Effective learning depends on timely, personalized support that helps students overcome individual challenges in real time. In this paper, we present the eTRainer system which meets this need by combining automated task generation, data-driven feedback, and streamlined course administration in one comprehensive platform. Built on a LAMP architecture (Linux/Apache/MySQL/PHP) and augmented by mathematical modules, the eTRainer enhances both student engagement and instructor workflow management.

From a student’s perspective, the eTRainer provides a dynamic environment for developing and refining essential skills. Knowledge Tracing algorithms identify knowledge gaps and adjust subsequent practice sessions accordingly. When a student demonstrates readiness to progress, the eTRainer presents more complex exercises; if confusion or repeated errors appear, the system changes the proposals accordingly. This feedback loop promotes self-regulated learning by pointing students toward the most relevant resources and tasks.

Beyond personalized adaptation, the eTRainer functions as a drill-based practice platform for standardized problem types (in the areas of mathematics and electrical engineering). The system can generate nearly arbitrary variations of typical exercises, ensuring that learners practice key methods without becoming overly reliant on a single problem instance. This diversity strengthens knowledge retention and prepares students for advanced coursework or final evaluations.

For instructors, eTRainer serves as a robust administrative tool. It automatically distributes problem sets, instantly tailoring each set to the student’s proficiency level, and consolidates points earned with minimal manual effort. By aggregating data and confirming prerequisite milestones, the eTRainer ensures that all students meet the conditions for exam eligibility. As a result, instructors gain a comprehensive, real-time view of overall class performance.

Because the eTRainer is designed with modular Python components, new exercise formats and updated teaching materials can be integrated quickly, keeping pace with evolving course requirements. Its web-based architecture further facilitates seamless deployment across a range of educational settings. As data accumulate over time, the embedded Knowledge Tracing algorithms can be refined to boost predictive accuracy and optimize practice routines.

Early results of the concept emphasize the platform’s potential to foster engagement: students commonly credit the instant feedback loop and repeated exercises for improving their exam preparation. In parallel, the instructor values the simplified point tracking, exam-eligibility checks, and cohort-wide performance monitoring - all of which streamline administrative duties. By combining key management functions with adaptive instructional tools, the eTRainer enriches the learning experience for both educators and students.

In summary, the eTRainer illustrates how an advanced Knowledge Tracing platform can act both as a transformative learning aid and as a crucial administrative resource. Its data-driven approach to task creation and individualized support elevates student performance and equips instructors with a transparent, efficient method of managing academic progress. As the system evolves, its blend of personalization and effortless administration promises to play an increasingly central role in creating a more student-centered higher education landscape.

Keywords: Knowledge Tracing, e-learning platform, eTRainer.