Early student dropout detection in Indian secondary education with special reference to selected districts in Tamil Nadu: a machine learning-based survival analysis approach

Author:

Venkatesan Raghul GandhiORCID,Mappillairaju BagavandasORCID

Publisher

Springer Science and Business Media LLC

Reference71 articles.

1. Venkatesan, R. G., & Mappillairaju, B. (2023). Detection of hotspots of school dropouts in India: A spatial clustering approach. PLoS One, 18(1), e0280034.

2. Department of School Education and Literacy M of E. District Information System for Education [Internet]. Government of India (2023). https://www.education.gov.in/sites/upload_files/mhrd/files/statistics-new/udise_21_22.pdf.

3. India, R. G. (2001). Census of India 2001. Provisional Popul Total New Delhi.

4. Banerji, M., & Mathur, K. (2021). Understanding school attendance: The missing link in schooling for all. Int J Educ Dev, 87, 102481.

5. Arthi, D., Rajalakshmi, M., & Ganapathy, K. Reasons for school dropouts in suburban areas near Villupuram district: A retrospective study. J Curr Res Sci Med [Internet]. 9900; https://journals.lww.com/jcsm/fulltext/9900/reasons_for_school_dropouts_in_suburban_areas_near.19.aspx.

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