LVII Riunione Scientifica SIEDS - Società italiana di economia demografia e statistica
Session: Composite indicators.
The covid-19 pandemic has disrupted people’s lives and forced governments to impose great sacrifices on their citizens. One of the areas where the pandemic has imposed the greatest sacrifices is certainly education. To avoid the complete suspension of educational activities, it was necessary to adopt a completely different type of teaching than that traditionally used. Schools and Universities around the world have responded to the crisis by moving teaching activities online. While the enormous effort made to quickly make the transition to these new ways of teaching was certainly appreciated, the transition certainly had a major impact on the lives of students and teachers and their families.
The aim of this paper is to evaluate the impact of e-learning on the different types of students, in terms of preparation, characteristics and social background. The challenge is to explore if the switch from on-site to online learning caused by the emergence is exacerbating existing educational inequalities penalising more vulnerable students. The risk is that the social and economic conditions of families have a major influence on the e-learning experience because less advantaged students are less likely to have access to relevant learning digital resources (e.g. laptop/computer, broadband internet connection) and less likely to have a suitable home learning environment (e.g. a quiet place to study or their own desk).
The study is based on the analysis of data collected at the University of Naples Federico II in June 2020. More than 19,000 students took part in a survey, carried out to monitor distance learning activities and perceptions. The paper exploits a factorial method to obtain a composite indicator measuring the family impact of distance learning. Then, the family impact is analysed, trying to understand if it takes different forms and intensity depending on the students’characteristics, the availability of computer equipment and the type of teaching used. Finally, quantile regression allows to differentiate the study of effects for different levels of family impact. Some considerations on the preferred teaching method for the future and on the effects that the closure of universities has had on the performance of students are also enclosed.
The results, although in many cases expected, allow to quantify, and visualising heterogeneity in the conditions and characteristics of students. For example, the study quantifies the difference in the family impact among students who predominantly experienced a quiet e-learning experience without changing family habits (they already had all the equipment available for their exclusive use) and students who were forced to share both the workstation and the device with family members engaged in smart working or other learning activities. Moreover, quantile regression detects the different effects of socio-demographics and IT equipment in case of low, medium, or high family impact.