# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 7 Conclusions  
The proposed algorithm provides a practical and adaptable framework for the early detection of students at risk of failure, offering a balance between predictive accuracy, interpretability, and ease of implementation. Its simplicity, ease of implementation, and minimal computational requirements make it highly accessible to a broad spectrum of educational institutions, regardless of their technological infrastructure. The model's iterative weekly application allows for continuous monitoring and timely interventions, ensuring that students receive support before their performance declines significantly. Additionally, the integration of [PERSON]'s V measure and the Youden index enables systematic predictor selection and the establishment of clear risk thresholds. The analysis showed that the most influential predictors included prior academic performance, with [PERSON]'s V values of 0.362 for grades and 0.325 for enrollment rates, as well as motivational factors, such as introjected motivation (0.409) and achievement motivation (0.393). Among the course engagement variables, weekly test performance and early assessments showed the highest predictive power, with Colloquium 1 and Colloquium 2 achieving [PERSON]'s V values of 0.593 and 0.503, respectively. The Youden index was applied to determine optimal thresholds for risk classification, with weekly thresholds ranging from 32 to 55 points, as shown in Table 11. These scores provide an objective basis for early risk detection and enable timely and targeted interventions. A key advantage of this approach is its methodological simplicity, relying on well-established statistical techniques that are widely accepted among educators. This increases the likelihood of adoption, particularly among senior instructors who may have reservations about more complex machine learning methods. Furthermore, the model combines objective indicators of prior knowledge, motivational characteristics, and student work habits, creating a holistic predictive model rather than relying solely on learning analytics from the LMS. By structuring the model as an algorithm, its automation is straightforward, making it scalable for courses with large student enrollments. The model is also designed to accommodate weekly variations in student behavior, allowing educators to track changes over time and adjust interventions accordingly. A clear methodological framework is provided, making it adaptable to different courses, either through direct implementation or minor adjustments. One of the most impactful aspects of the model is its intervention component, which enables educators to provide individualized support to at-risk students. The prospective analysis demonstrated that the model exhibited exceptionally high discriminative power in the week before the first major exam, ensuring that nearly all at-risk students were correctly identified in time for targeted interventions. The high sensitivity of the scoring system further guarantees that students in need of support are not overlooked. Despite these strengths, some challenges remain. The relatively small sample size in this study was a limitation, although the prospective analysis indicated that this did not significantly affect the model's performance. Additionally, collecting motivational and non-LMS data proved challenging, as it required voluntary student participation, potentially impacting response rates. While the model can be integrated into existing LMS platforms, some degree of manual processing may be required, posing an implementation challenge for large-scale adoption. Lastly, although alternative statistical methods (e.g., logistic regression) could be explored for further refinement, the choice of simpler techniques was intentional, prioritizing interpretability and practical application. The proposed model has already been successfully implemented in our prospective analysis, where it was directly applied to real teaching scenarios without modification.