Project- or discovery-based learning activities promote curiosity, enjoyment, and interest deriving from the stimulating context in which students operate. Providing a concrete contextualization of laboratory activities could improve student motivation and learning outcomes. In this contribution, a case study related to a workshop on laboratory activities proposed for Engineering Master students is presented, and designed with the aim of developing practical competencies, increasing problem-solving skills, and providing design abilities. Using the facilities available in the Measurements and Control Laboratory, the students, starting from concept knowledge acquired in basic subjects, such as Physics, Chemistry, Mathematics, and Electronics, design and implement their experiments, gaining a deeper understanding of core disciplinary concepts while strengthening soft and teamwork skills. The challenges and possibilities of these self-directed thinking and learning laboratory activities are also discussed.
LindseyBAHsuLSadaghianiH, et al.Positive attitudinal shifts with the physics by inquiry curriculum across multiple implementations. Phys Rev ST Phys Educ Res2012; 8: 010102.
3.
PizzolatoNFazioCSperandeo MineoRM, et al.Open-inquiry driven overcoming of epistemological difficulties in engineering undergraduates: a case study in the context of thermal science. Phys Rev ST Phys Educ Res2014; 10: 010107 (25pp).
4.
WeiYZhaiZGunnarssonK, et al.A guided enquiry approach to introduce basic concepts concerning magnetic hysteresis to minimize student misconceptions. Eur J Phys2014; 35: 065015.
5.
Persano AdornoDPizzolatoNFazioC. Elucidating the electron transport in semiconductors via Monte Carlo simulations: an inquiry-driven learning path for engineering undergraduates. Eur J Phys2015; 36: 055017.
6.
Persano AdornoDPizzolatoNFazioC. Long term stability of learning outcomes in undergraduates after an open-inquiry instruction on thermal science. Phys Rev Phys Educ Res2018; 14: 010108 (11pp).
7.
Persano AdornoD. Inquiry-based environments for bio-signal processing training in engineering education. Int J Mech Eng Educ2022; 50: 629–647.
8.
Persano AdornoDBellomonteL. Active learning in a real-world bioengineering problem: a pilot-study on ophthalmologic data processing. Comput Appl Eng Educ2019; 27: 485–499.
9.
TolomiczenkoGSangerT. Linking engineering and medical training: a USC program seeks to introduce medical and engineering students to medical device development. IEEE Pulse2015; 6: 32–36.
10.
Garduño-AparicioMRodríguez-ReséndizJMacias-BobadillaG, et al.A multidisciplinary industrial robot approach for teaching mechatronics-related courses. IEEE Trans Educ2018; 61: 55–62.
11.
SinghAFerryDMillsS. Improving biomedical engineering education through continuity in adaptive, experiential, and interdisciplinary learning environments. J Biomech Eng2018; 140: 081009.
12.
KohCTanHSTanKC, et al.Investigating the effect of 3D simulation-based learning on the motivation and performance of engineering students. J Eng Educ2010; 99: 237–251.
13.
Ortiz-EcheverriCJRodríguez-ReséndizJGarduño-AparicioM. An approach to STFT and CWT learning through music hands-on labs. Comput Appl Eng Educ2018; 26: 2026–2035.
14.
KimHHannafinMJ. Developing situated knowledge about teaching with technology via web-enhanced case-based activity. Comput Educ2011; 57: 1378–1388.
15.
EberhardtAWJohnsonOLKirklandWB, et al.Team-based development of medical devices: An engineering–business collaborative. J Biomech Eng2016; 138: 070803.
16.
Morss ClyneABilliarKL. Problem-based learning in biomechanics: advantages, challenges, and implementation strategies. J Biomech Eng2016; 138: 070804.
17.
LuoG. A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third year students. Int J Electr Eng Educ2019; 56: 51–61.
18.
BerryJLNolesKEberhardtA,et al.Accelerating clinical innovation in biomedical engineering education by using a digital portal for collaboration. J Biomech Eng2019; 141: 121003–5.
19.
BanchiHBellR. The many levels of inquiry. Sci Child2008; 46: 26–29.
20.
SadlerTDBurginSMcKinneyL, et al.Learning science through research apprenticeships: a critical review of the literature. J Res Sci Teach2010; 47: 235–256.
21.
BergCARBergendahlVCBLundbergBKS. Benefiting from an open-ended experiment? A comparison of attitudes to, and outcomes of, an expository versus an open-inquiry version of the same experiment. Int J Sci Educ2003; 25: 351–372.
22.
CappsCKCrawfordBA. Inquiry-based instruction and teaching about nature of science: are they happening?J Sci Teacher Educ2013; 24: 497–526.
23.
FlickLLedermanNG. Scientific inquiry and nature of science. In Contemporary Trends and Issues in Science Education, Flick L and Lederman NG (eds.), Vol. 25, 2006. ISBN 978-1-4020-2671-3.
24.
PahlGBeitzWFeldhusenJ, et al.Engineering design: a systematic approach. London: Springer-Verlag, 2007.
25.
KumarSKent HsiaoJ. Engineers learn “soft skills the hard way”: planting a seed of leadership in engineering classes. Lead Manag Eng2007; 7: 18–23.
26.
EnglishLDKingD. STEM learning through engineering design: fourth-grade students’ investigations in aerospace. IJ STEM Ed2015; 2: 14.
27.
GeorgiouKLarentzakisAPapavassiliouAG. Surgeons' and surgical trainees' acute stress in real operations or simulation: a systematic review. Surgeon2017; 15(6): 355–365.
28.
HernandoALazaroJGilE, et al.Inclusion of respiratory frequency information in heart rate variability analysis for stress assessment. IEEE J Biomed Health Inform2016; 20: 1016–1025.
29.
GrantcharovPDBoillatTElkabanyS, et al.Acute mental stress and surgical performance. BJS Open2019; 3: 119–125.
30.
CastanedaDEsparzaAGhamariM, et al.A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron2018; 4: 195–202.
31.
VescioBSalsoneMGambardellaA, et al.Comparison between electrocardiographic and earlobe pulse photoplethysmographic detection for evaluating heart rate variability in healthy subjects in short- and long-term recordings. Sensors2018; 18: 44.
32.
ScardullaFD’AcquistoLColombariniR, et al.A study on the effect of contact pressure during physical activity on photoplethysmographic heart rate measurements. Sensors2020; 20: 5052.
33.
Persano AdornoDGiaconiaGC. Processing f-NIRS brain oxygenation signals: an active learning environment designed for science and engineering students, submitted to Applied Sciences (2022).
34.
MeredithDJCliftonDCharltonP, et al.Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J Med Eng Technol2012; 36(1): 1–7. https://doi.org/10.3109/03091902.2011.638965
