## 3 Methodology
In this case, experts whose argumentation collectively corresponds to a level of theoretical analysis and practical experience at an average level are accepted--i.e., Ka \(\geq\) 0.5 [81]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Argumentation**} \\ \hline
**High** & **Average** & **Low** & **Source of Argumentation** \\ \hline
0.3 & 0.2 & 0.1 & Theoretical analysis conducted by the expert \\ \hline
0.5 & 0.35 & 0.2 & Practical experience of the expert \\ \hline
0.05 & 0.05 & 0.05 & Generalization of works by local authors \\ \hline
0.05 & 0.05 & 0.05 & Generalization of works by foreign authors \\ \hline
0.1 & 0.1 & 0.1 & Expert’s intuition \\ \hline \hline \end{tabular} Source: Author’s own work on the basis of [81].  
\end{table} 
Table 1: Survey to evaluate the argumentation coefficient (Ka) of the expert. Returning to the competence coefficient (Kk), if the minimum for Kz is set at 0.5 and for Ka at 0.55, the competence coefficient above which an expert can be qualified for the study is \((0.5+0.55)/2=0.525\). Based on the conducted analysis, 17 experts from the group of academic researchers engaged in e-learning and 23 from the group of practitioners using e-learning were qualified for the study. The next step is to determine the degree of agreement among experts regarding the assessment of individual survey questions, which will confirm or refute the appropriateness of the expert selection. This measure is determined using the Kendall/Babington-Smith concordance coefficient. The concordance coefficient, \(\omega\), can range from 0 to 1. A higher value of the coefficient corresponds to greater agreement among experts, with full agreement yielding \(\omega=1\). The concordance ranges can be interpreted as follows [81]:  
* Sufficient for the range--0.20-0.40;
* Good for the range--0.41-0.60;
* Very good for the range--0.61-0.80;
* Excellent for the range--0.81-0.95;
* Ideal for the range--0.96-1. To determine the concordance, the following actions will be taken:  
Calculation of the arithmetic mean of the sum of scores for evaluating individual questions. \[\hat{S}=\frac{\sum_{j=1}^{n^{*}}S_{j}}{n^{*}} \tag{2}\]  
Determination of the deviation of the sum of ranks of scores received by object j from the arithmetic mean of the sum of scores received by all objects, \(\mathrm{d_{j}}\) (3),  
\[\mathrm{d_{j}}=S_{j}-\hat{S},\quad j=1,2\ldots n^{*} \tag{3}\]  
Calculation of the indicator of the associated ranks assigned by each successive expert, TiT_i_Ti. If there are identical ranks among them, the formula is the following (4):  
\[\mathrm{T_{i}}=\sum_{k=1}^{1}\left(\mathrm{t_{k}^{3}-t_{k}}\right) \tag{4}\]  
i = 1, 2,..., m  
Here, the following pertains:  
l--is the number of tied ranks in the group;  
t\({}_{k}\)--is the number of identical ranks in each group. Determination of the concordance coefficient, using the formula (5)  
\[\dot{\omega}=\frac{\sum_{j=1}^{n^{*}}\mathrm{d_{j}^{2}}}{\frac{1}{12}m^{*2}(n ^{*3}-n^{*})-\frac{1}{12}m^{*}\ \sum_{i=1}^{m^{*}}T_{i}} \tag{5}\]  
In the final survey, only those questions were included that achieved a Kendall/ Babington-Smith concordance coefficient of at least'sufficient' for the range, i.e., \(\omega>0.20\backslash\mathrm{omega}>0.20\omega>0.20\). In the conducted research, this included 14 questions from the original list (stage B2B). The first part of the survey was devoted to issues related to the advantages of using e-learning in higher education.