Exploring the Efficacy of Simulation-Based Learning (SBL) for Enhancing Program Outcomes in Mechanical Engineering: A Case Study on Engineering Graphics

Authors

  • Pankaj Beldar Department of Mechanical Engineering, K.K.Wagh Institute of Engineering Education and research, Savitribai Phule Pune University
  • Atulkumar Patil Department of Mechanical Engineering, K.K.Wagh Institute of Engineering Education and research, Savitribai Phule Pune University
  • Mahesh Shelar Department of Mechanical Engineering, K.K.Wagh Institute of Engineering Education and research, Savitribai Phule Pune University
  • Vishal Galande Department of Robotics and Automation, K.K.Wagh Institute of Engineering Education and research, Savitribai Phule Pune University

DOI:

https://doi.org/10.16920/jeet/2026/v39i3/26089

Keywords:

Simulation based learning, Modern tools usage, Program Outcomes, Attainment.

Abstract

This research investigates the effectiveness of Simulation-Based Learning (SBL) in enhancing student comprehension of complex mechanical engineering concepts, such as fluid dynamics, thermodynamics, FEA, and mechanics. The integration of tools like MATLAB, SolidWorks, ANSYS Fluent, AutoCAD, FluidSim, BricsCAD, and GeoGebra allows students to simulate theoretical mechanics and engineering graphics, bridging the gap between theory and practice. Additionally, Machine Learning playgrounds offer hands-on AI experience. A key novelty is the development of an Automatic Engineering Drawing Sheet Evaluation Algorithm, utilizing Python-based image processing to automate the assessment of technical drawings, increasing efficiency and accuracy. GeoGebra further serves as a tool for assessing geometric transformations in engineering graphics. Integrating simulation tools into the first-year Engineering Graphics course has significantly enhanced Course Outcomes (CO) and Program Outcomes (PO), particularly PO5 (Modern Tool Usage), with a 94.29% improvement. Tools like AutoCAD and GeoGebra have enhanced student understanding of Orthographic Projections and Isometric Views, reflected in improved test scores and positive feedback. Case studies showcase how these tools align with various POs, highlighting the critical role of SBL in modern mechanical engineering education.

Downloads

Download data is not yet available.

Downloads

Published

2026-01-29

How to Cite

Beldar, P., Patil, A., Shelar, M., & Galande, V. (2026). Exploring the Efficacy of Simulation-Based Learning (SBL) for Enhancing Program Outcomes in Mechanical Engineering: A Case Study on Engineering Graphics. Journal of Engineering Education Transformations, 39(3), 146–162. https://doi.org/10.16920/jeet/2026/v39i3/26089

Issue

Volume 39, Issue 3, January 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Abburi, R., Praveena, M., & Priyakanth, R. (2021). TinkerCad—A web based application for virtual labs to help learners think, create and make. Journal of Engineering Education Transformations, 34(Special Issue). https://doi.org/10.16920/jeet/2021/v34i0/157209

Aluga, M. (2023). Application of CHATGPT in civil engineering. East African Journal of Engineering, 6(1). https://doi.org/10.37284/eaje.6.1.1272

Anees, M., Nagaraja, B. G., Yadava, G. T., & Patil, C. M. (2025). Experimental based Learning: Microcontroller Simulation Using Virtual Simulation Machine. Journal of Engineering Education Transformations, 38(3). https://doi.org/10.16920/jeet/2024/v38i3/24255

Beldar, P. (2025). Exploring the Efficacy of Open-Ended Questions in Theory-Based Subjects. Journal of Engineering Education Transformations, 38(4). https://doi.org/10.16920/jeet/2024/v38i4/25101

Beldar, P., Galande, V., Panchbhai, M., & Kavale, P. (2025). Fostering Engagement and Understanding: The Impact of Kolb’s Experiential Learning Theory on Teaching Theory of Machines. Journal of Engineering Education Transformations, 39(2). https://doi.org/10.16920/jeet/2025/v39i2/25153

Beldar, P. R. (2025). Case Study: Enhancing Learning in C Programming Through Gibbs Reflective Cycle. Journal of Engineering Education Transformations, 38(4). https://doi.org/10.16920/jeet/2024/v38i4/25096

Beldar, P. R., Munje, R. K., & Kadbhane, S. V. (2025). Enhancing Engineering Education through Drone Technology Skilling Program: Analyzing the Impact on Program Outcomes. Journal of Engineering Education Transformations, 39(2). https://doi.org/10.16920/jeet/2025/v39i2/25138

Beldar, P., Rakhade, R., Bahiram, M., & Kadbhane, S. (2025). Innovative Coding Teaching Methodologies: A Comprehensive Approach for Diverse Learners. Journal of Engineering Education Transformations, 39(2). https://doi.org/10.16920/jeet/2025/v39i2/25141

Bishara, D., Xie, Y., Liu, W. K., & Li, S. (2023). A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials. In Archives of Computational Methods in Engineering (Vol. 30, Issue 1). https://doi.org/10.1007/s11831-022-09795-8

Campos, N., Nogal, M., Caliz, C., & Juan, A. A. (2020). Simulation-based education involving online and on-campus models in different European universities. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-0181-y

Dahalan, F., Alias, N., & Shaharom, M. S. N. (2024). Gamification and Game Based Learning for Vocational Education and Training: A Systematic Literature Review. Education and Information Technologies, 29(2). https://doi.org/10.1007/s10639-022-11548-w

Davidovitch, L., Parush, A., & Shtub, A. (2006). Simulation-based learning in engineering education: Performance and transfer in learning project management. Journal of Engineering Education, 95(4). https://doi.org/10.1002/j.2168-9830.2006.tb00904.x

Deshpande, A. A., & Huang, S. H. (2011). Simulation games in engineering education: A state-of-the-art review. In Computer Applications in Engineering Education (Vol. 19, Issue 3). https://doi.org/10.1002/cae.20323

Hulme, K. F., Schiferle, M., Lim, R. S. A., Estes, A., & Schmid, M. (2021). Incorporation of modeling, simulation, and game-based learning in engineering dynamics education towards improving vehicle design and driver safety. Safety, 7(2). https://doi.org/10.3390/safety7020030

Karadoğan, E., & Karadoğan, F. (2019). Simulation-based learning modules for undergraduate engineering dynamics. Computer Applications in Engineering Education, 27(4). https://doi.org/10.1002/cae.22114

Kong, N. (2019). Active game-based learning of dynamics modeling and simulation in biomedical systems engineering. INFORMS Transactions on Education, 20(1). https://doi.org/10.1287/ited.2018.0205

Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlar, J., Damborsky, J., Pluskal, T., Sivic, J., & Mazurenko, S. (2023). Machine Learning-Guided Protein Engineering. In ACS Catalysis (Vol. 13, Issue 21). https://doi.org/10.1021/acscatal.3c02743

Mesquita, M. A. de, Mariz, F. B. de A. R., & Tomotani, J. V. (2017). The skateboard factory: A teaching case on discrete-event simulation. Production, 27(Specialissue). https://doi.org/10.1590/0103-6513.226216

Negahban, A. (2024). Simulation in engineering education: The transition from physical experimentation to digital immersive simulated environments. Simulation. https://doi.org/10.1177/00375497241229757

Nowparvar, M., Ashour, O., Ozden, S. G., Knight, D., Delgoshaei, P., & Negahban, A. (2022). An Assessment of Simulation-Based Learning Modules in an Undergraduate Engineering Economy Course. ASEE Annual Conference and Exposition, Conference Proceedings. https://doi.org/10.18260/1-2–41913

Praveena, K. S., Bhargavi, K., Mahadevaswamy, & Patil, C. M. (2025). Creating Interest On Microwave And Antenna Design Course Among Students Through Iovp Blended Teaching Method. Journal of Engineering Education Transformations, 38(3). https://doi.org/10.16920/jeet/2024/v38i3/24249

Rebello, C. M., Marrocos, P. H., Costa, E. A., Santana, V. V., Rodrigues, A. E., Ribeiro, A. M., & Nogueira, I. B. R. (2022). Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning. Processes, 10(2). https://doi.org/10.3390/pr10020250

Salazar-Peña, R., Pedroza-Toscano, M. A., López-Cuenca, S., & Zárate-Navarro, M. A. (2023). Project-based learning for an online course of simulation engineering: From bioreactor to epidemiological modeling. Education for Chemical Engineers, 42. https://doi.org/10.1016/j.ece.2022.12.002

Suthar, K. J., & Joshipura, M. H. (2023). The integrative approach of learning chemical engineering thermodynamics by using simulation-based exercises. Education for Chemical Engineers, 45. https://doi.org/10.1016/j.ece.2023.09.001

Zaher, A. A., Hussain, G. A., & Altabbakh, H. (2023). An Active Learning Approach for Applying STEAMeD-Based Education in Engineering Programs. International Journal of Engineering Pedagogy, 13(3). https://doi.org/10.3991/ijep.v13i3.34819

Most read articles by the same author(s)