# A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
## 5 Results
### Development of the Prediction Algorithm  
The scoring system is created at the end of the academic year when the values of the dependent variable, whether the student has passed or failed the course, are known. The described process of developing a scoring system that serves as a classification prediction model is illustrated by the flowchart in Figure 5. By following this process, teachers can create their own scoring system. They must independently determine the student activities in their course that they want to track and consider as potential predictor variables. The model is applicable to different courses and different activities within a course. Once teachers have established their scoring system, they can use it in subsequent academic years to predict student success in the course.  
The predictive model was designed as a weekly classification system to assess the likelihood that students will fail the course. The algorithm follows a structured process, as illustrated in Figure 6, ensuring that the predictions are continuously updated based on student engagement and performance data.  
The process begins with the definition of the course settings, including the course structure, the grading policies, and key assessment milestones. The prediction variables are then determined, selecting relevant indicators such as attendance, participation, and self-reported engagement. These variables are collected through student surveys and activity logs.  
Each week, the participation of students in the course activities is monitored, and based on their recorded participation a score is calculated. This score represents an aggregated measure of their academic performance and engagement up to that point. Students are then classified as \"at risk\" or \"not at risk\" based on a predefined cut-off threshold. If a student's score falls below this threshold, they are flagged as at risk of failing the course.  
This score-based classification is then used to provide personalized feedback to students, helping them understand their current risk level and encouraging corrective action if necessary. By making students aware of their status early in the semester, the system enables timely interventions that can improve their performance.  
The process repeats weekly, updating the predictions dynamically. The system runs until the final week of the semester, at which point the iterative predictions stop.  
This predictive model allows educators to implement targeted pedagogical strategies (feedback to the students) in real time. In our prospective study, these strategies include the following:  
Figure 5: Flowchart of the designing scoring system.  
Individual consultation invitations, encouraging students to discuss their challenges and receive tailored guidance;
* The integration of motivational narratives within lectures to reinforce resilience and engagement; and
* The organization of group-based supplementary exercises, fostering peer learning and collaborative problem-solving.  
Beyond direct interventions, predictive insights are also used to guide students toward specific learning resources. Based on their risk score, students are advised to review selected video lectures, attempt additional problem sets, or revisit fundamental concepts, ensuring that support is both personalized and actionable.