Explore Long- and Short-Run COVID-19 Impacts(6)

Abstract

EdTech which includes online education, computer assisted learning (CAL), and remote instruction was expanding rapidly even before the current full-scale substitution for in-person learning at all levels of education around the world because of the coronavirus pandemic. Studies of CAL interventions have consistently found large positive effects, bolstering arguments for the widespread use of EdTech. However CAL programs, often held after school, provide not only computer-based instruction, but often additional non-technology based inputs such as more time on learning and instructional support by facilitators. In this paper, we develop a theoretical model to carefully explore the possible channels by which CAL programs might affect academic outcomes among schoolchildren. We isolate and test the technology-based effects of CAL and additional parameters from the theoretical model, by designing a novel multi-treatment field experiment with more than four thousand schoolchildren in rural China. Although we find evidence of positive overall CAL program effects on academic outcomes, when we isolate the technology-based effect of CAL (over and above traditional pencil-andpaper learning) we generally find small to null effects. Our empirical results suggest that, at times, the “Tech” in EdTech may have relatively small effects on academic outcomes, which has important implications for the continued, rapid expansion of technologies such as CAL throughout the world.

原文链接：

https://www.nber.org/papers/w26953 .pdf

New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighborhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighborhoods, as well as in neighborhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-randomness in testing across New York City neighborhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.