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Emotion modeling for social robotics has the great potential to improve the life quality for the elderly and individuals with disabilities by making communication, care, and interactions more effective. It can help individuals with communication difficulties express their emotions. It can also be used to monitor the emotional well-being of elderly persons living alone and alert caregivers or family members if there are signs of distress. More broadly, emotion modeling is necessary to design robots closer and closer to human beings that can naturally interact with them by understanding their behavior and reactions. Here, we propose a deep learning technique for emotion classification using electroencephalogram (EEG) signals. We aim to recognize valence, arousal, dominance, and likability. Our technique uses the spectrogram from each of the 32 electrodes applied in the skull area. Then, we employ a Resnet101 convolutional neural network to learn a model capable of predicting several emotions. We built and tested our model on the DEAP dataset.

Although ubiquitous and fast access to the Internet allows us to admire objects and artworks exhibited worldwide from the comfort of our home, visiting a museum or an exhibition remains an essential experience today. Current technologies can help make that experience even more satisfying. For instance, they can assist the user during the visit, personalizing her experience by suggesting the artworks of her higher interest and providing her with related textual and multimedia content. To this aim, it is necessary to automatically acquire information relating to the active user. In this paper, we show how a deep neural network-based approach can allow us to obtain accurate information for understanding the behavior of the visitor alone or in a group. This information can also be used to identify users similar to the active one to suggest not only personalized itineraries but also possible visiting companions for promoting the museum as a vehicle for social and cultural inclusion.

Current image/video acquisition and analysis techniques allow for not only the identification and classification of objects in a scene but also more sophisticated processing. For example, there are video cameras today able to capture micro facial expressions, namely, facial expressions that occur in a fraction of a second. Such micro expressions can provide useful information to define a person's emotional state. In this article, we propose to use these features to collect useful information for designing and implementing increasingly effective interactive technologies. In particular, facial micro expressions could be used to develop interfaces capable of fostering the social and cultural inclusion of users belonging to different realities and categories. The preliminary experimental results obtained by recording the reactions of individuals while observing artworks demonstrate the existence of correlations between the action units (i.e., single components of the muscular movement in which it is possible to break down facial expressions) and the emotional reactions of a sample of users, as well as correlations within some homogeneous groups of testers.

The DAtaset of Joint Educational Entities (DAJEE) is a repository which hosts more than 20,000 educational resources crawled from the MOOC platform Coursera. The resources are divided per category according to the MOOC categorization on Coursera, which is, however, very shallow. This contribution focuses on a more meaningful categorization of the resources in DAJEE, tailored to their content. To achieve such goal, our approach enriches the resources in DAJEE with semantic entities by applying state-of-the-art semantic techniques. The result is a significant improvement of the categorization of the resources in DAJEE than the previous version.

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. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher's assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in Learning Analytics approaches. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map.

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.

A Concept Map provides a graphical, hierarchical, representation of a domain of knowledge, and can be used by teachers to define and organize the content of a course. The use of existing concept maps, possibly implemented by means of tested and suitable learning material, can be of great help for the teacher, while defining her/his own concept map. In this context, being able to establish the similarity between two concept maps is a significant asset, allowing to limit the analysis of other maps, and focus on those that can be of actual help for the teacher. This is true in a special way if the measure is computed 'according to one among many possible pedagogical criteria of similarity'. We are working on the definition of measures entailing similarity based on different pedagogical criteria. In this paper, we meet the problem of experimenting such with measures, and comparing them with baseline measures, while investigating the effectiveness of a new measure. To support this kind of analysis we propose here a web-based system. We show the characteristics of the system, and use it to compare the behaviour of three measures of similarity: the pre-requisite measure (also presented) computes the similarity of two maps with respect to a particular pedagogical aspect. The other two measures are used as a baseline. We conclude that the system has shown its usefulness and can be used for more extensive analysis of other measures.

The increasing trend of sharing educational resources on the World Wide Web has attracted several contributions from the research community. Since most Technology Enhanced Learning users retrieve resources from the Web for teaching or learning, the Web is recognised as a repository of educational material. A big issue in automatic retrieval of online resources is that the Web is a huge and mostly unorganised space. Hence, there is no guarantee that items retrieved by current search engines are appropriate for educational uses. Automatically identifying Web-content suitable for education is one of the most challenging objectives because it requires extraordinary attention. Studies in Technology Enhanced Learning have proposed several solutions to support the teaching and learning needs of instructors and pupils within an enclosed platform. Other studies offer different techniques for collecting resources from the open Web that have specific characteristics. This work aims to gather findings and results from such studies for establishing a set of guidelines for researchers in education, expecting to improve the development of future proposals in this field.

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.

This paper presents a newborn collaboration between heterogeneous AI competences. In particular, it describes current work on the integration of machine learning techniques for the automatic generation of contents for an Intelligent Tutoring System grounded on automated planning techniques. The joint use of these two approaches allows on the one hand to facilitate the task of instructional designers in defining and preparing courses, and on the other hand to dynamically support the use of content according to different users context.

In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.

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.

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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.

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.

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.