Abstract
Virtual Patients (VPs) and Affective Learning are promising tools in the research for enhancement of educational efficacy. The purpose of this research is to explore the effect of medical error and the emotions it evokes on learning by using these tools.
A sample of four undergraduate medical students took part in the experiment. Each student managed two VPs, while connected to biosignal recording devices (heart rate, skin conductance, brain activity, pupil diameter). Before and after managing the VPs, each student filled in a sheet for each VP, containing one competence self-evaluation question and one knowledge assessment question, so that possible differences in their responses could be spotted.
The results showed that: a) medical errors with VPs can probably have slight effects on the affect state as indicated by the biosignals, b) some of the errors made by the students with virtual patients did contribute to learning – for the rest of the errors there were no control questions. This research was unable to establish a correlation between the affect state following an error and the learning outcome.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Young, J.Q., Ranji, S.R., Wachter, R.M., Lee, C.M., Niehaus, B., Auerbach, A.D.: “July effect”: impact of the academic year-end changeover on patient outcomes: a systematic review. Ann. Intern. Med. 155, 309–315 (2011). https://doi.org/10.7326/0003-4819-155-5-201109060-00354
Bamidis, P., Dimitrova, V., Treasure-Jones, T., Poulton, T., Roberts, T.: Augmented Minds: Technology’s role in supporting 21st Century Doctors. Work. Eur. TEL Work. Learn. Prof. Dev. (2017)
Bradley, P.: The history of simulation in medical education and possible future directions. Med. Educ. 40, 254–262 (2006). https://doi.org/10.1111/j.1365-2929.2006.02394.x
Bamidis, P.D., Abakassova, G., Poulton, T.: Guest editorial: medical curricula transformations – EPBLNET. Mefanet J. 5, 4–5 (2011)
Ellaway, R., Candler, C., Greene, P., Smothers, V.: An architectural model for MedBiquitous virtual patients. MedBiquitous, pp. 1–15 (2006)
Bamidis, P.D.: Brain Function Assessment in Learning. In: Frasson, C., Kostopoulos, G. (eds.) First International Conference, Brain Function Assessment in Learning. Springer International Publishing, Patras (2017). https://doi.org/10.1007/978-3-319-67615-9
Eva, K.W.: Diagnostic error in medical education: where wrongs can make rights. Adv. Heal. Sci. Educ. 14, 71–81 (2009). https://doi.org/10.1007/s10459-009-9188-9
Kopp, V., Stark, R., Fischer, M.R.: Fostering diagnostic knowledge through computer-supported, case-based worked examples: effects of erroneous examples and feedback. Med. Educ. 42, 823–829 (2008). https://doi.org/10.1111/j.1365-2923.2008.03122.x
Poulton, T., Balasubramaniam, C.: Virtual patients: a year of change. Med. Teach. 33, 933–937 (2011). https://doi.org/10.3109/0142159X.2011.613501
Dafli, E., Fountoukidis, I., Hatzisevastou, C., Bamidis, P.D.: Curricular integration of virtual patients: a unifying perspective of medical teachers and students. BMC Med. Educ. 19, 1–11 (2019). https://doi.org/10.1186/s12909-019-1849-7
Dafli, E., Antoniou, P., Ioannidis, L., Dombros, N., Topps, D., Bamidis, P.D.: Virtual patients on the semantic web: a proof-of-application study. J. Med. Internet Res. 17, e16 (2015). https://doi.org/10.2196/jmir.3933
Picard, R.W.: Affective Computing for HCI. In: Proceedings 8th HCI Int. Human-Computer Interact. Ergon. User Interfaces. 829–833 (1999)
Gouizi, K., Bereksi Reguig, F., Maaoui, C.: Emotion recognition from physiological signals. J. Med. Eng. Technol. 35, 300–307 (2011). https://doi.org/10.3109/03091902.2011.601784
Picard, R.W., et al.: Affective learning - a manifesto. BT Technol. J. 22, 253–268 (2004). https://doi.org/10.1023/B:BTTJ.0000047603.37042.33
Menezes, M.L.R., et al.: Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset. Pers. Ubiquit. Comput. 21(6), 1003–1013 (2017). https://doi.org/10.1007/s00779-017-1072-7
Kusserow, M., Amft, O., Tröster, G.: Monitoring stress arousal in the wild. IEEE Pervasive Comput. 12, 28–37 (2013). https://doi.org/10.1109/MPRV.2012.56
OpenLabyrinth: User Guide version 3.2.1 (2014). http://demo.openlabyrinth.ca/documents/UserGuide.pdf
Emotiv web page. https://www.emotiv.com/
Empatica E4 product page. https://www.empatica.com/en-eu/research/e4/
Gazepoint GP3 product page. https://www.gazept.com/product/gazepoint-gp3-eye-tracker/
Edmondson, A.C.: Learning from mistakes is easier said than done: group and organizational influences on the detection and correction of human error. J. Appl. Behav. Sci. 40, 66–90 (2004). https://doi.org/10.1177/0021886304263849
Zhao, B.: Learning from errors: the role of context, emotion, and personality. J. Internet Bank. Commer. 32, 435–463 (2011). https://doi.org/10.1002/job
Vogel, S., Schwabe, L.: Learning and memory under stress: implications for the classroom. npj Sci. Learn. 1, 1–10 (2016). https://doi.org/10.1038/npjscilearn.2016.11
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kyriakidou, MR., Antoniou, P., Arfaras, G., Bamidis, P. (2020). The Role of Medical Error and the Emotions it Induces in Learning – A Study Using Virtual Patients. In: Frasson, C., Bamidis, P., Vlamos, P. (eds) Brain Function Assessment in Learning. BFAL 2020. Lecture Notes in Computer Science(), vol 12462. Springer, Cham. https://doi.org/10.1007/978-3-030-60735-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-60735-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60734-0
Online ISBN: 978-3-030-60735-7
eBook Packages: Computer ScienceComputer Science (R0)