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

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.

[1]  Nichol Castro,et al.  Spoken Word Recognition , 2018, The Oxford Handbook of Psycholinguistics.

[2]  D. Biber,et al.  Styles of stance in English: Lexical and grammatical marking of evidentiality and affect , 1989 .

[3]  Luciano da Fontoura Costa,et al.  Knowledge acquisition: A Complex networks approach , 2017, Inf. Sci..

[4]  Markus Brede,et al.  Multiplex lexical networks reveal patterns in early word acquisition in children , 2016, Scientific Reports.

[5]  Swapna Somasundaran,et al.  Recognizing Stances in Ideological On-Line Debates , 2010, HLT-NAACL 2010.

[6]  Zachary Estes,et al.  Emotion and memory: A recognition advantage for positive and negative words independent of arousal , 2013, Cognition.

[7]  Paul van der Cingel.  How to educate navigators in a complex world: making a case in higher professional education in The Netherlands , 2018 .

[8]  Yoed N. Kenett,et al.  Remotely Close Associations: Openness to Experience and Semantic Memory Structure , 2018, European Journal of Personality.

[9]  Yoed N. Kenett,et al.  Investigating the structure of semantic networks in low and high creative persons , 2014, Front. Hum. Neurosci..

[10]  M. V. P. Villalobos,et al.  Cambios en el concepto aprendizaje de estudiantes de pedagogía: análisis de disponibilidad léxica y grafos , 2018, Estudios filológicos.

[11]  Mitchel Resnick,et al.  Turtles, termites, and traffic jams - explorations in massively parallel microworlds , 1994 .

[12]  Massimo Stella,et al.  Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning , 2019, Big Data Cogn. Comput..

[13]  Joshua B. Tenenbaum,et al.  The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth , 2001, Cogn. Sci..

[14]  Amy Perfors,et al.  The “Small World of Words” English word association norms for over 12,000 cue words , 2018, Behavior Research Methods.

[15]  Michaela Gläser-Zikuda,et al.  An investigation of the influence of emotional factors on learning in physics instruction , 2003 .

[16]  Cynthia S. Q. Siew,et al.  Using network science to analyze concept maps of psychology undergraduates , 2018, Applied Cognitive Psychology.

[17]  Amy M. Masnick,et al.  Adolescents' and Emerging Adults' Implicit Attitudes about STEM Careers: "Science Is Not Creative". , 2016 .

[18]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[19]  Yoed N. Kenett,et al.  The Semantic Distance Task: Quantifying Semantic Distance With Semantic Network Path Length , 2017, Journal of experimental psychology. Learning, memory, and cognition.

[20]  Thomas T. Hills,et al.  Filling gaps in early word learning , 2018, Nature Human Behaviour.

[21]  Mason A. Porter,et al.  What are essential concepts about networks? , 2015, J. Complex Networks.

[22]  P. Zurn,et al.  Hunters, busybodies, and the knowledge network building associated with curiosity , 2019 .

[23]  D. Biber,et al.  Current Conceptions of Stance , 2012 .

[24]  M. Ashcraft.  Math Anxiety: Personal, Educational, and Cognitive Consequences , 2002 .

[25]  E. Ambrosini,et al.  Affective Norms for Italian Words in Older Adults: Age Differences in Ratings of Valence, Arousal and Dominance , 2017, PloS one.

[26]  J. Aitchison.  Words in the Mind: An Introduction to the Mental Lexicon , 1987 .

[27]  Saif Mohammad,et al.  Stance and Sentiment in Tweets , 2016, ACM Trans. Internet Techn..

[28]  Russell Tytler,et al.  STEM: country comparisons: international comparisons of science, technology, engineering and mathematics (STEM) education. Final report. , 2013 .

[29]  Yoed N. Kenett,et al.  Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity , 2019, Big Data Cogn. Comput..

[30]  Jonathan Rothwell,et al.  The Hidden STEM Economy. , 2013 .

[31]  Saif Mohammad,et al.  SemEval-2016 Task 6: Detecting Stance in Tweets , 2016, *SEMEVAL.

[32]  Ismo Koponen,et al.  Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support , 2018, Appl. Netw. Sci..

[33]  Diego R. Amancio,et al.  A Complex Network Approach to Stylometry , 2015, PloS one.

[34]  Danielle J. Navarro,et al.  Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations , 2012, Behavior Research Methods.

[35]  Nichol Castro,et al.  The multiplex structure of the mental lexicon influences picture naming in people with aphasia , 2019, J. Complex Networks.

[36]  Yoed N. Kenett,et al.  Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics , 2018, Complex..

[37]  Joseph L. Austerweil,et al.  Analyzing Knowledge Retrieval Impairments Associated with Alzheimer’s Disease Using Network Analyses , 2019, Complex..

[38]  D. Siegel.  The Developing Mind: How Relationships and the Brain Interact to Shape Who We Are , 2001 .

[39]  Manfred Prenzel,et al.  Research on Interest in Science: Theories, methods, and findings , 2011 .

[40]  Kalle Juuti,et al.  Connection between academic emotions in situ and retention in the physics track: applying experience sampling method , 2018, International journal of STEM education.

[41]  Melanie Mitchell,et al.  Complexity - A Guided Tour , 2009 .

[42]  Markus Brede,et al.  Multiplex model of mental lexicon reveals explosive learning in humans , 2017, Scientific Reports.

[43]  Diego R. Amancio,et al.  Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks , 2016, PloS one.

[44]  I. Koponen,et al.  Coherent Knowledge Structures of Physics Represented as Concept Networks in Teacher Education , 2010 .

[45]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[46]  L. Hamp-Lyons.  Stance and voice in written academic genres , 2014 .

[47]  Cynthia S. Q. Siew,et al.  Using Network Science to Understand Statistics Anxiety Among College Students , 2019, Scholarship of Teaching and Learning in Psychology.

[48]  S. Simon,et al.  Attitudes towards science: A review of the literature and its implications , 2003 .

[49]  Danielle S. Bassett,et al.  On Curiosity: A Fundamental Aspect of Personality, a Practice of Network Growth , 2018, Personality Neuroscience.

[50]  J. Russell,et al.  The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology , 2005, Development and Psychopathology.

[51]  S. Dehaene,et al.  Nonconscious semantic processing of emotional words modulates conscious access. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Yoed N. Kenett,et al.  Flexibility of thought in high creative individuals represented by percolation analysis , 2018, Proceedings of the National Academy of Sciences.

[53]  Thomas A. Schreiber,et al.  The University of South Florida free association, rhyme, and word fragment norms , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[54]  I. Koponen,et al.  Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism , 2019, Education Sciences.

[55]  M. Vitevitch.  What can graph theory tell us about word learning and lexical retrieval? , 2008, Journal of speech, language, and hearing research : JSLHR.