## 5 Data Analysis
### Model Results  
With the findings and index modification, adjustments were made to the initial model (Figure 1). In effect, the model was re-specified by considering the criterion of statistical significance between the regression coefficients and the theoretical support for making the modifications. Once the changes were made, the relationships found were statistically significant for the various coefficients and the levels of adjustments appropriate to the model presented in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Standard Deviation** & **Minimum** \\ \hline Total studying time in LMS & Spearman’s Rho & 0.351 \\  & p-value & 0.006 \\ Interaction with the LMS & Spearman’s Rho & 0.332 \\  & p-value & 0.005 \\ Interaction with virtual learning objects & Spearman’s Rho & 0.653 \\  & p-value & 0.003 \\ \hline Interaction in forums/wiki & Spearman’s Rho & 0.405 \\  & p-value & 0.001 \\ Interaction with learning tasks & Spearman’s Rho & 0.410 \\  & p-value & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlational analysis between learning behaviors and learning performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **MN** & **MT** & **CF** & **LD** \\ \hline Motivation (MT) & 0.472 & - & - & - \\ Communication and & 0.505 & 0.673 & - & - \\ Feedback (CF) & 0.711 & 0.567 & 0.458 & - \\ Learning Design (LD) & 0.379 & 0.525 & 0.493 & 0.528 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation analysis for model variables. The adjustment rates for the final model were acceptable \(\chi^{2}_{(3)}\)= 2.96; p \(>\) 0.05; GFI = 0.984; AGFI = 0.918; IFC = 1.00; RMSEA = 0.001; AIC = 26.96; ECVI = 0.396). The above indicates that the errors associated with estimating the parameters fall in the range of normal values and the residues are small. The hypotheses results are demonstrated in Table 6.  
In this study, all three hypotheses are positively significant at the 0.001 level, so the results indicate support for the following three hypotheses (H1, H2, H4). For Hypothesis 1 (H1), virtual teacher tutoring is significantly positively related to learning design levels (\(\beta\) = 0.57). The virtual tutor (teacher)'s LA-based interventions contribute to inferring actions to mediate content, select pedagogical strategies, design didactic sequences, select types of assessments, and convenient technological tools for learning design. For Hypothesis 2 (H2), motivation has a positive and significant impact on Learning Design levels (\(\beta\) = 0.57). In this sense, the virtual tutor's motivational actions to develop his teaching practice contribute to deduce the levels of Learning Design that imply the concreteness of the educational fact. Supported Hypothesis 4 (H4) demonstrates that the virtual tutor's Learning Design based on LA exerts a direct positive influence on the students' learning performance (\(\beta\) = 0.35). Consequently, when the virtual tutor performs actions to choose what, when, where, and how to teach (learning design), this is reflected in the students' learning performance. Regarding the direct and indirect effects present in the model (Figure 3), it is observed that the Tutoring (MN) and Motivation (MN) variables presented only indirect effects (using Learning Design) with coefficients 20 and 11, respectively. On the other hand, the variables Communication and Feedback (CF) and Learning Design (LD) showed direct effects with coefficients of 30 and 35 (H3). These last two variables showed a strong relationship with learning performance; that is, the higher the communication and learning design, the higher the student's learning performance and vice versa. Hypothesis 3 (H3) was rejected as original where CF and LD had direct effects; instead, CF provide direct effects to Learning Performance (LP). In terms of Hypothesis 5 (H5), the three variables (MN; MN and CF) together are reflected in Learning Design (LD) because Communication and Feedback (CF) have a direct impact on Learning Performance (LP).