The work of the EDSAFE centers around the SAFE Benchmarks Framework as we engage stakeholders to align equitable outcomes for all learners and improved working experiences for dedicated and innovative educators. We intend to clarify the urgency and specific areas of need to prevent failures in data management that compromise the potential for how responsible AI can be a lever for equity and innovation while protecting student privacy. Frameworks and benchmarks are important to innovation as a means of targeted guidance, focusing disparate efforts towards shared objectives and outcomes and ensuring the development of appropriate guidelines and guardrails.
The Australian Framework for Generative AI in Schools (the Framework) seeks to guide the responsible and ethical use of generative AI tools in ways that benefit students, schools, and society. The Framework supports all people connected with school education including school leaders, teachers, support staff, service providers, parents, guardians, students and policy makers.
The SHEILA project team ran a workshop on “Developing an evidence-based institutional learning analytics policy” at the 13th European Conference on Technology Enhanced Learning on 3 September at the University of Leeds.
The field learning analytics is established with the promise for the education sector to embrace the use of data for decision making. There are many examples of successful use of learning analytics to enhance student experience, increase learning outcomes, and optimize learning environments.
A framework to support dynamic adaptation behavior in Java EE enterprise systems and to develop self-managing applications. StarMX utilizes JMX features and can be integrated with different policy/rule engines to enable self-management capabilities.
The MS Rules Framework was a spirited attempt by MS to create a wide-ranging environment that could integrate rules held in different forms in different repositories, mange the deployment of rule sets out across an enterprise environment and even target a range of different rules engines.
L. Kagal, T. Berners-Lee, D. Connolly, и D. Weitzner. POLICY '06: Proceedings of the Seventh IEEE International Workshop on Policies for Distributed Systems and Networks, стр. 205--214. Washington, DC, USA, IEEE Computer Society, (2006)