Is your heart in your lecturing? A study of the Heart Rate of lecturers during lectures.

Author(s):

Aidan McGowan (presenting / submitting) Darryl Stewart Angela Allen Philip Hanna Des Greer Andrew McDowell

Conference:

ECER 2017

Network:

22. Research in Higher Education

Format:

Paper

Session Information

22 SES 11 C,

Paper Session

Time:

2017-08-24

17:15-18:45

Room:

K5.09

Chair:

Serap Emil

Contribution

Introduction

It is well established that increasing physical activity will increase heart rate, however there have been substantial amounts of study into the variability of HR in relation to cognitive activity and stress (Pérusse-Lachance, 2012). HR is the peripheral measure most used to assay affect and cognition (Guerra, 2015). As far back as the 1970s a series of experiments by Lacey (1970) and Lacey et al (1974) demonstrated that tasks requiring increased cognitive processing are associated with HR acceleration. Numerous experiments have been conducted measuring HR and cognitive tasks, including a reported increase in HR for computer gamers performing complex gaming tasks and by subjects performing difficult mental arithmetic (Turner & Carroll, 1985).

Abundant real world occupation studies have reported HR increases due to increased physical and cognitive task load, such as for air traffic controllers (Wilson & Eggemeier, 1991), fighter pilots (Wilson, 1993), miners (Montoliu, 1995) and professional musicians (Iñesta, 2005). There have been some educational based studies including a limited number of studies in the use of HR as a measure of student cognitive engagement in university lectures. Bligh carried out a series of classroom lecture studies showing that student HR decreased over the course of a 50-minute lecture (Bligh 1972, 1998, 2000). The decline in HR was interpreted as a measure of decreasing arousal, which Bligh considered as one component of cognitive engagement. Buchheit (2010) presented the HR profile of a student presenting his PhD, and showed that stress was likely to be very high at the start of the talk (as inferred from his HR reaching 87% of maximal values), but decreased continuously as the presentation progressed. Several HR peaks occurred with the toughest questions raised by the examiners.

While there are some HR studies of public speakers there have been surprisingly very few studies carried out in relation to university lecturers during a lecture. In one of the very few, Filaire (2010) conducted a study that involved the collection of beat-by-beat HR data of lecturers before and immediately after a two hour lecture. It concluded that there was generally an increase in the lecturer’s HR as recorded immediately after the lecture compared to the pre-lecture recording. Filaire then linked the increase, in conjunction with measured stress markers in the lecturer’s saliva, as evidence of increased stress.

Remarkably there are no known studies that accurately track lecturer HR activity during the lecture. The recent proliferation of accurate, cheap and unobtrusive wearable devices with biometric sensors presents a new opportunity to perform a relatively inexpensive, natural, large scale study on the HR patterns of lecturers during lectures.

Objectives - stages

General HR patterns during the lecture: The initial objective was to use wearable devices (Microsoft Band 2) to measure and record the HR of lecturers during a number of lectures and analyse the data to seek to identify any general patterns in HR during the length of the lecture.
HR and teaching activity: The objective was to analyse the HR data to seek to identify and potentially link repeatable HR patterns to the teaching activities of the lecturer during the lecture, such as presenting, demonstrating or answering difficult questions.
HR and potential influencing factors: The objective was to analyse how HR is affected by potential influencing factors, such as complexity of material being taught, the numbers of students attending, the time of day, how prepared the lecturer was, how familiar the lecturer was with the taught content, the amount of active learning within the lecture, how well the lecture slept the previous night, pre-lecture anxiety and general wellbeing.

Method

The study was conducted with 15 university lecturers teaching various Computing Science modules. All subjects volunteered to wear a HR monitor and have their HR recorded during a series of lectures. Lecturers that had diagnosed cardiovascular defects and smokers were not included. Each lecturer was given a Microsoft Band 2 wearable device and encouraged to wear it regularly. This was to ensure that a baseline resting heart rate could be established for each lecturer and also to lessen the potential influence on the results that measuring the HR during the lecture may have had. Immediately after each lecture session the lecturers uploaded their HR data to a secure central server which was accessible to the researchers. Additionally they completed a questionnaire to help determine other potential HR influencing factors such as pre-lecture anxiety, complexity of the subject material being taught, familiarity with taught content and general wellbeing. The lecturer was also videoed throughout the lecture. Analysis 1. General HR patterns during the lecture: an analysis of each lecturer’s HR for each lecture was made and this was then aggregated with all of the lecturer’s HR lecture data to produce a generalised lecture HR pattern for the individual lecturer. All of the individual HR lecture patterns were aggregated to produce an overall HR pattern for all lecturers. 2. HR and teaching activity: in order to establish if indeed themed cognitive teaching activities have an influence on HR, each lecture video was independently transcribed and sectioned by time into common teaching formats and lecture events. These included teacher lead activities such as teaching theory, programming demonstrations, live code development, questioning students and student lead activities such as individual or paired exercises, group discussions, peer demonstrations and student questions to the lecturer. A comparison of HR responses to these activities was made. A comparison was made with the known resting HR for each individual and the base rate for each lecturer during the lecture. 3. HR and other potential influencing factors: the results of the post-lecture questionnaire where used to statistically determine if HR variances were influenced by other factors such as pre-lecture anxiety, student questions and general wellbeing.

Expected Outcomes

The initial results have shown that similar to the findings of Buchheit (2010) and Kusserow (2012) that in most cases there is an elevated HR at the start of the lecture which then slowly declines. Several HR peaks are observed that may be correlated with physical activity, such as the lecturer moving around the lecture theatre. Furthermore, there are identifiable patterns of HR increase due to cognitive activities such as answering students’ questions and the teaching of materials that the lecturer deemed complex. Generally HR was highest during periods when the lecturer was providing explanations or demonstrating but decreased when students were engaged in individual or group work activities. There was a notable rise in HR towards the end of many lectures in instances when the lecturer was rushing to complete. The importance of Computer Science to the European and international economy cannot be understated, however the difficulty for new learners in the mass education university environment means that there are acknowledged issues with student attrition, learner attainment and quality of learning. The major contribution of this work is targeted at improving how Computer Science is taught as a discipline. Understanding HR profile of lecturers especially in association with teaching formats presents an opportunity to assess lecturing performance. It would follow that the ability to improve lecturing performance would lead to improved learning opportunities for students. While the subject basis in this study is on the lecture delivery of a Computer Science modules the researchers feel that there are many areas of commonality that will be of interest and comparable to other university subjects and their delivery.

References

Buchheit, M. & Mendez-Villanueva, A. Are 200 students really affecting heart rate variability and alpha-amylase activity? European Journal of Applied Physiology (2010) 109: 569. doi:10.1007/s00421-010-1373-2 Bligh DA. What’s the Use of Lectures? Jossey-Bass Publishers, SF (2000). Or Intellect Books (1998). Originally published in 1972. Boucsein, W.: Electrodermal activity. Plenum Press, New York (1992) Darnell, D., Krieg, P. (2014) Use of heart rate monitors to assess student engagement in lecture. The FASEB Journal vol. 28 no. 1 Supplement 721.25. Filaire, E., Portier, H., Massart, A. Ramat, L., Teixeira, A. (2010) Effect of lecturing to 200 students on heart rate variability and alpha-amylase activity. European Journal of Applied Physiology March 2010, Volume 108, Issue 5, pp 1035–1043 Iñesta, C., Terrados, N., García, D., & Pérez, J. A. (2008). Heart rate in professional musicians. Journal of Occupational Medicine and Toxicology (London, England), 3, 16. http://doi.org/10.1186/1745-6673-3-16 Kusserow, M., Amft, O., and Troster, G. (2012) Monitoring Stress Arousal in the Wild. IEEE Pervasive Computing (Volume: 12, Issue: 2, April-June 2013). Lacey, J., Obrist, B., Black, P, Brener, A., DiCara, L. (1974). Studies of heartrate and other bodily processes in sensorimotor behaviour. Cardiovascular psychophysiology, Aldine, Chicago Lacey, J, Lacey, B., Black, P. (1970). Some autonomic-central nervous system interrelationships Physiological correlates of emotion, Academic Press, New York Guerra, P., Sánchez-Adam A., Miccoli L., Polich J., Vila J. (2015) Heart rate and P300: Integrating peripheral and central indices of cognitive processing. Int J Psychophysiol. 2016 Feb;100:1-11. doi: 10.1016/j.ijpsycho.2015.12.008. Epub 2015 Dec 23. Montoliu, M., González, V., and Palenciano, L. (1995) Cardiac frequency throughout a working shift in coal miners. Ergonomics. 1995;38:1250–1263. doi: 10.1080/00140139508925186 Pérusse-Lachance, E., Tremblay, A., Chaput, JP., Poirier, P., Teasdale. (2012) Mental Work Stimulates Cardiovascular Responses through a Reduction in Cardiac Parasympathetic Modulation in Men and Women. Bioenergetics 2: 107. doi:10.4172/2167-7662.1000107 Turner, J., Carroll, D. (1985) Heart rate and oxygen consumption during mental arithmetic, a video game, and graded exercise: Further evidence of metabolically-exaggerated cardiac adjustments. Psychophysiology, 22 (1985), pp. 261–267 Wilson, G. & Eggemeier, F. (1991). Physiological measures of workload in multi-task environments (pp. 329-360). In Damos,