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

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, leadershiporiented 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 grouplevel mindset changes due to professional growth. FMNs open new scenarios for datainformed, multidisciplinary interventions aimed at professional training in innovation.publishedVersio

Forma mentis networks reconstruct how Italian high schoolers and international STEM experts perceive teachers, students, scientists, and school

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 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.Comment: 12 Pages, 5 Figure

Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Comment: 23 pages, 8 figure

Sensitivity analysis in a scoping review on police accountability : assessing the feasibility of reporting criteria in mixed studies reviews

In this paper, we report on the findings of a sensitivity analysis that was carried out within a previously conducted scoping review, hoping to contribute to the ongoing debate about how to assess the quality of research in mixed methods reviews. Previous sensitivity analyses mainly concluded that the exclusion of inadequately reported or lower quality studies did not have a significant effect on the results of the synthesis. In this study, we conducted a sensitivity analysis on the basis of reporting criteria with the aims of analysing its impact on the synthesis results and assessing its feasibility. Contrary to some previous studies, our analysis showed that the exclusion of inadequately reported studies had an impact on the results of the thematic synthesis. Initially, we also sought to propose a refinement of reporting criteria based on the literature and our own experiences. In this way, we aimed to facilitate the assessment of reporting criteria and enhance its consistency. However, based on the results of our sensitivity analysis, we opted not to make such a refinement since many publications included in this analysis did not sufficiently report on the methodology. As such, a refinement would not be useful considering that researchers would be unable to assess these (sub-)criteria

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

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

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, leadershiporiented 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 grouplevel mindset changes due to professional growth. FMNs open new scenarios for datainformed, multidisciplinary interventions aimed at professional training in innovation

Proceedings of the 11th Toulon-Verona International Conference on Quality in Services

The Toulon-Verona Conference was founded in 1998 by prof. Claudio Baccarani of the University of Verona, Italy, and prof. Michel Weill of the University of Toulon, France. It has been organized each year in a different place in Europe in cooperation with a host university (Toulon 1998, Verona 1999, Derby 2000, Mons 2001, Lisbon 2002, Oviedo 2003, Toulon 2004, Palermo 2005, Paisley 2006, Thessaloniki 2007, Florence, 2008). Originally focusing on higher education institutions, the research themes have over the years been extended to the health sector, local government, tourism, logistics, banking services. Around a hundred delegates from about twenty different countries participate each year and nearly one thousand research papers have been published over the last ten years, making of the conference one of the major events in the field of quality in services