Teachers’ Beliefs on the use of Digital Technologies at School: Text Mining of Open-Ended Questions

In the era of digital plenitude, educational institutions face persistent challenges in aligning their strategies with technological advancements, adopting a learning-ecosystem perspective. Following the digital overload imposed by the pandemic, teachers consider technology integration indispensable, but their extensive use in schools has also triggered resistance attitudes. This study aims to explore teachers’ beliefs on the use of digital technologies in education, focusing on three main aspects: (1) the perceived effectiveness of digital tools, (2) their impact on learning, and (3) the challenges related to their integration in teaching practices. To achieve this, we analyze 898 open-ended responses, collected through a questionnaire concerning digital learning ecosystems. To automate the analysis of open-ended responses, we adopt a Structural Topic Model (STM). STM, an extension of the Latent Dirichlet Allocation (LDA) topic modeling technique, integrates features from a correlated topic model and the sparse additive generative topic model. The underlying assumption of STM remains that the documents are based on a combination of topics. However, the model allows correlations between topic proportions, enabling exploration of how both the prevalence of topics and their associated content may vary based on covariates. The first results concern the identification of some themes that clearly fit with what the recent literature highlights, including the interplay between digital education and early childhood, as well as the pressing issue of teacher training in technology; the possibility of clustering teachers’ postures and attitudes according to their beliefs.