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

Abstract

This study investigates how students and researchers shape their knowledge and perception of educational topics. The mindset or forma mentis of 159 Italian high school students and of 59 international researchers in science, technology, engineering and maths (STEM) are reconstructed through forma mentis networks, i.e., cognitive networks of concepts connected by free associations and enriched with sentiment labels. The layout of conceptual associations between positively/negatively/neutrally perceived concepts is informative on how people build their own mental constructs or beliefs about specific topics. Researchers displayed mixed positive/neutral mental representations of "teacher", "student" and, "scientist". Students' conceptual associations of "scientist" were highly positive and largely non-stereotypical, although links about the "mad scientist" stereotype persisted. Students perceived "teacher" as a complex figure, associated with positive aspects like mentoring/knowledge transmission but also to negative sides revolving around testing and grading. "School" elicited stronger differences between the two groups. In the students' mindset, "school" was surrounded by a negative emotional aura or set of associations, indicating an anxious perception of the school setting, mixing scholastic concepts, anxiety-eliciting words, STEM disciplines like maths and physics, and exam-related notions. Researchers' positive stance of "school" included concepts of fun, friendship, and personal growth instead. Along the perspective of Education Research, the above results are discussed as quantitative evidence for test-and STEM anxiety co-occurring in the way Italian students perceive education places and their actors. Detecting these patterns in student populations through forma mentis networks offers new, simple to gather yet detailed knowledge for future data-informed intervention policies and action research.

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

“…In the following, a valence aura will identify the most frequent sentiment label (e.g. positive, negative, neutral) attributed to the semantic associates of a concept, analogously to previous studies (Stella et al, 2019;Stella, 2020). Also emotional auras will be considered, indicating the contextual feelings and emotions elicited by the conceptual associates of a word in a given semantic frame.…”

“…This section introduces the following elements: (i) the dataset about the online perception of the gender gap in STEM as retrieved from Twitter, (ii) the methodology behind the construction of text-based forma mentis networks, and (iii) the cognitive data used for detecting conceptual overlap in meaning between words and their valence. This section also reports on the benchmark data used for testing TFMNs, namely the text "Complexity Explained" (https://complexityexplained.github.io/ -Last Accessed: 16/03/2020) and the behavioural forma mentis network of international STEM researchers analysed in (Stella et al, 2019) and (Stella, 2020).…”

“…An example is represented by the finding that mindsets vehiculating positive emotions reach larger audiences on social media whereas negative emotional content can spread at faster rates (Ferrara and Yang, 2015). Another example for the relevance of mindset reconstruction is uncovering and acting upon traces of science anxiety in student populations in order to improve their learning experiences (Stella, 2020;Stella and Zaytseva, 2020) or detecting sexual harassment through large-scale web surveys (Karami et al, 2020).…”

“…Sentiment scores of positive/negative affect are also called word valence in psycholinguistics (Warriner et al, 2013;Recchia and Louwerse, 2015) and represent how positively or negatively a given concept was perceived in a behavioural study. Word valence and emotional profiles add more emotional contextual information about the stance reconstructed by conceptual associations (Stella, 2020). Although large-scale datasets about sentiment and emotions have only been recently made available to the scientific community by cognitive studies (Warriner et al, 2013), they have quickly become predominant in predicting a wide variety of human behaviour (Li et al, 2020) and information processing patterns such as consensus formation in social networks (Konstantinidis et al, 2017) or information sharing on microblogs (Ferrara and Yang, 2015).…”

Between Social and Semantic Networks: A Case Study on Classroom Complexity

“…Proximity can be either phonological or semantic [29]. It can also be obtained by free association between words, considering its valency [30]. In the present research, as we are investigating shared ideas in a group, proximity between words was obtained by a similarity index, considering the co-occurrence of evoked terms.…”