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Wikipedia is one of the most used repositories of free learning materials published by communities of experts: most of the students, and teachers do great use of it, despite the criticism about its reliability. Here we address the problem of helping teachers to build courses with Wikipedia pages to prepare on-The-fly courses. We use a web-based system called Wiki Course Builder, which allows teachers to query Wikipedia, by submitting a set of keywords, and having as a response four different ranked lists of retrieved Wikipedia pages, depending each list on a given retrieval metric implemented. In addition to the classic TF-IDF, Information Gain and Latent Semantic Indexing metrics, we propose a metric that highlights specific didactic aspects. This metric is based on the teacher's Teaching Styles namely, the Grasha teaching styles. Wikipedia pages are indexed with the Teaching Styles of the teachers that selected the given page; then, this value is compared with the Teaching Style of the teacher who is doing the search. The metric assigns higher rank to the Wikipedia pages that have a Teaching Style closer to the teachers'. We present an evaluation of the four retrieval metrics, showing that the teacher-styles based metric, Vs. The other three content-based metrics, allows for a better Wikipedia retrieval from a didactic point of view.

The public availability of datasets of teaching resources is an issue for the design, development and evaluation of Information Retrieval and Recommender Systems in Technology Enhanced Learning. Recently, the Dataset of Joint Educational Entities (DAJEE) has provided the community with a very exhaustive collection of resources coming from Massive Open Online Courses. This work proposes a representation of the DAJEE dataset according to Linked Data principles, interlinking the large amount of resources in DAJEE with the Web of Data. The transcript of the educational resources in DAJEE have been annotated through a Named Entity Recognition tool in order to create interlinks with the DBpedia entities. The DBpedia knowledge base provides additional information related to categories, that can be exploited to infer new knowledge and support reasoning processes.

The organization of the information in the knowledge economy hasbecome a priority business process. Better organization leads tofaster retrieval of relevant information. The process of searchingand sequencing didactic materials for course building is anarticulated and time-consuming process that requires considerableeffort by the user. The goal of this research is to implement aplatform for supporting the course building from Wikipedia articles. The selected materials will be automatically sequenced on the baseof prerequisite relation, in order to provide a knowledge graph, editable by the teacher, with prerequisite relationships, representing the optimum learning path for the students.

The designing and delivering of a new online course is a crucial task for teachers that have to face two main problems: building, or retrieving, and sequencing learning materials. Retrieving learning materials requires a great effort and a waste of time, while sequencing them requires an accurate didactic project. On the other hand, thanks to the Internet, teachers and instructional designers today can search and retrieve learning materials from Learning Objects Repositories freely available on the Web, such as Mertlot or Ariadne. In this paper we investigate the possibility of using the Wikipedia free encyclopedia, that is the biggest repository of educational material which is visited daily by about sixty million people with its 49 millions of registered people. It is a matter of facts that teachers consult this encyclopedia to arrange, integrate or enrich their courses. So here we propose a system, now at its early stage of development, aiming at supporting teachers to build courses basing on Wikipedia only. The system retrieves learning materials form Wikipedia and sequences them on the basis of the links embedded in the Wikipedia HTML pages, following a course building process based on the Grasha teaching styles and on a social didactic approach. A first questionnaire has been submitted to a sample of teachers with encouraging results.

"The success of training through virtual environments is due both to an interesting cost savings for industry and positive results for learners. However, the system behind this approach is usually hard to produce, being strongly domain-dependent and for this reason difficult to port on other domains. Here we present the design of a domain-independent training system based on simulation environments, capable to be reused in different domains and especially in the industrial ones, saving costs considerably. The main idea behind the system design is the separation between the domain logic and the trainer role. This distinction is achieved by the adoption of a modular and flexible agent-based system approach. We present a first case study for training advanced technicians to learn maintenance procedures for complex medium tension equipments."

"In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standard-compliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context."

"\"Training in industry is one of the most critical and expensive tasks to be faced by the management. Furthermore, in some cases, it is dangerous or even impossible to directly train operators on the real plants where security and safety problems may arise, making it very difficult to start training programs at low cost. For these reasons, the field of training in industry is rapidly developing using software or hardware solutions coming mainly from the following research areas: i) Human-Computer interaction, i.e., the use of complex and interactive human-machine interfaces, ii) plant simulators, i.e., software systems which are delivered with the plant itself to test and to learn complex tasks and processes, iii) Intelligent Training Systems, i.e., the availability of intelligent and personalized training systems where a virtual tutor guides users through a personalized learning path. In this paper we present the overall architecture of a system for industrial training, embedded into an Intelligent Tutoring System that can provide more effective and personalized training and learning in a context where working directly on real plants can be difficult and very expensive. In particular we present a simulator for training operators in using power plants, based on a multimedia and on interactive interface. This system is particularly suitable to be used for training in industrial electric and oil plants. Moreover, the system allows operators for collaborative problem solving. Currently the system is under delivery to an Italian Electric industry.\""

Our work carries on the idea of configuring personalized courses by means of automated planning techniques, preserving coherence with present standards for e-learning, in particular with SCORM properties. Starting from a previous prototype based on suitably defined learning objects, learning components, we intend to make it available in a wider contest. To this aim we design a mapping between our learning component specification and the definition of a SCO, a SCORM 1.2 compliant learning object. In this way we can extend conservatively the usual SCO by enriching it with those elements that are relevant for the process of automated configuration: while the original SCORM properties stay unchanged, we can then make course configuration with a SCORM compliant learning object. In order to obtain such a mapping, the ScormUni tag format has been devised, which is an extension of SCORM meta data. We show here how the above mentioned prototype has evolved into UniTag , a software module that extends Moodle (Version 1.6).