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Introducing mindset streams to investigate stances towards STEM in high school students and experts

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  • Brian, Kieran
  • Stella, Massimo

Abstract

We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging “math” and “learning”. These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between ‘maths”/“physics” and key motivational aspects of learning (“fun”, “work”, “failure”) in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge “maths” and “fun” only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers’ mindset stream, which rather bridges “math” and “fun” through positive, science-related associations. The mindset streams of both groups bridge “maths” and “physics” to “work” through mostly positive career-related jargon. Students’ mindset streams of “failure” and “math”/“physics” are dominated by negative associations with test anxiety, whereas researchers integrate “failure” and “math”/“physics” in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.

Suggested Citation

  • Brian, Kieran & Stella, Massimo, 2023. "Introducing mindset streams to investigate stances towards STEM in high school students and experts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    • Handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123006295
    DOI: 10.1016/j.physa.2023.129074
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    References listed on IDEAS

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    1. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    2. Cynthia S. Q. Siew & Dirk U. Wulff & Nicole M. Beckage & Yoed N. Kenett, 2019. "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics," Complexity, Hindawi, vol. 2019, pages 1-24, June.
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