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framework for data analysisTo test the three research questions (RQ), we first established a framework as the guideline for data analysis (Figure 2) and then the data analysis was conducted with the help of an online SPSS website www.spssau.com offering a ‘Statistical Package for the Social Sciences’(cf. Grum & Zydatiß 2016).

Figure 2:

Framework for the data analysis

The reliability analysis showed that the value of Cronbach α was 0.908, which meant the internal consistency of the questionnaire is excellent and the data could be used for further analysis. We used both parametric and non-parametric analyses. Usually, parametric analyses are adopted when the data are normally distributed and non-parametric analyses do not require the normal distribution. A normal distribution has a symmetric bell shape with equal mean and median located on the center of the distribution. Although the normality test showed that all data collected in this survey did not follow a normal distribution, parametric tests are “sufficiently robust to yield largely unbiased answers that are acceptably close to ‘the truth’ when analyzing Likert scale responses” (Sullivan and Artino, 2013).

Cronbach’s Alpha: the most commonly used reliability analysis

It is important to “calculate and report Cronbach’s alpha coefficient for internal consistency reliability for any scales or subscales one may be using” (Gliem and Gliem, 2003). The rules of thumb provided by George and Mallery (2003) indicate that a Cronbach’s α value above 0.70 was considered as the threshold to test for internal consistency.

Cronbach’s alpha	Internal consistency
α ≥ 0.9	Excellent
0.9 > α ≥ 0.8	Good
0.8 > α ≥ 0.7	Acceptable
0.7 > α ≥ 0.6	Questionable
0.6 > α ≥ 0.5	Poor
0.5 > α	Unacceptable

(Source: https://www.statisticshowto.com/cronbachs-alpha-spss/)

RQ1: attitudes and self-perception of digital competenceRQ 1 sought to ask about the student teachers’ general attitudes toward digital technologies and their self-perceptions of their digital competences. We created two new variables by grouping the 44 items in the first two sections and conducted a descriptive analysis. The results (Table 1) revealed that the average score for their perceived digital competences was 2.630 and the mean for their digital attitudes was 2.375, both of which were below 3 (between “2-mostly agree” and “3- slightly agree”). The student teachers’ digital attitudes were more positive than their digital competences perceived by them, and the difference was significant as the results of the paired t-test analysis show in Table 2. A t-test is a statistical test for hypothesis testing which shows how significant the differences between the means of two groups are. The significance could be told from the P-value, with P < 0.05 being statistically significant and P<0.001 highly significant. In this case, the two groups refer to the two variables: attitudes toward digital technologies and self-perceptions of digital competences.

Table 1:

Descriptive analysis on digital competence and attitudes

Table 2:

Paired t test analysis on digital competence and attitude

Sullivan and Artino (2013) argued that means are often of little value when data do not follow a classic normal distribution and in this case a frequency distribution of responses will likely be more helpful. The results of the frequency analysis in Figure 3 reveal that 90 % of student teachers had positive attitudes toward DT (34.8%+55.2%) and 75.7% of them had positive self-perceptions of their digital competences (18.2%+57.5%).

Figure 3:

Frequency analysis of perceived competences and digital attitudes

Figure 4 shows a more detailed distribution of responses concerning the constructs within digital competences. Nearly 90 % of the student teachers (57%+29%) were confident in their competence of using digital technologies to promote their professional engagement, the highest among all six constructs. Over 80 % of them agreed that they could use digital technologies to facilitate learners’ digital competence (39%+44%) and enhance teaching and learning (35%+46%). 76 % of them thought they could use digital technologies in the assessment of learners. But when it comes to digital resources, the number of participants who had positive perceptions dropped to 65 %. The most divided results were about empowering learners, with only 50 % of them perceiving that they were competent in this area.

Figure 4:

Frequency analysis of perceived digital competences

The frequency analysis of the four constructs regarding student teachers’ attitudes toward DT (Figure 5) reveals that 94 % of the participants (60%+34%) held positive attitudes toward the influence of DT on students and nearly 93 % of them were willing to use DT in their future classrooms (55%+38%). Yet they seemed to be more skeptical about data privacy and distraction, reflected in the less positive personal feelings toward DT.

Figure 5:

Frequency analysis of attitudes toward digital technologies

Data analysis – Terminology

Correlation is the degree of association between random variables. However, correlation is not the same as causation. Mathematically, a correlationRQ2: relationship between self-perceptions of digital competence and attitudes is expressed by a correlation coefficient that ranges from –1 (never occur together), through to 0 (absolutely independent) and to 1 (always occur together).

Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables. The estimated regression equation can be used to predict the value of the dependent variable in view of the influence exerted by the value of the independent variables.

The independent variable is the one which is controlled in an experiment, but does not depent on other variables (e.g., age). The dependent variable is the one that changes in response to the independent variable. Their relationship could be simplified as cause and effect: the change in the independent variable will affect the dependent variable.

To identify the relationship between the two variables of student teachers’ self-perceptions of their digital competence and their attitudes toward DT, correlation analyses and regression analyses were conducted.

As is indicated in Table 3, the correlation coefficients (Pearson r) for the ten constructs are all above 0 (p<0.01), which concludes that student teachers’ perceived digital competence and their attitudes are positively related and the relationship is statistically significant. The relationships are found to be remarkably strong (>0.500) especially between “Attitudes toward the influence of DT on students” and “Facilitating learners’ digital competence” (0.530), between “Willingness to use” and “Facilitating learners’ digital competence” (0.567) as well as “Teaching and learning” (0.503), and between “Personal feelings toward DT” and “Digital resources” (0.559).

Table 3:

Correlation analysis on perceived digital competence and attitudes

In order to more accurately describe the relationship between the two variables, two regression analyses were carried out: one with the attitudes as the dependent variable and the digital competence as the independent variable, and another analysis with the two variables exchanged. According to Table 4, when the “self-perceptions of one’s digital competences” is the dependent variable, and the four constructs of digital attitudes are independent variables, the value of R² is 0.374, which means “attitudes toward DT” can account for 37.4% of the reasons for the change of “Self-perceptions of one’s digital competences”. The regression coefficients indicate that “Personal feelings toward DT” (t=5.649，p=0.000<0.01), “Willingness to use” 0.202 (t=2.145，p=0.033<0.05) will produce significant positive effects on “Self-perceptions of one’s digital competence” of student teachers.

Table 4:

Regression analysis (Dependent variable: perceived digital competences)

Table 5 shows that perceived digital competences could predict 38.8% of the change in attitudes (R²=0.388), which means the more competent the student teachers perceived themselves to be, the more positive attitudes they would hold. However, among the six constructs of digital competence, only “facilitating learner’s digital competence“ (t=4.786，p=0.000<0.01) will have a significant, positive effect on the attitudes.

Table 5:

Regression analysis (X=attitudes towards DT as dependent variable， Y=six constructs of digital competence as independent variables). The result of regression analysis is an equation where the coefficients represent the relationship between each independent variable and the dependent variable.

To conclude, the correlation analysis and regression analysis show that student teachers’ self-perceptions of their digital competence were closely related to their attitudes toward DT and both variables affect each other positively. A positive coefficient in the regression analysis suggests that the mean of the dependent variable tends to increase with the increase of that of the independent variable. Perceived digital competence had a slightly stronger impact on attitudes (0.388) than attitudes had on digital competence (0.374).

RQ 3: influencing factorsIn order to find out whether factors such as age, gender, school types, and teaching experience may influence the attitudes and self-perceptions of student teachers toward DT, a one-way ANOVA analysis was used (with ANOVA standing for Analysis of Variance). This type of analysis is conducted to assess the interrelationship of two or more independent variables on a dependent variable. The one-way ANOVA analysis results are as follows:

 Gender and years of pre-service teacher education may have an influence on student teachers’ perceived competence and their attitudes, but other factors like age, school types, and whether student teachers were Lehramt Staatsexamen or Master of English Studies students did not make any difference.

 There was no statistically significant difference between gender and perceived digital competences on the whole, yet male students perceived themselves to be more digitally competent in facilitating learners’ digital competence (p=0.005), creating digital resources (p=0.025) and promote their professional engagement with digital technologies (p=0.014).

 Student teachers with more years of pre-service teacher education had significantly less positive attitudes toward DT than students with no or less than one-year experience of pre-service teacher training (p=0.040).