A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight

Whilst investigating student performance in design and arithmetic tasks, as well as during exams<b>, </b>electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capac...

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Main Authors: Clodagh Reid, Conor Keighrey, Niall Murray, Rónán Dunbar, Jeffrey Buckley
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/23/6857
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spelling doaj-ee5dfd09d72a424ebeba36dffd473a932020-12-01T00:02:57ZengMDPI AGSensors1424-82202020-11-01206857685710.3390/s20236857A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational InsightClodagh Reid0Conor Keighrey1Niall Murray2Rónán Dunbar3Jeffrey Buckley4Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, N37 HD68, IrelandFaculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, N37 HD68, IrelandFaculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, N37 HD68, IrelandFaculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, N37 HD68, IrelandFaculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, N37 HD68, IrelandWhilst investigating student performance in design and arithmetic tasks, as well as during exams<b>, </b>electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.https://www.mdpi.com/1424-8220/20/23/6857electrodermal activitywearablescognitive loadeducationbehavior
collection DOAJ
language English
format Article
sources DOAJ
author Clodagh Reid
Conor Keighrey
Niall Murray
Rónán Dunbar
Jeffrey Buckley
spellingShingle Clodagh Reid
Conor Keighrey
Niall Murray
Rónán Dunbar
Jeffrey Buckley
A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
Sensors
electrodermal activity
wearables
cognitive load
education
behavior
author_facet Clodagh Reid
Conor Keighrey
Niall Murray
Rónán Dunbar
Jeffrey Buckley
author_sort Clodagh Reid
title A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
title_short A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
title_full A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
title_fullStr A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
title_full_unstemmed A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
title_sort novel mixed methods approach to synthesize eda data with behavioral data to gain educational insight
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-11-01
description Whilst investigating student performance in design and arithmetic tasks, as well as during exams<b>, </b>electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.
topic electrodermal activity
wearables
cognitive load
education
behavior
url https://www.mdpi.com/1424-8220/20/23/6857
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