Research Article | DOI: https://doi.org/ISSRR-RA-25-017
Analysis and control of a Dynamic Model Involving Students Anxiety towards Mathematics
Abstract
Anxiety towards mathematics is the most common problem for many students in several universities. In this study, bifurcation analysis and multiobjective nonlinear model predictive control (MNLMPC) calculations are performed on a dynamic model involving students’ anxiety towards mathematics. Bifurcation analysis is a powerful mathematical tool used to deal with the nonlinear dynamics of any process. Several factors must be considered, and multiple objectives must be met simultaneously. The MATLAB program MATCONT was used to perform the bifurcation analysis. The MNLMPC calculations were performed using the optimization language PYOMO in conjunction with the state-of-the-art global optimization solvers IPOPT and BARON.The bifurcation analysis reveals a branch point and a Hopf bifurcation point, which leads to a limit cycle. This Hopf point was eliminated using an activation factor that involves the tanh function. The branch point enables the multiobjective nonlinear model predictive control calculations to converge to the Utopia point, which is the best solution.
References
-
Mohamed, S. H. & Tarmizi, R. A. Anxiety in mathematics learning among secondary school learners: A comparative study between Tanzania and Malaysia. Proc. Soc. Behav. Sci. 8, 498–504 (2010).
View at Publisher | View at Google Scholar -
Akin, A. & Kurbanoglu, I. N. The relationships between math anxiety, math attitudes, and self-efficacy: A structural equation model. Stud. Psychol. 53(3), 263 (2011).
View at Publisher | View at Google Scholar -
Maria de Lourdes, M., Monteiro, V. & Peixoto, F. Attitudes towards mathematics: Effects of individual, motivational, and social support factors. Child Dev. Res. 2012 (2012).
View at Publisher | View at Google Scholar -
Hoorfar, H. & Taleb, Z. Correlation between mathematics anxiety with metacognitive knowledge. Proc. Soc. Behav. Sci. 182, 737–741 (2015).
View at Publisher | View at Google Scholar -
Getahun, D. A., Adamu, G., Andargie, A. & Mebrat, J. D. Predicting mathematics performance from anxiety, enjoyment, value, and self-efficacy beliefs towards mathematics among engineering majors. Bahir Dar J. Educ. 16, 1 (2016).
View at Publisher | View at Google Scholar -
Zakaria, E. & Syamaun, M. The effect of realistic mathematics education approach on students’ achievement and attitudes towards mathematics. Math. Educ. Trends Res. 1(1), 32–40 (2017).
View at Publisher | View at Google Scholar -
Mazana, Y.M., Montero, C.S. & Olifage, C.R. Investigating Students’ Attitude Towards Learning Mathematics (2019).
View at Publisher | View at Google Scholar -
Mazana, M. Y., Montero, C. S. & Casmir, R. O. Assessing students’ performance in mathematics in Tanzania: the teacher’s perspective.Int. Electron. J. Math. Educ. 15(3), em0589 (2020).
View at Publisher | View at Google Scholar -
Teklu, S. W. & Terefe, B. B. Mathematical modeling analysis on the dynamics of university students animosity towards mathematics with optimal control theory. Sci. Rep. 12(1), 1–19 (2022).
View at Publisher | View at Google Scholar -
Teklu, S. W. Analysis of fractional order model on higher institution students’ anxiety towards mathematics with optimal control theory. Sci. Rep. 13(1), 6867 (2023).
View at Publisher | View at Google Scholar -
Dhooge, A., Govearts, W., and Kuznetsov, A. Y., MATCONT: “A Matlab package for numerical bifurcation analysis of ODEs”, ACM transactions on Mathematical software 29(2) pp. 141-164, 2003.
View at Publisher | View at Google Scholar -
Dhooge, A.,W. Govaerts; Y. A. Kuznetsov, W. Mestrom, and A. M. Riet , “CL_MATCONT”; A continuation toolbox in Matlab, 2004.
View at Publisher | View at Google Scholar -
Kuznetsov,Y.A. “Elements of applied bifurcation theory” .Springer,NY, 1998.
View at Publisher | View at Google Scholar -
Kuznetsov,Y.A.(2009).”Five lectures on numerical bifurcation analysis” ,Utrecht University,NL., 2009.
View at Publisher | View at Google Scholar -
Govaerts, w. J. F., “Numerical Methods for Bifurcations of Dynamical Equilibria”, SIAM, 2000.
View at Publisher | View at Google Scholar -
Dubey S. R. Singh, S. K. & Chaudhuri B. B. 2022 Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing, 503, 92-108. https://doi.org/10.1016/j.neucom.2022.06.111
View at Publisher | View at Google Scholar -
Kamalov A. F. Nazir M. Safaraliev A. K. Cherukuri and R. Zgheib 2021,
View at Publisher | View at Google Scholar -
Szandała, T. 2020, Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks. ArXiv. https://doi.org/10.1007/978-981-15-5495-7
View at Publisher | View at Google Scholar -
Sridhar. L. N. 2023 Bifurcation Analysis and Optimal Control of the Tumor Macrophage Interactions. Biomed J Sci & Tech Res 53(5). BJSTR. MS.ID.008470.DOI: 10.26717/BJSTR.2023.53.008470
View at Publisher | View at Google Scholar -
Sridhar LN. Elimination of oscillation causing Hopf bifurcations in engineering problems. Journal of Applied Math. 2024b; 2(4): 1826.
View at Publisher | View at Google Scholar -
Flores-Tlacuahuac, A. Pilar Morales and Martin Riveral Toledo; “Multiobjective Nonlinear model predictive control of a class of chemical reactors” . I & EC research; 5891-5899, 2012.
View at Publisher | View at Google Scholar -
Hart, William E., Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, and John D. Siirola. “Pyomo – Optimization Modeling in Python” Second Edition. Vol. 67.
View at Publisher | View at Google Scholar -
Wächter, A., Biegler, L. “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming”. Math. Program. 106, 25–57 (2006). https://doi.org/10.1007/s10107-004-0559-y
View at Publisher | View at Google Scholar -
Tawarmalani, M. and N. V. Sahinidis, “A polyhedral branch-and-cut approach to global optimization”, Mathematical Programming, 103(2), 225-249, 2005
View at Publisher | View at Google Scholar -
Sridhar LN. (2024)Coupling Bifurcation Analysis and Multiobjective Nonlinear Model Predictive Control. Austin Chem Eng. 2024; 10(3): 1107.
View at Publisher | View at Google Scholar -
Upreti, Simant Ranjan(2013); Optimal control for chemical engineers. Taylor and Francis.
View at Publisher | View at Google Scholar