## 4 Results
### User-Engagement Calculations to Assess Educational Web Project Parts Interaction
Thus, the shorter the course duration (Figure 2) --the less load on the infrastructure is created, and more profit is made not only due to the higher e-training cost, but also lower costs associated with the service disposal. This is important to consider. User-engagement calculation and monitoring along with correlation of the SLAs compliance levels will allow us to assess the technical equipment, infrastructure condition, as well as to determine which categories of users face particular online project bottlenecks. An equation to determine an online project user-engagement score with respect to \(n\) number of content entities has been developed. Our formula offers the possibility of its application both to a web project separate element and the service entirely; input parameters are the most important metrics for understanding the web project and its participants' interactions; the weights are calculated on a comparative basis of all project components. Below is Equation (1) for specific content \(i\). \[U_{i}=\frac{\max(R_{i},\ C)}{\max(V_{i},\ C)}\ W_{i}, \tag{1}\]  
where \(U_{i}\) is the user-engagement value. Its range depends on the minimum and maximum values available in the dataset for the arguments \(V_{i}\) and \(R_{i}\), \(V_{i}\) is the number of views/visitors/subscriptions on a certain online project topic. \(R_{i}\) is the number of reactions on a certain online project topic, where 0\(\leq R_{i}\leq V_{i}\). \(C\) is a constant used to avoid getting zero during the division. In our research, we set it to 0.5 because it is an artificially created value that does not count as a real user review \(R_{i}\) value. \(W_{i}\) is the weight parameter, which is different for each topic and its user-engagement \(U_{i}\) value. Max functions are used to obtain the maximum value between \(V_{i}\) or \(R_{i}\) and the constant. We found that more than 9% of courses do not have any user reviews, but the number of subscribers ranged from 0 to 1600. This is quite unclear and at the same time captivating because it means that there was insufficient user-engagement, probably due to a lack of a user- and content-understanding. Apart from that, we consider that users might have faced technical issues when viewing the content of these courses. This finding needs further study to determine what leads to this kind of user-behavior. Table 1 shows the number of courses according to the total number of subscribers (X-axis). We propose the following Equation (2) to calculate the weight value:  
\[W_{i}=\sum\
olimits_{j=1}^{k}f_{j}, \tag{2}\]  
where \(f_{i}\) is the relative frequency of a certain factor related to a web-resource category or topic, for instance, user interaction, total time spent for page visit, page load time, views, reviews, subscriptions, and comments left. We might also face an issue when the \(W_{i}\) equals zero. Then the user-engagement is equal to zero as well. This might happen in rare cases, because if the \(U_{i}\) has a high value without multiplying it by the weight coefficient, it is very likely that \(W_{i}\) will be greater than 0. \begin{table}
\begin{tabular}{c c} \hline \hline
**Subscribers Count Interval** & **Number of Courses** \\ \hline
0–5000 & 1119 \\
5000–10,000 & 52 \\
10,000–15,000 & 14 \\
15,000–20,000 & 6 \\
20,000–30,000 & 5 \\
50,000–70,000 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total subscribers count interval per Udemy business and finance courses. Figure 2: Business and finance course price and duration grouped by competency levels. As mentioned above, the output range of the user-engagement score is dependent on the arguments \(V_{i}\) and \(R_{i}\); in order to standardize it, adjusted to the range from 0 to 1, and receive a user-engagement score value for a topic \(i\), we propose the following simple Equation (3) below:  
\[Ustandard_{i}=\frac{U_{i}}{\max(U_{i})}, \tag{3}\]  
where we get the ratio of a certain category user-engagement to the max user-engagement scores.