Search

This paper articulates how the teaching of movement, physical activity and sport, through formal education and schooling, and the medium of physical education, affords opportunities for twenty-first-century character education. We begin by outlining our theoretical framework for character education and identify how character-traits developed through teaching morally and morality, can be pigeonholed into one of four categories, including performance, moral, civic and intellectual virtues. We then scaffold five models-based approaches to teaching physical education through memetic, progressive and transformative pedagogy based on their scope for educating the moral person. In doing so, we illuminate how model-based theories play a pivotal role when educating for character and illustrate the importance of physical education as a developmental platform for character, practical wisdom and human flourishing. [ABSTRACT FROM AUTHOR]

Scholars and industry stakeholders have exhibited an interest in identifying the underlying dimensions of viral memes. However, the recipe for creating a viral meme remains obscure. This study makes a phenomenological contribution by examining viral memes, exploring the antecedents (i.e., content‐related factors, customer‐related factors, and media‐related factors), consequences, and moderating factors using a mixed‐method approach. The study presents a holistic framework for creating viral memes based on the perceptions of customers and industry stakeholders. Four quantitative studies (i.e., a lab experiment, an online quasi‐experiment, an event study, and a brand recall study) validate the theoretical model identified in the qualitative study. The research underlines the potential of viral memes in marketing communications as they enhance brand recall and brand engagement. The study found that viral memes are topical and highly relatable and are thus well received by the target groups, which increases customer engagement and brand recall. Marketers can adopt the findings of this study to design content for memes that consumers find relevant, iconic, humorous, and spreadable. Furthermore, marketers can use customer‐related factors suggested in the theoretical framework for enhancing escapism, social gratification, and content gratification for their target customers which in turn shall organically increase their reach within their target segments and enhance brand performance in terms of brand recall and brand engagement. [ABSTRACT FROM AUTHOR]

This article examines the development of memetic witnessing, a vernacular strategy of repetition and remixing, in the testimony of Harriet Fernanders, a nineteenth-century woman called to testify before a congressional hearing about Ku Klux Klan violence in South Carolina during Reconstruction, and in the contemporary "#Say Her Name" social media campaign, which asks viewers to repeat the names of women killed by police violence. This article argues that memetic witnessing allows for important forms of identification and solidarity that refuse the persistent threat of erasure such subjects are vulnerable to through this important form of knowledge production. [ABSTRACT FROM AUTHOR]

In transfer learning (TL) for multiagent reinforcement learning (MARL), most popular methods are based on action advising scheme, in which skilled agents directly transfer actions, i.e., explicit knowledge, to other agents. However, this scheme requires an inquiry-answer process, which quadratically increases the computational load as the number of agents increases. To enhance the scalability of TL for MARL when all the agents learn from scratch, we propose an experience sharing based memetic TL for MARL, called MeTL-ES. In the MeTL-ES, the agents actively share implicit memetic knowledge (experience), which avoids the inquiry-answer process and brings highly scalable and effective acceleration of learning. In particular, we firstly design an experience sharing scheme to share implicit meme based experience among the agents. Within this scheme, experience from the peers is collected and used to speed up the learning process. More importantly, this scheme frees the agents from actively asking for the states and policies of other agents, which enhances scalability. Secondly, an event-triggered scheme is designed to enable the agents to share the experiences at appropriate timings. Simulation studies show that, compared with the existing methods, the proposed MeTL-ES can more effectively enhance the learning speed of learning-from-scratch MARL systems. At the same time, we show that the communication cost and computational load of MeTL-ES increase linearly with the growth of the number of agents, indicating better scalability compared to the popular action advising based methods. [ABSTRACT FROM AUTHOR]