Students are increasingly learning through online environments that deliver different experiences from traditional classrooms. In this study, participants’ learning environments were manipulated using two independent variables, each with two levels for a total of four conditions: study medium, which is the focus of this paper (text was presented either on paper or a screen), and in-text prompts (metacognitive or non-metacognitive in nature). Ninety-two participants were randomly assigned to one of the conditions in a between-subject design; during the study, they each read three expository texts, completed a comprehension test after each text, and responded to a survey at the end of the study. Participants who read text on paper tended to take more notes and spend more time studying than those who read from a screen, but comprehension differences were negligible. Results from this study can inform dialogue about the effects of technology in classrooms.
The U.S. Army is interested in extending the application of intelligent tutoring systems (ITS) beyond cognitive problem spaces and into psychomotor skill domains. In this paper, we present a methodology and validation procedure for creating expert model representations in the domain of rifle marksmanship. GIFT (Generalized Intelligent Framework for Tutoring) was used as the architecture to guide development efforts and was paired with an Army marksmanship simulator that collects behavioral information through sensor technologies. The models were based on expert data from eight members of the U.S. Army Marksmanship Unit's Service Rifle Team. The goal is to establish validated models that serve as artificial intelligence assessment criteria for driving a self-regulated training environment. We review the techniques applied to the data for model construction, the trends found in the data that are generalized across each expert informed through cross-fold validation practices, and discuss how the models will be used for driving real-time assessment. Results support the utility of generalized expert models across the fundamental components of rifle marksmanship as outlined in U.S. Army doctrine.
Distracted driving is becoming more prevalent as automobile use is commonplace and technology use grows in pervasiveness. The present study investigated the impact of cell phone, touch MP3, and external environmental distractions on commercial truck driving performance. Commercial truck drivers' performance and physiological responses were monitored while they drove a simulated cab through various control and distraction scenarios. The results support previous findings that distractions, particularly the phone and touch MP3, reduce driving performance and increase cognitive resource allocation in truck drivers.
Human factors (HF) can be implemented in various domains to improve usability, and healthcare is no exception. A student team from Georgia Tech was consulted by the Centers for Disease Control (CDC) about performing a straightforward user-centered redesign of the CDC’s immunization schedule. Using classic design principles (e.g., consistency, simplicity, clarity), the team created a prototype schedule that aims to produce HF-driven efficiency of use while maintaining a form that fits with end-user expectations of the schedule. The CDC implemented some of the team’s design recommendations in its 2017 immunization schedule, and more changes could be implemented in the 2018 version of the schedule.
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