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      Pandemic response policies’ democratizing effects on online learning

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          Significance

          In the face of rising university tuition costs and a longstanding skills gap in the US workforce, a growing number of people access higher and continuing education programs via online platforms. There are serious concerns that online learning disadvantages members of underserved communities, thereby exacerbating social inequalities. However, it is hard to evaluate these concerns at scale partly due to selection effects. Policy responses to the COVID-19 pandemic, such as nonessential business closures, suddenly changed how people spent their time, which allowed us to estimate effects on demand for online learning and how it varies along socioeconomic dimensions. Unlike most prior studies that find education technology to maintain or amplify inequities, we present causal evidence for its potential democratizing effects.

          Abstract

          The COVID-19 pandemic has changed peoples’ lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 ( n = 277,425). Exploiting the staggered adoption of actions to mitigate the spread of COVID-19 across states, we identify the causal effect at the neighborhood level. The adoption of nonessential business closures led to a 38% increase in new users and a 6% increase in engagement among existing users. We find that these increases are proportional across higher- and lower-income neighborhoods and neighborhoods with a high or low share of Black residents. This demonstrates the potential for online platforms to democratize access to knowledge and skills that are in high demand, which supports job security and facilitates social mobility.

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          Most cited references16

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          Suicide Mortality and Coronavirus Disease 2019—A Perfect Storm?

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            Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples.

            Increasingly, investigators append census-based socioeconomic characteristics of residential areas to individual records to address the problem of inadequate socioeconomic information on health data sets. Little empirical attention has been given to the validity of this approach. The authors estimate health outcome equations using samples from nationally representative data sets linked to census data. They investigate whether statistical power is sensitive to the timing of census data collection or to the level of aggregation of the census data; whether different census items are conceptually distinct; and whether the use of multiple aggregate measures in health outcome equations improves prediction compared with a single aggregate measure. The authors find little difference in estimates when using 1970 compared with 1980 US Bureau of the Census data or zip code compared with tract level variables. However, aggregate variables are highly multicollinear. Associations of health outcomes with aggregate measures are substantially weaker than with microlevel measures. The authors conclude that aggregate measures can not be interpreted as if they were microlevel variables nor should a specific aggregate measure be interpreted to represent the effects of what it is labeled.
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              Democratizing education? Examining access and usage patterns in massive open online courses.

              Massive open online courses (MOOCs) are often characterized as remedies to educational disparities related to social class. Using data from 68 MOOCs offered by Harvard and MIT between 2012 and 2014, we found that course participants from the United States tended to live in more-affluent and better-educated neighborhoods than the average U.S. resident. Among those who did register for courses, students with greater socioeconomic resources were more likely to earn a certificate. Furthermore, these differences in MOOC access and completion were larger for adolescents and young adults, the traditional ages where people find on-ramps into science, technology, engineering, and mathematics (STEM) coursework and careers. Our findings raise concerns that MOOCs and similar approaches to online learning can exacerbate rather than reduce disparities in educational outcomes related to socioeconomic status.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                16 March 2021
                11 March 2021
                11 March 2021
                : 118
                : 11
                : e2026725118
                Affiliations
                [1] aDepartment of Information Science, Cornell University , Ithaca, NY 14853;
                [2] bW. P. Carey School of Business, Arizona State University , Tempe, AZ 85287;
                [3] cSloan School of Management, Massachusetts Institute of Technology , Cambridge, MA 02142;
                [4] dDepartment of Policy Analysis and Management, Cornell University , Ithaca, NY 14853
                Author notes
                1To whom correspondence may be addressed. Email: kizilcec@ 123456cornell.edu .

                Edited by Douglas S. Massey, Princeton University, Princeton, NJ, and approved February 8, 2021 (received for review December 29, 2020)

                Author contributions: R.F.K., C.A.M., and K.C.S. designed research, performed research, analyzed data, and wrote the paper.

                2R.F.K., C.A.M., and K.C.S. contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-6283-5546
                https://orcid.org/0000-0002-6547-5897
                Article
                202026725
                10.1073/pnas.2026725118
                7980394
                33707215
                07a0f8e0-a4e8-41fc-9ab9-1e7942008de6
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 7
                Categories
                432
                Social Sciences
                Social Sciences

                online learning,equity,future of work,covid-19
                online learning, equity, future of work, covid-19

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