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IoT 24(1):

Editorial

Interlink Platform for School, Higher and Technical Education in India: Design Platform

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  • @ARTICLE{10.4108/eetiot.6152,
        author={Nitesh Ghodichor and Pratham Chopde and Mansi Choudhari and Saloni Rangari and Pratham Badge},
        title={Interlink Platform for School, Higher and Technical Education in India: Design Platform},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2025},
        month={4},
        keywords={Performance Analysis, Interlinked Platform, Dropout Prediction, Educational},
        doi={10.4108/eetiot.6152}
    }
    
  • Nitesh Ghodichor
    Pratham Chopde
    Mansi Choudhari
    Saloni Rangari
    Pratham Badge
    Year: 2025
    Interlink Platform for School, Higher and Technical Education in India: Design Platform
    IOT
    EAI
    DOI: 10.4108/eetiot.6152
Nitesh Ghodichor1,*, Pratham Chopde2, Mansi Choudhari2, Saloni Rangari2, Pratham Badge2
  • 1: SRK University
  • 2: Priyadarshini College of Engineering
*Contact email: niteshgho@gmail.com

Abstract

This research paper aims to fill the knowledge gap in understanding the factors that contribute to academic performance and dropout rates in the Indian education system. The study proposes a “Interlinked platform for school, higher and technical education in India”, a unified digital space for students, educators, administrators and government agencies. The platform is designed to track students' academic performance across different educational levels and visualize dropout rates. The research uses a comprehensive methodology that integrates data analysis techniques and visualization frameworks and uses Python libraries such as NumPy, Pandas, Matplotlib and Scikit-Learn. Student academic outcomes are analyzed using linear regression and K-means clustering, and dropout rates are predicted using logistic regression. The aim of the research is to provide institutions with valuable insights to understand the factors that contribute to dropout rates and to develop targeted interventions to address potential triggers of dropout.

Keywords
Performance Analysis, Interlinked Platform, Dropout Prediction, Educational
Received
2024-05-23
Accepted
2025-03-22
Published
2025-04-13
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.6152

Copyright © 2025 N. Ghodichor et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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