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Articles 38941 - 38942 of 38942
Full-Text Articles in Entire DC Network
Supporting Social Justice In Physical Education Teacher Education Programs: A Qualitative Analysis Of Adapted Pe Teachers' Perspectives, Ann-Catherine Sullivan, Christianna Blanchard-Alworth, Cynthia K. Piletic, Linda Hilgenbrinck
Supporting Social Justice In Physical Education Teacher Education Programs: A Qualitative Analysis Of Adapted Pe Teachers' Perspectives, Ann-Catherine Sullivan, Christianna Blanchard-Alworth, Cynthia K. Piletic, Linda Hilgenbrinck
Educational Leadership & Workforce Development Faculty Publications
Despite attention to sociocultural issues in Physical Education Teacher Education (PETE), the role of Adapted Physical Education (APE) teachers as social justice advocates remains underexplored. A primary responsibility of APE teachers is to provide specially designed instruction to students with disabilities (SWD), so they have access to the general physical education curriculum to make progress toward grade-level standards. This study presents a qualitative analysis of the perspectives of National Adapted Physical Education Teachers of the Year (APE TOYs) on advocating for social justice within PETE programs. A phenomenological approach with mixed-methods design was used. Findings emphasise the importance of fairness, …
Towards Dynamic Learner State: Orchestrating Ai Agents And Workplace Performance Via The Model Context Protocol, Mohan Yang, Nolan Lovett, Belle Li, Zhen Hou
Towards Dynamic Learner State: Orchestrating Ai Agents And Workplace Performance Via The Model Context Protocol, Mohan Yang, Nolan Lovett, Belle Li, Zhen Hou
Educational Leadership & Workforce Development Faculty Publications
Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional …