Skip to main content

Advertisement

Log in

Emotions and heuristics: the state of perplexity in mathematics

  • Original Article
  • Published:
ZDM Aims and scope Submit manuscript

Abstract

Using data provided by an empirical exploratory study with mathematics undergraduates, this paper discusses some key variables in the interaction between affective and cognitive dimensions in the perplexity state in problem solving. These variables are as follows: heuristics, mathematical processes, appraisal processes [pleasantness, attentional activity, control (self-other responsibility/control, situational control), certainty, goal-path obstacle, anticipated effort and mental flexibility], as well as the relationships these variables have with different emotions that make up perplexity. Fuzzy sets were introduced as a tool to capture and accurately reflect the diversity and subjectivity in the interplay between cognition and emotion. The descriptive analysis of the responses to a fuzzy rating scale-based questionnaire shows the interaction between variables linked to the dimensions of control and certainty and students’ ability to cope with perplexity in performance in mathematics. The study also adds novel considerations related to the function and interaction of mathematics cognitive processes that are linked to appraisal processes, namely, the perception of goal-path obstacle, attentional activity and mental flexibility that contributes to the ability to solve simpler problem components involved in mathematical performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Based on the definitions of Diccionario de la Lengua Española de la Real Academia (DRAE), Diccionario María Moliner 3ª ed. 1988 and Diccionario del Español Actual (DEA) de Manuel Seco et al. 1999.

  2. The Oxford English Dictionary defines perplexity as follows: “1. a. Inability to decide what to think or how to act owing to the involved, intricate, or complicated condition of circumstances or of the matters to be dealt with, generally also involving mental perturbation or anxiety…” (Oxford English Dictionary 2010, accessed September 2010).

  3. Diccionario de la Lengua Española de la Real Academia (DRAE).

References

  • Blanco-Fernández, A., M.R. Casals, M. R., Colubi, A., Corral, N., García-Bárzana, M., Gil, M. A., González-Rodríguez, G., López, M. T., Montenegro, M., Lubiano, M. A., Ramos-Guajardo, A. B., de la Rosa de Sáa, S., & Sinova, B. (2014). A distance-based statistical analysis of fuzzy number-valued data. International Journal of Approximate Reasoning, 55, 1487–1501.

    Article  Google Scholar 

  • Breiman, L., Friedman, J. H., Olshen, R., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group.

    Google Scholar 

  • Byers, W. (2007). How mathematicians think. Princeton: Princeton University Press.

    Google Scholar 

  • D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.

    Article  Google Scholar 

  • Dewey, J. (1910). How we think. Boston: D.C. Heath.

    Book  Google Scholar 

  • Diamond, P., & Kloeden, P. (1990). Metric spaces of fuzzy sets. Fuzzy Sets Systems, 35, 241–249.

    Article  Google Scholar 

  • Goldin, G. A. (2000). Affective pathways and representations in mathematical problem solving. Mathematical Thinking and Learning, 17(2), 209–219.

    Article  Google Scholar 

  • Goldin, G. A. (2004). Problem solving heuristics, affect and discrete mathematics. ZDM- Mathematics Education, 36(2), 56–60.

    Article  Google Scholar 

  • Gómez-Chacón, I. M. (2000). Affective influences in the knowledge of mathematics. Educational Studies in Mathematics, 43, 149–168.

    Article  Google Scholar 

  • Gómez-Chacón, I. M. (2012). Affective pathways and interactive visualization in the context of technological and professional mathematical knowledge. Nordic Studies in Mathematics Education, 17(3–4), 57–74.

    Google Scholar 

  • Gómez-Chacón, I. M. (2015). Meta-emotion and mathematical modeling processes in computerized environments. In B. Pepin & B. Rösken-Winter (Eds.), From beliefs and affect to dynamic systems in mathematics education. Exploring a mosaic of relationships and interactions (pp. 201–226). Switzerland: Springer.

    Google Scholar 

  • Gómez-Chacón, I. M., Romero, I. M., del Mar García López, M. (2016) Zig-zagging in geometrical reasoning in technological collaborative environments: a Mathematical Working Space-framed study concerning cognition and affect. ZDM Mathematics Education, 48(6), 909–924. doi:10.1007/s11858-016-0755-2.

    Article  Google Scholar 

  • Hesketh, T., Pryor, R., & Hesketh, B. (1988). An application of a computerized fuzzy graphic rating scale to the psychological measurement of individual differences. International Journal of Man-Machine Studies, 29, 21–35.

    Article  Google Scholar 

  • Lakatos, I. (1976). Proofs and refutations. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Lazarus, R. (1991). Emotion and adaptation. New York: Oxford University Press.

    Google Scholar 

  • Lubiano, M. A., de la Rosa de Sáa, S., Montenegro, M., Sinova, B., & Gil, M. A. (2016). Descriptive analysis of responses to ítems in questionnaies. Why not using a fuzzy rating scale? Information Sciences, 360, 131–148.

    Article  Google Scholar 

  • Muis, K. R. (2007). The role of epistemic beliefs in self-regulated learning. Educational Psychologist, 42, 173–190.

    Article  Google Scholar 

  • Muis, K. R., Psaradellis, C., Lajoie, S. P., Di Leo, I., & Chevrier, M. (2015). The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology, 42, 172–185.

    Article  Google Scholar 

  • Pekrun, R., & Stephens, E. J. (2012). Academic emotions. In K. Harris, S. Graham, T. Urdan, S. Graham, J. Royer & M. Zeidner (Eds.), Individual differences and cultural and contextual factors. APA educational psychology handbook (Vol. 2, pp. 3–31). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Polya, G. (1957). How to solve it? New York: Doubleday Anchor Books Edition.

    Google Scholar 

  • Scherer, K. R. (2000). Emotions as episodes of subsystem synchronization driven by nonlinear appraisal processes. In M. D. Lewis & I. Granic (Eds.), Emotion, development, and self-organization: Dynamic systems approaches to emotional development (pp. 70–99). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Schoenfeld, A. H. (1994). Reflections on doing and teaching mathematics. In A. H. Schoenfeld (Ed.), Mathematical thinking and problem solving (pp. 53–75). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Schukajlow, S. (2015). Is Boredom important for students performance? In K. Krainer & N. Vondrová (Eds.), Proceedings of the Ninth Congress of the European Society for Research in Mathematics Education (pp. 1273–1279). Prague: Charles University in Prague.

    Google Scholar 

  • Schukajlow, S., & Krug, A. (2014). Are interest and enjoyment important for students’ performance? In C. Nicol, S. Oesterle, P. Liljedahl & D. Allan (Eds.), Proceedings of the Joint Meeting of PME 38 and PME-NA 36 (Vol. 5, pp. 129–136). Vancouver: PME.

    Google Scholar 

  • Seco, M., Ramos, G., Andrés, O. (1999). Diccionario del Español Actual. Madrid: Aguilar.

    Google Scholar 

  • Silvia, P. J. (2010). Confusion and interest: The role of knowledge emotions in aesthetic experience. Psychology of Aesthetics Creativity and the Arts, 4, 75–80.

    Article  Google Scholar 

  • Smith, C. A., & Ellsworth, P. (1985). Patterns of cognitive appraisal in emotions. Journal of Personal and Social Psychology, 84(4), 813–838.

    Article  Google Scholar 

  • Thagard, P. (1992). Conceptual revolutions. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Trutschnig, W., & Lubiano, A. (2013). Package SAFD, CRAN R-project.org.

  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8, 199–249.

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the research grant Visiting Scholar Fellowship, University of California in Berkeley, Scholarship “Becas Complutense del Amo” 2015–16, Spain and by the Spanish Ministry of the Economy and Competitive Affairs under project EDU2013-44047-P.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Inés M. Gómez-Chacón.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez-Chacón, I.M. Emotions and heuristics: the state of perplexity in mathematics. ZDM Mathematics Education 49, 323–338 (2017). https://doi.org/10.1007/s11858-017-0854-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11858-017-0854-8

Keywords

Navigation