Forma mentis networks quantify crucial differences in STEM perception between students and experts

Abstract

In order to investigate how high school students and researchers perceive science-related (STEM) subjects, we introduce forma mentis networks. This framework models how people conceptually structure their stance, mindset or forma mentis toward a given topic. In this study, we build forma mentis networks revolving around STEM and based on psycholinguistic data, namely free associations of STEM concepts (i.e., which words are elicited first and associated by students/researchers reading “science”?) and their valence ratings concepts (i.e., is “science” perceived as positive, negative or neutral by students/researchers?). We construct separate networks for (Ns = 159) Italian high school students and (Nr = 59) interdisciplinary professionals and researchers in order to investigate how these groups differ in their conceptual knowledge and emotional perception of STEM. Our analysis of forma mentis networks at various scales indicate that, like researchers, students perceived “science” as a strongly positive entity. However, differently from researchers, students identified STEM subjects like “physics” and “mathematics” as negative and associated them with other negative STEM-related concepts. We call this surrounding of negative associations a negative emotional aura. Cross-validation with external datasets indicated that the negative emotional auras of physics, maths and statistics in the students’ forma mentis network related to science anxiety. Furthermore, considering the semantic associates of “mathematics” and “physics” revealed that negative auras may originate from a bleak, dry perception of the technical methodology and mnemonic tools taught in these subjects (e.g., calculus rules). Overall, our results underline the crucial importance of emphasizing nontechnical and applied aspects of STEM disciplines, beyond purely methodological teaching. The quantitative insights achieved through forma mentis networks highlight the necessity of establishing novel pedagogic and interdisciplinary links between science, its real-world complexity, and creativity in science learning in order to enhance the impact of STEM education, learning and outreach activities.

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

“…No new data were generated for this study. Notice that this work is based on the data provided by Stella and colleagues [16], which included cues, responses, and valence labels (positive/negative/neutral). The dataset was anonymous and it did not include individual demographics of students and researchers but only overall fractions of female/male participants and average age, which are reported in the following.…”

“…This manuscript uses the forma mentis networks released by Stella and colleagues and representative of 159 Italian high school students and of 59 international young researchers on complex systems [16]. The selected students were gathered independently on their school grades from three Italian high schools.…”

“…Within this complex picture, it becomes of utmost importance to develop general and simple techniques quantifying and detecting anxiety in the students' perception of their classroom settings, of STEM subjects, of potential stereotypes and, more in general, of the whole educational system. Forma mentis networks (FMN) were recently introduced by Stella and colleagues [16] and shown to be capable of detecting anxiety patterns through easily obtainable cognitive data. Building on these results, this paper adopts the framework of forma mentis networks for better understanding the perception toward educational topics and STEM of high school students in their final year of studies, with a focus on the Italian educational system.…”

“…FMNs combine network science and cognitive science methods and data [16,17] with the aim of stance detection, i.e., identifying whether an individual or a group is in favor or against a given topic. Differently from machine learning approaches, often solving stance detection through blackbox analyses of textual data [18], forma mentis networks produce a transparent representation of how individuals or populations of individuals perceive and associate knowledge about a specific topic.…”

“…The recent adoption of network science tools has shown how the large-scale, associative structure of word knowledge in the mental lexicon is highly informative of a wide variety of cognitive processes such as lexical processing [19,22,23,24], learning and cognitive development [25,26,27], text structuring and writing styles [28,29,30,31], creativity [32,33], and expertise levels in specific domains [34,5]. Analogously, forma mentis networks act as approximated reconstructions on the mental constructs built by individuals in their associative mental lexicon, representing their perceptions of the outer world [16,17].…”