# Using LSTM to Identify Help Needs in Primary School Scratch Students
## 4 Discussion  
In this research, we used the distance calculation presented in [6], and we implemented a first prototype model for predicting when a primary school student will need help while performing block-based programming exercises. One important motivation for this model is to ensure that the students will be properly helped when they are stuck. To achieve this goal, the model must be able to correctly identify the moment when a student has a mental block while he/she is carrying out Scratch exercises. During the user experiences, despite the fact that we sought to have an equal number of boys and girls, and to have a significant number of students whose mother tongue is different from Spanish, the reality is that there was an imbalance in the gender of the participants, as well as a large majority of students whose mother tongue is Spanish. User experiences were designed taking into account ethical aspects such as transparency and avoiding any kind of discrimination. In this first approach, we focused on trying to have a significant number of boys and girls, and studying the influence of gender, being mindful of its relevance to learning in the field of computer science, as studies like [45] show. In this sense, we intend to conduct larger experiments in the future. Even with such data, we consider that this first approach, which includes the data collection with the distance-calculation attribute and the predictive help model, is relevant, because there is no mention in the literature of repositories containing user interactions with block-based programming software, and solution distance metrics may help to build the suggested model. Looking at the calculated impact of the different features in Figure 11, it can be seen that the most important features are the student's skills, the total time, and the distance calculations. This means that the distance calculation introduced in Section 2.3.2 is a relevant feature that helps to improve the accuracy, reaching more than 40% impact. Our predictive model trained with the data collected from user experiences in a real learning environment obtained 99.2% accuracy if we consider the gender attribute in training, and 91.1% accuracy if we exclude the gender attribute in training. Despite the impact of gender being relatively low, as shown in Figure 11, it can be seen in Tables 3 and 4 that this attribute \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LSTM** & **Dropout** & **Loss** & **Binary** & **Val Loss** & **Val Binary** \\
**Layers** & **Layers** & **Loss** & **Accuracy** & **Val Loss** & **Accuracy** \\ \hline
1 & False & 0.015799 & 0.9574 & 0.04275 & 0.91122 \\
1 & True & 0.013381 & 0.95972 & 0.047054 & 0.91095 \\
2 & False & 0.015337 & 0.95727 & 0.045792 & 0.91122 \\
2 & True & 0.013264 & 0.94273 & 0.045034 & 0.91116 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model training results without gender feature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LSTM** & **Dropout** & **Loss** & **Binary** & **Val Loss** & **Val Binary** \\
**Layers** & **Layers** & **Accuracy** & **Accuracy** \\ \hline
1 & False & 0.017185 & 0.94459 & 0.0071762 & 0.99144 \\
1 & True & 0.019589 & 0.94277 & 0.0077519 & 0.99249 \\
2 & False & 0.017146 & 0.94621 & 0.0078189 & 0.98975 \\
2 & True & 0.020001 & 0.94273 & 0.0085079 & 0.99266 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model training results with gender feature. influences the accuracy of the model by around 8%. All this seems to indicate that gender may be a relevant attribute. Also, in Figure 10, gender is inversely correlated with the different skills, which means that, in general, in our experiment girls had a lower score in these skills. The small amount of data collected, and the high precision of the model when gender is present, makes us suspect that the model is overfitting, leading to it not properly identifying when a student requires help, due to gender, and this fact could cause frustration and loss of interest in the subject.