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 ecosys...
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| Formatua: | Online |
| Hizkuntza: | ingelesa |
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Firenze University Press, Genova University Press
2023
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| Sarrera elektronikoa: | ONIX_20230803_9791221501063_104 |
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Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869523428179968000 |
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| author | Evangelista, Adelia Sarra, Annalina Di Battista, Tonio |
| author_browse | Di Battista, Tonio Evangelista, Adelia Sarra, Annalina |
| author_facet | Evangelista, Adelia Sarra, Annalina Di Battista, Tonio |
| author_sort | Evangelista, Adelia |
| collection | Directory of Open Access Books |
| description | 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”. |
| format | Online |
| id | doab-20.500.12854ir-111711 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Firenze University Press, Genova University Press |
| publisherStr | Firenze University Press, Genova University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1117112025-07-17T10:01:25Z Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach Evangelista, Adelia Sarra, Annalina Di Battista, Tonio Student feedback digital learning ecosystem open-ended questions pandemic context structural topic models thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences 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”. 2023-08-05T04:05:38Z 2023-08-05T04:05:38Z 2023-08-03T15:06:24Z 2023 chapter ONIX_20230803_9791221501063_104 2704-5846 https://library.oapen.org/handle/20.500.12657/74908 9791221501063 https://directory.doabooks.org/handle/20.500.12854/111711 eng Proceedings e report open access image/png image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/74908/1/9791221501063-36.pdf https://library.oapen.org/bitstream/20.500.12657/74908/1/9791221501063-36.pdf Firenze University Press, Genova University Press 10.36253/979-12-215-0106-3.36 10.36253/979-12-215-0106-3.36 74113d79-2268-4658-88bb-6e8757c543b0 ASA 2022 Data-Driven Decision Making 9791221501063 6 Florence open access |
| spellingShingle | Student feedback digital learning ecosystem open-ended questions pandemic context structural topic models thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences Evangelista, Adelia Sarra, Annalina Di Battista, Tonio Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title | Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title_full | Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title_fullStr | Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title_full_unstemmed | Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title_short | Chapter Students’ feedback on the digital ecosystem: a structural topic modeling approach |
| title_sort | chapter students feedback on the digital ecosystem a structural topic modeling approach |
| topic | Student feedback digital learning ecosystem open-ended questions pandemic context structural topic models thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences |
| topic_facet | Student feedback digital learning ecosystem open-ended questions pandemic context structural topic models thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences |
| url | ONIX_20230803_9791221501063_104 |
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