weSPOT: a cloud-based approach for personal and social inquiry

Scientific inquiry is at the core of the curricula of schools and universities across Europe. weSPOT is a new European initiative proposing a cloud-based approach for personal and social inquiry. weSPOT aims at enabling students to create their mashups out of cloud-based tools in order to perform scientific investigations. Students will also be able to share their inquiry accomplishments in social networks and receive feedback from the learning environment and their peers

weSPOT: A personal and social approach to inquiry-based learning

weSPOT is a new European initiative proposing a novel approach for personal and social inquiry-based learning in secondary and higher education. weSPOT aims at enabling students to create their mash-ups out of cloud based tools and services in order to perform scientific investigations. Students will also be able to share their inquiry accomplishments in social networks and receive feedback from the learning environment and their peers. This paper presents the research framework of the weSPOT project, as well as the initial inquiry-based learning scenarios that will be piloted by the project in real-life educational settings

A report on e-portfolios : design features, uses, benefits, examples & emerging trends

This paper gives a more sophisticated response to the rapid (re)emergence of the e­portfolio buzz­word. Starting from a basic e­portfolio design pattern, a wide range of variations are explored. The aim is to establish a body of knowledge for guiding users and technology providers, so as to achieve an ever­more appropriate and fruitful alignment of needs, designs, platforms and informed choices. The key benefits of e­portfolio approaches are discussed, with some coverage of the variations, and suggested research and development directions. Deep and persistent diversity­creating factors are highlighted. A range of mini case studies from Warwick are then examined to throw further light upon the combinations of real and perceived needs, platform affordances and design choices. Finally, this is a fast evolving field, especially given the near ­ubiquitous adoption of platforms with e­portfolio­like elements (Facebook, LinkedIn etc). Technology and academic support services must look further forwards to emerging practices and requirements just at the edge of the institutional­perceptual horizon. We must be prepared to shape these potentially disruptive developments for the benefit of students, teachers, the institution and society

Tasks, cognitive agents, and KB-DSS in workflow and process management

The purpose of this paper is to propose a nonparametric interest rate term structure model and investigate its implications on term structure dynamics and prices of interest rate derivative securities. The nonparametric spot interest rate process is estimated from the observed short-term interest rates following a robust estimation procedure and the market price of interest rate risk is estimated as implied from the historical term structure data. That is, instead of imposing a priori restrictions on the model, data are allowed to speak for themselves, and at the same time the model retains a parsimonious structure and the computational tractability. The model is implemented using historical Canadian interest rate term structure data. The parametric models with closed form solutions for bond and bond option prices, namely the Vasicek (1977) and CIR (1985) models, are also estimated for comparison purpose. The empirical results not only provide strong evidence that the traditional spot interest rate models and market prices of interest rate risk are severely misspecified but also suggest that different model specifications have significant impact on term structure dynamics and prices of interest rate derivative securities.

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

A Data-informed Public Health Policy-Makers Platform

Hearing loss is a disease exhibiting a growing trend due to the number of factors, including but not limited to the mundane exposure to the noise and ever-increasing amount of older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform, responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in a framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers professionals involved in the policy development process in Croatia