Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School
Abstract
:1. Introduction
2. Methods
Forma Mentis Networks
3. Results
3.1. The Structure of Forma Mentis Networks Crucially Depends on Hubs
3.2. Hubs Related to Education Reveal the Stances toward Teachers, Study, and School
3.3. On the Perception of Anxiety and Fun
3.4. Hubs Related to Self-Perceptions Reveal Positive, Mostly Non-Stereotypical Attitudes toward Scientists and Students
4. Discussion
5. Conclusions
Supplementary Materials
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Network Measures of Forma Mentis Networks
References
- Mallow, J.V. Science anxiety: Research and action. In Handbook of College Science Teaching; National Science Teaching Association: Arlington, VA, USA, 2006; pp. 3–14. [Google Scholar]
- Zeidner, M. Test anxiety. In The Corsini Encyclopedia of Ppsychology; John Wiley & Sons: Hoboken, NJ, USA, 2010; pp. 1–3. [Google Scholar]
- Núñez-Peña, M.I.; Suárez-Pellicioni, M.; Bono, R. Effects of math anxiety on student success in higher education. Int. J. Educ. Res. 2013, 58, 36–43. [Google Scholar] [CrossRef] [Green Version]
- Lehtamo, S.; Juuti, K.; Inkinen, J.; Lavonen, J. Connection between academic emotions in situ and retention in the physics track: Applying experience sampling method. Int. J. Stem Educ. 2018, 5, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Siew, C.S.Q.; McCartney, M.J.; Vitevitch, M.S. Using network science to understand statistics anxiety among college students. Scholarsh. Teach. Learn. Psychol. 2019. [Google Scholar] [CrossRef]
- Bodin, M.; Winberg, M. Role of beliefs and emotions in numerical problem solving in university physics education. Phys. Rev. Spec. Top.-Phys. Educ. Res. 2012, 8, 010108. [Google Scholar] [CrossRef] [Green Version]
- Oludipe, D.; Awokoya, J.O. Effect of cooperative learning teaching strategy on the reduction of students’ anxiety for learning chemistry. J. Turk. Sci. Educ. 2010, 7, 30–36. [Google Scholar]
- Van der Cingel, P. How to educate navigators in a complex world: Making a case in higher professional education in The Netherlands. Complex. Gov. Netw. 2018, 4, 19–31. [Google Scholar]
- Cassady, J.C.; Johnson, R.E. Cognitive test anxiety and academic performance. Contemp. Educ. Psychol. 2002, 27, 270–295. [Google Scholar] [CrossRef] [Green Version]
- Finson, K.D.; Beaver, J.B.; Cramond, B.L. Development and field test of a checklist for the Draw-A-Scientist Test. Sch. Sci. Math. 1995, 95, 195–205. [Google Scholar] [CrossRef]
- Haynes, R.D. Whatever happened to the ‘mad, bad’scientist? Overturning the stereotype. Public Underst. Sci. 2016, 25, 31–44. [Google Scholar] [CrossRef] [Green Version]
- Rahm, J.; Downey, J. “A scientist can be anyone!” Oral histories of scientists can make “real science” accessible to youth. Clear. House 2002, 75, 253–257. [Google Scholar] [CrossRef]
- Beilock, S.L.; Rydell, R.J.; McConnell, A.R. Stereotype threat and working memory: Mechanisms, alleviation, and spillover. J. Exp. Psychol. Gen. 2007, 136, 256–276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shapiro, J.R.; Williams, A.M. The role of stereotype threats in undermining girls’ and women’s performance and interest in STEM fields. Sex Roles 2012, 66, 175–183. [Google Scholar] [CrossRef]
- Valenti, S.; Masnick, A.; Cox, B.; Osman, C. Adolescents’ and Emerging Adults’ Implicit Attitudes about STEM Careers: “Science Is Not Creative”. Sci. Educ. Int. 2016, 27, 40–58. [Google Scholar]
- Stella, M.; De Nigris, S.; Aloric, A.; Siew, C.S. Forma mentis networks quantify crucial differences in STEM perception between students and experts. PLoS ONE 2019, 14, e0222870. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stella, M.; Zaytseva, A. Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth. OSF Prepr. 2019. [Google Scholar] [CrossRef]
- 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), San Diego, CA, USA, 16–17 June 2016; pp. 31–41. [Google Scholar]
- De Deyne, S.; Navarro, D.J.; Storms, G. Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behav. Res. Methods 2013, 45, 480–498. [Google Scholar] [CrossRef] [Green Version]
- Smeets, T.; Jelicic, M.; Merckelbach, H. The effect of acute stress on memory depends on word valence. Int. J. Psychophysiol. 2006, 62, 30–37. [Google Scholar] [CrossRef]
- Dóczi, B. An Overview of Conceptual Models and Theories of Lexical Representation in the Mental Lexicon. In The Routledge Handbook of Vocabulary Studies; Routledge: Abingdon, UK, 2019; p. 46. [Google Scholar]
- Van Rensbergen, B.; Storms, G.; De Deyne, S. Examining assortativity in the mental lexicon: Evidence from word associations. Psychon. Bull. Rev. 2015, 22, 1717–1724. [Google Scholar] [CrossRef] [Green Version]
- Vitevitch, M.S.; Siew, C.S.Q.; Castro, N. Spoken Word Recognition. In The Oxford Handbook of Psycholinguistics; Oxford University Press: New York, NY, USA, 2018; p. 31. [Google Scholar]
- Neergaard, K.D.; Luo, J.; Huang, C.R. Phonological network fluency identifies phonological restructuring through mental search. Sci. Rep. 2019, 9, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Stella, M.; Beckage, N.M.; Brede, M. Multiplex lexical networks reveal patterns in early word acquisition in children. Sci. Rep. 2017, 7, 46730. [Google Scholar] [CrossRef] [Green Version]
- Stella, M.; De Domenico, M. Distance entropy cartography characterises centrality in complex networks. Entropy 2018, 20, 268. [Google Scholar] [CrossRef] [Green Version]
- Stella, M.; Beckage, N.M.; Brede, M.; De Domenico, M. Multiplex model of mental lexicon reveals explosive learning in humans. Sci. Rep. 2018, 8, 2259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Amancio, D.R. A complex network approach to stylometry. PLoS ONE 2015, 10, e0136076. [Google Scholar] [CrossRef] [PubMed]
- De Arruda, H.F.; Marinho, V.Q.; Costa, L.d.F.; Amancio, D.R. Paragraph-based representation of texts: A complex networks approach. Inf. Process. Manag. 2019, 56, 479–494. [Google Scholar] [CrossRef] [Green Version]
- Machicao, J.; Corrêa, E.A., Jr.; Miranda, G.H.; Amancio, D.R.; Bruno, O.M. Authorship attribution based on life-like network automata. PLoS ONE 2018, 13, e0193703. [Google Scholar] [CrossRef]
- Stella, M.; Ferrara, E.; De Domenico, M. Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. USA 2018, 115, 12435–12440. [Google Scholar] [CrossRef] [Green Version]
- Kenett, Y.N.; Faust, M. A semantic network cartography of the creative mind. Trends Cogn. Sci. 2019, 23, 271–274. [Google Scholar] [CrossRef]
- Stella, M.; Kenett, Y.N. Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity. Big Data Cogn. Comput. 2019, 3, 45. [Google Scholar] [CrossRef] [Green Version]
- Tyumeneva, Y.; Kapuza, A.; Vergeles, K. Distinctive Ability of Concept Maps for Assessing Levels of Competence. Pilot study. Educ. Stud. 2017, 150–170. [Google Scholar] [CrossRef]
- Koponen, I.T.; Mäntylä, T. Generative role of experiments in physics and in teaching physics: A suggestion for epistemological reconstruction. Sci. Educ. 2006, 15, 31–54. [Google Scholar] [CrossRef]
- Nousiainen, M.; Koponen, I.T. Concept maps representing knowledge of physics: Connecting structure and content in the context of electricity and magnetism. Nord. Stud. Sci. Educ. 2010, 6, 155–172. [Google Scholar] [CrossRef]
- Koponen, I.T.; Kokkonen, T. A Systemic view of the learning and differentiation of scientific concepts. Frontline Learn. Res. 2014, 5, 140–166. [Google Scholar]
- Koponen, M.; Asikainen, M.; Viholainen, A.; Hirvonen, P. Using network analysis methods to investigate how future teachers conceptualize the links between the domains of teacher knowledge. Teach. Teach. Educ. 2019, 79, 137–152. [Google Scholar] [CrossRef]
- Koponen, I.T.; Kokkonen, T.P.; Nousiainen, M.K. Complex Dynamic Systems View on Conceptual Change. Complicity 2017, 14, 7–19. [Google Scholar] [CrossRef]
- Subramaniam, K.; Kirby, B.; Harrell, P.; Long, C. Using Concept Maps to Reveal Prospective Elementary Teachers’ Knowledge of Buoyancy. Electron. J. Sci. Educ. 2019, 23, 1–18. [Google Scholar]
- Kinchin, I.M.; Möllits, A.; Reiska, P. Uncovering Types of Knowledge in Concept Maps. Educ. Sci. 2019, 9, 131. [Google Scholar] [CrossRef] [Green Version]
- Newman, M. Networks; Oxford University Press: Oxford, UK, 2018. [Google Scholar]
- Bruun, J.; Andersen, I.V. Network maps of student work with physics, other sciences, and math in an integrated science course. arXiv 2017, arXiv:1708.01389. [Google Scholar]
- Bruun, J. Networks as integrated in research methodologies in PER. In Proceedings of the Physics Education Research Conference 2016, Sacramento, CA, USA, 20–21 July 2016; pp. 11–17. [Google Scholar]
- Ahmad, W.; Nooraishya, W.; Ali, N.M. A Study on Persuasive Technologies: The Relationship between User Emotions, Trust and Persuasion. Int. J. Interact. Multimed. Artif. Intell. 2018, 5. [Google Scholar] [CrossRef]
- Jantsch, E. Inter-and transdisciplinary university: A systems approach to education and innovation. High. Educ. 1972, 1, 7–37. [Google Scholar] [CrossRef]
- Cramer, C.B.; Porter, M.A.; Sayama, H.; Sheetz, L.; Uzzo, S.M. Network Science in Education: Transformational Approaches in Teaching and Learning; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
- Tsiouplis, E.; Stamovlasis, D. Rethinking Educational Reforms through a Complex Dynamical Systems Approach: Preliminary Report from an Empirical Research. Northeast J. Complex Syst. (NEJCS) 2019, 1, 3. [Google Scholar] [CrossRef] [Green Version]
Measure | Students’ FMN | Experts’ FMN | Null Model (Students) | Null Model (Experts) |
---|---|---|---|---|
Concepts | 4483 | 1616 | 4483 | 1616 |
Associations | 11,728 | 3185 | 11,728 | 3185 |
Clust. Coef. | 0.045 | 0.042 | 0.035 (2) | 0.025 (2) |
Assor. Coef. | −0.34 | −0.048 | −0.035 (9) | −0.026 (9) |
Diameter | 7 | 10 | 11 (1) | 10 (1) |
Mean Distance | 4.08 | 4.53 | 3.99 (3) | 4.27 (8) |
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Stella, M. Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School. Educ. Sci. 2020, 10, 17. https://doi.org/10.3390/educsci10010017
Stella M. Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School. Education Sciences. 2020; 10(1):17. https://doi.org/10.3390/educsci10010017
Chicago/Turabian StyleStella, Massimo. 2020. "Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School" Education Sciences 10, no. 1: 17. https://doi.org/10.3390/educsci10010017
APA StyleStella, M. (2020). Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School. Education Sciences, 10(1), 17. https://doi.org/10.3390/educsci10010017