Search

The Web offers an unprecedented number of resources and has become the most popular source of information for students shaping their understanding of a new topic, and for instructors selecting relevant material for learning and teaching activities. Even though search engines are the most widely used tools for searching for educational content, the realities of the learning and teaching processes make the retrieval and evaluation of educational resources more complex than they are for other goods or services. The lack of recourse to educational metadata in web pages, as well as the size of the Web itself, call for specific techniques to be adopted for a more effective ranking of educational content. In this study, we propose an innovative approach based on semantic technologies. The SemanticSearch approach described in this paper leverages knowledge graph representation of teaching contexts and proposes a new ranking method for rating educational web content. In the literature we find an Educational Ranking Principle that ranks web pages for a specific teaching context. In this study, we integrate the Educational Ranking Principle with semantic data to extend the experimentation and analyse performance further. We undertake an evaluation involving university teachers, considering more than 70 queries to measure the SemanticSearch performance against the Educational Ranking Principle in addition to two state-of-the-art methodologies: Tf-Idf and BM25F. Paired t-tests of four accuracy measures provide statistical evidence for improvements made by using SemanticSearch method when compared to the three baselines.

A concept map provides a graphic hierarchical means of representing how knowledge is structured in a domain. It visually organizes a set of concepts showing their mutual relations. An analysis of the similarities between two concept maps can produce significant results in any fields where Intelligent Knowledge Management is used, such as Healthcare, Policy Development, Energy and Waste Management, Resource Consumption Sustainability, Mobility, Safety, Citizen Empowerment, and, of course, Education. In an educational setting, a concept map conveys the various concepts connected by relations of dependence that a course must cover. However, the similarity between two concept maps for education has to be measured according to criteria that take into consideration the pedagogical properties of the maps, i.e., not only considering the structural aspects of the maps themselves. An automated analysis of the similarity between two concept maps can allow the teacher to reflect on different interpretations of the knowledge domain of a certain course as well as to assess how existing learning material can be implemented in a new course. Research into this aspect of concept mapping appears to be relatively scarce. This paper proposes criteria to assess the similarity of two concept maps, also based on pedagogical features, with the aim of providing teachers with better support during the course creation process. Each criterion is implemented through a specific measure function. The measures are then shown to be sensitive to their criterion rationale by evaluating them against a collection of random case studies.

Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.

Among more dramatic effects, the COVID-19 scenario also raised the need for new online information and communication services, promoting the spread of software solutions whose usefulness will last well beyond the pandemic situation. Particularly in the cultural heritage domain, it has been unveiled the relevance of new AI-based approaches, able to dynamically aggregate information and making them available for a customized fruition aimed to the individual cultural growth. Here, we integrate machine learning techniques for the automatic generation of contents for an intelligent tutoring system grounded on automated planning techniques. We present a solution for semantic, intelligent creation of personalized cultural contents, born as a lesson-making assistant, but developed as to become a multi-function “cultural crossover”, useful in the frame of a wide range of planning, dissemination, and managing activities for cultural heritage contents.

Learning Management Systems are among the most popular e-learning tools. Over the last few years, however, scientific research has made considerable progress in developing valuable resources currently unavailable in most Learning Management Systems, including solutions aimed at providing students with personalized support throughout the learning process, which is an essential requirement in continuing education. Observing and modelling the learner, and adapting their learning experience accordingly means opening up new technological and, above all, methodological perspectives in e-learning. The work described in this paper is part of the Open Learning project, in which business-based and university researchers aim to combine the most frequently used e-learning technologies, Learning Management Systems, with the benefits of customized systems so as to develop an innovative learning content delivery system based on the personalization of the learning experience. The proposed system integrates moodle with an engine, LS-Plan, which provides automated sequencing of the learning material based on the learner’s knowledge and learning styles. This paper describes the new system and presents the results of tests conducted in the domain of Italian Neorealist Cinema.