Draft:E-khool LMS
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| Type | Private |
|---|---|
| Industry | IT |
| Founded | 13-01-2016 |
| Headquarters | Technopark, Trivandrum |
| Brands | e-KHOOL |
| Parent | Resbee Info Technologies Private Limited |
| Website | https://ekhool.com/ |
e-KHOOL is a cloud-based Learning Management System (LMS) that provides an integrated system for digital learning, learner management, assessment, and educational analytics across academic, training, and enterprise environments.[1]. In addition to conventional LMS functions, e-KHOOL offers data from the platform for the reserach studies related to artificial intelligence, educational data mining, learning analytics, recommender systems, learner performance prediction, and course sentiment analysis[2][3][4][5][6][7][8].
History
[edit]e-KHOOL LMS was developed by Resbee Info Technologies Pvt. Ltd., an Indian software company established in 2016[1]. The LMS software has evolved from a traditional learning management system into a research-supported digital learning environment, offering datasets and application scenarios for research studies in AI-assisted education, personalized learning, course recommendation, and learner analytics[2][3][5][6]. Also, academic books and peer-reviewed research publications[2][3][5][1] frequently cited the e-KHOOL LMS due to the usage of data for their research studies.
Platform
[edit]The platform provides web-based and mobile-accessible learning management capabilities, including:[8]
- Course and programme management
- Learning content delivery
- Online assessments and examinations
- Assignment management
- Question bank management
- Discussion forums
- Certificate generation
- Learning analytics and reporting
- Learner and trainer portals
- Notification management
- Institution administration
These functional components play a major role to collect the dataset useful for the research involving educational analytics, personalized learning, and recommendation systems[2][3][4][5].
Research
[edit]e-KHOOL LMS cited as an experimental platform in multiple peer-reviewed studies investigating artificial intelligence and educational technology. Research associated with the platform includes:
- Course recommendation systems[2][4][5][6].
- Learning analytics and learner behaviour analysis[5][8].
- Learner performance prediction using deep learning models[3][7].
- Educational data mining[3][5].
- Sentiment analysis for personalized course recommendation[5].
- Cloud-based collaborative learning environments[1].
- Time-series modelling for student performance prediction[6]
- Evaluation of learning environments and learner outcomes[8].
Several published studies utilized anonymized learner interaction data from the platform to analyse recommendation algorithms, clustering techniques, deep learning models, and educational intelligence frameworks[2][3][5][6].
Intellectual Property
[edit]Due to the effective research studies, e-KHOOL LMS received multiple patent grants and intellectual property filings related to learning management, educational analytics, artificial intelligence, and recommendation technologies[9]
- ^ a b c d D, Binu; B R, Rajakumar (2021). Artificial Intelligence in Data Mining. Elsevier Academic Press. doi:10.1016/C2019-0-01255-1. ISBN 978-0-12-820601-0.
- ^ a b c d e f Naik, N. Venkatesh; K, Madhavi (2024). "Collaborative E-Learning Application with Course Recommendation in Cloud Computing". International Journal of Software Engineering and Knowledge Engineering. 23 (6). doi:10.1142/S0219649224500886.
- ^ a b c d e f g Alzubi, Ahmad (2022). "Learner Performance Prediction in the E-Learning Platform Using the Optimized Deep Long Short-Term Memory Classifier". International Journal of Pattern Recognition and Artificial Intelligence. 20 (2). doi:10.1142/S021969132150051X.
- ^ a b c A, Madhavi; A, Nagesh (2023). "FLICM Clustering with Matrix Factorization Based Course Recommendation in an E-learning Platform". Web Intelligence. 21 (4). doi:10.3233/WEB-220121.
- ^ a b c d e f g h i P, Vijaya; M, Selvi (2022). "An Approach Using E-Khool User Log Data for E-Learning Recommendation System". International Journal of Software Engineering and Knowledge Engineering. 21 (3). doi:10.1142/S0219649222500411.
- ^ a b c d e A, Madhavi; A, Nagesh (2024). "Sentiment-Based Hierarchical Deep Learning Framework Using Hybrid Optimization for Course Recommendation in E-learning". Complex & Intelligent Systems. 12: 1661–1690. doi:10.1007/s40745-024-00580-x.
- ^ a b Radhakrishnan, Saravanan; V, Vijayarajan (2025). "NARX_CNN: Hybrid Deep Learning Approach for Student's Performance Prediction Using Time Series Data". SN Computer Science. 6: 543. doi:10.1007/s42979-025-04047-5.
- ^ a b c d M. P. J., Santosh Kumar; Kolli, Chandra Sekhar (2026). "Triangular Evaluation of Learning Environments: Correlations and Outcome". Journal of Information & Knowledge Management. (17): 4. doi:10.1142/S1793962326500200.
- ^ "e-KHOOL LMS Patents".
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