With rapid digital innovation, social support strategies are increasingly embedded in Academic Social Networking Sites (ASNSs) to shape user perceptions and behaviors. However, limited empirical research has examined ...
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Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cogn...
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In the process of shifting from traditional teachercentred systems to more student-engagement ones, Augmented Reality (AR) is coming into its own as a way of improving how information is delivered and received. Howeve...
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Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, th...
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The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market exp...
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This study evaluates the relevance of Phillips’ reward taxonomy in the context of contemporary video game design, with implications for modern gamification strategies. Phillips’ taxonomy categorizes game rewards int...
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Motor skills education often requires experienced professionals who can provide personalized instruction. Unfortu-nately, the availability of high-quality training can be limited for specialized tasks, such as high pe...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Motor skills education often requires experienced professionals who can provide personalized instruction. Unfortu-nately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have proposed AI -assistance for motor skills instruction, ranging from rehabilitation to surgical robot tele-operation. However, these works often make simplifying assumptions on the student learning process, and fail to model how a teacher's assistance interacts with different individuals' abilities when determining optimal teaching strategies. Inspired by the idea of scaffolding from educational psychology, we leverage shared autonomy, a framework for combining user inputs with robot autonomy, to aid with curriculum design. Our key insight is that the way a student's behavior improves in the presence of assistance from an autonomous agent can highlight which sub-skills might be most “learnable” for the student, or within their Zone of Proximal Development. We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills. In a user study $(\mathrm{n}=50)$ , where we teach high performance racing in a simulated environment of the Thunderhill Raceway Park with the CARLA Autonomous Driving simulator, we show that Z-COACH helps identify which skills each student should first practice, leading to an overall improvement in driving time, behavior, and smoothness. Our work shows that increasingly available semi-autonomous capabilities (e.g. in vehicles, robots) can not only assist human users, but also help teach them.
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