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Wikipedia has become the mostused source of didactic materials with its immense number of Wiki pages,freely available to anyone. Wiki Course Builder is a Web platform designed to help teachers building courses composed by Wikipedia pages only,directly taken by the Wikipedia free encyclopedia.One of the interesting and novel features is the Community module, that provides a collaborative environment, through a suitable Graphic User Interface, where teachers can interact with all the courses in the platform. The Community is a graphical visualization of the Wikipedia pages used by all the teachers in the systems, grouped in clusters, each representing a particular topic. Another characteristic is the information about the Teaching Styles of those teachers that have used that page in their courses. This feature has a twofold advantage. First, each Wikipedia page is tagged with the Teaching Styles. Second, this social aspect gives useful suggestions for possible extensions or changes in the courses. We present a complete use case showing the usefulness of the social Community environment to speed up and improve the course building activity.

In recent years, there has been a radical change in the world of teaching and training. This is causing numerous schools, universities and companies to adopt the most modern Information and Communication Technologies, mainly based on the Web, for distance education. Moreover, the widespread use of web-based environments is producing a considerable volume of data which could be used to monitor the learning processes to improve them. While Educational Data Mining analyzes this data from a technical point of view, Learning Analytics focuses on educational aspects to optimize online learning opportunities, involving all the stakeholders. In this paper, we present a case study concerning the analysis of data generated by a learning process, in a Learning Management System (LMS). The main goal is to test a particular Business Intelligence platform, the Knime platform, to extract hidden significant educational features from data. The case study strengthens our approach, with interesting pedagogical results.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others.

One of the major problems facing our cities is the disposal of the huge amount of waste produced every day. A possible solution is represented by recycling. In this article, we propose a system for automatic recognition and extraction of materials from the unsorted waste, which takes advantage of Computer Vision and Machine Learning techniques. The system can classify the material of incoming objects and grasp, and insert them into proper bins. For the material classification phase, the system analyzes the information captured by a Near-Infrared (NIR) camera and an RGB camera. Experimental tests performed on real-world datasets show encouraging accuracy values.

The personalized fruition of cultural heritage, especially with the health crisis we are currently experiencing, poses challenges to the learning and educational processes that could benefit from the use of intelligent tools. By emphasizing the interactivity of the learning process, in particular, the recent Technology Enhanced Learning (TEL) methodologies can represent an enabling factor for the construction of the common knowledge. This paper pursues the idea of integrating different heterogeneous artificial intelligence techniques for the personalized administration of stimuli in a dynamic learning environment. With this in mind, this paper describes a system, called WikiTEL and its first instantiation to Cultural Heritage visits in the Matera city.

The retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories (\(p < .01\)), and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications.

The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed.

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task. In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.

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.

During the phases of course construction, in Learning Management Systems, a teacher can be valuably helped by system's recommendations about learning objects to include in the course. A usual protocol is in that the teacher performs a query, looking for suitable learning material, and the system proposes a list of learning objects, with information shown for each one; then the teacher is supposed to make her choice, basing on the displayed information. Here we present a Recommender System for Learning Objects retrieved from Learning Objects Repositories, that is based on a ``social teacher model", based on the similarities with the teacher in the system, and the potential model evolutions over time. The proposed system is available as a Moodle plug-in. In the paper we show the details of the information decorating the learning objects retrieved after a query, the definition of the teacher model, and the similarity measure underlying the recommendation strategy.

The Knowledge Society is increasing the demand for tools to manage the didactic knowledge stored in Learning Objects Repositories, and needed by teachers to generate courseware. In this respect, still there is a lack of automated tools for the analysis and retrieval of learning resources from such repositories. Here we propose the use of the OLAP technique to help teachers to specify a didactic ontology by which performing quantitative and qualitative analysis of Internet-based Learning Objects Repositories. The related system is presented, together with a case study based on real repositories.

With its 5,006,202 articles, 49 millions of registered people and on average 800 new articles per day, Wikipedia provides a knowledge base for teachers and instructional designers to build didactic materials. As a matter of fact, teachers often consult this encyclopaedia to arrange, integrate or enrich their courses. Moreover, with the exponential growth of the Internet, didactic materials are freely available and usable by teachers, instructional designers and students from Learning Objects Repositories such as Mertlot or Ariadne and others. On the other hand, designing and delivering a new course is a crucial task for teachers, who have to face two main problems: building or retrieving and sequencing learning materials. Retrieving or building learning materials requires a great effort and is time-consuming, while sequencing requires an accurate didactic project. In this paper we present a sequencing engine of learning materials, embedded in the Wiki Course Builder system, a system capable of retrieving and sequencing Wikipedia web pages, taking into account both the teacher model based on the Grasha teaching styles and on a social didactic approach. The main goal is to support teachers building on-the-fly courses, i.e., building courses quickly, with a few clicks of the mouse. An important feature of the system is represented by its ability to allow teachers to interact with the recommended learning path through a graph-based interface where they can directly modify the proposed learning path, adding or deleting Wikipedia pages. A first questionnaire has been submitted to a sample of teachers with encouraging results.

Selecting and sequencing a set of Learning Objects (LOs) to build a course may turn out to be quite a challenging task. In this paper we focus on such an aspect, related to the verification and respect of the relationships of pedagogical dependence existing between two LOs added to a course (meaning that if a given LO has another one as "pre-requisite", then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). In our approach the sequencing of LOs in the course can still be managed by the instructor, basing on her/his taste and preferences, yet s/he can also be helped by a set of suggestions, related to the pre-requisite relationships existing among the LOs selected for the course. Such suggestions (such relationships, in effect) can be computed automatically and provide the instructor with significant help and guidance. We show a light-weight formalization of the LO, and how it can be "represented" by a set of WikiPedia Pages ("topics"); then we show how such set of topics, together with a set of relevant hypotheses we previously defined, can help establish the dependence relationship existing between two LOs. In this endeavor we exploit the classification in categories available for the WikiPedia topics, and obtain interesting results for our framework, in terms of precision and recall of the dependence relationships.

Concept maps represent a significant tool in education, used to plan and guide learning activities and to help teachers in some endeavors such as analyzing and refining their teaching strategies, retrieving suitable learning material, and supporting the provision of adaptive guidance in adaptive learning environments. Here we propose seven measures of similarity among concept maps, representing course modules. They deal with both structural and didactic aspects of the maps, to find out educational similarities among their associated course modules. The performance of the proposed similarity measures are analyzed and evaluated by means of some significant case studies.

The process of carefully choosing and sequencing a set of Learning Objects (LOs) to build a course may reveal to be quite a challenging task. In this work we focus on an aspect of such challenge, related to the verification and respect of the relationships of pedagogical dependence that holds between two LOs added to a course (meaning that if a given LO has another one as “pre-requisite”, then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). An innovative Machine learning-based approach for the identification of these kinds of relationships is proposed.

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.

This paper presents an approach to artificial intelligence planning based on linear temporal logic (LTL). A simple and easy-to-use planning language is described, PDDL-K (Planning Domain Description Language with control Knowledge), which allows one to specify a planning problem together with heuristic information that can be of help for both pruning the search space and finding better quality plans. The semantics of the language is given in terms of a translation into a set of LTL formulae. Planning is then reduced to "executing" the LTL encoding, i.e. to model search in LTL. The feasibility of the approach has been successfully tested by means of the system Pdk, an implementation of the proposed method.

The Teacher Model renders also a network of concepts and learning materials used in teacher's courses.The model comprises a concept of learning material reputation, based on the usage of such material.The model can support rapid analysis and selection of learning material for course construction.The model evolves dynamically, while the teacher participates in her/his community of teachers. On line education is a student centred activity, and most of the research in this field focuses on students; yet the quality of teaching is undoubtedly the basic ingredient for a successful learning. In particular, fostering new forms of collaboration between students and teachers, i.e. pursuing co-learning aspects of e-learning, probably needs giving teachers new means of collaboration, also among themselves. In this paper, we tackle the aim of providing the teacher with social collaboration tools, to support the process of course construction. Such a process comprises several distinct steps, from concept mapping, through selection of suitable learning material, to the final stages of delivery in a Learning Management System. It is an heavy process, through which teachers have to spend a lot of time to build or to retrieve the right learning material from local databases or from specialized repositories on the web. The support we foresee should exploit the knowledge of the whole teaching community, in which the teacher acts, to help her/him in doing the above described job. By "knowledge" we mean basically a representation of the ways of usage of learning materials, by the teachers in the community for their courses. To start on a solid footing, here we address the topic of modeling the teacher. The model we define aims to give teachers a personalized support, encompassing consideration for their own pedagogy, teaching styles, and teaching experience during course creation. It is deemed to consider all those issues in a dynamic way and to guide the teacher towards the best didactic choices.

Communities of Practice (CoPs) may be interpreted as kinds of a vertical evolution of social networks, where members share common interests in a particular domain or area, and exchange practical experiences to increase their knowledge and skills with respect to that specific field. In this paper we present some evaluation aspects of an experiment conducted within the framework of the European project UnderstandIT. The experiment involved the use of a CoP providing an educational program on Web2.0 Technologies for education for Vocational Education and Training teachers. The CoP was designed on the basis of the foundational Wenger’s concepts of domain, community and practice. In particular, we present a study of some social aspects of the CoP dynamics, basing our study on some evaluation metrics coming from the Social Network Analysis research area, i.e., using metrics such as betweenness, centrality, and closeness, in order to elicit useful relationships information. The experimental results confirm the goodness of the use of such approach for the elicitation of hidden information in the communicative network processes.

In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student's needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory. In this paper we propose a fine-grained student model, embedded into an Adaptive Educational Hypermedia, LS-Plan provided as plug-in for Moodle. In this way we satisfy the two most important requirements: a fine-grained personalization and a large diffusion. In particular, the substantial modification proposed in this contribution regards the methodology to evaluate the knowledge of the single student which currently has a low granularity level. The experiments showed that the new system has improved the evaluation mechanism by adding information that students and teachers can use to keep track of learning progress.

"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."

Nowadays, teachers tend to build their own didactic local repository composed by learning objects retrieved from web repositories or, in most cases, by self-made didactic material. In this way they do not share their teaching experience, loosing a precious shortcut to a fast professional update and to an improvement of their teaching activity. In this paper we address the problem of helping teachers to retrieve didactic material from a repository through a didactic social network where teachers with similar Teaching Styles, can help each other in retrieving educational material. To this aim a teaching-styles based social network is built following the Grasha TS paradigm. We present a first evaluation of the network embedded in a web application.

"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.\""

This work addresses the issue of personalised tours in cultural heritage domains, by exploiting methods and techniques developed for e-learning, in particular those that regard personalised e-learning courses. The main idea of our work is that a tour in a museum or in an archaeological site, either fully virtual or blended, may be managed through an e-learning environment. We propose the use of the LECOMPS5, an e-learning environment, to provide museums or other cultural sites with the capability of automatically planning personalised tours, according to visitors' needs and interests. We also show an example of an application of this system to an ancient archaeological site called Lucus Feroniae, showing how an e-learning platform can be successfully used for guiding visitors.

Curriculum Sequencing is one of the most interesting challenges in learning environments, such as Intelligent Tutoring Systems and e-learning The goal is to automatically produce personalized sequences of didactic materials or activities, on the basis of each individual student's model In this paper we present the extension of the LS-Lab framework, supporting an automated and flexible comparison of the outputs coming from a variety of Curriculum Sequencing algorithms over the same student models The main aim of LS-Lab is to provide researchers or teachers with a ready-to-use and possibly extensible environment, supporting a reasonably low-cost experimentation of several sequencing algorithms The system accepts a student model as input, together with the selection of the algorithms to be used and a given learning material; then the algorithms are applied, the resulting courses are shown to the user, and some metrics computed over the selected characteristics are presented, for the user's appraisal.

This paper presents LS-Plan , a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman's model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student's Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the student's navigation, and on the basis of learning assessments, the adaptation algorithm can propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.

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).

This paper presents the planning system Pdk (Planning with Domain Knowledge), based on the translation of planning problems into Linear Time Logic theories, in such a way that finding solution plans is reduced to model search. The model search mechanism is based on temporal tableaux. The planning language accepted by the system allows one to specify extra problem dependent information, that can be of help both in reducing the search space and finding plans of better quality.

This paper describes a methodology based on the object-oriented programming paradigm, to support the design and implementation of a symbolic computation system. The requirements of the system are related to the specification and treatment of mathematical structures. This treatment is considered from both the numerical and the symbolic points of view. The resulting programming system should be able to support the formal definition of mathematical data structures and methods at their highest level of abstraction, to perform computations on instances created from such definitions, and to handle abstract data structures through the manipulation of their logical properties. Particular consideration is given to the correctness aspects. Some examples of convenient application of the proposed design methodology are presented.

The “planning as satisfiability” approach for classical planning establishes a correspondence between planning problems and logical theories, and, consequently, between plans and models. This work proposes a similar framework for contingency planning: considering contingent planning problems where the sources of indeterminism are incomplete knowledge about the initial state, non-inertial fluents and non-deterministic actions, it shows how to encode such problems into Linear Time Logic. Exploiting the semantics of the logic, and the notion of conditioned model introduced in this work, a formal characterization is given of the notion of contingent plan (a plan together with the set of conditions that ensure its executability).