- ASA 2022 Data-Driven Decision Making
- Edited by Enrico di Bella, Luigi Fabbris, Corrado Lagazio
Students’ feedback on the digital ecosystem: a structural topic modeling approach
- Adelia Evangelista
- Annalina Sarra
- Tonio Di Battista
- © 2023 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-0106-3.36
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,
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-7596-9719
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-0974-0799
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0003-2139-7273
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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
Metadata License
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
Metadata License
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