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

1
Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone N37 HD68, Ireland
2
Department of Learning, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6857; https://doi.org/10.3390/s20236857
Received: 16 October 2020 / Revised: 10 November 2020 / Accepted: 26 November 2020 / Published: 30 November 2020
Whilst investigating student performance in design and arithmetic tasks, as well as during exams, 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. View Full-Text
Keywords: electrodermal activity; wearables; cognitive load; education; behavior electrodermal activity; wearables; cognitive load; education; behavior
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MDPI and ACS Style

Reid, C.; Keighrey, C.; Murray, N.; Dunbar, R.; Buckley, J. A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight. Sensors 2020, 20, 6857.

AMA Style

Reid C, Keighrey C, Murray N, Dunbar R, Buckley J. A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight. Sensors. 2020; 20(23):6857.

Chicago/Turabian Style

Reid, Clodagh; Keighrey, Conor; Murray, Niall; Dunbar, Rónán; Buckley, Jeffrey. 2020. "A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight" Sensors 20, no. 23: 6857.

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