Designing professional learning

The Designing Professional Learning report provides a snapshot of the key elements involved in creating effective and engaging professional learning in a globally dispersed market. AITSL commissioned Learning Forward to undertake this study to give greater guidance around the ‘how’ of professional learning. Learning design involves making careful decisions based on an integration of theories, research and models of human learning in order to contribute to the effectiveness of professional learning. This work is not presented as definitive findings, but seeks to draw attention to observed trends and areas of commonality between learning designs that have demonstrated success. Following an analysis of a broad range of professional learning activities, a Learning Design Anatomy was developed to provide a framework for understanding the elements of effective professional learning. Each learning design element is framed by a detailed series of questions that challenge users to refine and clarify aims, intended learning outcomes and the most effective ways in which to engage—taking into consideration the unique context for learning. Examples of professional learning design are provided to illustrate elements of the Anatomy. The report is designed to be of use to teachers, school leaders, policy makers, system administrators and professional learning providers. It is intended that this report and the Anatomy will serve as provocation for a broader conversation about the composition of professional learning and the elements that establish the strongest correlation between participants, environment, delivery and action

Good Perspectives for Social Protection in Angola

Good Perspectives,Social Protection, Angola

啟發另類思維 嶺南大學推服務研習課程

原載於2017年3月28日《晴報》。https://commons.ln.edu.hk/osl_press/1025/thumbnail.jp

Service-Learning Times : programme booklet 2017/18 semester 1

Service-Learning (S-L) integrates academic study with meaningful community service to create opportunities for students and staff to make positive impact locally, regionally, and globally. In line with Lingnan’s motto “Education for Service”, Service-Learning and Research Scheme (SLRS) aims to provide opportunities where students can apply subject-specific knowledge to the real world, while collaboration partners can benefit from the knowledge and innovation that faculty and students bring to these projects. Innovation and entrepreneurship are central to SLRS as it is a priority for liberal arts students to understand and engage with the impact of technology on the humanities, and vice versa. Innovation and entrepreneurship can give new impetus to community service and capacity building, and through this, the making of global citizenship for the 21st Century. All 4-year curriculum undergraduate students starting from the 2016- 17 academic year must satisfactorily complete at least one S-L course to meet graduation requirement. This booklet highlights popular courses with S-L components. Students wishing to experience the best of S-L should plan early and act quick while places are available.https://commons.ln.edu.hk/sl_times/1000/thumbnail.jp

Lingnan University grooms social innovators

原載於2017年6月21日《The Standard》。(只有英文) Originally published in The Standard 21st June 2017. (English only)https://commons.ln.edu.hk/osl_press/1028/thumbnail.jp

Spectral Methods from Tensor Networks

A tensor network is a diagram that specifies a way to "multiply" a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks. In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems. An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy). Orbit recovery problems over finite groups can often be solved via standard tensor methods. However, for infinite groups, no general algorithms are known. We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2). Our algorithm extends to the more general heterogeneous case.Comment: 30 pages, 8 figure

Social Policies in a Period of Crisis

Social Policies, Period of Crisis