LMS-FAIR-India

LMS-FAIR-India is a FAIR-compliant synthetic dataset designed to facilitate privacy-preserving research in learning analytics, artificial intelligence, and educational data science. The dataset represents realistic Learning Management System (LMS) interaction patterns derived from a private university academic environment while ensuring that no personally identifiable information (PII) or real user data is exposed. The dataset includes multi-dimensional academic and behavioral records such as student demographics (synthetically generated), course metadata, enrollment mappings, LMS interaction logs (e.g., login events, content access, assignment submissions), assessment outcomes, and derived engagement metrics. These data components are structured in a relational format and accompanied by standardized schemas and metadata to ensure interoperability and reuse. A hybrid synthetic data generation approach was employed, combining statistical distribution modeling and rule-based simulation to preserve the structural, temporal, and behavioral characteristics of real-world LMS systems. The dataset maintains statistical fidelity and supports downstream machine learning and learning analytics applications without compromising user privacy. LMS-FAIR-India adheres to the FAIR principles: Findable: Assigned a persistent identifier (DOI) and indexed metadata Accessible: Freely available via an open repository Interoperable: Provided in standard formats (CSV/JSON) with schema definitions Reusable: Includes comprehensive documentation, data dictionary, and usage guidelines This dataset is intended for researchers, educators, and developers working on: Learning analytics and student behavior modeling AI/ML applications in education Privacy-preserving data mining Educational data benchmarking and reproducibility studies By providing a realistic yet privacy-safe alternative to sensitive educational datasets, LMS-FAIR-India contributes to advancing open science and enabling responsible data sharing in academic ecosystems.