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


Autoři: Massimo Stella aff001;  Sarah de Nigris aff003;  Aleksandra Aloric aff004;  Cynthia S. Q. Siew aff005
Působiště autorů: Institute for Complex Systems Simulation, University of Southampton, Southampton, United Kingdom aff001;  Complex Science Consulting, Lecce, Italy aff002;  Institute for Web Science and Technologies, University of Koblenz-Landau, Koblenz, Germany aff003;  Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, Belgrade, Serbia aff004;  Department of Psychology, University of Warwick, Coventry, United Kingdom aff005;  Department of Psychology, National University of Singapore, Singapore, Singapore aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222870

Souhrn

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.

Klíčová slova:

Anxiety – Emotions – Human learning – Learning – Mathematical physics – Psycholinguistics – Semantics – Schools


Zdroje

1. Osborne J, Simon S, Collins S. Attitudes towards science: A review of the literature and its implications. International journal of science education. 2003;25(9):1049–1079. doi: 10.1080/0950069032000032199

2. Krapp A, Prenzel M. Research on interest in science: Theories, methods, and findings. International journal of science education. 2011;33(1):27–50. doi: 10.1080/09500693.2010.518645

3. Valenti S, Masnick A, Cox B, Osman C. Adolescents’ and Emerging Adults’ Implicit Attitudes about STEM Careers:” Science Is Not Creative”. Science Education International. 2016;27(1):40–58.

4. Ashcraft MH. Math anxiety: Personal, educational, and cognitive consequences. Current directions in psychological science. 2002;11(5):181–185. doi: 10.1111/1467-8721.00196

5. Rothwell J. The hidden STEM economy. Brookings; 2013.

6. Marginson S, Tytler R, Freeman B, Roberts K. STEM: country comparisons: international comparisons of science, technology, engineering and mathematics (STEM) education. Final report. 2013.

7. Aitchison J. Words in the mind: An introduction to the mental lexicon. John Wiley & Sons; 2012.

8. Siegel DJ. The developing mind: How relationships and the brain interact to shape who we are. Guilford Publications; 2015.

9. Gray B, Biber D. Current conceptions of stance. In: Stance and voice in written academic genres. Springer; 2012. p. 15–33.

10. Mohammad S, Kiritchenko S, Sobhani P, Zhu X, Cherry C. Semeval-2016 task 6: Detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016); 2016. p. 31–41.

11. Biber D, Finegan E. Styles of stance in English: Lexical and grammatical marking of evidentiality and affect. Text-interdisciplinary journal for the study of discourse. 1989;9(1):93–124. doi: 10.1515/text.1.1989.9.1.93

12. Stella M, Ferrara E, De Domenico M. Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences. 2018;115(49):12435–12440. doi: 10.1073/pnas.1803470115

13. Somasundaran S, Wiebe J. Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Association for Computational Linguistics; 2010. p. 116–124.

14. Mohammad SM, Sobhani P, Kiritchenko S. Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT). 2017;17(3):26. doi: 10.1145/3003433

15. Siew CSQ, Wulff DU, Beckage N, Kenett Y. Cognitive Network Science: A review of research on cognition through the lens of network representations, processes, and dynamics. 2018.

16. Steyvers M, Tenenbaum JB. The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive science. 2005;29(1):41–78. doi: 10.1207/s15516709cog2901_3 21702767

17. Vitevitch MS. What can graph theory tell us about word learning and lexical retrieval? Journal of Speech, Language, and Hearing Research. 2008. doi: 10.1044/1092-4388(2008/030) 18367686

18. Vitevitch MS, Siew CSQ, Castro N. Spoken Word Recognition. The Oxford Handbook of Psycholinguistics. 2018; p. 31.

19. Stella M, Beckage NM, Brede M, De Domenico M. Multiplex model of mental lexicon reveals explosive learning in humans. Scientific reports. 2018;8(1):2259. doi: 10.1038/s41598-018-20730-5 29396497

20. Stella M, Beckage NM, Brede M. Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific reports. 2017;7:46730. doi: 10.1038/srep46730 28436476

21. Hills TT, Siew CSQ. Filling gaps in early word learning. Nature Human Behaviour. 2018;2(9):622. doi: 10.1038/s41562-018-0428-y 31346280

22. Stella M. Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning. Big Data and Cognitive Computing. 2019;3(1):10. doi: 10.3390/bdcc3010010

23. De Deyne S, Navarro DJ, Storms G. Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior research methods. 2013;45(2):480–498. doi: 10.3758/s13428-012-0260-7 23055165

24. Kenett YN, Levi E, Anaki D, Faust M. The semantic distance task: Quantifying semantic distance with semantic network path length. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2017;43(9):1470. doi: 10.1037/xlm0000391 28240936

25. De Deyne S, Navarro DJ, Perfors A, Brysbaert M, Storms G. The “Small World of Words” English word association norms for over 12,000 cue words. Behavior research methods. 2018; p. 1–20.

26. Amancio DR. A complex network approach to stylometry. PloS one. 2015;10(8):e0136076. doi: 10.1371/journal.pone.0136076 26313921

27. Akimushkin C, Amancio DR, Oliveira ON Jr. Text authorship identified using the dynamics of word co-occurrence networks. PloS one. 2017;12(1):e0170527. doi: 10.1371/journal.pone.0170527 28125703

28. Zemla JC, Austerweil JL. Analyzing Knowledge Retrieval Impairments Associated with Alzheimer’s Disease Using Network Analyses. Complexity. 2019;2019. doi: 10.1155/2019/4203158 31341377

29. Castro N, Stella M. The multiplex structure of the mental lexicon influences picture naming in people with aphasia. Journal of Complex Networks. 2019;. doi: 10.1093/comnet/cnz012

30. Kenett YN, Anaki D, Faust M. Investigating the structure of semantic networks in low and high creative persons. Frontiers in human neuroscience. 2014;8:407. doi: 10.3389/fnhum.2014.00407 24959129

31. Kenett YN, Levy O, Kenett DY, Stanley HE, Faust M, Havlin S. Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences. 2018;115(5):867–872. doi: 10.1073/pnas.1717362115

32. Stella M, Kenett YN. Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity. Big Data and Cognitive Computing. 2019;3(3):45. doi: 10.3390/bdcc3030045

33. Zurn P, Bassett DS. On Curiosity: A Fundamental Aspect of Personality, a Practice of Network Growth. Personality Neuroscience. 2018;1. doi: 10.1017/pen.2018.3

34. Lydon-Staley DM, Zhou D, Blevins AS, Zurn P, Bassett DS. Hunters, busybodies, and the knowledge network building associated with curiosity.

35. Christensen AP, Kenett YN, Cotter KN, Beaty RE, Silvia PJ. Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality. 2018;32(4):480–492. doi: 10.1002/per.2157

36. Siew CSQ. Using network science to analyze concept maps of psychology undergraduates. Applied Cognitive Psychology. 2018;. doi: 10.1002/acp.3484

37. Valenzuela Castellanos MF, Pérez Villalobos M, Bustos C, Salcedo Lagos P. Cambios en el concepto aprendizaje de estudiantes de pedagogía: análisis de disponibilidad léxica y grafos. Estudios filológicos. 2018;(61):143–173.

38. Siew CSQ, McCartney MJ, Vitevitch MS. Using network science to understand statistics anxiety among college students. Scholarship of Teaching and Learning in Psychology. 2019;. doi: 10.1037/stl0000133

39. Posner J, Russell JA, Peterson BS. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and psychopathology. 2005;17(3):715–734. doi: 10.1017/S0954579405050340 16262989

40. Warriner AB, Kuperman V, Brysbaert M. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior research methods. 2013;45(4):1191–1207. doi: 10.3758/s13428-012-0314-x 23404613

41. Adelman JS, Estes Z. Emotion and memory: A recognition advantage for positive and negative words independent of arousal. Cognition. 2013;129(3):530–535. doi: 10.1016/j.cognition.2013.08.014 24041838

42. Gaillard R, Del Cul A, Naccache L, Vinckier F, Cohen L, Dehaene S. Nonconscious semantic processing of emotional words modulates conscious access. Proceedings of the National Academy of Sciences. 2006;103(19):7524–7529. doi: 10.1073/pnas.0600584103

43. Koponen IT, Pehkonen M. Coherent knowledge structures of physics represented as concept networks in teacher education. Science & Education. 2010;19(3):259–282. doi: 10.1007/s11191-009-9200-z

44. Koponen IT, Nousiainen M. Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support. Applied Network Science. 2018;3(1):14. doi: 10.1007/s41109-018-0072-5

45. Sayama H, Cramer C, Porter MA, Sheetz L, Uzzo S. What are essential concepts about networks? Journal of Complex Networks. 2016;4(3):457–474. doi: 10.1093/comnet/cnv028

46. Koponen IT, Nousiainen M. Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism. Education Sciences. 2019;. doi: 10.3390/educsci9010018

47. Cramer CB, Porter MA, Sayama H, Sheetz L, Uzzo SM. Network Science In Education: Transformational Approaches in Teaching and Learning. 1st ed. Springer Publishing Company, Incorporated; 2018.

48. Nelson DL, McEvoy CL, Schreiber TA. The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers. 2004;36(3):402–407. doi: 10.3758/BF03195588

49. Fairfield B, Ambrosini E, Mammarella N, Montefinese M. Affective norms for Italian words in older adults: age differences in ratings of valence, arousal and dominance. PloS one. 2017;12(1):e0169472. doi: 10.1371/journal.pone.0169472 28046070

50. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: Homophily in social networks. Annual review of sociology. 2001;27(1):415–444. doi: 10.1146/annurev.soc.27.1.415

51. Laukenmann M, Bleicher M, Fuß S, Gläser-Zikuda M, Mayring P, von Rhöneck C. An investigation of the influence of emotional factors on learning in physics instruction. International Journal of Science Education. 2003;25(4):489–507. doi: 10.1080/09500690210163233

52. Lehtamo S, Juuti K, Inkinen J, Lavonen J. Connection between academic emotions in situ and retention in the physics track: applying experience sampling method. International journal of STEM education. 2018;5(1):25. doi: 10.1186/s40594-018-0126-3 30631715

53. Resnick M. Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Mit Press; 1997.

54. van der Cingel P. How to educate navigators in a complex world: making a case in higher professional education in the Netherlands. Complexity, governance and networks. 2018;4(1).

55. Mitchell M. Complexity: A guided tour. Oxford University Press; 2009.

56. Cramer C, Gera R, Panagakou E, Porter MA, Sayama H, Sheetz L, et al. Proceedings of NetSciEd 2018. OSF Preprints; 2018.

57. de Arruda HF, Silva FN, Costa LdF, Amancio DR. Knowledge acquisition: A Complex networks approach. Information Sciences. 2017;421:154–166. doi: 10.1016/j.ins.2017.08.091


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PLOS One


2019 Číslo 10