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

Analysis of academic performance and dropout rates in the Indian education system

Authors

  • Nitesh Ghodichor SRK University https://orcid.org/0000-0001-5391-1914
  • Pratham Chopde Priyadarshini College of Engineering
  • Mansi Choudhari Priyadarshini College of Engineering
  • Saloni Rangari Priyadarshini College of Engineering
  • Pratham Badge Priyadarshini College of Engineering

DOI:

https://doi.org/10.4108/eetiot.6152

Keywords:

Performance Analysis, Interlinked Platform, Dropout Prediction, Educational

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.

Downloads

<br data-mce-bogus="1"> <br data-mce-bogus="1">

References

Amal Alhadabi & Aryn C. Karpinski (2020) Grit, self-efficacy, achievement orientation goals, and academic performance in University students, International Journal of Adolescence and Youth, 25:1, 519-535, DOI: 10.1080/02673843.2019.1679202

Nabil, Aya & Seyam, Mohammed & Abou-Elfetouh, Ahmed. (2021). Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks. IEEE Access. PP. 1- 1. 10.1109/ACCESS.2021.3119596.

Tadese M, Yeshaneh A, Mulu GB. Determinants of good academic performance among university students in Ethiopia: a cross-sectional study. BMC Med Educ. 2022 May 23;22(1):395. doi: 10.1186/s12909-022-03461-0. PMID: 35606767; PMCID: PMC9125903.

Chin-Wei Teoh, Sin-Ban Ho, Khairi Shazwan Dollmat, and Ian Chai. 2022. An Evolutionary Algorithm-Based Optimization Ensemble Learning Model for Predicting Academic Performance. In Proceedings of the 2022 11th International Conference on Software and Computer Applications (ICSCA '22). Association for Computing Machinery, New York, NY, USA, 102–107. https://doi.org/10.1145/3524304.3524320.

Albreiki, B., Zaki, N., & Alashwal, H. (2021, September 16). A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552

G. Feng, M. Fan and Y. Chen, "Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining," in IEEE Access, vol. 10, pp. 19558-19571, 2022, doi: 10.1109/ACCESS.2022.3151652.

Abdul Bujang, S. D., Selamat, A., Krejcar, O., Mohamed, F., Cheng, L. K., Chiu, P. C., & Fujita, H. (2023). Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review. IEEE Access, 11, 1970–1989. https://doi.org/10.1109/access.2022.3225404

Chamberlin, Kelsey & Yasué, Maï & Chiang, I-Chant. (2018). The impact of grades on student motivation. Active Learning in Higher Education. 24. 10.1177/1469787418819728.

Bucos, Marian & Drăgulescu, B.. (2018). Predicting student success using data generated in traditional educational environments. TEM Journal. 7. 617-625. 10.18421/TEM73-19.

Sadasivam, R., Paramasivam, S., Prakash raj, N., & Saravanan, M. (2022). Students career prediction. International Journal of Health Sciences, 6(S5), 1357–1365. https://doi.org/10.53730/ijhs.v6nS5.8883

Garg, Mausam & Chowdhury, Poulomi & Sheikh, Illias. (2023). Determinants of school dropouts in India: a study through survival analysis approach. Journal of Social and Economic Development. 10.1007/s40847-023-00249-w.

Mr. Basavaraj Biradar, Dr. S.Y. SWADI, Reviews on school dropouts and research gap. International Journal of Research and Analytical Reviews (IJRAR).

Kirazoğlu, Cem. (2009). The investigation of school-dropout at the secondary level of formal education: the stated reasons by the school administrators and school counselors: a preliminary study. Procedia - Social and Behavioral Sciences. 1. 905-914. 10.1016/j.sbspro.2009.01.161.

Buchhorn, J., & Wigger, B. U. (2021). Predicting Student Dropout: A Replication Study Based on Neural Networks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3929194

Garcia-Zanabria, G.; Gutierrez-Pachas, D.A.; Camara-Chavez, G.; Poco, J.; Gomez-Nieto, E. SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration. Appl. Sci. 2022, 12, 5785. https://doi.org/ 10.3390/app12125785

Ladeira, Pedro & de Lima, Leandro & Krohling, Renato. (2022). A visualization tool for data analysis on higher education dropout: a case study at UFES.

Downloads

Published

13-04-2025

How to Cite

[1]
N. Ghodichor, P. Chopde, M. Choudhari, S. Rangari, and P. Badge, “Interlink Platform for School, Higher and Technical Education in India: Design Platform: Analysis of academic performance and dropout rates in the Indian education system”, EAI Endorsed Trans IoT, vol. 11, Apr. 2025.

Issue

Section

IoT, Machine Learning and Data Analytics for Smart Environment