Raising awareness of the accessibility challenges in mathematics MOOCs

MOOCs provide learning environments that make it easier for learners to study from anywhere, at their own pace and with open access to content. This has revolutionised the field of eLearning, but accessibility continues to be a problem, even more so if we include the complexity of the STEM disciplines which have their own specific characteristics. This work presents an analysis of the accessibility of several MOOC platforms which provide courses in mathematics. We attempt to visualise the main web accessibility problems and challenges that disabled learners could face in taking these types of courses, both in general and specifically in the context of the subject of mathematics

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

A Review of the Literature and Implications for People with Disabilities (E-Human Resources Literature Review)

To accomplish this overview, an extensive review of the literature on information technology applications to the employment process was conducted. Three human resources related uses of the Internet are explored in this review of current literature: E-recruiting, E-benefits/HR, E-training. Each of these areas can have a significant impact on employees with disabilities, especially given the growth of business’ use of the Web. If E-recruiting is not accessible, it could prevent people from applying for or even finding open positions. E-training, if not accessible, could create a new barrier to the advancement of individuals who are unable to access online training to improve or update their skills. E-benefits, while likely to make enrollment and other activities easier for many employees, may become an obstacle for individuals with certain disabilities if not designed to be accessible. In addition, we examined the literature for any current discussion of access issues for applicants and employees with disabilities by business. In the remainder of this introduction, we also cover the World Wide Web and accessibility issues for people with disabilities, legislation relevant to Internet accessibility, and studies of Web accessibility

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Planning and managing the cost of compromise for AV retention and access

Long-term retention and access to audiovisual (AV) assets as part of a preservation strategy inevitably involve some form of compromise in order to achieve acceptable levels of cost, throughput, quality, and many other parameters. Examples include quality control and throughput in media transfer chains; data safety and accessibility in digital storage systems; and service levels for ingest and access for archive functions delivered as services. We present new software tools and frameworks developed in the PrestoPRIME project that allow these compromises to be quantitatively assessed, planned, and managed for file-based AV assets. Our focus is how to give an archive an assurance that when they design and operate a preservation strategy as a set of services, it will function as expected and will cope with the inevitable and often unpredictable variations that happen in operation. This includes being able to do cost projections, sensitivity analysis, simulation of “disaster scenarios,” and to govern preservation services using service-level agreements and policies