Research into Performance Index-Dependent Student Learning Dynamics
DOI:
https://doi.org/10.7250/ecce-2026-0001Keywords:
computational cybernetics, continuing education, control engineering education, control nonlinearities, industrial psychology, key performance indicator, optimal controlAbstract
The problem of effective student learning of a new course within the framework of rational student goal setting is an “eternal” applied problem for the social and technical sciences. Each rational goal setting of the student was formulated in the form of a non-obvious mathematical construct of a nonlinear objective function that determined the minimized functional for the corresponding optimal control problem. Within the framework of the author’s approach to nonlinear modelling of various optimal goal-settings in student study of a new course, 35 optimal control problems for 35 pedagogically-admissible algebraic constructs for minimized functionals were mathematically posed, Optimica-formulated and numerically solved in JModelica-{1.17; 2.14}. As part of further generalization and psychological and pedagogical interpretation of the obtained graphical results of the numerical modelling, the following six strategies for studying a new course by a student were formulated: “Lazy Student” (Strategy A); “Procrastinator” (Strategy B); “Growing Student” (Strategy C); “Steady Student” (Strategy D); “Midterm Hero” or “Halfway Hero” (Strategy E), and “Starting Hero” or “Sprinting Hero” (Strategy F). The six strategies mentioned above for student learning of a new course seem to be a fairly concise summary of the individual educational efforts of both a school student, a university student, and a working professional.
References
G. F. Raggett et al., “A student-related optimal control problem”, Bulletin of the Institute of Mathematics and Its Applications, vol. 17, pp. 133–136, 1981.
H. Bondi, “Note on “A student related optimum control problem” by Raggett, Hempson, and Jukes”, Bulletin of the Institute of Mathematics and Its Applications, vol. 18, pp. 10–11, 1982.
W. Woodside, “A student optimal control problem or how to pass courses with the minimum expenditure of effort”, Applied Mathematics Notes, Canadian Mathematical Society, vol. 7, pp. 2–13, 1982.
M. Parlar, “Some extension of a student related optimal control problem”, IMA Bulletin, vol. 20, pp. 180–181, 1984.
M. S. Klamkin, “Mathematical modelling: A student optimal control problem and extensions”, Mathematical Modelling, vol. 6, no. 1, pp. 49–64, Jan. 1985. https://doi.org/10.1016/0270-0255(85)90021-1
T. C. E. Cheng and K. L. Teo, “Further extensions of a student-related optimal control problem”, Mathematical Modelling, vol. 9, no. 7, pp. 499–506, Jan. 1987. https://doi.org/10.1016/0270-0255(87)90057-1
C. S. Lee and G. Leitmann, “On a student-related optimal control problem”, Journal of Optimisation Theory and Applications, vol. 65, no. 1, pp. 129–138, Apr. 1990. https://doi.org/10.1007/BF00941164
C. S. Lee and G. L. Leitmann, “Some stabilizing study strategies for a student-related problem under uncertainty”, Dynamics and Stability of Systems, vol. 6, no. 1, pp. 63–78, Jan. 1991. https://doi.org/10.1080/02681119108806107
Y. H. Chen, “A revisit to the student learning problem”, Optimal Control Applications and Methods, vol. 12, no. 4, pp. 263–272, Oct.–Dec. 1991. https://doi.org/10.1002/oca.4660120405
G. Leitmann and C. S. Lee, “A discrete stabilizing study strategy for a student related problem under uncertainty”, in Optimal Control: Calculus of Variations, Optimal Control Theory and Numerical Methods, R. Bulirsch, A. Miele, J. Stoer, and K. Well, Eds., Basel: Birkhäuser Basel, 1993, pp. 173–185. https://doi.org/10.1007/978-3-0348-7539-4_13
A. Buratto and B. Viscolani, “An optimal control student problem and a marketing counterpart”, Mathematical and Computer Modelling, vol. 20, no. 6, pp. 19–33, Sep. 1994. https://doi.org/10.1016/0895-7177(94)90021-3
A. Buratto and B. Viscolani, “Optimal study/advertising plan for a knowledge/goodwill evolution process with rank memory”, Journal of Information and Optimisation Sciences, vol. 18, no. 2, pp. 211–228, May 1997. https://doi.org/10.1080/02522667.1997.10699328
A. C. Smith et al., “Dynamic analysis of learning in behavioral experiments”, Journal of Neuroscience, vol. 24, no. 2, pp. 447–461, Jan. 2004. https://doi.org/10.1523/JNEUROSCI.2908-03.2004
A. Mehmood, “Modeling framework for understanding the dynamics of learning performance in education systems”, in 23rd International Conference of the System Dynamics Society, Boston, USA, July 2005. https://proceedings.systemdynamics.org/2005/proceed/papers/MEHMO324.pdf
L. Bao and E. F. Redish, “Model analysis: Representing and assessing the dynamics of student learning”, Physical Review Physics Education Research, vol. 2, no. 1, Feb. 2006, Art. no. 010103. https://doi.org/10.1103/PhysRevSTPER.2.010103
D. Brunetto, C. Andra, N. Parolini, and M. Verani, “Student interactions during class activities: a mathematical model,” Communications in Applied and Industrial Mathematics, vol. 9, no. 2, pp. 91–105, Dec. 2018. https://doi.org/10.2478/caim-2018-0011
S. Goldt, M. S. Advani, A. M. Saxe, F. Krzakala, and L. Zdeborova, “Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup”, Journal of Statistical Mechanics: Theory and Experiment, vol. 2020, no. 12, Dec. 2020, Art. no. 124010. https://doi.org/10.1088/1742-5468/abc61e
D. K. N. Rachim, “Study of the lazy nature of physics students using the quadratic optimal control method”, Jurnal Penelitian dan Pengembangan Pendidikan Fisika, vol. 6, no. 2, pp. 279–288, Dec. 2020. https://doi.org/10.21009/1.06214
H. Asanuma et al., “Statistical mechanical analysis of catastrophic forgetting in continual learning with teacher and student networks”, Journal of the Physical Society of Japan, vol. 90, no. 10, Oct. 2021, Art. no. 104001. https://doi.org/10.7566/JPSJ.90.104001
P. Castaldi and N. Mimmo, “Investigation of the student-professor interaction and self-learning ability for an aerospace engineering student”, IFAC-PapersOnLine, vol. 54, no. 12, pp. 1–6, Jan. 2021. https://doi.org/10.1016/j.ifacol.2021.11.001
O. P. Chornyi, L. V. Herasymenko, and V. V. Busher, “The learning process simulation based on differential equations of fractional orders”, CTE Workshop Proceedings, vol. 8, pp. 473–483, Mar. 2021. https://doi.org/10.55056/cte.301
M. Kaffenberger and L. Pritchett, “A structured model of the dynamics of student learning in developing countries, with applications to policy,” International Journal of Educational Development, vol. 82, Apr. 2021, Art. no. 102371. https://doi.org/10.1016/j.ijedudev.2021.102371
J. W. Kooken, R. Zaini, and I. Arroyo, “Simulating the dynamics of self-regulation, emotion, grit, and student performance in cyber-learning environments,” Metacognition and Learning, vol. 16, no. 2, pp. 367–405, Aug. 2021. https://doi.org/10.1007/s11409-020-09252-6
K. L. Teo, B. Li, C. Yu, and V. Rehbock, Eds., “Introduction”, in Applied and Computational Optimal Control: A Control Parametrization Approach, vol. 171. Cham: Springer International Publishing, 2021, pp. 1–20. https://doi.org/10.1007/978-3-030-69913-0_1
K. L. Teo, B. Li, C. Yu, and V. Rehbock, Eds., “Elements of Optimal Control Theory”, in Applied and Computational Optimal Control: A Control Parametrization Approach, vol. 171. Cham: Springer International Publishing, 2021, pp. 173–216. https://doi.org/10.1007/978-3-030-69913-0_6
D. Lewis, “Modeling student engagement using optimal control theory”, Journal of Geometric Mechanics, vol. 14, no. 1, pp. 131–150, Feb. 2022. https://doi.org/10.3934/jgm.2021032
S. W. Teklu and B. B. Terefe, “Mathematical modeling analysis on the dynamics of university students animosity towards mathematics with optimal control theory”, Scientific Reports, vol. 12, no. 1, Jul. 2022, Art. no. 11578. https://doi.org/10.1038/s41598-022-15376-3
C. C. J. Dominé, L. Braun, J. E. Fitzgerald, and A. M. Saxe, “Exact learning dynamics of deep linear networks with prior knowledge”, Journal of Statistical Mechanics: Theory and Experiment, vol. 2023, Nov. 2023, Art. no. 114004. https://doi.org/10.1088/1742-5468/ad01b8
M. Qiu, “Dynamic model construction and correlation analysis of college students’ academic performance”, Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 2, pp. 963–973, Mar. 2023. https://doi.org/10.3233/JCM-226473
A. El Bhih, Y. Benfatah, H. Hassouni, O. Balatif, and M. Rachik, “Mathematical modeling, sensitivity analysis, and optimal control of students awareness in mathematics education”, Partial Differential Equations in Applied Mathematics, vol. 11, Sep. 2024, Art. no. 100795. https://doi.org/10.1016/j.padiff.2024.100795
J. G. Vergaño-Salazar, M. Del Valle, C. Muñoz, J. Miranda, A. Precht, and J. Valenzuela, “Modeling learning-oriented motivation in health students: a system dynamics approach,” BMC Psychology, vol. 12, no. 1, Sep. 2024, Art. no. 512. https://doi.org/10.1186/s40359-024-02014-y
P. Castaldi and N. Mimmo, “Representing the dynamics of student learning and interactions with a university curriculum”, IFAC-PapersOnLine, vol. 58, no. 16, pp. 211–216, Jan. 2024. https://doi.org/10.1016/j.ifacol.2024.08.488
C. N. Loong and C.-C. Chang, “Control knowledge tracing: Modeling students’ learning dynamics from a control-theory perspective”, Computers and Education: Artificial Intelligence, vol. 7, Dec. 2024, Art. no. 100292. https://doi.org/10.1016/j.caeai.2024.100292
E. Papageorgiou, J. Wong, M. Khalil, and A. J. Cabo, “Nonlinear effort-time dynamics of student engagement in a web-based learning platform: A Person-oriented transition analysis”, Learning Analytics, vol. 12, no. 2, pp. 237–258, Aug. 2025. https://doi.org/10.18608/jla.2025.8663
M. Zine, F. Harrou, and Y. Sun, “Understanding student behavioral dynamics in Moodle through chaos theory and interpretable machine learning”, SN Computer Science, vol. 6, no. 8, Dec. 2025, Art. no. 1011. https://doi.org/10.1007/s42979-025-04595-w
J. Åkesson, K.-E. Årzén, M. Gäfvert, T. Bergdahl, and H. Tummescheit, “Modeling and optimisation with Optimica and JModelica.org – Languages and tools for solving large-scale dynamic optimisation problems”, Computers & Chemical Engineering, vol. 34, no. 11, pp. 1737–1749, Nov. 2010. https://doi.org/10.1016/j.compchemeng.2009.11.011
A. V. Perig, N. N. Golodenko, V. M. Skyrtach, and A. G. Kaikatsishvili, “Hydraulic analogy method for phenomenological description of the learning processes of technical university students”, European Journal of Contemporary Education, vol. 7, no. 4, pp. 764–789, Dec. 2018. https://doi.org/10.13187/ejced.2018.4.764
A. V. Liuta, A. V. Perig, M. A. Afanasieva, and V. M. Skyrtach, “Didactic games as student-friendly tools for learning hydraulics in a technical university’s undergraduate curriculum”, Industry and Higher Education, vol. 33, no. 3, pp. 198–213, Jan. 2019. https://doi.org/10.1177/0950422218824507
A. V. Perig, N. N. Golodenko, O. V. Lapchenko, V. M. Skyrtach, A. A. Kostikov, and O. V. Subotin, “Recent postdigital transformations of undergraduate learning processes in the study of multidisciplinary materials science”, International Journal of Continuing Engineering Education and Life-Long Learning, vol. 29, no. 3, pp. 251–291, Jul. 2019. https://doi.org/10.1504/IJCEELL.2019.10021595
A. V. Perig, N. N. Golodenko, R. S. Martynov, and A. G. Kaikatsishvili, “Educational research into socio-economic dynamics of university graduate employment: Triple analogy-based physics-and-engineering approach to labor market oscillations”, WORK: A Journal of Prevention, Assessment & Rehabilitation, vol. 65, no. 1, pp. 3–29, Dec. 2019. https://doi.org/10.3233/WOR-193054
A. V. Perig et al., “Engineering pedagogy course mapping”, Acta Metallurgica Slovaca, vol. 28, no. 1, pp. 49–67, 2022. https://doi.org/10.36547/ams.28.1.1411
J. A. C. Hattie and G. M. Donoghue, “Learning strategies: a synthesis and conceptual model”, npj Science of Learning, vol. 1, Aug. 2016, Art. no. 16013. https://doi.org/10.1038/npjscilearn.2016.13
R. N. Jazar, “Time optimal control”, in Theory of Applied Robotics: Kinematics, Dynamics, and Control, R. N. Jazar, Ed. Cham: Springer International Publishing, 2022, pp. 731–757. https://doi.org/10.1007/978-3-030-93220-6_13
O. V. Perig, “DataSet for Perig’s manuscript on performance index-dependent student learning dynamics – ver 26112024”, Nov. 2024. https://doi.org/10.13140/RG.2.2.19324.24966
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