Chapter Students' feedback on the digital ecosystem: a structural topic modeling approach
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 e...
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Other Authors: | , |
Format: | Electronic Book Chapter |
Language: | English |
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Florence
Firenze University Press, Genova University Press
2023
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Series: | Proceedings e report
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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245 | 1 | 0 | |a Chapter Students' feedback on the digital ecosystem: a structural topic modeling approach |
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520 | |a 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". | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Society & social sciences |2 bicssc | |
653 | |a Student feedback | ||
653 | |a digital learning ecosystem | ||
653 | |a open-ended questions | ||
653 | |a pandemic context | ||
653 | |a structural topic models | ||
773 | 1 | 0 | |7 nnaa |o OAPEN Library UUID: ASA 2022 Data-Driven Decision Making |
856 | 4 | 0 | |a www.oapen.org |u https://library.oapen.org/bitstream/20.500.12657/74908/1/9791221501063-36.pdf |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/111711 |7 0 |z DOAB: description of the publication |