Statistical methods to estimate the impact of remote teaching on university students’ performance

Autor: Bruno Bertaccini, Simone Del Sarto, Silvia BACCI, CARLA RAMPICHINI, Leonardo Grilli
Rok vydání: 2023
Předmět:
Zdroj: Quality & Quantity.
ISSN: 1573-7845
0033-5177
Popis: The COVID-19 pandemic manifested around the World since February 2020, leading to disruptive effects on many aspects of people social life. The suspension of face-to-face teaching activities in schools and universities was the first containment measure adopted by the Governments to deal with the spread of the virus. Remote teaching has been the emergency solution implemented by schools and universities to limit the damages of schools and universities closure to students’ learning. In this contribution we intend to suggest to policy makers and researchers how to assess the impact of emergency policies on remote learning in academia by analysing students’ careers. In particular, we exploit the quasi-experimental setting arising from the sudden implementation of remote teaching in the second semester of academic year 2019/2020: we compare the performance of the cohort 2019/2020, which represents the treatment group, with the performance of the cohort 2018/2019, which represents the control group. We distinguish the impact of remote teaching at two levels: degree program and single courses within a degree program. We suggest to use Difference-In-Differences approach in the former case and multilevel modeling in the latter one. The proposal is illustrated analysing administrative data referred to freshmen of cohorts 2018/2019 and 2019/2020 for a sample of degree programs of the University of Florence (Italy).
Databáze: OpenAIRE
Externí odkaz: