Possibilities offered by progression modeling as an active learning strategy in basic engineering courses

Authors

  • Hernán Paz Penagos Universidad Escuela Colombiana de Ingeniería Julio Garavito

DOI:

https://doi.org/10.26507/paper.4413

Keywords:

Active learning, Physical-mathematical representation, Pedagogical strategy, Educational engineering, Progression modeling, Conceptual understanding

Abstract

Models become dynamic tools that reveal the underlying complexity of phenomena by allowing the description, explanation, and prediction of an object's behavior through physical-mathematical and/or graphical representation. Ignoring this strategy can limit the true understanding of phenomena, since the opportunity for appropriation and conceptual significance of knowledge is lost in contrast to the traditional arbitrary construction of conceptual relationships and short-term memorization. The problem investigated is in science education and is based on Henri Poincaré and D. Ausubel's learning theories, which emphasize the importance of attributing meaning to symbols and representations beyond mere memorization and their application to the field of engineering. By conceiving progression modeling as a gradual and creative activity that ranges from simple representations to sophisticated models, this article proposes a teaching strategy through progression modeling that can overcome the traditional fragmentation of knowledge, thus transforming science education from a passive process of absorption to an active journey of discovery and construction. The methodological design is qualitative and involves instrumental and multiple case studies. To validate the teaching strategy, two interventions were conducted in the media course of the electronic engineering program at the Colombian Julio Garavito School of Engineering during the 2024-2024 semesters. The purpose was to promote the development of students' cognitive and metacognitive skills, such as analysis, synthesis, conceptualization, information representation, systemic and critical thinking, self-regulation of learning, and academic independence. The data collection instrument was participant observation, through which students' verbalizations and actions (how they acted) were compared in the work groups, to investigate and interpret changes in the cognitive and metacognitive dimensions. The results obtained highlighted the importance of considering progression modeling in engineering education, which favors a multiplicity of perspectives for constructive interaction and cooperation in a true reciprocity of exchange for integration and synthesis when identifying the transmission medium hidden in a black box. It also rescues the self-regulatory function of learning processes – which directs, controls efforts and manages persistence to face the difficulty in solving the problem – and academic autonomy with responsibility – which makes the empowerment of subjects possible – for the metacognitive development of the students themselves.

Author Biography

Hernán Paz Penagos, Universidad Escuela Colombiana de Ingeniería Julio Garavito

Ingeniería Electrónica

References

Adúriz-Bravo, A. (2012). Algunas características clave de los modelos científicos relevantes para la educación química. Educación Química, 23, 1-9. https://doi.org/10.1016/S0187-893X(17)30151-9

Ausubel, D. (2002). Adquisición y retención del conocimiento. Una perspectiva cognitiva. España: Paidós

Bartolomé, E. (2025). Didactic models for active and inquiry-based learning of machines and mechanisms. International Journal of Mechanical Engineering Education. https://doi.org/10.1177/03064190241310053

Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms, F. Weinert & R. Kluwe (Ed.): Metacognition, motivation, and understanding. Hillsdale, NJ: Erlbaum

Bunge, M. (1973). Method, Model and Matter. Dordrecht: Reidel Publishind Company. https://doi.org/10.1007/978-94-010-2519-5

Clement, J. J. y Rea-Ramírez M. A. (2008). Model Based Learning and Instruction in Science. Vol. 2. ISSN electrónico: 2213-2260 https://doi.org/10.1007/978-1-4020-6494-4. https://doi.org/10.1007/978-1-4020-6494-4

Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053. https://doi.org/10.1080/095006900416901

Fei, X., Bin, C., Rui, L., & Hu, S. (2020). A Model-Based System Engineering Approach for aviation system design by applying SysML modeling. Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020, 1361–1366. https://doi.org/10.1109/CCDC49329.2020.9164443

Flavell, J. (1978). Metacognitive aspect of problem solving in the nature of intelligence. In L.B. Resnick, (Ed.). (Pp. 231-235). New Jersey: Hillsdale. https://doi.org/10.4324/9781032646527-16

Khan, S. (2011). What’s Missing in Model-Based Teaching. Journal of Science Teacher Education, 22(6), 535–560. https://doi.org/10.1007/s10972-011-9248-x

Lavi, R., Dori, Y. J., & Dori, D. (2021). Assessing Novelty and Systems Thinking in Conceptual Models of Technological Systems. IEEE Transactions on Education, 64(2), 155–162. https://doi.org/10.1109/TE.2020.3022238

Lavi, R., Dori, Y. J., Wengrowicz, N., & Dori, D. (2020). Model-Based Systems Thinking: Assessing Engineering Student Teams. IEEE Transactions on Education, 63(1), 39–47. https://doi.org/10.1109/TE.2019.2948807

Louca, L. T., & Zacharia, Z. C. (2012). Modeling-based learning in science education: cognitive, metacognitive, social, material and epistemological contributions. Educational Review, 64(4), 471–492. https://doi.org/10.1080/00131911.2011.628748

Lu, K. L., & Chen, Y. Y. (2021). Model-based design, analysis and assessment framework for safety-critical systems. Proceedings - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2021, 25–26. https://doi.org/10.1109/DSN-S52858.2021.00023

Poincaré, H. (2008) El valor de la ciencia. Edición Oviedo. ISBN 9788483670675, págs. 384. España.

Pozo, J. y Monereo, C. (Coord.). (1999). El aprendizaje estratégico. Enseñar a aprender desde el currículo. Madrid: Santillana.

Ramírez, C. A., Thompson, A. E., & Gorthala, R. (2024). A Design for Testability (DFT) strategy for the development of a highly complex safety-critical system using a Model-Based Systems Engineering (MBSE) approach. ISSE 2024 - 10th IEEE International Symposium on Systems Engineering, Proceedings. https://doi.org/10.1109/ISSE63315.2024.10741151

Rogoff, B. (1993). Aprendices del pensamiento. El desarrollo cognitivo en el contexto social. Barcelona: Paidós

Schmidt, K. (1995). Problem-based learning: An introduction. Instructional Science, (22), pp. 247-250. https://doi.org/10.1007/BF00891778

Zárate-Navarro, M. A., Schiavone-Valdez, S. D., Cuevas, J. E., Warren-Vega, W. M., Campos-Rodríguez, A., & Romero-Cano, L. A. (2024). STEM activities for heat transfer learning: Integrating simulation, mathematical modeling, and experimental validation in transport phenomena education. Education for Chemical Engineers, 49, 81–90. https://doi.org/10.1016/j.ece.2024.06.004

Zimmerman, B., Bandura, A. y Martínez-Pons, M. (1992). Self-motivation for academic attainment: the role of self-efficacy beliefs and personal goal setting, American Educational Research Journal, 29, pp. 663-76. https://doi.org/10.3102/00028312029003663

How to Cite

[1]
H. Paz Penagos, “Possibilities offered by progression modeling as an active learning strategy in basic engineering courses”, EIEI ACOFI, Sep. 2025.

Downloads

Download data is not yet available.

Published

2025-09-08
Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views
Escanea para compartir
QR Code
Crossref Cited-by logo