“I can learn everything online”: An Analysis of TikTok’s Notional Machines for Learning Programming

• Maria Barbieri IFMS
• Jamili Amaral IFMS
• Rodrigo Duran IFMS

DOI: https://doi.org/10.5753/wei.2024.2325

Abstract

Unsurprisingly, many instructors and students resort to non-formal educational spaces, especially video-based social networks such as TikTok, when tasked to learn to program, particularly when formal training is scarcely provided. However, it is unclear what kind of educational content such platforms provide to teachers and the quality of such content. In this work, we aim to alleviate this gap by investigating programming videos on TikTok extracting explanations – the Notional Machines – used by presenters. We analyzed 300 videos on these platforms to classify the Notional Machines used to explain topics such as variables, loops, conditionals, and functions. Our results show that, in general, the majority of the videos do not offer an explicit definition of concepts, or use shallow or “textbook” explanations, including some deemed dangerous in the literature. Unfortunately, we could not find new terminology or language that would allow educators to communicate with students from primary and secondary levels successfully. While more work is necessary to analyze a larger corpus of videos, we offer a cautionary tale for using unvetted content as a replacement for well-prepared learning materials.

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