:Leveraging online learning tools and encouraging transfer of learning to practice remains a critical challenge to successful continuing professional development (CPD) offerings. Four sets of factors are essential to the transfer of learning from CPD into practice: learner characteristics, instructional design, content, and environment. Through incorporating elements of educational theories/frameworks into the planning of online CPD activities, educators can maximize opportunities for learning transfer. In this article, we highlight four educational theories/frameworks that provide useful insight to tackle these interrelated factors in online CPD: Self-Determination Theory considers the intrinsic and extrinsic motivation of participants, which can be encouraged through flexibility, customization, and choices available in online formats. Practical Inquiry Model encourages intentionally planning and embedding opportunities for reflection and dialogue in online activities to enhance knowledge application. Virtual Communities of Practice can be used to transcend spatial and temporal boundaries, promoting interactions and relationships where participants learn from peers. Finally, Professional Learning Networks can be fostered through developing interpersonal connections and sharing resources for informal and flexible learning. Online CPD is likely to increase in the future, and educators should consider elements of these educational theories/frameworks in the design and delivery of CPD to support participants' application of newly acquired knowledge.
Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments because of their larger capturing and labeling requirement. Physics-based controllers are effective in this regard, albeit less controllable. In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments. Starting with an agent that can imitate individual animation clips, we use Generative Adversarial Networks to adapt high-level controls, such as speed and heading, to action distributions that correspond to the original animations. Further fine-tuning through the deep reinforcement learning enables the agent to recover from unseen external perturbations while producing smooth transitions. It then becomes straightforward to create autonomous agents in dynamic environments by adding navigation modules over the entire process. We evaluate our approach by measuring the agent's ability to follow user control and provide a visual analysis of the generated motion to show its effectiveness.
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