Towards medical education neuroscience: pilot results of integrative affective analytics from clinical skills workshops
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1
Aristotle University of Thessaloniki, Faculty of Health Sciences, Medical School, Greece
Introduction. Neuroscience advances have provided insights on the interplay of emotion and cognition in the human brain [1]. Studies have revealed that emotional capacities can be even more influential than cognitive abilities in personal success and career achievements [2]. Specifically in learning, engagement and motivation have been revealed to be crucial factors for effective and participatory learning processes whose importance has been proven from several research endeavors [3]. From their inception, theories of affect in learning offered as the most prudent approach [4] the definition and use of measurable quantities from classical macroscopic observations. Analytics technologies offer new ways for learning assessment. This quantification of learning activities facilitates data collection. These measurements then can be used for analysis and aggregation into metric of high value and impact [5]. Such technologies facilitate data mining statistical methods for the derivation of conclusions from quantitative data in the field of affective learning research. This work reports on endeavors to bring together a host of sensors and analysis tools in order to explore the viability of integrative neurophysiological data as mood and emotion assessors during extra-curricular clinical skill training of undergraduate students.
Methods. The presented case study was conducted during the 4th "Essential Skills in the Management of Surgical Cases- esmsc" course, which took place between 9-11 May in Athens, Greece in the Experimental Research Centre ELPEN. Medical students in teams of 6 were presented with a suturing technique and practiced suturing for 20 minutes approximately on pork tissue. After each session a different team was rotated in the workshop station. There was no limit to the amount of sutures the participants could made nor was there a goal of sutures to be reached. All rotating students were asked to volunteer to the study and rotating pairs were selected each time to focus the integrative affective environment on them. A total of 10 participants participated in this study. Each participating learner pair was given a Fitbit activity tracker to wear on their non-dominant hand to measure heart rate. They were also in view of a Microsoft HD web-cam recording their faces in order to use these data for emotion facial extrapolation. The emotion states that were extrapolated from the video were anger, contempt, disgust, fear, happiness, neutral, sadness and surprise. An EMOTIV EPOC unobtrusive EEG sensor was mounted on both participants to record 14-channel EEG and measure mental state conditions. The device’s software offered pre-validated assessors for mental states (frustration, engagement, meditation, excitement and valence. Devices were synchronized by hand at the moment of the first suture practice. Additionally, the time of each suture’s completion was noted for each participant. The resulting data was 13 time series (of duration approximating that of the experiment) for emotion and mental states and the time series of heart beat for each participant of the study. The emotion and mental states’ approximations were presented by a value between 0 and 1, while 0 indicated absence of a state. The time series were cut according to all the sutures that was complete by the participants. Since the duration of each suture was not constant, the time series were resampled to a range of 1 to 100, representing the percentage of completion of each suture. Also, since the baseline values of the mental and emotion states different for each participant, they were transformed to Z-scores. By these transformations, the time series were comparable between and within subjects.
Results. On average the participants completed 7 sutures (7.28) with median value of 6, although there was high variability. We calculated, across participants, the mean time needed to complete a suture, which was 155 seconds. We defined a learning curve of the mean time needed for a suture versus the order of the sutures. The result was a trend of decrease in time needed for a suture which was not deemed significant. Furthermore, we compared the Z-scores of excitement and frustration during the course of completion of sutures versus the order of the sutures. The excitement Z-score was maximized at the last 80% of the first suture and the fist 30% of the second. Concerning the next 4 sutures it was lower than the first two and had some increase at the end of each suture. The Frustration Z-score was maximized during the whole first suture and then was close to zero for the duration of the other sutures. Additionally, out of the 8 emotion states, 4 were deemed irrelevant to the learning process (anger, disgust, contempt, neutral). The other emotion states, namely sadness, happiness, fear and surprise were tested for fluctuations. Concerning the relevant states, fear decreased over the course of suturing for 8 out of 10 participants, sadness was increased in 7, happiness was increased in 6 while surprise was decreased in 7.
Discussion. The first results presented here are in accordance to empirical observations from the clinical education process. Specifically, the excitement increase at the beginning and end of the workflow conform to the engagement of the learner with a new subject and her subsequent anticipation for closure of one and start of another learning task. It is interesting to note that fear and surprise decrease along with sadness increase over the course of suturing, for the majority of learners, could be construed as an indirect corroborating evidence of decreasing engagement and increasing boredom. The ambivalence of happiness increase in 6 (against 4) of the subject pool in the happiness score leaves room open for further specific study. These first corroborations between empirical observations and part of the data of one sensor from the deployed integrative environment are the starting point for further research. Ongoing endeavor is currently focused on integrating more sensor data and incorporating validation methods in the experimentation process. The breadth of insights that these results offer demonstrate the breadth of potential of a fully evidence based validated affective analytics integrative environment and its promising role in medical education powered by neuroscience.
Acknowledgements
Authors would like to thank all academic staff (clinical skills tutors) involved in the organisation of the ESMC workshop and undergraduate students Nikos Staikoglou and Giannis Papadimitriou for organizing and facilitating the pilot experimentation
References
[1] Cytowic, R. E. (2002). Synesthesia: A union of the senses. MIT press.
[2] Goleman, D. (2006). Emotional intelligence. Bantam.
[3] Bransford, J.D., Brown, A.L. and Cocking, R.R. (Eds.) (2000) ‘How people learn: brain, mind, experience and school’, Committee on Developments in the Science of Learning, NRC Commission on Behavioral and Social Sciences and Education, Washington, DC: National Academy Press.
[4] Picard RW, Papert S, Bender W, Blumberg B, Breazeal C, Cavallo D, Machover T, Resnick M, Roy D, Strohecker C. (2004). Affective learning—a manifesto. BT technology journal. 2004 Oct 1;22(4):253-69.
[5] Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1-57.
Keywords:
Education,
Affective Analytics,
Medical Education Informatics,
Unobtrusive measurements,
Signal Processing, Computer-Assisted
Conference:
SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.
Presentation Type:
Oral Presentation in SAN 2016 Conference
Topic:
Oral Presentations
Citation:
Antoniou
PE,
Kartsidis
P,
Xefteris
S,
Arfaras
G,
Konstantinidis
E and
Bamidis
PD
(2016). Towards medical education neuroscience: pilot results of integrative affective analytics from clinical skills workshops.
Conference Abstract:
SAN2016 Meeting.
doi: 10.3389/conf.fnhum.2016.220.00035
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Received:
29 Jul 2016;
Published Online:
30 Jul 2016.
*
Correspondence:
Dr. Panagiotis E Antoniou, Aristotle University of Thessaloniki, Faculty of Health Sciences, Medical School, Thessaloniki, Thessaloniki, 54124, Greece, pantonio@otenet.gr
Prof. Panagiotis D Bamidis, Aristotle University of Thessaloniki, Faculty of Health Sciences, Medical School, Thessaloniki, Thessaloniki, 54124, Greece, bamidis@med.auth.gr