One of the many drastic changes caused by Covid-19 was the quick implementation of distance learning which represented a great technological challenge to many teachers and students. In fact, Italy ranks 24th amongst the 27-EU member countries in digital competitiveness which testifies to the significant delays and gaps in basic digital skills amongst the population. Based on the difficulties encountered in organizing distance learning, we assumed that teachers' perceived stress increased. Given that transversal skills can be associated with this relationship, (...) we hypothesized that among these skills, self-efficacy mediated the relationship between the difficulties in organizing distance learning and perceived stress. Since we targeted teachers from Italy and other European countries, we also hypothesized that this mediator effect would be different for both samples. Our sample was composed of a total of 366 primary/middle school teachers of which 86% female. After doing a mediation analyses with Process, Hayes' Model 4, we confirmed H1 but not H2: resulting in a partial mediation effect of self-efficacy for each individual group of teachers. Since difficulties of distance learning can affect the perception of stress, we believe that the promotion of transversal skills, such as self-efficacy, can better equip teachers when facing stressful situations. (shrink)

More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized. In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N (...) = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design. (shrink)