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The field of Digital Humanities (DH) is still in its infancy, with multifaceted aspects that are very open for debate (Pavlidis et al., 2018; Luhmann & Burghardt, 2022). These debates have raised the interest for developing complementary knowledge using ingenuine human and computer interventions to solve DH-related problems. However, concerns about humanists shying away from considering DH as a discipline have arisen. This can be linked to the blurred boundaries that humanists are familiar with diverse methods that can differ from those applied in DH (Clement, 2016).
Unsurprisingly, computer interventions are not limited to understanding application packages, social media, and web browsing alone but also to learn how to program. More importantly, humanists are bound to ask some central questions before learning to program, like; Why should humanists consider learning to program? And why humanists should not rely alone on open-source (drag and drop) applications designed for ease of use? Existing studies have shown that programming allows humanists to perform both quantitative and qualitative data analysis on a large scale that may be impossible to carry out with other. Nevertheless, reports have shown that learning programming has low satisfaction, high-dropout rates, and can be challenging, especially for non-technical students (Marcolino & Barbosa, 2017)
With regards to the mentioned above challenges, it is pertinent to democratize knowledge and teach humanists programming through a “Thought processing” approach like Computational Thinking (CT) (Wing, 2011). Employing CT concepts in DH may help not to depend on or scavenge works from computer scientists and programmers. CT concepts have the potential help in providing cognitive activities by formulating a problem that admits computational solutions. However, little is known about the epistemological landscape and how humanists apply CT concepts in programming education.
The aim of this paper is to better understand how teaching and learning programming within DH can be improved based on the analysis of existing educational practices. Data collected from submitted assignments & essays from two cohorts of DH master’s students from Linnaeus University were the sources for our exploration & analysis. Data was manually coded applying directed content analysis where a CT framework was used as predetermine categories for inquiry (Hsieh & Shannon, 2005). The categories showed strengths, opportunities, and weaknesses as insights for epistemological landscapes of learning programming by these DH master students. These insights can be used to revise, further develop and co-design programming & CT modules in this field.
