22,210 research outputs found
Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques
[EN]The application of the Information and Communication Technologies to teaching and learning processes is linked to the development of new tools and services that can help students and teachers. Learning platforms are a clear example of this. They are
very popular tools in eLearning contexts and provide different kinds of learning applications and services. In addition, these environments also register most of the interactions between the learning process stakeholders and the system. This information could potentially be used to make decisions but usually it is stored as raw data, which is very difficult to understand. This work presents a system that employs visual learning analytic techniques to facilitate the exploitation of that information. The system presented includes several tools that make possible to explore issues such as: when interaction is carried out, which contents are the most important for users, how they interact with others, etc. The system was tested in the context of a software engineering subject, taking into account the stored logs of five academic years. From this analysis it is possible to see how visual analytics can help decision-making and in this context how it helps to improve educational processes
Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression
In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream
SciTech News Volume 71, No. 1 (2017)
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Innovating Pedagogy 2015: Open University Innovation Report 4
This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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