Document Type

Article

Publication Title

Journal of Behavioral and Brain Science

Abstract

Training can now be delivered on a large scale through mobile and web-based platforms in which the learner is often distanced from the instructor and their peers. In order to optimize learner engagement and maximize learning in these contexts, instructional content and strategies must be engaging. Key to the de-velopment and study of such content and strategies, and adaptation of instruc-tional techniques when learners become disengaged, is the ability to objec-tively assess engagement in real-time. Previous self-reported metrics, or expen-sive EEG-based engagement measures are not appropriate for large-scale plat-forms due to their complexity and cost. Here we describe the development and testing of a measurement and classification technique that utilizes non-invasive physiological and behavioral monitoring technology to directly assess engage-ment in classroom, simulation, and live training environments. An experimen-tal study was conducted with 45 students and first responders in a unmanned aircraft systems (UAS) training program to assess the ability to accurately as-sess learner engagement and discriminate between levels of learner engagement within classroom, simulation and live environments via physiological and be-havioral inputs. A series of engagement classifiers were developed using car-diovascular, respiratory, electrodermal, movement, and eye-tracking features that were able to successfully classify engagement levels at an accuracy level of 85% with eye-tracking features included or 81% without eye-tracking fea-tures. This approach is capable of monitoring, assessing, and tracking learner engagement across learning situations and contexts, and providing real-time and after action feedback to support instructors in modulating learner engage-ment.

First Page

165

Last Page

178

DOI

10.4236/jbbs.2020.103010

Publication Date

2020

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