The article presents an open educational resource (OER) to introduce humanities students to data analysis with Python. The article beings with positioning the OER within wider pedagogical debates in the digital humanities. The OER is built from our research encounters and committed to computational thinking rather than technicalities. Furthermore, we argue that students best learn with the `whole game' methodology. Learners need to be exposed to meaningful activities as soon and as far as possible. The article presents two examples that implement our approach. The first introduces Python as a data analysis language to students of the humanities. It is different because it concentrates on the principles of the computational thinking behind data analysis rather than programming details. The second example takes the students into the world of machine learning and the whole game of social and cultural research with it. Students learn useful skills such as web scraping but will also run their own machine learning algorithms to follow concrete research questions.

Blanke Tobias, Colavizza Giovanni, van Hout Zarah (2023). An open educational resource to introduce data analysis in Python for the Humanities. EDUCATION FOR INFORMATION, 39(2), 105-119 [10.3233/EFI-230020].

An open educational resource to introduce data analysis in Python for the Humanities

Colavizza Giovanni;
2023

Abstract

The article presents an open educational resource (OER) to introduce humanities students to data analysis with Python. The article beings with positioning the OER within wider pedagogical debates in the digital humanities. The OER is built from our research encounters and committed to computational thinking rather than technicalities. Furthermore, we argue that students best learn with the `whole game' methodology. Learners need to be exposed to meaningful activities as soon and as far as possible. The article presents two examples that implement our approach. The first introduces Python as a data analysis language to students of the humanities. It is different because it concentrates on the principles of the computational thinking behind data analysis rather than programming details. The second example takes the students into the world of machine learning and the whole game of social and cultural research with it. Students learn useful skills such as web scraping but will also run their own machine learning algorithms to follow concrete research questions.
2023
Blanke Tobias, Colavizza Giovanni, van Hout Zarah (2023). An open educational resource to introduce data analysis in Python for the Humanities. EDUCATION FOR INFORMATION, 39(2), 105-119 [10.3233/EFI-230020].
Blanke Tobias; Colavizza Giovanni; van Hout Zarah
File in questo prodotto:
File Dimensione Formato  
efi_2023_39-2_efi-39-2-efi230020_efi-39-efi230020.pdf

accesso aperto

Descrizione: Articolo
Tipo: Versione (PDF) editoriale / Version Of Record
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 2.21 MB
Formato Adobe PDF
2.21 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/948801
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact