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that 73.9% of the teachers agreed that professional development programmes must be more demand driven, that is, more specific courses are needed for teachers, and 3.1% disagreed.
The teachers believed that professional development curriculum must be related to their beliefs and behaviour and be demand-driven.
The median for each construct has also been computed, and the results show that the respondents agreed that nine of the 10 constructs were factors influencing their usage behaviour of online professional development. Their modes and medians were 2. These constructs were performance expectancy, effort expectancy, facilitating conditions, OPD content and activities, evidence of students’ performance, challenges of OPD, teacher’s experiences with OPD, knowledge and beliefs, behavioural intention to use OPD and online teacher-training usage. The percentage who agreed was greater or equal to 60. On the other hand, for social influence, the mode and median were 3. This means that most of the respondents (56.9%) were neutral regarding social influence being a contributor to their beliefs about teacher online professional development (see Appendix R).
Further analysis determines to what extent social influence, facilitating conditions, performance expectancy, effort expectancy and behavioural intention predict or influence the usage behaviour of OPD courses. Previous studies (Shaper & Pervan, 2007; Venkatesh et al., 2003) have shown that these constructs account for almost 70% of the dependent variable in predicting behavioural intention to adopt or use a technology or system. A hierarchical multiple linear regression was chosen to answer this question as in such analysis the researcher would be able to see which variable added significant change in predicting user’s use behaviour of online teacher-training programmes. Moreover, it allowed the researcher to enter the variables in steps. The researcher entered the variable related to UTAUT model first followed by those of teacher learning model. The next section presents the multiple regression analysis.
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predictions regarding secondary school teachers’ behavioural intention to use online teacher- training programmes and their beliefs about online professional development. A hierarchical multiple linear regression method of analysis was used to measure the strength of the link between each of a set of explanatory variables (independent variables) and a single response (or dependent) variable (Landau & Everitt; 2004). This method was used to understand to what extent the variables or constructs are predictors of teachers’ intention to use online professional development and teachers’ usage of such a system. Furthermore, multiple regression was used to assess if the facilitating and inhibiting factors could be used to predict teacher beliefs about OPD. It also assessed the relationship among the set of variables. As it may be the first time that UTAUT model has been used in the Mauritian context of online professional development for teachers, it is imperative to reconsider its factors and the interactions among them. Furthermore, having combined two models (teacher learning and the UTAUT model) in this study, and that the constructs (PE, EE, SI and FC) from UTAUT have already been proven to impact on the intention and usage of technology, it was necessary to explore which set of variables was able to predict teachers’ intention and usage of OPD other than the four found in UTAUT.
The variables were evaluated to determine their impact on the dependent variable. In this study, the researcher used multiple regression to show how the predictor variables influenced the dependent variables such as BI to use the online learning and also the teachers’
usage behaviour.
The assumptions that must be met for the hierarchical multiple linear regression to be valid are: 1) the random independent variables must be normally distributed and linearly related to dependent variable; 2) the independent variables must be free of error; 3) the variance of the dependent variable as a function of the independent variables must be constant, which is referred to as homoscedasticity; and 4) the independent variables must be relatively uncorrelated. Before the hierarchical multiple regression analysis was conducted, preliminary analyses were performed to ensure there was no violation of assumptions of normality, linearity and multicollinearity. The variance inflation factor (VIF) and tolerance values were calculated to find out whether there was multicollinearity between the variables.
Any VIF of 10 or above and tolerance values of .10 or less (equivalent to a VIF of 10) showed serious problems of multicollinearity regarding independent variables (Cohen et al., 2003;
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pp. 423–424). Both the VIF and the tolerance value was 1.00 for Behavioural intention to use OPD. The VIF values varied from 1.68 to 3.67, and the tolerance values ranged from .27 to .60 when the four independent variables FC, PE, EE and SF were added. The Pearson correlation varied from .52 to .86 (see Appendix T). While adding the five other variables (teachers’ experience, challenges of online teacher training, knowledge and beliefs, content and activities and evidence of students’ performance) to the existing five, the VIF values varied from 1.43 to 4.76, the tolerance values ranged from .21 to .70 (see Appendix S) and the Pearson correlation ranged from .50 to .86 as shown in Appendix T. “The level of multicollinearity can be assessed by looking at the predictor variables. The predictor variables should not have a strong relationship with each other; the stronger the relationship between the predictors, the higher the degree off multicollinearity of the betas” (Walker, 2011, p. 7). The levels of tolerance are not beneath .1 and the VIF scores are below 10, the comparative starting point levels that highlight concern with the data. Therefore, the predictive variables do not excessively influence each other. Based on these results, the researcher decided to conduct multiple regression. According to Krawthol and Anderson (2001), the significance level was set at p < .05, being the normal level used while working on significance. The researcher looked at the unstandardised coefficient beta weights and the standardised beta weights of each predictive variable to be able to check the significance and the importance of each predictive variable. Furthermore, the R squared was used to assess the interactions among the numerous predictive variables and the dependent variable. The predictive variables are: behavioural intention to use OPD, facilitating conditions, effort expectancy, performance expectancy, social factors, teachers' experiences, challenges of online teacher training, knowledge and beliefs, content and activities and evidence of students’ performance. The dependent outcome variable is online teacher-training use behaviour.
The calculated Pearson Correlations values among the 10 predictive variables are shown in Appendix T. None of the correlations attained the 1.0, so the analysis indicates that no two variables are strongly related. Summary models were created during the hierarchical multiple regression analysis.
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Table 6.18: Model Summary of Hierarchical Multiple Regression
Model R R
Square
Adjusted R Square
Std.
Error of the Estimate
Change Statistics R
Square Change
F Change
df1 df2 Sig. F Change
1 .79a .63 .62 7.20 .63 105.61 1 63 .000
2 .79b .63 .62 7.23 .00 .47 1 62 .495
3 .86c .74 .72 6.14 .11 24.84 1 61 .000
4 .87d .76 .75 5.89 .03 6.34 1 60 .014
5 .92e .84 .83 4.81 .08 31.17 1 59 .000
6 .92f .85 .83 4.79 .00 1.21 1 58 .260
7 .92g .81 .83 4.78 .00 1.30 1 57 .259
8 .96h .93 .92 3.37 .08 58.56 1 56 .000
9 .96i .93 .92 3.39 .00 .66 2 54 .523
a. Predictors: (Constant), Behavioural intention to use OPD
b. Predictors: (Constant), Behavioural intention to use OPD, Social factors
c. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy d. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions
e. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions, Performance expectancy
f. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions, Performance expectancy, knowledge and beliefs1
g. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions, Performance expectancy, knowledge and beliefs1, Evidence of student performance
h. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions, Performance expectancy, knowledge and beliefs1, Evidence of student performance, Content and activities
i. Predictors: (Constant), Behavioural intention to use OPD, Social factors, Effort expectancy, Facilitating conditions, Performance expectancy, knowledge and beliefs1, Evidence of student performance, Content and activities, challenges of OPD, Teachers' experiences
j. Dependent Variable: Online teacher training usage
Table 6.18 shows the result of the predictive variables. A nine-stage hierarchical multiple regression was carried out to study how the set of independent variables are related to the dependent variable. Behavioural intention to use OPD was the predictor of online
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teacher-training usage in model 1. The R value was .79; thus, there is a high positive relationship between the predictor variable and online teacher-training usage. The R2 (.63 or 63%) was greatly significant at F (1, 63) = 105.61, p < 0.001, since it could account for 63%
of the variance. Consequently, behavioural intention to use OPD contributed significantly to online teacher-training usage.
Two predictors (behavioural intention to use OPD and social factors) were used in model 2, where there was no improvement over the previous model. The R value was .79 and an R2 remained .63, thus accounted for 63% of the variance. The change in R2 was not significant F (1, 62), p > 0.05. This showed that social factors could not predict online teacher-training usage.
With three predictors (behavioural intention to use OPD, social factors and effort expectancy) model 3 gave an improved value for R = .86, with an R2 of .74, accounted for 74% of the variance. The change in R2 was very significant F (1, 61) = 24.84, p < 0.001, and subsequently the result showed that a good predictor of online teacher-training usage was effort expectancy.
For model 4, there were four predictor variables (behavioural intention to use OPD, social factors, effort expectancy and facilitating conditions), the value of R has improved slightly to .87, with an R2 of .76, and thus 76% of the variance had been accounted for.
R2 was significant F (1, 60) = 6.34, p < 0.05. Therefore, facilitating conditions was a predictor of online teacher-training usage.
The five-predictor variable behavioural intention to use OPD, social factors, effort expectancy, facilitating conditions and performance expectancy) of model 5 showed an improvement of the R value from .87 to .92 with an R2 of .84, explaining 84% of the variance.
The R2 change was very significant F (1, 59) = 31.17, p < 0.001. Therefore, these results showed that performance expectancy contributed significantly to online teacher-training usage.
For the model with the six predictor variables (behavioural intention to use OPD, social factors, performance expectancy, facilitating conditions, effort expectancy, knowledge and beliefs), the value of R = .92 and R2 = .85, accounted for 85% of the variance, but the change in R2 was not significant F (1, 58), p > 0.05.
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In model 7, there were seven predictor variables (behavioural intention to use OPD, social factors, facilitating conditions, performance expectancy, knowledge and beliefs, effort expectancy and evidence of student performance); there was no improvement in the value of R = .92 as well as R2 = .85, thus 85% of the variance has been reckoned for and the change in R2 was not significant F (1, 57), p > 0.05 also. Hence, knowledge and beliefs and evidence of students’ performance could not predict online teacher-training usage.
Model 8, with eight predictors (behavioural intention to use OPD, social factors, effort expectancy, facilitating conditions, performance expectancy, knowledge and beliefs, evidence of student performance, content and activities), the value of R has changed from .92 to .96 with R2 = .93 which accounted for 93% of the variance. The R2 change was very significant F (1, 56) = 31.17, p < 0.001. So, it was clear that content and activities contributed significantly to online teacher-training usage.
In model 9, there were 10 predictor variables (behavioural intention to use OPD, social factors, facilitating conditions, performance expectancy, knowledge and beliefs, effort expectancy, evidence of student performance, content and activities, challenges of OPD, teachers' experiences) with R value of .96 and R2 = .93, accounted for 93% of the variance.
The change in R2 was not significant F (1, 54), p > 0.05. Therefore, challenges of OPD and teachers’ experiences did not have any effect on online teacher-training usage.
The result of ANOVA in Table 6.19 provides us with the significance of each of the nine models. All the nine models were significant as all the p values were less than .001 (p
< .001). Model 1 has the largest F value and it has only one predictor.
Table 6.19: ANOVA Results of the Nine-Model-Hierarchical Regression Analysis
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 5473.15 1 5473.15 105.61 .000b
Residual 3265.09 63 51.83
Total 8738.25 64
2 Regression 5497.75 2 2748.88 52.59 .000c
Residual 3240.49 62 52.27
Total 8738.25 64
3 Regression 6435.59 3 2145.12 56.83 .000d
Residual 2302.66 61 37.75
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Model Sum of
Squares
df Mean Square F Sig.
Total 8738.246 64
4 Regression 6655.75 4 1663.94 47.941 .000e
Residual 2082.50 60 34.71
Total 8738.25 64
5 Regression 7375.62 5 1475.12 63.87 .000f
Residual 1362.63 59 23.10
Total 8738.25 64
6 Regression 7405.30 6 1234.22 53.70 .000g
Residual 1332.95 58 22.98
Total 8738.25 64
7 Regression 7435.02 7 1062.15 46.46 .000h
Residual 1303.23 57 22.86
Total 8738.25 64
8 Regression 8101.20 8 1012.65 89.02 .000i
Residual 637.05 56 11.38
Total 8738.25 64
9 Regression 8116.31 10 811.63 70.47 .000j
Residual 621.93 54 11.52
Total 8738.25 64
The β coefficients (see Appendix S) for the constant and five predictors of online teacher-training usage were as followed: Constant β = -2.83, t = -0.94, p = 0.35: not significant; Behavioural intention to use OPD, β = 0.14, t=1.94, p = .06: not significant;
Social factor, β = -0.06, t = -1.13, p =.26: not significant; Effort expectancy, β = 0.22, t = 3.99, p < 0.001: significant; Facilitating conditions, β = 0.18, t = 2.73, p = .008: significant;
Performance expectancy, β = 0.26, t = 3.60, p = .001: significant; knowledge and beliefs, β = 0.03, t = 0.66, p = .52: not significant; evidence of students’ performance, β = -0.25, t = - 3.99, p < 0.001: significant; content and activities, β = 0.56, t = 7.69, p < 0.001: significant;
Challenges of OPD, β =0 .07, t = 1.12, p = .27: not significant; Teachers’ experiences, β = - 0.04, t = -0.49, p = .63: not significant. The model: Online teacher-training usage = 0.22 (Effort expectancy) + 0.18 (Facilitating conditions) + 0.26 (Performance expectancy) – 0.25 (Evidence of students’ performance) + 0.56 (Content and activities). Performance expectancy, Effort expectancy, facilitating conditions, Content and activities and Evidence
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of student performance are coded as 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree and 5 = strongly disagree.
Figure 6.1: Histogram
Figure 6.1 indicates that a compact concentration of the predictive variables has an influence on online teacher-training use behaviour. The tallest bars on the graph have the greatest influence on online teacher-training use behaviour. They are: Performance expectancy (p = .001), Effort expectancy (p = .000), Facilitating conditions (p = .008), Content and activities (p = .000) and Evidence of students’ performance (.000). The equal intervals along the abscissa (x-axis, predictors) imitate the numerous class intervals relative to student outcomes.
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