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Quantifying the Impact of Severe Weather Conditions on Online Learning During the COVID-19 Pandemic

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Artificial Intelligence in Education (AIED 2021)

Abstract

From October to November 2020 the Philippines was struck by eight typhoons, two of which caused widespread flooding, utilities interruptions, property destruction, and loss of life. How did these severe weather conditions affect online learning participation of students pursuing their undergraduate and graduate studies in the midst of the COVID-19 pandemic? We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle Learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes were not significant. These findings suggested that students continued to invest their time and energy on activities that have a direct bearing on their final grades.

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References

  1. Brodersen, K.: CausalImpact: a new open-source package for estimating causal effects in time series. https://opensource.googleblog.com/2014/09/causalimpact-new-open-source-package.html. Accessed 25 Jan 2021

  2. Brodersen, K.H., Gallusser, F., Koehler, J., Remy, N., Scott, S.L.: Inferring causal impact using Bayesian structural time-series models. Ann. Appl. Stat. 9(1), 247–274 (2015)

    Article  MathSciNet  Google Scholar 

  3. Friedrich, A., Flunger, B., Nagengast, B., Jonkmann, K., Trautwein, U.: Pygmalion effects in the classroom: teacher expectancy effects on students’ math achievement. Contemp. Educ. Psychol. 41, 1–12 (2015)

    Article  Google Scholar 

  4. Google. CausalImpact. http://google.github.io/CausalImpact/CausalImpact.html. Accessed 29 Jan 2021

  5. IEA, TIMSS 2019 International Results in Mathematics and Science. https://timssandpirls.bc.edu/timss2019/international-results/wp-content/themes/timssandpirls/download-center/TIMSS-2019-International-Results-in-Mathematics-and-Science.pdf. Accessed 09 Jan 2021

  6. Lalu, G.P., Student group wants academic freeze until floods clear, internet fixed. https://newsinfo.inquirer.net/1361470/student-group-wants-academic-freeze-until-floods-clear-internet-is-fixed. Accessed 25 Jan 2021

  7. Larsen, K.: MarketMatching Package Vignette. https://cran.r-project.org/web/packages/MarketMatching/vignettes/MarketMatching-Vignette.html. Accessed 29 Jan 2021

  8. Philippines Department of Education, PISA 2018 National Report of the Philippines. https://www.deped.gov.ph/wp-content/uploads/2019/12/PISA-2018-Philippine-National-Report.pdf. Accessed 03 Dec 2020

  9. Szumski, G., Karwowski, M.: Exploring the Pygmalion effect: The role of teacher expectations, academic self-concept, and class context in students’ math achievement. Contemp. Educ. Psychol. 59, 101787 (2019)

    Article  Google Scholar 

  10. UNESCO. Philippines: Education and Literacy. http://uis.unesco.org/en/country/ph. Accessed 25 Jan 2021

  11. UNICEF & SEAMEO, SEA-PLM 2019 Main Regional Report, Children’s Learning in 6 Southeast Asian Countries. https://www.seaplm.org/index.php?option=com_content&view=article&id=44&Itemid=332. Accessed 03 Dec 2020

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Acknowledgements

We would like to thank Hiroyuki Kuromiya and Hiroaki Ogata of Kyoto University, the Ateneo Research Institute for Science and Engineering (ARISE), and the Ateneo Laboratory for the Learning Sciences for their support in this research.

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Correspondence to Ezekiel Adriel Lagmay or Ma. Mercedes T. Rodrigo .

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Lagmay, E.A., Rodrigo, M.M.T. (2021). Quantifying the Impact of Severe Weather Conditions on Online Learning During the COVID-19 Pandemic. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_41

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  • DOI: https://doi.org/10.1007/978-3-030-78270-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78269-6

  • Online ISBN: 978-3-030-78270-2

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