# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 6 Discussion
This discrepancy may be attributed to the way LMS Merlin records these activities: The system records only whether an activity was accessed, lacking finer engagement metrics, such as time spent reviewing materials or depth of interaction, which may limit its predictive reliability. As a result, recorded engagement with these resources may not always reflect real learning activity, making them a less reliable predictor compared to interactive assessments such as tests and quizzes. In line with [9], our study underscores the importance of using historical academic performance data to predict future outcomes. Student assessment activities throughout the semester (_Test 1_, _Test 2_, _Test 4_, _Colloquium 1_, _Colloquium 2_) consistently emerge as strong predictors of _Success_ in all weeks following their completion (Cramer's V values between 0.322 and 0.593). These assessments represent critical intervention points. Identifying students with low performance early enables timely adjustments to instructional methods, including adaptive learning resources, targeted feedback, and structured peer support. The scoring system was developed using classical statistical methods, with numerical variables transformed into categorical, dichotomous variables based on the Youden index, which optimally separates students at risk of failing from those predicted to succeed. The relationship between key variables and _Success_ was quantified using Cramer's V coefficients, allowing us to assess the strength of the association between predictor variables and student performance. Cramer's V and the Youden index were selected because of their suitability for the analysis of categorical data, the optimization of threshold values, and their practical in terpretability in the field of education. Cramer's V quantifies the strength of association between nominal or ordinal variables and is therefore particularly suitable for assessing the relationship between early course performance indicators and final student outcomes. Unlike [PERSON]'s correlation coefficient, which is designed for continuous variables and assumes a linear relationship, Cramer's V captures nonlinear dependencies and allows for easy interpretation of categorical predictors [34]. The Youden index has been used to establish optimal cut-off points for academic risk classification. While ROC curves provide a comprehensive assessment of classification performance by plotting sensitivity against specificity, they do not inherently define a single optimal threshold. The Youden index, on the other hand, directly identifies the threshold that maximizes both true-positive and true-negative rates, making it particularly effective for balancing sensitivity and specificity in early intervention strategies [32]. The resulting scoring system and its associated algorithm function as a predictive classification model, forecasting student _Success_ throughout all 15 weeks of instruction. What distinguishes this model is its transparency, ease of interpretation, and practical applicability. While complex machine learning models may offer marginally higher predictive accuracy, this approach provides educators with direct insights into the contribution of each variable to student performance, making it particularly suited for educational interventions. Our study builds on previous research [13; 14; 15], reinforcing the importance of interpretable models that not only achieve strong predictive performance but also offer actionable insights into their decision-making processes. By providing clear, interpretable predictions, this model enables instructors to implement proactive teaching strategies. For example, educators can use early warning indicators to tailor instructional pacing, design adaptive assignments, or introduce peer mentoring programs to support at-risk students. The robustness of the model was evaluated in two phases: first in its development dataset and later in a new cohort where real-time interventions were introduced.