## 3 Methodology
### Data  
We utilize data from the \"Preparate para la Vida Universitaria\" (PPVU) course, hosted on the Moodle LMS and developed by the Universidad de Antioquia. This eight-week course focuses on logical-mathematical reasoning and problem-solving strategies to support students preparing for university entrance exams. It is open to learners worldwide, although we believe most participants are based in or near the Antioquia region in Colombia. The course requires an estimated 48 h of study, with a recommended pace of six hours per week. It is structured into five units containing explanatory videos and interactive modules. Modules are delivered either through Moodle's lesson activity or as multimedia content. These formats are treated separately when defining features (see Tables 1 and 2), and includes 12 graded assessments--six workshops (30% of the final grade) and six entrance-exam-style simulations (70% of the final grade). Two additional diagnostic exams are provided to assess students' prior knowledge, though they do not contribute to the final grade. A distinctive feature of the course is its self-paced design: students may complete activities in any order, repeat them as needed, and retain only their highest score. Those achieving a final average grade of 3.5 or higher (on a 0-5 scale) receive a certificate of participation. We anonymized and collected all data by the institution's data policy, which students accepted upon enrollment. We did not access any personal information, and we securely stored the data on institutional servers with access restricted to authorized researchers. We quantify the degree of linearity of the student's learning path through a Linearity Score computed over the 12 evaluative activities of the course. This score measures how closely a student's assessment sequence follows an ascending order. A Linearity Score of 1 indicates perfect linearity, where a student completes the assessments in strictly ascending order from 1 to 12 or with repetitions while maintaining the ascending sequence (e.g., 1, 2, 2, 2, 3, 4, 5, 5, 5). In contrast, scores closer to 0 represent a lack of linearity, where the order of assessments appears disordered (e.g., 2, 5, 3, 6, 5). Figure 3 shows the distribution of the Linearity Score across students. We can see the diversity in how students resolve the course assessment, ranging from highly linear patterns (scores near 1) to highly disordered ones (scores near 0). This variability in assessment order reflects the self-paced, flexible nature of the course and highlights differences in how studentsapproach the learning material [33; 34]. The algorithm used to compute the Linearity Score is detailed in Algorithm A1. Figure 4 shows the distribution of the students according to the percentage of the overall course assessment they completed, highlighting a steep dropout rate from the beginning of the course. In this context, \"early stages\" refers to the first completed evaluative activities, since students can follow the course in any order. Many students disengage before reaching 5% of the Cumulative Weight Assessment (CWA); among those who reach 5%, more than half drop out before completing 25% of the assessments, with a particularly sharp decline observed between the 5% and 20% CWA. This early dropout pattern emphasizes the importance of developing predictive models to accurately identify at-risk students in the initial phases of the course.