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.File | Dimensione | Formato | |
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