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Auto-Generative Learning Objects (AGLO) are reusable learning templates that can be instantiated with content based on random numbers. The templates are filled with content generated automatically by metaprogramming. The DSEL platform is a web application that processes the AGLO templates and generates interactive HTML content for the students. The platform includes user authentication based on Google, Facebook and Yahoo in order to avoid creating new credentials. Each HTTP session is stored in the database in order to analyse the progress of the learning process for each student. The DSEL platform processes the evaluation of symbols and then replaces the content of HTML. The symbols may have text values or SVG (Scalar Vector Graphics) content. The platform is responsible for processing the interaction between student and the correct answer. It evaluates the student answer by comparing it to the computed answer. Another aspect of the interaction is about selecting the right feedback to be shown to the student based on the given answer. The DSEL platform manages the persistence of the student's interactions. Since the HTML content is dynamically generated each symbol is posted in a MySql database through a PHP persistence module. The navigation on the AGLO objects hierarchy tree is monitored and stored in the database. Each visit to an AGLO and its generated content is stored in the database to be further investigated. The result of the student answer evaluation is stored in the database too. The learning progress is showed in a web page for each user and globally for the administrator. The platform is prepared to be evaluated for two disciplines namely, Data Structures and Algorithms and Algorithm Design and Analysis. As perspective, we intent to implement an analytics module to gather conclusions from the AGLO learning approach. [ABSTRACT FROM AUTHOR]

Technology development affects all areas of social life. In the digital age we live in, education must keep up with new trends. This can be achieved by transforming how learning is organized into a modern, efficient and flexible one. Young people in our society are almost technology dependent, using electronic devices for both learning, socializing and leisure. The field of e-learning has significant potential for research as it is a quite new concept in the current socio-economic context of Romania. Digital technology produces certain changes in the learning environment such as virtual learning space, independent and individualized learning, real-time monitoring of work. The concept of learning object is defined very vaguely and therefore there is a variety of learning objects models and new proposals appear continuously. The most widely used e-learning standards are SCORM, IEEE LOM, IMS, LRMI, CISCO. A generative model is an effective way to learn using independent learning. Generating generative models on computer disciplines and information technology would be an effective support for students. Interactive questions play an important role in e-learning and offer a wide range of benefits to both the student and the tutor. The generative model has emerged to improve the use of learning objects. Auto-generative learning objects (AGLO) retain the philosophy of the generative model, but generation is different. The AGLO approach uses metaprogramming to generate content, and the main difference is that this generation relies heavily on random numbers and dynamic content. Following the study of existing objects, we identified the generative learning objects as having potential for further development. [ABSTRACT FROM AUTHOR]

The paper presents the technology for building e-Science cyberinfrastructure which enables integration of regular cloud computing environment with big data facilities and stream data processing. The developed technology is aimed to support uniform dynamic interaction with the user during composite application building and execution, as well as result analysis. The core concept of the proposed approach is based on the set of domain-specific knowledge including description of a) semantics of problem domain objects; b) used software and data services; c) data formats and access protocols. Linking all these knowledge parts together facilitates automatic solution of technological integration issues. It enables providing the user with high-level domain-specific tools for complex tasks' description, which can be automatically translated into particular calls of cloud computing services or BigData analytics tasks. The developed technology uses interactive workflow (IWF) technique to interconnect services of different kind: computation, data analytics, external data sources, interactive visualization systems [ABSTRACT FROM AUTHOR]