Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension

Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model. We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning

Personalised correction, feedback, and guidance in an automated tutoring system for skills training

In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input

What do Europeans do at Work? A Task-Based Analysis: European Jobs Monitor 2016

[Excerpt] Europe continues its recovery from the economic slump caused by the global financial crisis in 2008, exacerbated by the euro zone single currency crisis in 2010–2011. In 2014–2015, aggregate employment levels rose faster than at any time since 2008 and over four million new jobs were created in the 28 EU Member States. The fifth annual European Jobs Monitor report looks at employment shifts at Member State and aggregate EU level from the second quarter of 2011 to the second quarter of 2015. Part 1 presents the jobs-based approach, used to describe employment shifts quantitatively (how many jobs were created or destroyed) and qualitatively (what kinds of jobs). This approach relies on breaking employment down into detailed ‘job’ cells, with a job defined as ‘a specific occupation in a specific sector’, for example, a health professional in the health sector, or a skilled craft/tradesman in car manufacturing. A particular focus is placed on the time profile of recent shifts in employment structure. Ranking the jobs according to their wage and educational levels – or a broader multidimensional index of job quality – adds a qualitative dimension to the analysis. In this year’s report, a further level of detail is provided by measuring the intensity involved in carrying out different categories of task: physical, intellectual and social in terms of the job’s content, methods and work organisation, as well as the tools used (such as information and communication technologies (ICT) and machinery). Parts 2 and 3 of the report introduce a new set of indicators on the task content, methods and tools used at work. Derived from international databases on work and occupations, these indicators enable the analysis to go beyond characterising jobs by quality alone – to give a detailed account of what Europeans do at work and how they do it. The indicators provide valuable new insights on the structural differences and recent evolution of European labour markets, as well as a better understanding of labour input in the production process and the changing nature of the skills required. The jobs-based approach has been used since the 1990s to assess the extent to which employment structures in developed economies are polarising, leading to a shrinking middle quintile, or upgrading as the demand for, and supply of, highly qualified workers increases. These are the two main patterns identified in recent analysis of developed economy labour markets, although more recent analysis in the US – corroborated by some evidence in this report – suggests that a downgrading of the employment structure (relatively faster growth at the lower end of the wage distribution) is also emerging as an alternative pattern

A hybrid method for the analysis of learner behaviour in active learning environments

Software-mediated learning requires adjustments in the teaching and learning process. In particular active learning facilitated through interactive learning software differs from traditional instructor-oriented, classroom-based teaching. We present behaviour analysis techniques for Web-mediated learning. Motivation, acceptance of the learning approach and technology, learning organisation and actual tool usage are aspects of behaviour that require different analysis techniques to be used. A hybrid method based on a combination of survey methods and Web usage mining techniques can provide accurate and comprehensive analysis results. These techniques allow us to evaluate active learning approaches implemented in form of Web tutorials

A Model-Driven Engineering Approach for ROS using Ontological Semantics

This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A Model-Driven Engineering Approach for ROS using Ontological Semantic

A reflective characterisation of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in