(2023) PCE 605-617

  • 16-19/06/2023
    An Italian large case study on Emergency Remote Teaching: factors and predictors which affect Higher Education students’ attitude
    Matteo Bozzi, Roberto Mazzola, Italo Testa, Juliana Elisa Raffaghelli, Susanna Sancassani, Maurizio Zani
    PCE, The Paris Conference on Education (Paris – France)
    Atti 605-617 (2023) [ISSN 2578-0962] – doi 10.22492/issn.2758-0962.2023.52



Research on concerns about Emergency Remote Teaching has focused on teaching and management strategies, with some studies considering learners’ satisfaction, reactions, learning and overall acceptance. The present large case study, based on a survey on 3,920 undergraduate and postgraduate learners, aimed at investigating Politecnico di Milano students’ self-reported experiences of the Emergency Remote Teaching after identifying the empirical factors characterising such experience and the predictors of the students’ responses.

Participants’ evaluation was expressed based on a five-point Likert scale, whereby a score equal to 3 corresponded to neutrality. We validated the questionnaire empirically through factor analysis. This questionnaire consisted of 66 items across 6 sections and focused on different latent variables as well as socio-economic information about the students.

Our findings highlighted both the students’ assessment of their overall online learning experience of Emergency Remote Teaching and the change in their metacognitive strategies and self-efficacy as a consequence of the new learning approach. These results did not appear to depend on the learners’ gender or their educational level of degree study, while the academic year of attendance seemed to influence their opinion on teaching.

Moreover, the change in the learning approach that learners experienced in the passage from bachelor to master’s programmes was discovered to be a further predictor which might be more critical for females than males.

Finally, implications for policy makers and higher education institutions for online learning in the post-pandemic scenario are discussed.