## 2 Materials and Methods
### Data Collection
Finally, to obtain the interaction index for each student, various aspects regulated by the University's Online Education Unit were considered in accordance with the interests of the University's pedagogical model. The Online Education Unit determined that this index should include:  
* The number of activities and resources visited by students \(n_{A\bigoplus R}\). * The number of total activities and resources enabled by the teacher in the classroom \(N_{A\bigoplus R}\) and the number of activities only enabled by the teacher \(N_{A}\). * The number of interactive learning objects and interactive activities completed by the student \(n_{ILO\bigoplus IA}\) (considering interactive objects as those of the H5P, IMS, or SCORM type and interactive activities as questionnaires, lessons, and forums). * The total time spent by the student solely on solving interactive objects, interactive and communication activities \(t_{ILO\bigoplus IA\bigoplus C}\) (excluding homework delivery times, consulting calendars or grades, reading or viewing static resources, among others). * The number of experience points accumulated by the student was awarded by the LevelUp XP plugin \(p_{EXP}\). * The rank achieved in the classroom ranking system was awarded by the LevelUp XP plugin \(r_{EXP}\). Using all these parameters, a variable was designed that is obtained from the contribution of the majority of these metrics on a percentage scale named cumulative interaction \(i_{c}\), which can be calculated using the expression:  
\[i_{c}=50\frac{n_{A\bigoplus R}}{N_{A\bigoplus R}}+\frac{50}{1-e^{-4.595\frac {n_{ILO\bigoplus IA}}{N_{A}}}}+\frac{50}{1+e^{-0.00765\cdot t_{ILO\bigoplus IA \oplus C}}}-50 \tag{1}\]  
As can be seen in Equation (1), the ratio between the number of activities visited by students and the total number of activities facilitated by the teacher was assigned 50% of the weight of this variable. Similarly, the proportion between the interactive activities, the total number of activities designed in each subject, and the time spent by the students in interactive activities was given a weight of 25% each. Next, considering that the number of experience points obtained by the student in the development of each subject does not have a defined scale and can continue to grow indefinitely depending on the activities considered in the set of rules and that the range obtained by the LevelUp XP plugin depends on all the configurations and thresholds that the teacher has defined for each score, which are defined by default in the institutional template but can be modified according to the needs of each teacher; for this reason, the inclusion of these two variables, experience points \(p_{EXP}\) and XP rank \(r_{EXP}\) from the LevelUp plugin, along with the cumulative interaction \(i_{c}\) were modeled using a shallow neural network to provide an output on a scale of 1 to 100 named interaction index \(I_{int}\)[21]. The configuration of this shallow neural network is presented in Figure 2.  
To obtain this neural network model, a database was created for the variables of interest--experience score, experience range, and cumulative interaction--comprising the observations of 147 students from different classrooms whose level of interaction was outstanding as observed by their teachers. Next, the teachers who taught these students subjects in the current period were asked to assign an interaction score on a scale of 1 to 100, and the average of these scores for each student was used as the interaction index in the training phase. With these scores, several sequential neural network models were trained using the TensorFlow v2.10.0 and Keras v2.12.0 libraries with RStudio v2023.06.2, using leaky ReLU activation functions in the hidden layer and linear activation in the output layer. An Adam optimizer was used with a coefficient of 0.001 and Mean Absolute Error (MAE) as the cost function (loss).