## 5 Discussions and Conclusions  
We explored machine learning techniques to predict student performance in the non-linear logic reasoning PPVU course at the Universidad de Antioquia, focusing on its flexible learning path. This non-linear structure requires dynamically updating the features at the moment of prediction to capture relevant student interactions accurately. To address this, we introduced the concept of a Feature Aggregation Time Point (FATP), where features are extracted based on the student's Cumulative Weight Assessment (CWA), ensuring the feature set reflects student activity up to a specific performance threshold. Since no standard set of features exists for predicting performance in non-linear courses, we extracted engagement, behavior, and performance features. We used these features to train a binary classifier for early student performance prediction and compared it against a non-machine-learning baseline. Our analysis confirmed that when updated at the FATP, these features effectively represent and predict student performance, significantly improving classification accuracy. We adopted a three-phase methodology: (a) binary classification to predict whether a student would achieve a certificate-granting grade, (b) cluster analysis to identify online learning patterns among students, and (c) a correlation analysis between student performance and the behavioral clusters, including a feature importance analysis through the selected machine learning models. The non-linear nature of the PPVU course required selecting the optimal lower Cumulative Weight Assessment (CWA\({}_{L}\)) to ensure balanced data samples and setting the FATP based on the student's progress relative to the upper threshold (CWA\({}_{U}\)). Our analysis of students who completed at least 55% of the course revealed that models significantly outperformed the baseline, with Logistic Regression demonstrating strong performance across all CWA\({}_{U}\) thresholds. Although Random Forest performed better at specific thresholds, Logistic Regression offered a favorable balance of accuracy, computational efficiency, and interpretability. The combination of behavior and perfor  
Figure 14: Students’ final grade performance for each cluster at different CWA\({}_{U}\) thresholds with top features. mance features yielded the best results, showing that while engagement features contribute to predictions, their importance diminishes when combined with other feature types. Our approach enabled early predictions with an F1-score of 0.73 at 20% \(\text{CWA}_{U}\) and 0.76 at 40% \(\text{CWA}_{U}\) for binary classification. Our feature importance analysis showed that certain engagement features, such as course navigation and forum participation, had relatively low importance across all \(\text{CWA}_{U}\) thresholds. However, engagement features related to time spent on educational resources were consistently more influential. Our clustering analysis revealed how students interact with different resources (multimedia, video, simulations, and workshops) and how these behaviors relate to performance at varying \(\text{CWA}_{U}\) thresholds. Early and sustained engagement with multimedia and video was key to success, especially in high-performing clusters. As students progressed to higher \(\text{CWA}_{U}\) levels, simulations and workshops became more influential, highlighting their role in assessing later-stage performance. We also identified distinct self-regulated learning (SRL) patterns: students who strategically engaged with simulations and workshops consistently outperformed others across all thresholds. These high-performing clusters demonstrated strong SRL practices, such as time management and resource prioritization. They also showed lower linearity scores and higher forum participation, reflecting an adaptive learning approach that favored exploration over rigid sequences. This flexible behavior highlights the value of diverse engagement strategies tailored to individual learning needs.