Becoming the Expert - Interactive Multi-Class Machine Teaching

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.Comment: CVPR 201

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Enabling Robots to Communicate their Objectives

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then it selects those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped. Our main contribution is to recognize that unlike robots, humans will not be exact in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain. We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.Comment: RSS 201

Education Workforce Initiative: Initial Research

The purpose of this initial research is to offer evidenced possibilities in the key areas of education workforce roles, recruitment, training, deployment and leadership, along with suggested areas for further research to inform innovation in the design and strengthening of the public sector education workforce. The examples described were identified through the process outlined in the methodology section of this report, whilst we recognise that separation of examples from their context is problematic – effective innovations are highly sensitive to context and uncritical transfer of initiatives is rarely successful. The research aims to support the Education Workforce Initiative (EWI) in moving forward with engaging education leaders and other key actors in radical thinking around the design and strengthening of the education workforce to meet the demands of the 21st century. EWI policy recommendations will be drawn from a number of country level workforce reform activities and research activity associated with the production of an Education Workforce Report (EWR). This research has informed the key questions, approach and structure of the EWR as outlined in the Education Workforce Report Proposal. Issues pertaining to teaching and learning in primary and secondary education are at the centre of the research reported here; the focus is on moving towards schools as safe places where all children/ young people are able to engage in meaningful activity. The majority of the evidence shared here relates to teachers and school leaders; evidence on learning support staff, district officials and the wider education workforce is scant. Many of the issues examined are also pertinent to the early childhood care and education sector but these are being examined in depth by the Early Childhood Workforce Initiative. Resourcing for the Education Workforce was out of scope of this initial research but the EC recognises, as outlined in the Learning Generation Report, that provision of additional finance is a critical factor in achieving a sustainable, strong and well-motivated education workforce, particularly but not exclusively, in low and middle income countries. The next stage of EWI work will consider the relative costs of current initiatives and modelling of the cost implications of proposed reforms. EWI aims to complement the work on teacher policy design and teacher career frameworks (including salary structures) being undertaken by other bodies and institutions such as Education International, the International Task Force on Teachers for 2030 and the Teachers’ Alliance, most particularly by bringing a focus on school and district leadership, the role of Education Support Professionals (ESPs) and inter-agency working