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Students’ feedback on the digital ecosystem: a structural topic modeling approach

  • Adelia Evangelista
  • Annalina Sarra
  • Tonio Di Battista

Starting from March 2020, strict containment measures against COVID-19 forced the Italian Universities to activate remote learning and supply didactic methods online. This work is aimed at showing students’ perceptions towards a learning-teaching experience practised within a digital learning ecosystem designed in the period of first emergency and then re-proposed for the blended mode. Specifically, students, attending six teaching large courses held by four professors in two different Italian universities, were asked to express their impression in a text guided by questions, requiring the reflections and clarification of their and inner deep thoughts on the ecosystem. To automate the analysis of the resulting open-ended responses and avoid a labour-intensive human coding, we focused on a machine learning approach based on structural topic modelling (STM). Alike to Latent Dirichlet Allocation model (LDA), STM is a probabilistic generative model that defines a document generated as a mixture of hidden topics. In addition, STM extends the LDA framework by allowing covariates of interest to be included in the prior distributions for open-ended-response topic proportions and topic word distributions. Based on model diagnostics and researchers’ expertise, a 10-topic model is best fitted the data. Prevalent topics described by respondents include: “Physical space”, “Bulding the community: use of Whatsapp”, “Communication and tools”, “Interaction with Teacher”, “Feedback”.

  • Keywords:
  • Student feedback,
  • digital learning ecosystem,
  • open-ended questions,
  • pandemic context,
  • structural topic models,
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Adelia Evangelista

University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-7596-9719

Annalina Sarra

University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-0974-0799

Tonio Di Battista

University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0003-2139-7273

  1. Blei, D. M., Ng, A. Y. , Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, pp. 993–1022.
  2. Carrillo, C., Flores, M.A. (2020). Covid-19 and teacher education: a literature review of online teaching and learning practices. European Journal of Teacher Education, 43, pp. 466–487.
  3. Chang, V., Fisher, D. (2003). The validation and application of a new learning environment instrument for online learning in higher education, in Technology-rich learning environments: A future perspective, eds. M.S. Khine, D. Fisher , London: World Scien
  4. Kenneth, B.,Watanabe, K.,Wang, H., Nulty, P., Obeng, A. , M¨uller, S., Matsuo, A. (2018). quanteda: an R package for the quantitative analysis of textual data. Journal of Open Source Software, 3 (30), pp. 774.
  5. Kuhn, K. D. (2018) Topics and trends in aviation incident reports. Transportation Research Part C: Emerging Technologies, 87, pp. 105–122.
  6. Pereira, S.P., Fernandes, R.L., Flores, M.A. (2021) Teacher education during the covid-19 lockdown: insights from a formative intervention approach involving online feedback. Education Sciences, 11 (400), pp. 1–14.
  7. Pennycook, A. (2005). Teaching with the flow: fixity and fluidity in education. Asia Pacific Journal of Education, 25(1), pp. 29–43.
  8. R Core Team (2022). R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.
  9. Rivoltella, P. C. (2021). Apprendere a distanza. Teorie e metodi, Raffaello Cortina Editore. Milano, Italia.
  10. Roberts, M. E., Stewart, B. M., Tingley, D., Airoldi, E. M. (2013). The structural topic model and applied social science, in Advances in neural information processing systems workshop on topic models: computation, application, and evaluation.
  11. Roberts, M. E., Stewart, B. M., Tingley, D. (2019). stm: an R package for structural topic models. Journal of Statistical Software, 91(2). DOI: 10.18637/jss.v091.i02
  12. Rodriguez, M. Y., Storer, H. (2019). stm: a computational social science perspective on qualitative data exploration: using topic models for the descriptive analysis of social media data. Journal of Technology in Human Services, 38(1), pp. 54–86.
  13. Rothschild, J. E., Howat, A. J., Shafranek, R. M. , Busby, E. C. (2019). Pigeonholing partisans: stereotypes of party supporters and partisan polarization. Political Behavior, 41(2), pp. 423–443.
  14. Selwyn, N., Jandri´c, P. (2020). Postdigital living in the age of covid-19: unsettling what we see as possible. Postdigital Science and Education, 2(3), pp. 989–1005.
  15. Zafari, B., Ekin, T. (2019). Topic modelling for medical prescription fraud and abuse detection. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(3), pp. 751–769.
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  • Publication Year: 2023
  • Pages: 203-208
  • Content License: CC BY 4.0
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  • Publication Year: 2023
  • Content License: CC BY 4.0
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Chapter Information

Chapter Title

Students’ feedback on the digital ecosystem: a structural topic modeling approach

Authors

Adelia Evangelista, Annalina Sarra, Tonio Di Battista

Language

English

DOI

10.36253/979-12-215-0106-3.36

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

ASA 2022 Data-Driven Decision Making

Book Subtitle

Book of short papers

Editors

Enrico di Bella, Luigi Fabbris, Corrado Lagazio

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press, Genova University Press

DOI

10.36253/979-12-215-0106-3

eISBN (pdf)

979-12-215-0106-3

eISBN (xml)

979-12-215-0107-0

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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