A multidisciplinary journal that focuses on public health and technology, public health informatics, mass media campaigns, surveillance, and innovation in public health practice and research. Also dedicated to rapid open data sharing during epidemics.

Socio-scientific reasoning (SSR) is key to helping students take informed positions around socio-scientific issues (SSI). SSR comprises four competencies: recognising complexity of SSI’s, multiple perspectives around SSI’s, the need for ongoing inquiry around SSI’s, and skepticism around different parties’ claims made about SSI’s. The Quantitative Assessment of SSR (QuASSR) provides a promising measurement framework, but there are still important questions around the ability of this instrument to measure transfer across different scenarios and change in SSR over an intervention. Further, prior work suggests that the four competencies may constitute a progression. We explored the ability of the QuASSR to measure transfer of SSR across three different SSI’s using 2-faceted and multi-faceted Rasch models. We used path analysis to test the hypothesis that competencies associated with SSR formed a progression. We found transfer or neartransfer of SSR across the three scenarios, and that the competencies comprise a unidimensional hierarchy. Perspective-taking is a necessary bridge between students’ understanding of complexity and the higher-level competencies of inquiry and skepticism. Inquiry and skepticism were found to be conditionally independent upon accounting for perspective-taking, supporting the idea that seeing multiple perspectives around SSI’s is central to development of the other SSR competencies.

Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’s learning activities and cognitive load.

The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant’s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions.