Recurrence Analysis in Engineering Education: A Formative Strategy as a Prospective Tool for Improving Academic Quality
DOI:
https://doi.org/10.29105/mdi.v14i23.355Keywords:
Engineering education, Curriculum development, Dynamical systems, Data analysis, Mathematical modelsAbstract
Engineering education faces the challenge of incorporating analytical tools capable of addressing nonlinear phenomena and complex dynamical systems. The aim of this study is to design and theoretically support a learning unit based on Recurrence Analysis (RA) as a formative strategy in engineering programs. The research follows a qualitative curriculum design approach grounded in educational design research. The methodology included a systematic literature review on engineering education and Education 4.0, a disciplinary analysis of RA, and the instructional design of a 16-week course focused on dynamical systems analysis using recurrence plots and recurrence quantification analysis (RQA).
The proposal integrates theoretical foundations, computational laboratory activities, and applications in mechanical, electrical, and biomedical signals within a project-based learning framework. Results suggest that RA can be incorporated as an interdisciplinary educational tool that strengthens modeling skills, data analysis, and computational thinking. This work contributes to curriculum innovation in engineering education and provides a foundation for future classroom implementation and empirical evaluation.
References
Ruay Garcés, R., González Bravo, P., & Plaza Taucare, E. (2016). ¿Cómo abordar la renovación curricular en la educación superior? Alteridad. Revista de Educación, 11(2), 157–170. https://www.redalyc.org/pdf/4677/467749196002.pdf
Consejo de Rectores de las Universidades Chilenas (CRUCH) (2012). Innovación Curricular en las Universidades del Consejo de Rectores. Reflexiones y procesos en las Universidades del Consejo de Rectores Prácticas Internacionales. ISBN: 978-956-7581-03-0.
Arriaga Cárdenas, O. G., & Lara Magaña, P. C. (2023). La innovación en la educación superior y sus retos a partir del COVID-19. Revista Educación, 47(1). https://doi.org/10.15517/revedu.v47i1.51979
Garcés, G. y Peña, C. ( 2020 ). Ajustar la Educación en Ingeniería a la Industria 4.0: Una visión desde el desarrollo curricular y el laboratorio . Revista de Estudios y Experiencias en Educación . https://doi.org/10.21703/rexe.20201940garces7
Carbajal-Amaya, R. V. (2020). La Universidad del futuro y la Revolución 4.0. Hacia una Universidad innovadora. Análisis prospectivo. Revista Electrónica Calidad En La Educación Superior, 11(2), 15–26. https://doi.org/10.22458/caes.v11i2.3321
Rey-Sánchez, S. P., Vergara-Calderón, R. S., Rodriguez-Barboza, J. R., Pablo-Huamani, R. (2023). Educación 4.0 en estudiantes universitarios peruanos en situación postpandemia: Education 4.0 in Peruvian university students in post-pandemic situation. Scientific Research Journal CIDI, 3 (5), 1-22. https://doi.org/10.53942/srjcidi.v3i5.110
Gupta, A., Sawhney, S., Nanda, A., Shabaz, M., Ofori, I. (2023). Transforming Learning to Online Education 4.0 during COVID‐19: Stakeholder Perception, Attitude, and Experiences in Higher Education Institutions at a Tier‐III City in India. Education Research International, 2023 (1), 3217552. https://doi.org/10.1155/2023/3217552
Tobar Litardo, J. E., Rodríguez Wong, C. A., Garcés Suárez, E. F. (2023). La formación de los docentes para la enseñanza de la industria 4.0 en la educación superior. RECIAMUC, 7 (2), 180-194. https://doi.org/10.26820/reciamuc/7.(2).abril.2023.180-194
Qian, Y., Vaddiraju, S., Khan, F. (2023). Safety education 4.0– A critical review and a response to the process industry 4.0 need in chemical engineering curriculum. Safety Science, 161, 106069. https://doi.org/10.1016/j.ssci.2023.106069
Cano Ibarra, S. T., Galván Morales, P., Briseño Canchola, S., Plascencia Cano, M. A. (2022). Probabilidad y Estadística Basada en Retos: Enfoque Educativo STEM y Educación 4.0 (Challenge-Based Probability And Statistics: Stem Educational Approach And Education 4.0). Pistas Educativas, 43 (141). https://pistaseducativas.celaya.tecnm.mx/index.php/pistas/article/view/2710/2166
Goldin, T., Rauch, E., Pacher, C., Woschank, M. (2022). Reference architecture for an integrated and synergetic use of digital tools in education 4.0. Procedia Computer Science, 200, 407-417. https://doi.org/10.1016/j.procs.2022.01.239
Layco, E. P. (2022). Mathematics Education 4.0: TeachersCompetence and SkillsReadiness in Facing the Impact of Industry 4.0 on Education. Journal of Positive School Psychology, 6 (2), 1233-1259. https://www.journalppw.com/index.php/jpsp/article/view/1642
Gonzales, G., Costan, F., Suladay, D., Gonzales, R., Enriquez, L., Costan, E., Atibing, N. M., Aro, J. L., Evangelista, S. S., Maturan, F., Selerio, E., Ocampo, L. (2022). Fermatean fuzzy DEMATEL and MMDE algorithm for modelling the barriers of implementing education 4.0: Insights from the Philippines. Applied Sciences, 12 (2), 689. https://doi.org/10.3390/app12020689
Srivani, V., Hariharasudan, A., Pandeeswari, D. (2022). English language learning using education 4.0 in Karimnagar, India. World Journal of English Language, 12 (2), 325. https://doi.org/10.5430/wjel.v12n2p325
Velásquez Pérez, T., Flórez Villamizar, L., Castro Silva, H. F. (2022). Pedagogías emergentes & educación 4.0: hacia un modelo de enseñanza holístico. Revista Boletín Redipe, 11 (1), 551-564. https://doi.org/10.36260/rbr.v11i1.1662
Jam, N. A. M., Puteh, S. (2022). Exploring the teaching and learning indicators towards education 4.0 in MTUN, Malaysia. International Journal of Information and Education Technology, 12 (2), 179-184. https://doi.org/10.18178/ijiet.2022.12.2.1602
Miranda, J., Navarrete, C., Noguez, J., Molina-Espinosa, J., Ramírez-Montoya, M., Navarro-Tuch, S. A., Bustamante-Bello, M., Rosas-Fernández, J., Molina, A. (2021). The core components of education 4.0 in higher education: Three case studies in engineering education. Computers & Electrical Engineering, 93, 1- 13. https://doi.org/10.1016/j.compeleceng.2021.107278
Matsumoto-Royo, K., Ramírez-Montoya, M. S., Conget, P. (2021). Opportunities to develop lifelong learning tendencies in practice-based teacher education: Getting ready for education 4.0. Future Internet, 13 (11), 292. https://doi.org/10.3390/fi13110292
López-Bernal, D., Balderas, D., Ponce, P., Molina, A. (2021). Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet, 13 (8), 193. https://doi.org/10.3390/fi13080193
Ramírez-Montoya, M. S., Loaiza-Aguirre, M. I., Zúñiga-Ojeda, A., Portuguez-Castro, M. (2021). Characterization of the Teaching Profile within the Framework of Education 4.0. Future Internet, 13 (4), 91. https://doi.org/10.3390/fi13040091
Sinche Crispín, F. V., Gordillo Flores, R. E., Baldeón Tovar, M. T., Medina Pelaiza, L. E., Armada Pacheco, J. (2021). El reto de la Educación 4.0 a nivel universitario de cara a la emergencia por Covid-19. Boletín de Malariología y Salud Ambiental, 61 (4), 717-724. https://doi.org/10.52808/bmsa.7e5.614.020
Ishak, R., Mansor, M. (2020). The relationship between knowledge management and organizational learning with academic staff readiness for education 4.0. Eurasian Journal of Educational Research, 20 (85), 169- 184. https://dergipark.org.tr/en/pub/ejer/issue/52308/685555
Alda, R., Boholano, H., Dayagbil, F. (2020). Teacher Education Institutions in the Philippines towards Education 4.0. International Journal of Learning, Teaching and Educational Research, 19 (8), 137-154. https://doi.org/10.26803/ijlter.19.8.8
Hımmetoglu, B., Aydug, D., Bayrak, C. (2020). Education 4.0: Defining the teacher, the student, and the school manager aspects of the revolution. Turkish Online Journal of Distance Education, 21(Special IssueIODL), 12-28. https://doi.org/10.17718/tojde.770896
Bujang, S. D. A., Selamat, A., Krejcar, O., Maresova, P., Nguyen, N. T. (2020). Digital Learning Demand for Future Education 4.0—Case Studies at Malaysia Education Institutions. Informatics, 7 (2), 13. https://doi.org/10.3390/informatics7020013
Jamaludin, R., McKay, E., Ledger, S. (2020). Are we ready for Education 4.0 within ASEAN higher education institutions? Thriving for knowledge, industry and humanity in a dynamic higher education ecosystem?. Journal of Applied Research in Higher Education, 12 (5), 1161-1173. https://doi.org/10.1108/JARHE-06-2019-0144
Eckmann, J.-P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters, 4(9), 973–977. https://doi.org/10.1209/0295-5075/4/9/004
Marwan, N. (2008). A historical review of recurrence plots. European Physical Journal Special Topics, 164, 3–12. https://doi.org/10.1140/epjst/e2008-00829-1
Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5), 237–329. https://doi.org/10.1016/j.physrep.2006.11.001
Webber, C. L., Jr., & Marwan, N. (Eds.). (2015). Recurrence quantification analysis: Theory and best practices. Springer. https://doi.org/10.1007/978-3-319-07155-8
Pánis, R., Adámek, K., & Marwan, N. (2023). Averaged recurrence quantification analysis. European Physical Journal Special Topics, 232, 47–56. https://doi.org/10.1140/epjs/s11734-022-00686-4
Rysak, A., Sedlmayr, M., & Gregorczyk, M. (2023). Revealing fractionality in the Rössler system by recurrence quantification analysis. European Physical Journal Special Topics, 232, 83–98. https://doi.org/10.1140/epjs/s11734-022-00740-1
Marwan, N., & Webber, C. L., Jr. (2014). Mathematical and computational foundations of recurrence quantifications. In C. L. Webber Jr. & N. Marwan (Eds.), Recurrence quantification analysis: Theory and best practices (pp. 3–43). Springer.
Zou, Y., Thiel, M., Romano, M. C., Read, P., & Kurths, J. (2008). Recurrence analysis of quasiperiodicity in experimental fluid data. European Physical Journal Special Topics, 164, 23–33. https://doi.org/10.1140/epjst/e2008-00831-7
Braun, T., Kraemer, K. H., & Marwan, N. (2022). Recurrence flow measure of nonlinear dependence. European Physical Journal Special Topics, 232. https://doi.org/10.1140/epjs/s11734-022-00687-3
Hirata, Y., & Aihara, K. (2011). Erratum: Devaney's chaos on recurrence plots. Physical Review E, 83.
Suresha, S., Sujith, R., Emerson, B., & Lieuwen, T. (2016). Nonlinear dynamics and intermittency in a turbulent reacting wake with density ratio as bifurcation parameter. Physical Review E, 94, 042206. https://doi.org/10.1103/PhysRevE.94.042206
Schinkel, S., Dimigen, O., & Marwan, N. (2008). Selection of recurrence threshold for signal detection. European Physical Journal Special Topics, 164, 15–53. https://doi.org/10.1140/epjst/e2008-00833-5
Syta, A., Jonak, J., Jedliński, Ł., & Litak, G. (2012). Failure diagnosis of a gear box by recurrences. Journal of Vibration and Acoustics, 134. https://doi.org/10.1115/1.4005846
Kecik, K., Smagala, A., & Liubytska, K. (2022). Ball bearing fault diagnosis using recurrence analysis. Materials, 15(17), 5940. https://doi.org/10.3390/ma15175940
García, S., Romo, M., & Figueroa-Nazuno, J. (2013). Characterization of ground motions using recurrence plots. Geofísica Internacional, 52, 209–227. https://doi.org/10.1016/S0016-7169(13)71473-9
Baghdadi, G., Amiri, M., Falotico, E., & Laschi, C. (2021). Recurrence quantification analysis of EEG signals for tactile roughness discrimination. International Journal of Machine Learning and Cybernetics, 12, 1–22. https://doi.org/10.1007/s13042-020-01224-1
Furman, Ł., Duch, W., Minati, L., & Tołpa, K. (2023). Short-time Fourier transform and embedding method for recurrence quantification analysis of EEG time series. European Physical Journal Special Topics, 232, 135–149. https://doi.org/10.1140/epjs/s11734-022-00683-7
Frilot, C., Kim, P. Y., Carrubba, S., McCarty, D. E., Chesson, A. L., & Marino, A. A. (2015). Analysis of brain recurrence. In C. L. Webber Jr. & N. Marwan (Eds.), Recurrence quantification analysis (pp. 213–251). Springer. https://doi.org/10.1007/978-3-319-07155-8_7
Frolov, N., Pitsik, E., Maksimenko, V., et al. (2023). Applying recurrence time entropy to identify changes in event-related potentials. European Physical Journal Special Topics, 232, 161–168. https://doi.org/10.1140/epjs/s11734-022-00743-y
Badarin, A., Brusinskii, N., Grubov, V., et al. (2024). Recurrency time entropy of brain wave rhythms as an indicator of performance on visual search tasks in schoolchildren. European Physical Journal Special Topics. https://doi.org/10.1140/epjs/s11734-024-01348-3
Calderón-Juárez, M., González Gómez, G. H., Echeverría, J. C., et al. (2023). Recurrence quantitative analysis of heart rate variability during intradialytic hypotension. European Physical Journal Special Topics, 232, 111–121. https://doi.org/10.1140/epjs/s11734-022-00688-2
Calderón-Juárez, M., Gutiérrez Alvarado, D. A., González Gómez, G. H., et al. (2023). Recurrence plot analysis of heart rate variability in end-stage renal disease. European Physical Journal Special Topics, 232, 99–110. https://doi.org/10.1140/epjs/s11734-022-00682-8
López Pérez, D., Bokde, A. L. W., & Kerskens, C. M. (2023). Complexity analysis of heartbeat-related signals in brain MRI time series as a potential biomarker for ageing and cognitive performance. European Physical Journal Special Topics, 232, 123–133. https://doi.org/10.1140/epjs/s11734-022-00696-2
Kędra, M. (2023). Dam-induced changes in river flow dynamics revealed by RQA. European Physical Journal Special Topics, 232, 209–215. https://doi.org/10.1140/epjs/s11734-022-00689-1
Li, S., Zhao, Z., & Liu, F. (2008). Identifying spatial pattern of NDVI series dynamics using recurrence quantification analysis. European Physical Journal Special Topics, 164, 127–139. https://doi.org/10.1140/epjst/e2008-00839-y
Rivas-Cisneros, D. E. (2023). Use of recurrence plots to find mutations in deoxyribonucleic acid sequences. Complex Systems, 32(1), 89–100. https://doi.org/10.25088/ComplexSystems.32.1.89
Rivas-Cisneros, D. E., y Rivas-Cisneros, A. E. (2025). Análisis de recurrencia: de señales de ingeniería a la mejora continua en procesos de manufactura. Multidisciplinas de la Ingeniería, 13(22), 120–128. https://doi.org/10.29105/mdi.v13i22.343
Rivas Cisneros, D., Díaz Romero, D. & Alcorta García, E. (2025). Reccurence Analysis as a Tool for Identifying Incipient Faults. Memorias del Congreso Nacional de Control Automático 2025, pp. 50-55. https://doi.org/10.58571/CNCA.AMCA.2025.009
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