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In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

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.

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.

LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, pre-course, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced, based on the initial learner’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 output a new Learning Objects Sequence, to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LECOMPS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures, the As a Whole and the Layered approaches. The results are encouraging, both for the system on the whole and for the adaptive components.

LS-Plan is a system capable to provide educational hypermedia with adaptivity. During the student’s navigation in hyperspace, the system, by means of a suitable interface, receives in input, for each visited learning node, a behavioral pattern, containing the student’s knowledge acquired or not acquired in that node and measured by a post-test questionnaire. Then, by means of the adaptation algorithms, the system returns a Learning Object Sequence to the hypermedia, just to be recommended to the student. In this work we investigate in detail the adaptation algorithms together with the didactic strategies behind them, through an application to a real learning domain, subdivided in five case studies. Moreover, we show in detail how the automated planner, embedded into the system, allows consistency checks during the arrangement of the pool of Learning Nodes, allowing the teacher to define possible didactic strategies during automatic course personalization. Finally, the performance of the system is evaluated by means of an experimental design with positive results.

Identifying the pre-requisite relationships among learning objects is a crucial step for faculty and instructional designers when they try to adapt them for delivery in their general education distance courses. We propose a general-purpose content-based approach for facilitating this step by means of semantic analysis techniques: the learning objects are associated to WikiPedia pages (topics), and their dependency is obtained using the classification of those topics supported by Wikipedia Miner.

In this work we address the problem of automatically finding prerequisite relations among learning materials in order to help instructional designers to speed up the course building process. Ours is a data-driven approach, where a (machine) learner is trained to classify predecessor/successor relationships, given two didactic materials in a textual form. As the training set we use the learning materials extracted from Coursera. A first evaluation shows promising results.

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.

In recent years, there has been a radical change in the world of education and training that is causing that many schools, universities and companies are adopting the most modern technologies, mainly based on Web architectures and Web 2.0 instruments and tools, for learning, managing and sharing of knowledge. In this context, an e-Learning system can reach its maximum potential and effectiveness if it could take advantage of the information in its possession and process it in an intelligent and personalized way. The Educational Data Mining is an emergent field of research where the approach to personalization makes use of the log data generated by learners during their training process, to dynamically update users learning profiles such as skills and learning styles and identify students behavioral patterns. In this paper we present a case study of a data mining approach, based on cluster analysis, in order to support the detection of learning styles in a community of learners, following the Grasha-Riechmann learning styles model. As an e-learning framework we used the Moodle LMS platform and studied the log files generated by a course taken by a community of learners. The first experimental results suggest a connection between clusters and learning styles, reinforcing the use of this approach.

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.

Nowadays, thanks to the Internet, the request for distance learning is increasing. Private companies and public institutions are very sensitive to the saving of costs that such type of education could implicate, i.e., with no limits in time and place. Besides, the results obtained by this kind of learning are comparable to those reached by the classic in-presence lessons. Consequently, instructional designers need didactic instruments to build courses in a rapid way, especially if they would build their courses starting from learning objects already posted into Learning Object Repositories by other peers. Our work proposes a system, at a very early stage of development, with the aim to help instructional designers to find and retrieve learning objects suitable for their courses, starting from the classic conceptual map of the course to be built. The system contains an OLAP module to let teachers to drill-down into concepts as well.

Building a new course is a complex task for teachers: the entire process requires different steps, starting with the concept map building and ending with the delivery of the learning objects to students through a learning management system. 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. Consequently, having a system supporting this phase is a very important challenge, considering that each teacher expresses her own pedagogy as well. Here we propose a novel Teacher Model that helps teachers to build new courses effectively. The model is based both on a didactic semantic network containing concepts and learning material and on Teaching Styles as proposed in the literature by Grasha. This framework gives teachers the possibility to share their teaching experience as well. A first experimentation of the system gives positive results.

"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.

Personalization and Adaptation are among the main challenges in the field of e-learning, where currently just few Learning Management Systems, mostly experimental ones, support such features. In this work we present an architecture that allows moodle to interact with the Lecomps system, an adaptive learning system developed earlier by our research group, that has been working in a stand-alone modality so far. In particular, the Lecomps responsibilities are circumscribed to the sole production of personalized learning objects sequences and to the management of the student model, leaving to moodle all the rest of the activities for course delivery. The Lecomps system supports the “dynamic” adaptation of learning objects sequences, basing on the student model, i.e., learner’s Cognitive State and Learning Style. Basically, this work integrates two main Lecomps tasks into moodle, to be directly managed by it: Authentication and Quizzes.

Visiting a real or virtual museum or an archaeological site can be a hard task, especially in case of large sites provided with many works of art or ancient ruins. For this reason most historical sites provide guided tours, to improve visitors satisfaction and interest. In this work we explore the use of an e-learning environment, called Lecomps5 , to provide museums or other cultural sites with the capability of automatically planning personalized tours, according to visitors needs and interests. Lecomps5 allows a domain expert, through a suitable GUI, to build a pool of learning components concerning a given site. Then the system, by means of an embedded planner, generates a personalized tour through the works of art, on the basis of the visitor's artistic interests and needs. We propose a first 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 as well.

In the context of distance learning, Adaptive Web-based Educational System focus on personalization and adaptation, that is on “learner’s satisfaction”. In this paper we address the other side of the coin, that is the "teacher’s satisfaction" problem, which is quite seldom taken into account in educational systems. We present a new version of the Lecomps5 Web-based Educational System, a system capable of providing personalization and adaptation on the basis of learner’s knowledge, learning styles and learning progresses. In this new version, a framework provides the teacher with an easy and flexible tool for managing learning material, expressing different didactic strategies and sequencing personalized courses by means of an embedded planner. Such functionalities are supported by the system basing on evaluations of learner’s knowledge, learning styles, and learning progresses. We report on a first controlled experiment, we made to evaluate the “teacher’s satisfaction”.

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 shows the expressive power of the functional programming language Standard ML (SML) in the context of computer algebra. It is focused on a special application of the p-adic lifting technique, the Hensel algorithm, that is utilized in a symbolic but also numeric context. This experiment demonstrates that SML provides a suitable frame for the implementation of abstract algebraic notions together with the possibility to code related algorithms in a generic way on the corresponding level of abstraction.

This paper presents several methodological and technological aspects for the treatment of mathematical objects by very high level abstract specification of data and methods. A homogeneus environment is defined for the design and implementation at the highest abstract level. The presentation is developed from the point of view of both formal specification and implementation languages. Indeed specification and programming are considered as uniform actions under the same general conceptual model. The conceptual model is completely supported by the Object-Oriented methodology. Particular interest is devoted to Algorithmic Logic, as a logic specification language, and to Loglan as an innovative Object-Oriented programming language.

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.