Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth

Abstract

Reconstructing a “forma mentis”, a mindset, and its changes, means capturing how individuals perceive topics, trends and experiences over time. To this aim we use forma mentis networks (FMNs), which enable direct, microscopic access to how individuals conceptually perceive knowledge and sentiment around a topic, providing richer contextual information than machine learning. FMNs build cognitive representations of stances through psycholinguistic tools like conceptual associations from semantic memory (free associations, i.e., one concept eliciting another) and affect norms (valence, i.e., how attractive a concept is). We test FMNs by investigating how Norwegian nursing and engineering students perceived innovation and health before and after a 2-month research project in e-health. We built and analysed FMNs by six individuals, based on 75 cues about innovation and health, and leading to 1,000 associations between 730 concepts. We repeated this procedure before and after the project. When investigating changes over time, individual FMNs highlighted drastic improvements in all students’ stances towards “teamwork”, “collaboration”, “engineering” and “future”, indicating the acquisition and strengthening of a positive belief about innovation. Nursing students improved their perception of ‘robots” and “technology” and related them to the future of nursing. A group-level analysis related these changes to the emergence, during the project, of conceptual associations about openness towards multidisciplinary collaboration, and a positive, leadership-oriented group dynamics. The whole group identified “mathematics” and “coding” as highly relevant concepts after the project. When investigating persistent associations, characterising the core of students’ mindsets, network distance entropy and closeness identified as pivotal in the students’ mindsets concepts related to “personal well-being”, “professional growth” and “teamwork”. This result aligns with and extends previous studies reporting the relevance of teamwork and personal well-being for Norwegian healthcare professionals, also within the novel e-health sector. Our analysis indicates that forma mentis networks are powerful proxies for detecting individual- and group-level mindset changes due to professional growth. FMNs open new scenarios for data-informed, multidisciplinary interventions aimed at professional training in innovation.

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

“…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).…”

“…However, these approaches did not explore the influence that emotions and sentiment can have over information processing (Warriner et al, 2013;Dóczi, 2019;Rudkowsky et al, 2018;Li et al, 2020). The introduction of behavioural forma mentis networks (BFMNs) (Stella et al, 2019;Stella and Zaytseva, 2020) tackled such gap by combining conceptual associations with sentiment scores and providing access to knowledge structure and positive/negative perceptions of concepts in a given mindset. However, BFMNs' mindset reconstruction comes at the cost of involving individuals in a cognitive experiment (cf.…”

“…Compared to previous approaches using behavioural forma mentis networks (Stella, 2020;Stella and Zaytseva, 2020;Stella et al, 2019), the current analysis introduces also an emotional profiling of stances as encapsulated in the reconstructed mindsets. Emotional profiling is defined in terms of considering how many associates of a concept, in the TFMN, elicit a given emotion.…”

“…This work built networks of conceptual associations between concepts. Several independent studies have reported that on such networks, metrics like network distance are powerful proxies for quantifying semantic relatedness (Kenett et al, 2017;Stella and Zaytseva, 2020). Network distance d i j is defined as the minimum number of links (here conceptual associations) connecting any two words i and j on a network structure (Siew et al, 2019).…”

“…On multiplex lexical networks, links of any colour/layer can be used (Stella et al, 2018a;Stella, 2019). Measures based on network distance such as closeness centrality have been found to identify also concepts of relevance from a cognitive perspective when detecting key words for word learning (Stella et al, 2018a;Stella, 2019), language processing (Kenett et al, 2017;Siew et al, 2019;Castro and Stella, 2019) and knowledge exploration (Stella and Zaytseva, 2020;Akimushkin et al, 2017;Amancio, 2015). Closeness centrality (Siew et al, 2019) c(i) is attributed to node i by checking how distant it is to its connected neighbours, in formulas: (Stella et al, 2018a).…”