13.56
Std. Deviation 11.06 6.611 9.885 2.599 4.177
Assumption Tests Data Screening
Data screening was conducted on each of the predictor and criterion variables using box and whisker plots to identify outliers. Visual inspection identified several outliers. Records associated with outliers were inspected, and no obvious entry errors were identified. Further, because few of the outliers were extreme, and model significance was unchanged by r emoval of outlier cases, the outliers were included in the analysis (Warner, 2013). The box and whisker plots can be found in Figure 1.
Assumptions
The assumption of normality was tested using the Kolmogorov-Smirnov (K-S) test of normality. Both reflection (p < .001) and critical reflection (p < .001) were significant.
However, because visual inspection of the Q-Q plots indicated normal distribution and multiple regression is robust to the assumption of normal distribution (Williams, Grajales, &
Kurkiewicz, 2013), it was reasonable to proceed with the regression analysis. See Table 2 for results from the K-S test, and Figure 2 for the Q-Q plots.
Table 2 Tests of Normality Kolmogorov-Smirnova Statistic df. Sig. Reflection .145 242 .000 Critical .099 242 .000
a. Lilliefors Significance Correction
Figure 2: Q-Q plots for the transformative factors of reflection and critical reflection, demonstrating normality of distribution.
The assumptions of bivariate outliers, linearity, and multivariate normal distribution were tested using scatterplots. No extreme outliers were identified, meeting the assumption of
bivariate outliers. Lines of best fit indicated linear relationships amongst all variables, thus meeting the assumption of linearity. Finally, the data appeared to be normally distributed, and therefore the assumption of multivariate normal distribution was met. See Figure 3 for the scatter plots.
Figure 3. Scatterplot of predictor and criterion variables, showing no extreme outliers, linearity, and normal distribution.
A Variance Inflation Factor (VIF) test was run to test the assumption of non-
mulitcollinearity for each of the predictor variables on both criterion variables. The assumption of non-mulitcollinearity (α = .05) was met for reflection, where (X1 = 2.84), (X2 = 2.15), and (X3
= 3.75); and critical reflection, where (X1 = 2.84), (X2 = 2.15), and (X3 = 3.75). See Table 3 for
Table 3 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 8.975 .789 11.376 .000
Teaching .050 .020 .211 2.461 .015 .352 2.840
Social -.021 .029 -.055 -.733 .464 .466 2.147
Cognitive .125 .026 .476 4.834 .000 .267 3.752
a. Dependent Variable: Reflection
Table 4 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) .607 1.254 .484 .629
Teaching .025 .032 .066 .773 .440 .352 2.840
Social -.041 .047 -.066 -.890 .374 .466 2.147
Cognitive .263 .041 .623 6.389 .000 .267 3.752
a. Dependent Variable: Critical
Results Null Hypothesis One
There is no significant predictive relationship between the community of inquiry, as measured by the teaching presence, social presence, and cognitive presence subscales, and transformative learning, as measured by the reflection subscale, in online, graduate business courses.
Results of Null Hypothesis One
A multiple regression was run to determine if there was a significant predictive relationship between the linear combination of predictor variables (teaching presence, social presence, and cognitive presence) and the criterion variable (reflection) in online, graduate business courses. The linear combination of teaching, social, and cognitive presence predicted 38.4% of variance in reflection scores (R2 = .38). The reflection model summary can be found
in Table 5. Table 5
Model Summary
Model R R² Adjusted R² RMSE
1 0.620 0.384 0.376 2.053
Analysis of an ANOVA found a significant relationship between the linear combination of predicator variables and the criterion variable (α = .05), where F(3, 238) = 49.42, p < .001. Therefore, the null hypothesis is rejected. Results of the ANOVA can be found in Table 6. Table 6
ANOVA
Model Sum of Squares df Mean Square F p
1 Regression 624.7 3 208.239 49.42 < .001
Residual 1002.8 238 4.213
Total 1627.5 241
The regression model found that teaching presence (X1 = p = .01) and cognitive presence
(X3 = p < .001) were significant predictors of reflection (Y1), while social presence (X2 = p = .46)
was not a significant predictor (α = .05). Results of the regression analysis can be found in Table 7.
Table 7 Coefficients
Model Unstandardized Standard Error Standardized t p
1 intercept 8.975 0.789 11.376 < .001
Teaching 0.050 0.020 0.211 2.461 0.015
Social -0.021 0.029 -0.055 -0.733 0.464
Cognitive 0.125 0.026 0.476 4.834 < .001
Null Hypothesis Two
There is no significant predictive relationship between the community of inquiry, as measured by the teaching presence, social presence, and cognitive presence subscales, and transformative learning, as measured by the critical reflection subscale, in online, graduate business courses.
Results of Null Hypothesis Two
A multiple regression was run to determine if there was a significant predictive relationship between the linear combination of predictor variables (teaching presence, social presence, and cognitive presence) and the criterion variable (critical reflection) in online, graduate business courses. The linear combination of teaching, social, and cognitive presence predicted 39.7% of variance in reflection scores (R2 = .39). The critical reflection model
summary can be found in Table 8. Table 8
Model Summary
Model R R² Adjusted R² RMSE
1 0.630 0.397 0.390 3.264
Analysis of an ANOVA found a significant relationship between the linear combination of predicator variables and the criterion variable (α = .05), where F(3, 238) = 52.28, p < .001.
Table 9 ANOVA
Model Sum of Squares df Mean Square F p
1 Regression 1670 3 556.83 52.28 < .001
Residual 2535 238 10.65
Total 4206 241
The regression model found that cognitive presence (X3 = p < .001) was significantly
predictive of critical reflection (Y2), while teaching presence (X1 = p = .44), and social presence
(X2 = p = .37), were not significant predictors (α = .05). Results of the regression analysis can
be found in Table 10. Table 10
Coefficients
Model Unstandardized Standard Error Standardized t p
1 intercept 0.607 1.254 0.484 0.629
Teaching 0.025 0.032 0.066 0.773 0.440
Social -0.041 0.047 -0.066 -0.890 0.374
CHAPTER FIVE: CONCLUSIONS
Overview
This study found that there is a significant predictive relationship between a community of inquiry and transformative learning in online, graduate business courses. These findings fit with the conceptual similarities between these theories, primarily by the educational and
psychological traditions on which they are based, as well as the conception of reflective thinking used to frame each. This study adds to the limited research on fostering transformative learning online, by providing a framework within which to study and promote it. The focus on business students, and the use of an instrument that measures a single dimension of transformative learning, presented limitations to the study. Suggestions for future research include replicating the study in different populations, using a more holistic transformative learning instrument, examining specific learning activities and their effect on community of inquiry and
transformative learning, and providing further validation for the Reflection Questionnaire in online settings.
Discussion
The purpose of this study is to measure the relationship between a community of inquiry and transformative learning in online, graduate business courses. Two models were used to measure the relationship: the linear combination of the teaching, social, and cognitive presence scales of the community of inquiry, and the reflection scale of transformative learning; and, the linear combination of the teaching, social, and cognitive presence scales of the community of inquiry, and the critical reflection scale of transformative learning. Both models demonstrated a significant relationship between a community of inquiry and transformative learning. Within the models, cognitive presence was significantly related to both factors of transformative learning—