There have been substantial amounts of study over a lengthy period of time into the variability of heart rate (HR) in relation to cognitive activity. 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 (1974) demonstrated that tasks requiring increased cognitive processing are associated with HR acceleration. Numerous clinical 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 also reported similar HR increases due to increased cognitive task load, such as for air traffic controllers (Wilson & Eggemeier, 1991), fighter pilots (Wilson, 1993) and university lecturers (Filaire, 2010). Increased memory load (number of items) was shown to be accompanied by accelerated HR (Backs & Selijos, 1994 : Pearson & Freeman 1991). Indeed Cranford’s study (2014) directly linked HR to varying degrees of cognitive load in problem solving and concluded that HR monitoring has further significant potential use in measuring cognitive load during the learning process. Scholey (1999) reasoned that these observed increases in HR during cognitive processing are the body’s facilitation of the delivery of metabolic substrates to the brain that are then utilised by neural mechanisms underpinning cognitive performance. However there have been a very 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 50minute 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. In addition, Bligh reported a single event where a question from a student resulted in an elevation of HR in other students. Darnell and King (2014) expanded on this work, and concurred with Bligh that there appears to be a decrease in average HR across a 50 minute lecture class and a temporary increase in HR in response to student questions. In addition, they concluded that pairshare sessions resulted in elevated average HR. The devices used in most HR cognitive studies were generally expensive and obtrusive. Consequently most of the studies suffered from small sample sizes and limited sampling points. The prominent nature of the HR measuring device likely also affected the results with the students acutely aware throughout the experiment that their HR was being sampled. Indeed Anttonen and Surakka (2005) pointed out the need for new methods for inconspicuous heart measurement. 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 biometric effects on students during a series of lectures. Objectives stages The initial objective is to use wearable devices (Microsoft Band 2) to measure and record heart rate of a large representative number of students during a number of lectures and analyse the data to seek to identify any general patterns in HR during the length of the lecture. To analyse the data to seek to identify and potentially link repeatable HR patterns to the cognitive activities of students during the lecture. To relate the analysis of the data to the relative effectiveness of the various interactive and noninteractive teaching methods employed during the lectures to potentially increase future student cognitive engagement and lecturer performance with the aim to increase overall student attainment.
|Number of pages||4|
|Publication status||Published - 25 Aug 2017|
|Event||ECER 2017 - Copenhagen, Denmark|
Duration: 22 Aug 2017 → 25 Aug 2017
|Period||22/08/2017 → 25/08/2017|
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Learner analytics of student programmers: The use of innovative technologies to better understand the learning behaviours of student programmersAuthor: McGowan, A., Dec 2021
Student thesis: Doctoral Thesis › Doctor of PhilosophyFile