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Investigating student engagement, learning, and attendance during the COVID-19 disruption: a machine learning analysis of online, hybrid, and offline learning modes

Crossref DOI link: https://doi.org/10.1186/s40536-026-00292-z

Published Online: 2026-03-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Kalelioğlu, Filiz

Acun Çelik, Sıla
License Information

Text and Data Mining valid from 2026-03-12

Version of Record valid from 2026-03-12
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Article History

Received: 28 November 2025

Accepted: 16 February 2026

First Online: 12 March 2026

Declarations

:

: This research was a quantitative synthesis of publicly available material. Thus, ethical approval from the authors’ Institutional Review Board was not required.

: The authors declare no competing interests.

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