IDEAS home Printed from https://ideas.repec.org/a/ags/aaeatr/310265.html
   My bibliography  Save this article

Impact of COVID-19 - Related Transition to Online Instruction on Student Achievement

Author

Listed:
  • Holderieath, Jason J.
  • Crosby, Michael K.
  • McConnell, T. Eric
  • Jackson, D. Paul

Abstract

Distance education and online delivery of course materials are not new in the United States. However, the sudden mass movement of entire universities online is new. The COVID-19 pandemic forced many universities to move their instruction online, over a weekend in some cases. This article explores the effects on student achievement by estimating a Poisson model of course grade outcomes to find that Spring 2020 term was not statistically significant in its effects on students completing the course, passing the course, and earning an "A" in the course. Graphically analyzed, the data show a possibility of different types of effects for different students, courses, and professors. Further research with more data is needed to understand the effect entirely.

Suggested Citation

  • Holderieath, Jason J. & Crosby, Michael K. & McConnell, T. Eric & Jackson, D. Paul, 2021. "Impact of COVID-19 - Related Transition to Online Instruction on Student Achievement," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 3(1), March.
    • Handle: RePEc:ags:aaeatr:310265
    DOI: 10.22004/ag.econ.310265
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/310265/files/AETR_2020_029RRPDFIssue_v3.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.310265?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Melo, Grace & Sanhueza, Dérgica & Morales, Sarahi & Pena-Levano, Luis, 2021. "What does the Pandemic Mean for Experiential Learning? Lessons from Latin America," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 3(3), September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. MacKinnon, J G, 1989. "Heteroskedasticity-Robust Tests for Structural Change," Empirical Economics, Springer, vol. 14(2), pages 77-92.
    2. Gu, Chen & Kurov, Alexander & Wolfe, Marketa Halova, 2018. "Relief Rallies after FOMC Announcements as a Resolution of Uncertainty," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 1-18.
    3. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    4. Belke, Ansgar & Volz, Ulrich, 2018. "Capital flows to emerging market and developing economies: global liquidity and uncertainty versus country-specific pull factors," IDOS Discussion Papers 23/2018, German Institute of Development and Sustainability (IDOS).
    5. Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 701-712, October.
    6. Salisu, Afees A. & Adediran, Idris A., 2019. "Assessing the inflation hedging potential of coal and iron ore in Australia," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    7. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    8. Cooney, John W. & Moeller, Thomas & Stegemoller, Mike, 2009. "The underpricing of private targets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 51-66, July.
    9. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    10. Samii, Cyrus & Aronow, Peter M., 2012. "On equivalencies between design-based and regression-based variance estimators for randomized experiments," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 365-370.
    11. Fortin, Bernard & Lacroix, Guy & Montmarquette, Claude, 2000. "Are Underground Workers More Likely to be Underground Consumers?," Economic Journal, Royal Economic Society, vol. 110(466), pages 838-860, October.
    12. Richard H. Spady & Sami Stouli, 2018. "Simultaneous Mean-Variance Regression," Bristol Economics Discussion Papers 18/697, School of Economics, University of Bristol, UK.
    13. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    14. Bernd Hayo & Kentaro Iwatsubo, 2022. "Who Is Successful in Foreign Exchange Margin Trading? New Survey Evidence from Japan," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    15. Damian Kozbur, 2017. "Testing-Based Forward Model Selection," American Economic Review, American Economic Association, vol. 107(5), pages 266-269, May.
    16. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    17. repec:lan:wpaper:2454 is not listed on IDEAS
    18. repec:lan:wpaper:2375 is not listed on IDEAS
    19. Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1350-1361, July.
    20. Psaradakis, Zacharias & Sola, Martin, 1996. "On the power of tests for superexogeneity and structural invariance," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 151-175.
    21. Panos Pashardes & Nicoletta Pashourtidou, 2011. "Consumer welfare from publicly supplemented private goods: age and income effects on demand for health care," Empirical Economics, Springer, vol. 41(3), pages 865-885, December.
    22. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2017. "Sampling-based vs. Design-based Uncertainty in Regression Analysis," Papers 1706.01778, arXiv.org, revised Jun 2019.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaeatr:310265. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.