IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v92y2024ics0038012124000235.html
   My bibliography  Save this article

Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques

Author

Listed:
  • Ragni, Alessandra
  • Ippolito, Daniel
  • Masci, Chiara

Abstract

This study employs multilevel propensity score techniques in an innovative analysis pipeline to assess the impact of hybrid teaching – a blend of face-to-face and online learning – on student performance within engineering programs at Politecnico di Milano implemented in response to Covid-19 pandemic. Analyzing students’ credits earned and grade point average, the investigation compares outcomes of students engaged in hybrid teaching against those solely in face-to-face instruction preceding the Covid-19 waves. Tailored multilevel models for earned credits and grade point averages are fitted onto meticulously constructed dataframes, minimizing potential biases stemming from variables such as gender, age, previous instruction, admission scores, and student origins across the two groups. Accounting for program variations in the methodology, our findings suggest marginal overall disparities in student performance, indicating, on average, a subtle rise in earned credits and a slight decrease in grade point averages for those exposed to hybrid teaching. Multilevel models reveal significant program-specific variations, offering valuable insights into the effectiveness of hybrid teaching in diverse educational contexts.

Suggested Citation

  • Ragni, Alessandra & Ippolito, Daniel & Masci, Chiara, 2024. "Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:soceps:v:92:y:2024:i:c:s0038012124000235
    DOI: 10.1016/j.seps.2024.101824
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012124000235
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2024.101824?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duha Tore Altindag & Elif S. Filiz & Erdal Tekin, 2021. "Is Online Education Working?," NBER Working Papers 29113, National Bureau of Economic Research, Inc.
    2. Contini Dalit & Di Tommaso Maria Laura & Muratori Caterina & Piazzalunga Daniela & Schiavon Lucia, 2022. "Who Lost the Most? Mathematics Achievement during the COVID-19 Pandemic," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 22(2), pages 399-408, April.
    3. Eva Blondeel & Patricia Everaert & Evelien Opdecam, 2021. "And Then There Was COVID-19: Do the Benefits of Cooperative Learning Disappear When Switching to Online Education?," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    4. Orlov, George & McKee, Douglas & Berry, James & Boyle, Austin & DiCiccio, Thomas & Ransom, Tyler & Rees-Jones, Alex & Stoye, Jörg, 2021. "Learning during the COVID-19 pandemic: It is not who you teach, but how you teach," Economics Letters, Elsevier, vol. 202(C).
    5. Philipp Hansen & Lennart Struth & Max Thon & Tim Umbach, 2021. "The Impact of the COVID-19 Pandemic on Teaching Outcomes in Higher Education," ECONtribute Discussion Papers Series 073, University of Bonn and University of Cologne, Germany.
    6. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    7. Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
    8. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    9. Michael S. Kofoed & Lucas Gebhart & Dallas Gilmore & Ryan Moschitto, 2024. "Zooming to Class? Experimental Evidence on College Students' Online Learning during COVID-19," American Economic Review: Insights, American Economic Association, vol. 6(3), pages 324-340, September.
    10. Maria De Paola & Francesca Gioia & Vincenzo Scoppa, 2022. "Online Teaching, Procrastination And Students’ Achievement: Evidence From Covid-19 Induced Remote Learning," Working Papers 202202, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    11. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    12. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    13. Leonardo Grilli & Carla Rampichini, 2010. "Selection bias in linear mixed models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 309-329.
    14. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    15. King, Gary & Nielsen, Richard, 2019. "Why Propensity Scores Should Not Be Used for Matching," Political Analysis, Cambridge University Press, vol. 27(4), pages 435-454, October.
    16. Chenyin Gao & Shu Yang & Jae Kwang Kim, 2023. "Soft calibration for selection bias problems under mixed-effects models," Biometrika, Biometrika Trust, vol. 110(4), pages 897-911.
    17. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    18. T Gonzalez & M A de la Rubia & K P Hincz & M Comas-Lopez & Laia Subirats & Santi Fort & G M Sacha, 2020. "Influence of COVID-19 confinement on students’ performance in higher education," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-23, October.
    Full references (including those not matched with items on IDEAS)

    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. Hardt, David & Nagler, Markus & Rincke, Johannes, 2022. "Can peer mentoring improve online teaching effectiveness? An RCT during the COVID-19 pandemic," Labour Economics, Elsevier, vol. 78(C).
    2. Jasjeet Singh Sekhon & Richard D. Grieve, 2012. "A matching method for improving covariate balance in cost‐effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 695-714, June.
    3. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    4. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    5. Riccardo D'Alberto & Francesco Pagliacci & Matteo Zavalloni, 2023. "A socioeconomic impact assessment of three Italian national parks," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 114-147, January.
    6. Adeola Oyenubi & Martin Wittenberg, 2021. "Does the choice of balance-measure matter under genetic matching?," Empirical Economics, Springer, vol. 61(1), pages 489-502, July.
    7. Wang-Sheng Lee, 2013. "Propensity score matching and variations on the balancing test," Empirical Economics, Springer, vol. 44(1), pages 47-80, February.
    8. Wichman, Casey J. & Ferraro, Paul J., 2017. "A cautionary tale on using panel data estimators to measure program impacts," Economics Letters, Elsevier, vol. 151(C), pages 82-90.
    9. Emma Lappi, 2023. "Help from the past—coworker ties and entry wages after self-employment," Small Business Economics, Springer, vol. 60(3), pages 1171-1196, March.
    10. Badruddoza, Syed & Amin, Modhurima Dey, 2023. "Impacts of Teaching Modality on U.S. COVID-19 Spread in Fall 2020 Semester," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 5(1), January.
    11. Solomon Asfaw & Silvio Daidone & Benjamin Davis & Josh Dewbre & Alessandro Romeo & Paul Winters & Katia Covarrubias & Habiba Djebbari, 2012. "Analytical Framework for Evaluating the Productive Impact of Cash Transfer Programmes on Household Behaviour – Methodological Guidelines for the From Protection to Production Project," Working Papers 101, International Policy Centre for Inclusive Growth.
    12. Siddique Abu Bakkar, 2020. "Identity-based Earning Discrimination among Chinese People," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-42, January.
    13. Yuri M. Zhukov, 2014. "Theory of Indiscriminate Violence," Working Paper 365551, Harvard University OpenScholar.
    14. Maria De Paola & Francesca Gioia & Vincenzo Scoppa, 2022. "Online Teaching, Procrastination And Students’ Achievement: Evidence From Covid-19 Induced Remote Learning," Working Papers 202202, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    15. Cantone, Giulio Giacomo & Tomaselli, Venera, 2023. "Quasi-experimental network-based design for semantic analysis of small clusters of bi-polar online reviews," SocArXiv v7u3h, Center for Open Science.
    16. Kirk Bansak, 2021. "Estimating causal moderation effects with randomized treatments and non‐randomized moderators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 65-86, January.
    17. Baron, Opher & Callen, Jeffrey L. & Segal, Dan, 2023. "Does the bullwhip matter economically? A cross-sectional firm-level analysis," International Journal of Production Economics, Elsevier, vol. 259(C).
    18. Kube, Roland & von Graevenitz, Kathrine & Löschel, Andreas & Massier, Philipp, 2019. "Do voluntary environmental programs reduce emissions? EMAS in the German manufacturing sector," Energy Economics, Elsevier, vol. 84(S1).
    19. K. Poehlmann & R. Helm & O. Mauroner & J. Auburger, 2021. "Corporate spin-offs’ success factors: management lessons from a comparative empirical analysis with research-based spin-offs," Review of Managerial Science, Springer, vol. 15(6), pages 1767-1796, August.
    20. Akoh Fabien Yao & Maxime Sèbe & Laura Recuero Virto & Abdelhak Nassiri & Hervé Dumez, 2024. "The effect of LNG bunkering on port competitiveness using multilevel data analysis [L'effet du soutage par GNL sur la compétitivité des ports à l'aide de l'analyse de données à plusieurs niveaux]," Post-Print hal-04611804, HAL.

    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:eee:soceps:v:92:y:2024:i:c:s0038012124000235. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.