The mighty awk: a great tool, but not the one I start with teaching people interested in applying computational methods. What I notice is that it moves 'too qui

The mighty awk: a great tool, but not the one I start with teaching people interested in applying computational methods. What I notice is that it moves 'too quick' on prompt execution--great for sysadmins, developers and on the job data analysts, but a 'slower' tool is often easier for newcomers to get their heads around, who'd often like to see what happens between steps as well as natural language error messages when something goes wrong. KNIME, Orange and GSheets + Apps Script fill this niche to some extent (and I've been wanting to give ENSO Lang a try), enabling rapid prototyping, iteration and visual clues when steps fail. Then, through repeatedly running the same steps in these environments, it suddenly 'clicks' for some that many of the data preprocessing steps follow a similar pattern, and that much time can be saved using scripting. You have to tease out this eagerness somewhat, by having learners first run through the more arduous process of clicking, dragging, navigating and debugging through a 'slow' interface. In this regard, some have started to create workflows and test suites around software onboarding, which will yield some valuable insights into what pedagogical strategies work best for different computational skill levels [0][1][2]. [0]: https://jku-vds-lab.at/publications/2022_eurovis_dashboard_onboarding/ https://jku-vds-lab.at/publications/2022_eurovis_dashboard_o... [1]: https://jku-vds-lab.at/publications/2022_visinf_compare_evaluations_onboarding/ https://jku-vds-lab.at/publications/2022_visinf_compare_eval... [2]: https://ideah.pubpub.org/pub/yia9z29r/release/1 https://ideah.pubpub.org/pub/yia9z29r/release/1