# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 6 Discussion  
The scoring system was designed based on 16 variables. A direct numerical comparison of the relationships between different prediction variables and their impact on student performance across studies is limited due to methodological differences. However, the result remains qualitatively the same; it is crucial to identify which variables are relevant predictors, regardless of the method used. Prior academic performance--specifically _School grade_ (_[PERSON]'s V = 0.362_) and _Matriculation percentage_ (_[PERSON]'s V = 0.325_)--emerges as the strongest predictor. This aligns \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Week** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC-ROC** \\ \hline Week 1 & 0.80 & 0.78 & 0.84 & 0.81 \\ Week 2 & 0.77 & 0.75 & 0.84 & 0.79 \\ Week 3 & 0.83 & 0.88 & 0.68 & 0.78 \\ \hline
**Week 4** & 0.93 & 0.92 & 0.95 & 0.93 \\ \hline \hline \end{tabular} Note: The week preceding first midterm exam is highlighted in light blue.  
\end{table} 
Table 13: Performance metrics of the predictive model per week in the 2024/2025 academic year. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Week** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC-ROC** \\ \hline Week 6 & 0.81 & 0.79 & 0.90 & 0.84 \\ Week 7 & 0.81 & 0.79 & 0.90 & 0.84 \\ Week 8 & 0.77 & 0.73 & 0.90 & 0.81 \\ Week 9 & 0.77 & 0.73 & 0.90 & 0.81 \\ Week 10 & 0.81 & 0.82 & 0.80 & 0.81 \\ Week  
with the foundational nature of mathematics, where new concepts build upon previously acquired knowledge. Motivational variables also played a crucial role, with both intrinsic (_Toward accomplishment_, _Cramer's V = 0.393_) and extrinsic components (_Introjected_, _[PERSON]'s V = 0.409_) emerging as consistent predictors across all weeks. These findings align with previous research [5; 6], confirming that motivation significantly influences engagement and persistence, making it an essential component of predictive modeling. However, a key challenge in incorporating both academic and non-academic variables lies in their method of collection. Unlike the variables obtained directly through the LMS, these data were collected using two voluntary questionnaires. In particular, 43% of the students did not complete them, leading to missing data that could impact the reliability of the predictions. Missing responses may introduce selection bias, where students who voluntarily participate in surveys differ systematically from those who do not, potentially skewing model results. To ensure the robustness of the model, it is essential to address this limitation. Educators should clearly communicate the purpose and benefits of the prediction model to students, emphasizing how their participation can lead to better support and intervention strategies. Actively encouraging students to complete both questionnaires in the first week of instruction is crucial to ensuring the reliability of predictions. Increasing response rates, for instance, by embedding surveys into early course activities, could further enhance prediction accuracy. The variables that capture students' engagement in solving tasks throughout the semester (task engagement metrics: _Number of learning tests_, _Maximum of learning tests_, _Number of practice tests_, _Maximum of practice tests_) play a particularly significant role in predicting student outcomes. Our findings align with previous research [7; 8], which also identified time spent on various course components, such as tests and assignments, along with quiz and assignment scores, as key predictors of overall course performance. Specifically, these task engagement metrics emerge as significant predictors of _Success_ in at least 6 out of 15 weeks. In contrast, variables _Video_ and _Presentation_ appear as relevant predictors in fewer weeks.