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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.
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.
This pilot study adds to the understanding of interventions to manage acculturative stress experienced during undergraduate study abroad programs. It was designed to evaluate the impact of cognitive behavioral strategy training combined with intentional practice during mixed reality (MR) simulations on acculturative stress. Participants included a convenience sample of undergraduate nursing students enrolled in a faculty-led study abroad course. Students participated in two MR simulations within a virtual environment over back-to-back days. Simulations were written by nursing faculty experts to reproduce realistic clinical situations students may encounter while studying in Peru. The Generalized Self-Efficacy Scale, State-Trait Anxiety Inventory, and Slater-Usoh-Steed questionnaire were administered. Quantitative biometric indices assessed during simulations included estimates of heart rate, blood pressure, respiratory rate, oxygen saturation and alterations in sweat gland activity reflective of changes in emotional state (galvanic skin response). There were changes in biometric indices within each simulation, but indices were not different between pre- and post- cognitive behavioral strategy training. Intentional reflective writing before, during, and after the study abroad program demonstrated an iterative cycle of reflection on action and mindfulness. These qualitative data suggest that pre-departure cognitive based behavior stress management strategies paired with simulated practice prior to departure may be one way to help nursing students deal with acculturative stress during study abroad experiences.
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