Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4-10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement-disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.

Non-invasive stress estimation in simulative endoscopy setting: literature review

One of the most common stressors is so-called "occupational stress." It is defined as the sum of physical, mental and physiological responses to work in situations where the workload or stress associated with it intensifies for an extended period of time. It is a gradual process in which individual cognitive assessments of occupational stressors generate adverse health events and may lead to burnout. Since it has become a major problem in the medical field, studying, measuring and limiting it have been set as goals for the future.

How Valid Are Cortisol and Galvanic Skin Responses in Measuring Student Stress During Training? Comment on the Psychological Effects of Simulation Training

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Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs

Applied behavior analysis (ABA) has become a popular behavioral therapy in the special education needs (SEN) community. ABA is used to manage SEN students’ behaviors by solving problems in socially important settings, and puts emphasis on having precise measurements on physical and observable events. In this work, we present how Internet of Things (IoT) technologies can be applied to enhance ABA therapy in normal SEN classroom settings. We measured (1) learning performance data, (2) learners’ physiological data, and (3) learning environment sensors’ data. Upon preliminary analysis, we have found that learners’ physiological data is highly diverse, while learner performance seems to be related to learners’ electrodermal activity. Our preliminary findings suggest the possibility of enhancing ABA for SEN with IoT technologies.

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, 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.

Complex Systems Research in Educational Psychology: Aligning Theory and Method

The purpose of this work is to provide an overview of complex systems research for educational psychologists. We outline a philosophically and theoretically sourced definition of complex systems research organized around complex, dynamic, and emergent ontological characteristics that is useful and appropriate for educational psychology. A complex systems approach is positioned as a means to align underexplored elements of existing theory with appropriate interaction dominant theoretical models, research methods, and equation-based analytic techniques. We conclude with a brief discussion of several foundational topics for complex systems research in educational psychology.

Complex Systems Research and Evaluation in Engineering Education

The purpose of this theory-to-practice paper is to discuss complex systems research needs within engineering education. We provide a comprehensive definition of complex systems educational research (Hilpert & Marchand, under review; Jacobson et al., 2016) and an overview of methods specific to the approach (Hollenstein, 2013; Koopsman & Stavalomsis, 2016; Strogatz, 1994). After this, we delineate a research-based framework that can be used to develop and conduct complex systems research and evaluation. We identify two areas within the field of engineering education where complex systems research can be useful: 1) educational research focused on student interaction and cognition and 2) assessment and evaluation of collaboratives such as grant funded projects and communication/ publication networks. We discuss existing literature in these spaces, and then outline the critical research needs for engineering education. We address each of these critical needs with an eye on theory as well as methodological and analytic techniques that can be used to design and conduct complex systems research and evaluation in engineering education settings and contexts. The result is a set of specific guidelines that researchers can use to move complex systems research forward in engineering education. This material is based upon work supported by the National Science Foundation under Grant Number NSF DUE #1245018 and partial support was also provided by the National Institute of General Medical Sciences, Grant No. P20GM109025.