Tecnologie, autonomia e internazionalizzazione. L'italiano L2 di cinesi

The development of autonomy in language learning is a strategic element in the enhancement of the internationalisation process of universities. In the Web 2.0 society the virtual spaces of e-learning provide support for, and empowerment of, autonomous learning traditionally carried out individually in Self-Access Language Centres. Autonomous language learning is now enriched by cooperative activities in virtual communities of learning through forums, wikis and chats without limits either of space or of time. The present paper takes a critical look at an L2 autonomous learning pathway undertaken online by Chinese exchange students in a "virtual self-access". This learning model proved to be engaging and motivating and facilitated the students' first approach to autonomous learning

Q-CP: Learning Action Values for Cooperative Planning

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance

An agent system to support student teams working online

Online learning is now a reality, with distributed learning and blended learning becoming more widely used in Higher Education. Novel ways in which undergraduate and postgraduate learning material can be presented are being developed, and methods for helping students to learn online are needed, especially if we require them to collaborate with each other on learning activities. Agents to provide a supporting role for students have evolved from Artificial Intelligence research, and their strength lies in their ease of operation over networks as well as their ability to act in response to stimuli. In this paper an application of a software agent is described, aimed at supporting students working on team projects in the online learning environment. Online teamwork is problematical for a number of reasons, such as getting acquainted with team members, finding out about other team members’ abilities, agreeing who should do which tasks, communications between team members and keeping up to date with progress that has been made on the project. Software agents have the ability to monitor progress and to offer advice by operating in the background, acting autonomously when the need arises. An agent prototype has been developed in Prolog to perform a limited set of functions to support students. Team projects have a planning, doing and completing stage, all of which require them to have some sort of agent support. This agent at present supports part of the planning stage, by prompting the students to input their likes, dislikes and abilities for a selection of task areas defined for the project. The agent then allocates the various tasks to the students according to predetermined rules. The results of a trial carried out using teams working on projects, on campus, indicate that students like the idea of using this agent to help with allocating tasks. They also agreed that agent support of this type would probably be helpful to both students working on team projects with face to face contact, as well as for teams working solely online. Work is ongoing to add more functionality to the agent and to evaluate the agent more widely

Benefits, Limitations and Best Practices of Online Coursework…Should Accounting Programs Jump on Board?

The evolution of online teaching has evolved as quickly and vivaciously as the adoption of the World Wide Web. While there were and are skeptics, research shows that not only is online learning more convenient and makes educational available anytime and anywhere, it has the potential, in some cases, to be an improved tool for educating. To ensure maximized learning outcomes, and to experience the blessing and not the curse of online coursework, it is critical that universities embrace it wholeheartedly and follow online pedagogical best practices in developing and executing online courses. In addition, there are some courses where special forethought should be made to ensure online learning is effective. Courses that are more computational necessitate this consideration. This document serves to provide strategies and best practices on how to obtain excellence and maximized outcomes from online education. It examines research to date and outlines: the benefits and challenges of online learning, strategies and best practices for online educating, and considerations for online accounting coursework

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018