# How to Improve the Digital Competence for E-Learning?
## 4 Results  
The Digitest allows us to assess students' digital competence in ten dimensions. These dimensions were taken as a basis for the SEM analysis, where we aimed to predict the development of digital knowledge and skills. The model combined the dimensions of digital competence; more precisely, we determined if and how attitudes play a role in digital knowledge and skills development and how individually developed knowledge and skills predict the knowledge and skills which need social settings for development. In order to answer the first research question about how attitudes towards digital device usage predict the development of digital knowledge and skills, we tested Theoretical Model 1 about how the attitudes were influencing all the digital knowledge and skills dimensions. The model revealed that, based on our data, only one attitude dimension statistically significantly predicts the development of operational skills. The analysis revealed that behavioural intention (students prefer digital devices or environments to other ways of doing things) does have a small effect on the development of operational skills (see Table 2 for the values). The model had a good fit with the data: \(\chi^{2}\) = 765.404, df = 620, \(\chi^{2}\)/df = 1.23, RMSEA = 0.023, CFI = 0.980, TLI = 0.977, WRMR = 0.885. For answering the second research question about how the individually developable skills have an effect on the digital skills for learning which need social settings for developing, another SEM analysis was conducted for testing Theoretical Model 2. The analysis resulted in a model (Figure 4) which suggests that the individually developable operational skills predict the development of knowledge and skills of creating digital materials/content and programming (also individually developable skills). The latter play a role in predicting digital knowledge and skills for learning which require social settings for development (communicating in the digital world, protecting oneself and others in the digital world and legal behaviour in the digital world). All the connections in the model are significant, with the exception of the connection between programming skills and protecting oneself and others in the digital world. The model had a good fit with the data: \(\chi^{2}\) = 318.326, df = 219, \(\chi^{2}\)/df = 1.45, RMSEA = 0.033, CFI = 0.982, TLI = 0.980, WRMR = 0.857. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dimension** & **COMM** & **CREAT** & **PROG** & **OPER** & **LEG** & **PROT** \\ \hline AT\_BINT & \(-\)0.050 & \(-\)0.059 & 0.093 & 0.173 * & 0.009 & \(-\)0.157 \\ AT\_BATT & 0.199 & 0.079 & 0.074 & 0.106 & 0.290 & 0.319 \\ AT\_PC & 0.187 & 0.435 & 0.494 & 0.353 & 0.055 & 0.137 \\ AT\_SA & 0.091 & \(-\)0.204 & \(-\)0.222 & \(-\)0.172 & 0.187 & 0.026 \\ \hline \end{tabular}
\end{table} 
Table 2: Regression coefficients in the SEM model predicting digital knowledge and skills dimensions based on the attitudes dimensions of the digital competence for learning. The value marked with * is considered statistically significant. (AT\_BINT—behaviour intention, AT\_BATT—behaviour attitudes, AT\_PC—perceived control, AT\_SA—social aspects, COMM—communicating in the digital world, CREAT—creating digital materials, PROG—programming digital content, OPER—performing operations with digital tools, LEG—legal behaviour in the digital world, PROT—protecting oneself and others in the digital world). The results indicate that social aspects (value of peers' use of digital tools, perception of others' support), perceived control (students deciding whether to use digital devices and believing in one's own abilities) and behavioural attitudes (anxiety and belief in the value of digital tools) are not significant factors when discussing the variance of the development of digital knowledge and skills for learning. However, behavioural intention (preferring digital devices or environments for learning) positively affects operational skills.