# Mapping computational thinking mindsets between educational levels with cognitive network science

arXiv: https://arxiv.org/abs/2007.09402 · HTML: https://mlsanity.com/paper/2007.09402/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 6 of 6)

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- [Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students](https://mlsanity.com/paper/2305.18320/) (2023)
- [Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review](https://mlsanity.com/paper/2108.13800/) (2021)
- [Recovering network topology and dynamics via sequence characterization](https://mlsanity.com/paper/2206.15190/) (2022)
- [Cognitive networks reconstruct mindsets about STEM subjects and educational contexts in almost 1000 high-schoolers, University students and LLM-based digital twins](https://mlsanity.com/paper/2602.14749/) (2026)

## Abstract (arXiv)

Computational thinking is a way of reasoning about the world in terms of data. This mindset channels number crunching toward an ambition to discover knowledge through logic, models and simulations. Here we show how computational cognitive science can be used to reconstruct and analyse the structure of computational thinking mindsets (forma mentis in Latin) through complex networks. As a case study, we investigate cognitive networks tied to key concepts of computational thinking provided by: (i) 159 high school students enrolled in a science curriculum and (ii) 59 researchers in complex systems and simulations. Researchers' reconstructed forma mentis highlighted a positive mindset about scientific modelling, semantically framing data and simulations as ways of discovering nature. Students correctly identified different aspects of logic reasoning but perceived "computation" as a distressing, anxiety-eliciting task, framed with math jargon and lacking links to real-world discovery. Students' mindsets around "data", "model" and "simulations" critically revealed no awareness of numerical modelling as a way for understanding the world. Our findings provide evidence of a crippled computational thinking mindset in students, who acquire mathematical skills that are not channelled toward real-world discovery through coding. This unlinked knowledge ends up being perceived as distressing number-crunching expertise with no relevant outcome. The virtuous mindset of researchers reported here indicates that computational thinking can be restored by training students specifically in coding, modelling and simulations in relation to discovering nature. Our approach opens innovative ways for quantifying computational thinking and enhancing its development through mindset reconstruction.