Minerva: An Intelligent Education Platform for Predicting Students' Academic Risks Using Machine Learning

Minerva is an intelligent education platform based on machine learning technology, with core functions of predicting students' academic risks and helping teachers analyze student performance in real time. The platform uses a full-stack architecture, including a Web application, Android mobile application, and shared backend services, supporting multi-terminal collaboration. The project is maintained by pach24, sourced from GitHub (link: https://github.com/pach24/Minerva), and released on 2026-06-16. Its core value lies in enabling teachers to shift from passive response to active intervention in students' academic issues through data-driven decision support.

In the wave of digital transformation, the education sector faces important issues such as identifying students with academic difficulties and providing data-driven decision support. The Minerva project emerged as an intelligent analysis platform designed specifically for educational scenarios, building a data bridge between teachers and students through machine learning technology to meet this era's needs.

Minerva uses a modern full-stack architecture, including three core components: Web application, Android mobile application, and shared backend services. The backend serves as the data hub, processing multi-terminal requests, executing machine learning model inference, and returning results; the Web application provides rich data visualization interfaces (such as class trends, individual risk scores, etc.); the Android application emphasizes portability, supporting viewing student status anytime, anywhere. The multi-terminal architecture ensures data consistency and real-time performance, allowing access to the latest information regardless of the device used.

Minerva's machine learning model is optimized for the time-series characteristics of educational data, capable of capturing dynamic changes in student performance (such as combinations of risk indicators like declining grades, reduced attendance, delayed homework, etc.). Compared with traditional rule-based evaluation, the model can detect complex correlations that are difficult for humans to perceive—for example, fluctuations in specific subject grades plus changes in library visit frequency may indicate upcoming difficulties for students.

Minerva brings multiple values to frontline teachers: 1. Reduces time spent on data organization and analysis, allowing teachers to focus more on teaching; 2. The risk early warning function helps shift from 'firefighting' passive response to 'preventive' active intervention; 3. Makes educational decisions more objective and data-driven, providing data support for teachers' professional judgments and improving the precision of intervention measures.

Minerva represents an important development direction of educational technology, but it also faces challenges such as data privacy protection, algorithm fairness, and avoiding over-reliance on technology while neglecting humanistic care. Successful educational intelligence should be a tool to assist teachers rather than replace them. In the future, we look forward to more similar innovative projects that transform the potential of artificial intelligence into actual improvements in educational quality.