When teaching robotics, instructors face the challenge of finding an effective approach to bridge theoretical concepts and practical applications. Both computer simulations and hands-on laboratory experiments provide learners with opportunities for active, immersive, and experiential learning. As students progress from introductory to advanced topics and from theory to practice, their performance is contingent upon earlier knowledge and may increase, remain unchanged, or decrease. The question that arises is whether computer simulation can serve as a viable foundation for fostering an understanding of theory that enables the subsequent grasp of advanced practical concepts in robotics. Put another way, when students are introduced to the field of robotics through computer simulation, how will they perform when presented with advanced hands-on tasks involving the construction of physical robots to solve problems in physical space? To answer this question, we examined undergraduate student performance (n = 107) across two robotics courses—an introductory course using computer simulation (Robot Operating System, Rviz, and GAZEBO) and an advanced course using physical hardware (Puzzlebot), leveraging the hardware's capability for AI tasks such as machine vision (Nvidia Jetson Nano development kit). Our findings suggest that student performance increased as they progressed from using computer simulation to engaging with hardware in the physical environment, further suggesting that teaching with computer simulations provides an adequate foundation to learn and complete more advanced tasks.
Álvarez-PérezPRLópez-AguilarDGonzález-MoralesMO, et al.Academic engagement and dropout intention in undergraduate university students. J of Coll Stud Retention: Res, Theory Pract2021. DOI: 10.1177/15210251211063611.
2.
DaempflePA. An analysis of the high attrition rates among first year college science, math, and engineering majors. J of Coll Stud Retention: Res, Theory & Pract2003; 5: 37–52.
3.
JeniferJBRozekCSLevineSC, et al.Effort(less) exam preparation: Math anxiety predicts the avoidance of effortful study strategies. J of Exp Psychol: Gen2022; 151: 2534–2541.
4.
LancasterTCotarlanC. Contract cheating by STEM students through a file sharing website: A COVID-19 pandemic perspective. Int J Educ Integr2021; 17: 3.
5.
García-HolgadoAMenaJGarcía-PeñalvoFJ, et al.Gender equality in STEM programs: A proposal to analyse the situation of a university about the gender gap. In: 2020 IEEE global engineering education conference (EDUCON), 2020, pp.1824–1830.
6.
García-HolgadoAVerdugo-CastroSGonzálezC, et al.European proposals to work in the gender gap in STEM: A systematic analysis. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje2020; 15: 215–224.
7.
PivaERovelliP. Mind the gender gap: The impact of university education on the entrepreneurial entry of female and male STEM graduates. Small Bus Econ2022; 59: 143–161.
8.
Verdugo-CastroSGarcía-HolgadoASánchez-GómezMC. The gender gap in higher STEM studies: A systematic literature review. Heliyon2022; 8: e10300.
9.
WangM-TDegolJL. Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educ Psychol Rev2017; 29: 119–140.
10.
BeierMEKimMHSaterbakA, et al.The effect of authentic project-based learning on attitudes and career aspirations in STEM. J Res Sci Teach2019; 56: 3–23.
11.
HoachlanderGYanofskyD. Making STEM real. Educ Leadersh2011; 68: 60–65.
12.
HuC-CYehH-CChenN-S. Enhancing STEM competence by making electronic musical pencil for non-engineering students. Comput Educ2020; 150: 103840.
13.
KelleyTRKnowlesJG. A conceptual framework for integrated STEM education. Int J of STEM Educ2016; 3: 11.
14.
SimarroCCousoD. Engineering practices as a framework for STEM education: A proposal based on epistemic nuances. Int J of STEM Educ2021; 8: 1–12.
15.
JongDVan JoolingenTRW. Scientific discovery learning with computer simulations of conceptual domains. Rev Educ Res1998; 68: 179–201.
16.
CrespoRLopez-CaudanaEPonceP. An immersive week for undergraduate engineering students for developing IoT competencies A case study at tecnologico De monterrey in Mexico. In: 2019 IEEE 11th international conference on engineering education (ICEED), 2019, pp.90–95.
17.
O’KellyJGibsonJP.RoboCode & problem-based learning: a non-prescriptive approach to teaching programming. 2006, pp.217–221.
18.
DahmKDHeskethRPSavelskiMJ. Is process simulation used effectively in ChE courses?Chem Eng Educ2002; 36: 192–203.
19.
ChoHJZhaoKLeeCR, et al.Active learning through flipped classroom in mechanical engineering: Improving students’ perception of learning and performance. IJ STEM Ed2021; 8: 46.
20.
AmigudA.Post-traditional learning analytics: How data and information technology transform learning environment. In: Emerging trends in learning analytics. Leiden: Brill, 2019, pp.13–25.
21.
ColeDJ. Bridging Traditional and Authentic Assessment.
22.
DrewVMackieL. Extending the constructs of active learning: Implications for teachers’ pedagogy and practice. Curriculum J2011; 22: 451–467.
23.
FreemanSEddySLMcDonoughM, et al.Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci USA2014; 111: 8410–8415.
24.
MachemerPLCrawfordP. Student perceptions of active learning in a large cross-disciplinary classroom. Active Learn In High Educ2007; 8: 9–30.
25.
Sutherland-SmithW. Crossing the line: Collusion or collaboration in university group work?Aust Univ’ Rev2013; 55: 51–58.
26.
TrullàsJCBlayCSarriE, et al.Effectiveness of problem-based learning methodology in undergraduate medical education: A scoping review. BMC Med Educ2022; 22: 104.
27.
HelleLTynjäläPOlkinuoraE. Project-based learning in post-secondary education–theory, practice and rubber sling shots. High Educ2006; 51: 287–314.
28.
AdderleyK. Project methods in higher education. UK: Society for Research into Higher Education, 1975.
29.
MorganA. Theoretical aspects of project-based learning in higher education. Br J Educ Technol1983; 14: 66–78.
30.
SivanALeungRWWoonC, et al.An implementation of active learning and its effect on the quality of student learning. Innov Educ Train Int2000; 37: 381–389.
31.
WangY. Open space learning circle and active learning in English communication class. Eur J of Soc Sci2009; 11: 477–485.
32.
LazarAM. Who is studying in groups and why?Coll Teach1995; 43: 61–65.
33.
WatkinsCCarnellELodgeC. Effective learning in classrooms. UK: SAGE Publications, 2007.
34.
Plato. The Laws. UK: Taylor & Francis, 2005.
35.
RussellB. On education. Routledge, 2014.
36.
RosenthalJS. Active learning strategies in advanced mathematics classes. Stud in High Educ1995; 20: 223–228.
37.
HungH-T. Flipping the classroom for English language learners to foster active learning. Comput Assist Lang Learn2015; 28: 81–96.
38.
FinkelsteinNDAdamsWKKellerCJ, et al.When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Phys Rev ST Phys Educ Res2005; 1: 010103.
39.
JongDLinnTZachariaMC,et al.Physical and virtual laboratories in science and engineering education. Science2013; 340: 305–308.
40.
ChoiHCrumpCDuriezC, et al.On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward. Proc Natl Acad Sci USA2021; 118: e1907856118.
41.
RuttenNvan JoolingenWRvan der VeenJT. The learning effects of computer simulations in science education. Comput Educ2012; 58: 136–153.
42.
SmetanaLKBellRL. Computer simulations to support science instruction and learning: A critical review of the literature. Int J Sci Educ2012; 34: 1337–1370.
43.
JohnsonDWJohnsonRSmithK. Active learning: Cooperation in the college classroom. Minnesota: Interaction Book Company, 1998.
44.
SmithKA. Cooperative learning: Making “groupwork” work. Vol. 67. In C Bonwell and T Sutherlund (eds) Active learning: Lessons from practice and emerging issues. New Directions for Teaching and Learning San Francisco: Jossey-Bass, 1996: 71–82.
45.
AmigudAHosseiniS. Collaboration, collusion, and barter-cheating: An analysis of academic help-seeking behaviors. Assess Eval in High Educ2023. DOI: 10.1080/02602938.2023.2259631.
46.
MukhopadhyayMPalSNayyarA, et al.Facial emotion detection to assess learner’s state of mind in an online learning system. In: Proceedings of the 2020 5th international conference on intelligent information technology. New York, NY, USA: Association for Computing Machinery, pp.107–115.
47.
ShenLWangMShenR. Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. J of Educ Technol Soc2009; 12: 176–189.
48.
BrownJSCollinsADuguidP. Situated cognition and the culture of learning. Educ Researcher1989; 18: 32–42.
49.
HsuRTsaiT. Assessing the impact of a project-based learning robotics course with integrating of STEM education using content analysis method. Eur J of STEM Educ2022; 7: 09.
50.
SchnotzWBannertM. Construction and interference in learning from multiple representation. Learn Instr2003; 13: 141–156.
51.
DeciELRyanRM. Self-determination theory: A macrotheory of human motivation, development, and health. Can Psychol/Psychologie Canadienne2008; 49: 182.
52.
ReeveJ. Self-determination theory applied to educational settings. Handbook of Self-Determination Res2002; 2: 183–204.
53.
WangHZhouCWuY.Smart cup, wisdom creation: A project-based learning initiative for maker education. IEEE, 2016, pp.486–488.
54.
JohnsonDWJohnsonRT. Cooperative learning: The foundation for active learning. Active learning—Beyond the future.
55.
ChengSHwangGChenC. From reflective observation to active learning: A mobile experiential learning approach for environmental science education. Br J Educ Technol2019; 50: 2251–2270.
56.
YeomanPWilsonS. Designing for situated learning: Understanding the relations between material properties, designed form and emergent learning activity. Br J Educ Technol2019; 50: 2090–2108.
57.
GuerraMShealyT. Teaching user-centered design for more sustainable infrastructure through role-play and experiential learning. J Profl Issues Eng Educ Pract2018; 144: 05018016.
58.
DichevaDHodgeA.Active learning through game play in a data structures course. 2018, pp.834–839.
59.
PhippsMPhippsCKaskS, et al.University Students’ perceptions of cooperative learning: Implications for administrators and instructors. J of Exper Educ2001; 24: 14–22.
60.
YataCOhtaniTIsobeM. Conceptual framework of STEM based on Japanese subject principles. Int J of STEM Educ2020; 7: 12.
61.
GhobakhlooM. Industry 4.0, digitization, and opportunities for sustainability. J Cleaner Prod2020; 252: 119869.
LópezHAPoncePMolinaA, et al.Design framework based on TEC21 educational model and education 4.0 implemented in a capstone project: A case study of an electric vehicle suspension system. Sustainability2021; 13: 5768.
64.
Nolazco-FloresJASwain-OropezaR. Engineering school women faculty evaluation in Tec21 competence educational model. In: IEEE, 2021, pp.851–856.
65.
Swain-OropezaRRenteria-SalcedoJA. Tec21 can be an educational model for a VUCA world. In: 2019 IEEE 11th international conference on engineering education (ICEED), 2019, pp.147–152.
66.
Blanco-ClaracoJL.A tutorial on SE(3) transformation parameterizations and on-manifold optimization. arXiv preprint arXiv:210315980.
CassS. Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects-[hands on]. IEEE Spectr2020; 57: 14–16.
