Paolo Mengoni

This study delves into the educational possibilities and user experiences offered by a Metaverse platform, utilizing immersive Virtual Reality (VR) to bolster engagement and learning in communication and grammar. In the wake of advancing digital technologies, there's an imperative to reevaluate conventional educational practices for their effectiveness in engaging students and fostering in-depth learning. Despite VR's potential in education, integrating these immersive experiences effectively into pedagogical frameworks and ensuring their accessibility and usability poses significant challenges. Our research employed a mixed-method approach, blending quantitative analysis to gauge user engagement, learning effectiveness, and acceptance of technology with qualitative feedback for deeper insight into user experiences. Participants navigated specially designed Metaverse scenarios, engaging with AI avatars through interactive tasks and narrative explorations across two distinct modules focused on grammar intricacies and the evolution of search technologies. The quantitative findings underscore a substantial correlation between users' interest and their intent to utilize the platform, accounting for 59% of the variance in intent. Qualitative responses highlight a robust engagement level and favorable perceptions of the Metaverse experience, particularly driven by the method being interesting. Nonetheless, identified areas for improvement in technology and ease of use underscore the necessity for a holistic approach that melds pedagogical efficacy with technological advancement to fully leverage the educational potential of immersive learning environments.

In this paper, we aim to explore the impact of the COVID-19 (Coronavirus disease 2019) pandemic on the global tourism industry and the changes in users' online reviews of listings on the Airbnb platform in this context. As a result of the outbreak, countries have introduced travel restriction policies to limit the movement of people and control the spread of the epidemic, which has dealt a severe blow to the global tourism industry. We want to provide an in-depth study of the impact of the COVID-19 epidemic on the tourism industry, as well as to explore the changes in the focus and attention of users on the travel experience. This is informative for tourism industry practitioners and policy makers to help better understand market demand and develop more rational policies. We selected online reviews on the Airbnb platform to explore the industry impact of the epidemic. Through topic modeling and sentiment analysis, this paper investigates the changes in users' discussion focus and attention level before, during and after the epidemic, and explores the reasons for them. The results show that the number of non-local visitors plummeted during the epidemic, while visitors paid more attention to topics related to environmental hygiene and personal health. In addition, this paper finds that user reviews have a very clear positive bias. From the triple perspective of platform, hosts, and users, this paper explores the reasons behind the phenomenon.

The recent advancements in Artificial Intelligence have paved the way for remarkable achievements in tasks that have traditionally posed challenges even for humans

Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients’ clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient’s reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist’s when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.

This work proposes an innovative visual tool for real-time continuous learners analytics. The purpose of the work is to improve the design, functionality, and usability of learning management systems to monitor user activity to allow educators to make informed decisions on e-learning design, usually limited to dashboards graphs, tables, and low-usability user logs. The standard visualisation is currently scarce, and often inadequate to inform educators about the design quality and students engagement on their learning objects. The same low usability can be found in learning analytics tools, which mostly focus on post-course analysis, demanding specific skills to be effectively used, e.g., for statistical analysis and database queries. We propose a tool for student analytics embedded in a Learning Management System, based on the innovative visual metaphor of interface morphing. Artificial intelligence provides in remote learning immediate feedback, crucial in a face-to-face setting, highlighting the students’ engagement in each single learning object. A visual metaphor is the representation of a person, group, learning object, or concept through a visual image that suggests a particular association or point of similarity. The basic idea is that elements of the application interface, e.g., learning objects’ icons and student avatars, can be modified in colour and dimension to reflect key performance indicators of learner’s activities. The goal is to provide high-affordance information on the student engagement and usage of learning objects, where aggregation functions on subsets of users allow a dynamic evaluation of cohorts with different granularity. The proposed visual metaphors (i.e., thermometer bar, dimensional morphing, and tag cloud morphing) have been implemented and experimented within academic-level courses. Experimental results have been evaluated with a comparative analysis of user logs and a subjective usability survey, which show that the tool obtains quantitative, measurable effectiveness and the qualitative appreciation of educators. Among metaphors, the highest success is obtained by Dimensional morphing and Tag cloud transformation.