An Intelligent Platform for Real-Time Academic Progress Evaluation and Data-Driven Learning Enhancement
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Zenodo
2025
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| author | Jay, Kanjariya Harekrushn, Vaghashiya Niyati, Patel Devangini, Parmar , Associate Professor, Dr.Shailendra Kumar Mishra |
| author_facet | Jay, Kanjariya Harekrushn, Vaghashiya Niyati, Patel Devangini, Parmar , Associate Professor, Dr.Shailendra Kumar Mishra |
| contents | <p><strong><span lang="EN-US">The project, presents the design and implementation of an intelligent online academic progress evaluation system that combines state-of-the-art web technologies with advanced artificial intelligence to provide holistic insights into students’ academic performance. Unlike conventional platforms that pri- marily display static grades and scores, it leverages modern data processing and AI-driven analytics to convert raw educational data into actionable and personalized feedback. The system architecture integrates Next.js for building a responsive and dynamic frontend, supported by a Node.js backend that manages the core business logic. For persistent and scalable data storage, the platform employs NeonDB (PostgreSQL) in conjunction with Prisma ORM, ensuring efficient querying, data integrity, and seamless integration with modern development workflows. Authentication and authorization mechanisms emphasize security and ease of access through a hybrid model involving JWT (JSON Web Tokens), Passport.js, and federated identity via Google OAuth, thereby enhancing both reliability and user convenience. The AI capability of the system is realized through the GROQ API, integrated with the LLaMA-3.3-70b-versatile large language model. These models enable natural language understanding and generation, empowering the system to conduct tasks such as gen- erating personalized learning feedback, performing performance trend forecasting, and producing automated evaluation reports for educators and institutions. By interpreting performance records, assignment results, and behavioral data, the system provides tailored recommendations that support learner improvement, reduce educator workload, and aid institutions in achieving data- driven educational decisions. In essence, the platform bridges the gap between raw academic data and meaningful educational insights. It transforms academic evaluation from a traditional grade-centric process into a comprehensive, adaptive, and pre- dictive learning support system. This work demonstrates the potential of integrating modern web frameworks with advanced AI methodologies to enrich educational analytics, optimize stu- dent outcomes, and promote personalized learning experiences at scale</span></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17104283 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | An Intelligent Platform for Real-Time Academic Progress Evaluation and Data-Driven Learning Enhancement Jay, Kanjariya Harekrushn, Vaghashiya Niyati, Patel Devangini, Parmar , Associate Professor, Dr.Shailendra Kumar Mishra Academic progress evaluation, online assessment, student performance analytics, machine learning, AI-assisted education. <p><strong><span lang="EN-US">The project, presents the design and implementation of an intelligent online academic progress evaluation system that combines state-of-the-art web technologies with advanced artificial intelligence to provide holistic insights into students’ academic performance. Unlike conventional platforms that pri- marily display static grades and scores, it leverages modern data processing and AI-driven analytics to convert raw educational data into actionable and personalized feedback. The system architecture integrates Next.js for building a responsive and dynamic frontend, supported by a Node.js backend that manages the core business logic. For persistent and scalable data storage, the platform employs NeonDB (PostgreSQL) in conjunction with Prisma ORM, ensuring efficient querying, data integrity, and seamless integration with modern development workflows. Authentication and authorization mechanisms emphasize security and ease of access through a hybrid model involving JWT (JSON Web Tokens), Passport.js, and federated identity via Google OAuth, thereby enhancing both reliability and user convenience. The AI capability of the system is realized through the GROQ API, integrated with the LLaMA-3.3-70b-versatile large language model. These models enable natural language understanding and generation, empowering the system to conduct tasks such as gen- erating personalized learning feedback, performing performance trend forecasting, and producing automated evaluation reports for educators and institutions. By interpreting performance records, assignment results, and behavioral data, the system provides tailored recommendations that support learner improvement, reduce educator workload, and aid institutions in achieving data- driven educational decisions. In essence, the platform bridges the gap between raw academic data and meaningful educational insights. It transforms academic evaluation from a traditional grade-centric process into a comprehensive, adaptive, and pre- dictive learning support system. This work demonstrates the potential of integrating modern web frameworks with advanced AI methodologies to enrich educational analytics, optimize stu- dent outcomes, and promote personalized learning experiences at scale</span></strong></p> |
| title | An Intelligent Platform for Real-Time Academic Progress Evaluation and Data-Driven Learning Enhancement |
| topic | Academic progress evaluation, online assessment, student performance analytics, machine learning, AI-assisted education. |
| url | https://doi.org/10.5281/zenodo.17104283 |