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A longitudinal analysis of Moodle log data during and after COVID-19: a cross-continental comparison of universities in France and Vietnam
* 1 , 2 , 3 , 4
1  Digital Economy and E-Commerce, Vietnam-Korea University of Information and Communication Technology, The University of Da Nang (UDN), Da Nang, 550000, Vietnam.
2  GREDEG (CNRS) - Groupe de Recherche en Droit, Économie, Gestion (Research group of Law, Economics and Management), Université Côte d’Azur, Nice, 06300 NICE, France.
3  L@UCA, Université Côte d’Azur, Nice, 06103 Nice CEDEX 2, France.
4  Laboratoire I3S (CNRS), Université Côte d’Azur, Nice, 06300 NICE, France.
Academic Editor: EMILIO ABAD-SEGURA

Abstract:

Introduction

Although learning management systems (LMSs) emerged in the late 1990s, their adoption in higher education accelerated sharply during the COVID-19 pandemic. The sudden shift to online teaching drew scholars’ attention to technology acceptance in crisis or emergency contexts (Alturki & Aldraiweesh, 2021). While the “new normal” appears to have influenced students’ LMS use (Misiejuk et al., 2023), continuance usage, understood as sustained post-adoption usage (Bhattacherjee, 2001), has received limited attention. Moreover, international comparisons remain relatively scarce and often insufficiently account for cultural and institutional differences, leaving an important research gap. This study therefore examines how students’ LMS behaviors differ across cultural contexts across two stages of environmental change: during and after COVID-19.

Method

This study conducts a longitudinal analysis of students’ behavior in an LMS, comparing two higher-education contexts (Carvalho et al., 2026): Université Côte d’Azur (France) and the University of Da Nang (Vietnam). We measure students’ Moodle use across two periods (during and after COVID-19), enabling us to observe behavioral change over time and examine dynamic relationships. Three analyses will be conducted: (1) behavioral analysis: intensity, diversity, and relative engagement; (2) trajectory modeling: multilevel growth-curve models and latent class growth analysis; (3) cross-institution comparison: comparing the distribution of trajectory types.

Result

Our expected contributions are twofold. First, we provide a typology of LMS-use trajectories that characterizes patterns of digital learning practices over time. Second, we offer evidence on how contextual factors (including learning culture, course design, and pedagogical practices) shape the sustainability of these practices in higher education.

Keywords: Higher education, LMS continuance use, Moodle logs data, Cultural contexts comparisons

 
 
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