Abstract
eLearning has been evolved in a gradual and consistent way. Along with this evolution several specialized and disparate systems appeared to fulfill the needs of teachers and students such as repositories of learning objects, intelligent tutors, or automatic evaluators. This heterogeneity poses issues that are necessary to address in order to promote interoperability among systems. Based on this fact, the standardization of content takes a leading role in the eLearning realm. This article presents a survey on current eLearning content standards. It gathers information on the most emergent standards and categorizes them according three distinct facets: metadata, content packaging and educational design. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
The ability to map instructions running in a microprocessor to a reconfigurable processing unit (RPU), acting as a coprocessor, enables the runtime acceleration of applications and ensures code and possibly performance portability. In this work, we focus on the mapping of loop-based instruction traces (called Megablocks) to RPUs. The proposed approach considers offline partitioning and mapping stages without ignoring their future runtime applicability. We present a toolchain that automatically extracts specific trace-based loops, called Megablocks, from MicroBlaze instruction traces and generates an RPU for executing those loops. Our hardware infrastructure is able to move loop execution from the microprocessor to the RPU transparently, at runtime, and without changing the executable binaries. The toolchain and the system are fully operational. Three FPGA implementations of the system, differing in the hardware interfaces used, were tested and evaluated with a set of 15 application kernels. Speedups ranging from 1.26 × to 3.69 × were achieved for the best alternative using a MicroBlaze processor with local memory. © 2013 João Bispo et al.

Abstract
This paper presents a novel approach to accelerate program execution by mapping repetitive traces of executed instructions, called Megablocks, to a runtime reconfigurable array of functional units. An offline tool suite extracts Megablocks from microprocessor instruction traces and generates a Reconfigurable Processing Unit (RPU) tailored for the execution of those Megablocks. The system is able to transparently movebcomputations from the microprocessor to the RPU at runtime. A prototype implementation of the system using a cacheless MicroBlaze microprocessor running code located in external memory reaches speedups from 2.2x to 18.2x for a set of 14 benchmark kernels. For a system setup which maximizes microprocessor performance by having the application code located in internal block RAMs, speedups from 1.4x to 2.8x were estimated.

Abstract
The teaching-learning process is increasingly focused on the combination of the paradigms "learning by viewing" and "learning by doing." In this context, educational resources, either expository or evaluative, play a pivotal role. Both types of resources are interdependent and their sequencing would create a richer educational experience to the end user. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. The Seqins is a sequencing tool of digital resources that has a fairly simple sequencing model. The tool communicates through the IMS LTI specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning. © Ricardo Queirós, José Paulo Leal and José Campos.

Abstract

Abstract
English is by far the most used language on the web. In some domains, the existence of less content in the users' native language may not be problematic and even help to cope with the information overload. Yet, in domains such as health, where information quality is critical, a larger quantity of information may mean easier access to higher quality content. Query translation may be a good strategy to access content in other languages, but the presence of medical terms in health queries makes the translation process more difficult, even for users with very good language proficiencies. In this study, we evaluate how translating a health query affects users with different language proficiencies. We chose English as the non-native language because it is a widely spoken language and it is the most used language on the web. Our findings suggest that non-English-speaking users having at least elementary English proficiency can benefit from a system that suggests English alternatives for their queries, or automatically retrieves English content from a non-English query. This awareness of the user profile results in higher precision, more accurate medical knowledge, and better access to high-quality content. Moreover, the suggestions of English-translated queries may also trigger new health search strategies.