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Computer-Aided Teaching Using SimMechanics and Matlab for Project-Based Learning in a Robotics Course

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Abstract

The present article reports a computer-aided teaching strategy, which is novel, using a MATLAB toolbox (i.e. SimMechanics) for project-based learning in a mechanical engineering robotics course (i.e. robot analysis). The effectiveness of the proposed teaching strategy was evaluated using multiple lenses of critical reflection such as student-reflection using numerous students’ surveys, and students’ grades comparison. Furthermore, the performance of this new teaching strategy was compared with a traditional teaching method, during which, traditional MATLAB solvers (e.g. ODE) were employed rather than SimMechanics. The results showed that the proposed teaching strategy helped students more than traditional teaching method to better understand and visualize ‘robot analysis’ concepts such as 3D drawing, dynamic modelling, and solving differential equations. The majority of students were more involved in learning, were able to achieve better learning outcomes, and performances.

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References

  1. Behrens A et al (2009) MATLAB meets LEGO mindstorms—a freshman introduction course into practical engineering. IEEE Trans Educ 53(2):306–317

    Article  Google Scholar 

  2. Ruiz J-RR, Espinosa AG, Romeral L (2009) A computer model for teaching the dynamic behavior of AC contactors. IEEE Trans Educ 53(2):248–256

    Article  Google Scholar 

  3. Edwards CH, Penney DE, Calvis DT (2016) Differential equations and boundary value problems. Pearson Education Limited, London

    Google Scholar 

  4. Auerbach JE, Concordel A, Kornatowski PM, Floreano D (2019) Inquiry-based learning with RoboGen: an open-source software and hardware platform for robotics and artificial intelligence. IEEE Trans Learn Technol 12(3):356–369. https://doi.org/10.1109/TLT.2018.2833111

    Article  Google Scholar 

  5. Dos Santos Lopes MS, Gomes IP, Trindade RMP, Da Silva AF, Lima ACDC (2017) Web environment for programming and control of a mobile robot in a remote laboratory. IEEE Trans Learn Technol 10(4):526–531. https://doi.org/10.1109/TLT.2016.2627565

    Article  Google Scholar 

  6. Odry Á, Fullér R, Rudas IJ, Odry P (2020) Fuzzy control of self-balancing robots: a control laboratory project. Comput Appl Eng Educ. https://doi.org/10.1002/cae.22219

    Article  Google Scholar 

  7. Ziaeefard S, Miller MH, Rastgaar M, Mahmoudian N (2017) Co-robotics hands-on activities: a gateway to engineering design and STEM learning. Robot Auton Syst 97:40–50. https://doi.org/10.1016/j.robot.2017.07.013

    Article  Google Scholar 

  8. Atmatzidou S, Demetriadis S (2016) Advancing students’ computational thinking skills through educational robotics: a study on age and gender relevant differences. Robot Auton Syst 75:661–670. https://doi.org/10.1016/j.robot.2015.10.008

    Article  Google Scholar 

  9. Hosseinzadeh N, Hesamzadeh MR (2012) Application of project-based learning (PBL) to the teaching of electrical power systems engineering. IEEE Trans Educ 55(4):495–501

    Article  Google Scholar 

  10. Sanchez-Romero J-L, Jimeno-Morenilla A, Pertegal-Felices ML, Mora-Mora H (2019) Design and application of project-based learning methodologies for small groups within computer fundamentals subjects. IEEE Access 7:12456–12466

    Article  Google Scholar 

  11. Macías-Guarasa J, Montero JM, San-Segundo R, Araujo Á, Nieto-Taladriz O (2006) A project-based learning approach to design electronic systems curricula. IEEE Trans Educ 49(3):389–397

    Article  Google Scholar 

  12. Barak M, Dori YJ (2005) Enhancing undergraduate students’ chemistry understanding through project-based learning in an IT environment. Sci Educ 89(1):117–139

    Article  Google Scholar 

  13. Tseng K-H, Chang C-C, Lou S-J, Chen W-P (2013) Attitudes towards science, technology, engineering and mathematics (STEM) in a project-based learning (PjBL) environment. Int J Technol Des Educ 23(1):87–102

    Article  Google Scholar 

  14. Wurdinger S, Qureshi M (2015) Enhancing college students’ life skills through project based learning. Innov High Educ 40(3):279–286

    Article  Google Scholar 

  15. Bédard D, Lison C, Dalle D, Côté D, Boutin N (2012) Problem-based and project-based learning in engineering and medicine: determinants of students’ engagement and persistance. Interdiscip J Probl Based Learn 6(2):8

    Google Scholar 

  16. Lattimer H, Riordan R (2011) Project-based learning engages students in meaningful work: students at High Tech Middle engage in project-based learning. Middle Sch J 43(2):18–23

    Article  Google Scholar 

  17. Montero E, Gonzalez MJ (2008) Student engagement in a structured problem-based approach to learning: a first-year electronic engineering study module on heat transfer. IEEE Trans Educ 52(2):214–221

    Article  Google Scholar 

  18. Jollands M, Jolly L, Molyneaux T (2012) Project-based learning as a contributing factor to graduates’ work readiness. Eur J Eng Educ 37(2):143–154

    Article  Google Scholar 

  19. Capraro RM, Capraro MM, Morgan JR (2013) STEM project-based learning: An integrated science, technology, engineering, and mathematics (STEM) approach. Springer, Berlin

    Book  Google Scholar 

  20. Chua KJ, Yang W, Leo H (2014) Enhanced and conventional project-based learning in an engineering design module. Int J Technol Des Educ 24(4):437–458

    Article  Google Scholar 

  21. Hernandez W, Maldonado-Correa JL (2016) Power performance verification of a wind turbine by using the Wilcoxon signed-rank test. IEEE Trans Energy Convers 32(1):394–396

    Article  Google Scholar 

  22. Karim F, Majumdar S, Darabi H, Chen S (2017) LSTM fully convolutional networks for time series classification. IEEE Access 6:1662–1669

    Article  Google Scholar 

  23. Souza IM, Andrade WL, Sampaio LM, Araujo ALSO (2018) A systematic review on the use of LEGO® robotics in education. In: 2018 IEEE frontiers in education conference (FIE), 2018. IEEE, pp 1–9

  24. Zhang L, Nouri J (2019) A systematic review of learning computational thinking through Scratch in K-9. Comput Educ 141:103607

    Article  Google Scholar 

  25. Hoxha B, Mavridis N, Fainekos G (2015) VISPEC: a graphical tool for elicitation of MTL requirements. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2015. IEEE, pp 3486–3492

  26. Tanoto A, Gómez JV, Mavridis N, Li H, Rückert U, Garrido S (2013) Teletesting: path planning experimentation and benchmarking in the Teleworkbench. In: 2013 European conference on mobile robots, 2013. IEEE, pp 343–348

  27. Torres F, Candelas-Herías FA, Puente Méndez ST, Pomares J, Gil P, Ortiz Zamora FG (2006) Experiences with virtual environment and remote laboratory for teaching and learning robotics at the University of Alicante.

  28. Yadegaridehkordi E, Noor NFBM, Ayub MNB, Affal HB, Hussin NB (2019) Affective computing in education: a systematic review and future research. Comput Educ 142:103649

    Article  Google Scholar 

  29. Costa S, Brunete A, Bae B-C, Mavridis N (2018) Emotional storytelling using virtual and robotic agents. Int J Humanoid Rob 15(03):1850006

    Article  Google Scholar 

  30. Al-Ibadi A, Nefti-Meziani S, Davis S (2020) Controlling of pneumatic muscle actuator systems by parallel structure of neural network and proportional controllers (PNNP). Front Robot AI 7:1–10

    Article  Google Scholar 

  31. Irshaidat M, Soufian M, Al-Ibadi A, Nefti-Meziani S (2019) A novel elbow pneumatic muscle actuator for exoskeleton arm in post-stroke rehabilitation. In: 2019 2nd IEEE international conference on soft robotics (RoboSoft), 2019. IEEE, pp 630–635

  32. Biggs JB, Collis KF (2014) Evaluating the quality of learning: the SOLO taxonomy (Structure of the observed learning outcome). Academic Press, Cambridge

    Google Scholar 

  33. Jimoyiannis A (2013) Using SOLO taxonomy to explore students’ mental models of the programming variable and the assignment statement. Themes Sci Technol Educ 4(2):53–74

    Google Scholar 

  34. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge University of Canberra, the University of Auckland, Nazarbayev University, and the American University of the Middle East Kuwait for their support.

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Correspondence to Shahid Hussain.

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Hussain, S., Jamwal, P.K. & Munir, M.T. Computer-Aided Teaching Using SimMechanics and Matlab for Project-Based Learning in a Robotics Course. Int J of Soc Robotics 14, 85–94 (2022). https://doi.org/10.1007/s12369-021-00769-7

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  • DOI: https://doi.org/10.1007/s12369-021-00769-7

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