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Hundreds of studies have explored student evolution acceptance because evolution is a core concept of biology that many undergraduate biology students struggle to accept. However, this construct of “evolution acceptance” has been defined and measured in various ways, which has led to inconsistencies across studies and difficulties in comparing results from different studies. Many studies and essays have offered evaluations and perspectives of evolution acceptance instruments, but publications with a focus on consensus building across research teams is still needed. Further, little attention has been paid to how evolution acceptance instruments may be interpreted differently by students with varied religious backgrounds. Funded by a Research Coordination Network in Undergraduate Biology Education grant from the National Science Foundation, we gathered 16 experts from different disciplinary and religious backgrounds to review current evolution acceptance instruments and create a guide to the strengths and weaknesses of these instruments, including appropriate contexts for using these instruments and their potential weaknesses with different religious populations. Finally, in an attempt to move the field forward, we articulated a consensus definition of evolution acceptance that can be used to guide future instrument development.

 

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.