IDEAS home Printed from https://ideas.repec.org/a/vrs/manmar/v18y2023is1p315-339n7.html
   My bibliography  Save this article

Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach

Author

Listed:
  • Campeanu Emilia Mioara

    (1 Bucharest University of Economic Studies, Bucharest, Romania)

  • Boitan Iustina Alina

    (2 Bucharest University of Economic Studies, Bucharest, Romania)

  • Anghel Dan Gabriel

    (3 Bucharest University of Economic Studies, Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania)

Abstract

Fostering student engagement to acquire knowledge and achieve academic performance requires understanding how students engage in learning and its influence on academic achievement. This provides valuable insights that help improve learning experiences and outcomes. The paper relies on a mixed methods approach by expanding the traditional dimensions of student engagement and by employing a machine learning framework to identify which specific dimension of student engagement exhibits the main impact on student academic achievement. A questionnaire-based survey is conducted for the period 2020-2021 among a cohort of Romanian students. The outcomes of this preliminary exploratory analysis are further embedded into a machine learning framework by performing a LASSO regression. The findings reveal that the most relevant dimensions of student engagement, during remote education, that contribute the most to outcomes were represented by the behavioural, social, cognitive, and emotional engagement dimensions. Furthermore, the switch to online education appeared to have inverted the positive relationship between social and cognitive engagement and academic achievement. Despite the inherent challenges, the student’s interest in class participation and homework completion was stimulated, and they managed to adapt without difficulty to study independently.

Suggested Citation

  • Campeanu Emilia Mioara & Boitan Iustina Alina & Anghel Dan Gabriel, 2023. "Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach," Management & Marketing, Sciendo, vol. 18(s1), pages 315-339, December.
    • Handle: RePEc:vrs:manmar:v:18:y:2023:i:s1:p:315-339:n:7
    DOI: 10.2478/mmcks-2023-0017
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/mmcks-2023-0017
    Download Restriction: no
    File URL: https://libkey.io/10.2478/mmcks-2023-0017?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. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    3. Alina Mihaela Dima & Mihail Busu & Vanesa Madalina Vargas, 2022. "The mediating role of students’ ability to adapt to online activities on the relationship between perceived university culture and academic performance," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1253-1281, December.
    4. Agoestina Mappadang & Khusaini Khusaini & Melan Sinaga & Elizabeth Elizabeth, 2022. "Academic interest determines the academic performance of undergraduate accounting students: Multinomial logit evidence," Cogent Business & Management, Taylor & Francis Journals, vol. 9(1), pages 2101326-210, December.
    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. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    2. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    3. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    4. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    5. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    6. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    7. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    8. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    9. Perrot-Dockès Marie & Lévy-Leduc Céline & Chiquet Julien & Sansonnet Laure & Brégère Margaux & Étienne Marie-Pierre & Robin Stéphane & Genta-Jouve Grégory, 2018. "A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-14, October.
    10. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    11. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    12. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    13. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    14. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    16. Hui Xiao & Yiguo Sun, 2020. "Forecasting the Returns of Cryptocurrency: A Model Averaging Approach," JRFM, MDPI, vol. 13(11), pages 1-15, November.
    17. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    18. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
    19. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    20. Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.

    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:vrs:manmar:v:18:y:2023:i:s1:p:315-339:n:7. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.