# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 2 State of the Field  
Recent research highlights the significant influence of learning motivation as a key predictor of academic success, while advances in LMS data analytics have created new opportunities for real-time performance monitoring. Leveraging insights about student motivational factors and interactions within LMS enables educators to take proactive measures and implement early intervention strategies. As academic institutions increasingly focus on improving retention rates and overall student success, early prediction models are emerging as important tools to identify at-risk students and develop targeted support mechanisms. While traditional prediction approaches have relied predominantly on statistical analysis, recent developments in machine learning (ML) have improved both the accuracy and adaptability of these models. In STEM subjects, where the subject matter is often particularly challenging, motivation to learn plays a crucial role in academic performance. Studies have shown a strong correlation between higher levels of intrinsic motivation and better academic outcomes, suggesting that students who demonstrate greater engagement in STEM subjects are more likely to persevere despite challenges [20]. For example, [PERSON] et al. conducted a nationwide study showing that interventions aimed at promoting a growth mindset were effective in improving students' grades across different demographic groups, highlighting the important link between motivation and academic success [20]. Similarly, [PERSON] et al. examined the negative effects of math anxiety, an issue closely linked to motivation, and emphasized its predictive role in STEM avoidance [21]. These findings underscore the need to develop targeted interventions that address both motivational barriers and academic difficulties and reinforce the importance of incorporating psychological factors into academic support systems to maximize their effectiveness. The results of learning motivation analysis can be integrated with LMS interaction logs to provide comprehensive insights into student engagement and behavioral patterns, both of which are critical to academic success. [PERSON] and [PERSON] emphasized that examining various aspects of student interactions within LMS platforms can reveal early indicators of risk that facilitate data-driven decision-making for the implementation of targeted interventions [22]. Furthermore, [PERSON] et al. developed an early warning system that utilizes eBook interaction logs to identify students at risk, demonstrating the potential of LMS data in establishing timely academic support mechanisms [23]. In addition, [PERSON] and [PERSON]'s research confirmed that LMS data can be effectively used to create predictive models that can identify at-risk students as early as the fifth week of the semester and achieve satisfactory predictive accuracy [24]. Statistical models, such as logistic regression, are often used to predict academic performance due to their simplicity and interpretability. For example, [PERSON] et al. developed a binary logistic regression model to identify students at risk in blended learning environments. The independent variables included attendance, participation in online discussions, and the completion of assignments. The model achieved a reported predictive accuracy of 0.82, underscoring the importance of engagement metrics in identifying students who may struggle, consistent with the findings that passive participation does not effectively predict performance [25]. [PERSON] and [PERSON] examined the relationship between admission criteria and academic performance using correlation and regression analyses. The study demonstrated a significant relationship, reporting that metrics such as high school GPA and standardized test scores significantly correlated with college performance. This underscores the importance of using admission data as a predictive tool for academic success in higher education institutions [26].