文章基本信息

标题：Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors
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作者：Meredith Carroll ; Mitchell Ruble ; Mark Dranias 等
期刊名称：Journal of Behavioral and Brain Science
印刷版ISSN：2160-5866
电子版ISSN：2160-5874
出版年度：2020
卷号：10
期号：3
页码：165-178
DOI：10.4236/jbbs.2020.103010
语种：English
出版社：Scientific Research Pub
摘要：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 development and study of such content and strategies, and adaptation of instructional techniques when learners become disengaged, is the ability to objectively assess engagement in real-time. Previous self-reported metrics, or expensive EEG-based engagement measures are not appropriate for large-scale platforms 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 engagement in classroom, simulation, and live training environments. An experimental study was conducted with 45 students and first responders in a unmanned aircraft systems (UAS) training program to assess the ability to accurately assess learner engagement and discriminate between levels of learner engagement within classroom, simulation and live environments via physiological and behavioral inputs. A series of engagement classifiers were developed using cardiovascular, 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 features. 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 engagement.
关键词：Learner Engagement;Physiological Monitoring;Eye Movements;Electrodermal Activity;Cardiovascular Physiology