This study presents the development of an immersive multisensory system for the artwork ”A Cuca” by Tarsila do Amaral, designed for visually impaired individuals. The system integrates 3D models, audio descriptions, NFC tags, and an iOS app with native accessibility features, ensuring ease of use, scalability, and applicability in cultural spaces. The development process includes requirement gathering, technology selection, refinement of 3D models, and the integration of NFC tags with the iOS app. The goal is to provide visually impaired individuals with autonomy in interpreting and appreciating art, thereby promoting cultural accessibility. A systematic literature mapping was also conducted to identify existing studies on multisensory technologies for art appreciation by visually impaired audiences. The findings highlight the effectiveness of combining tactile and auditory stimuli, offering guidelines to enhance user experience and autonomy. This approach demonstrates the potential of multisensory technologies to foster inclusion and critical engagement with art.

User reviews have become an essential source for evaluating quality attributes in mobile applications. Although prior research has examined privacy, security, and digital accessibility for people with disabilities through user feedback, comprehensive studies simultaneously analyzing accessibility, usability, and user experience (UX) through this lens remain scarce. This study expands on our earlier work by conducting a systematic literature review (SLR) to explore the connections between these three dimensions through user feedback analysis. From 670 studies published since 2013, we selected 42 for thematic analysis. The selected papers reflect the field’s multidisciplinary nature, encompassing computer science, health sciences, and social sciences, with most studies focusing on applications in both physical and mental health domains. The findings highlight accessibility as an ongoing challenge for users with disabilities and those with specific needs. Key results include: (i) a disparity between two positive and six negative accessibility factors; (ii) usability findings revealing six strengths (e.g., performance, design simplicity) but fourteen shortcomings (e.g., navigation difficulties, unintuitive interfaces); (iii) nine core UX elements, with accessibility and usability as central components; and (iv) thirty-three actionable recommendations for future research. This study provides a holistic understanding of how usability and UX improvements can enhance accessibility. The findings aim to assist developers and organizations in creating more inclusive mobile applications while guiding future research directions.

From a Human-Computer Interaction perspective, data visualizations are visual representations of data that improve users' cognitive capabilities during a task. In particular, UX data visibility can raise a team's engagement with the UX design and better inform product decisions. However, researchers and professionals lack a foundation to build new UX data visualizations. In this context, this paper describes a Systematic Mapping of the Literature that aims to consolidate the state of the art on UX data visualizations. To guide the open coding of the findings, we defined ten questions that span the Visual Information Seeking Mantra and the four levels of Munzner's analysis framework. We identified 28 well-known and seven custom chart formats, with the node-link diagram arising as the most popular. Most of the visualized data comes from software logs, and there is a lack of exploration of UX metrics, acoustic data, and demographic data as data sources. Regarding the Visual Information Seeking Mantra, visualizations had a zoom and filter function and a details on demand function for most chart formats. However, most chart formats lacked overview functions. Our findings provide a broad overview of the literature that can support the creation of new UX data visualizations.

Improving education through virtual reality is challenging due to the high costs of developing systems and the lack of teacher engagement in project conception. This project introduces and evaluates a system that enables teachers to create their own lessons in virtual reality without prior programming knowledge, addressing these challenges. By integrating Learning Management tools, Learning Analytics, and Multi-user Connection Servers, we developed a system that allows multiple users to coexist in the same virtual environment and perform various tasks. Our system also enables educators to evaluate students based on their behavior. In EduVR, teachers can configure activities in advance and retrieve results. Heuristic evaluations of EduVR’s integrated environment design have demonstrated its potential as a model for developing new educational platforms, setting a new standard for virtual reality educational systems.

The foundation of scientific research lies in robust data collection methodologies, increasingly embracing a data-driven paradigm within the realm of psychological studies. This paper outlines our extensive undertaking of a psychophysiological investigation utilizing the web-based platform UnnCyberpsy for seamless data aggregation and processing. Data collection stands as a pivotal phase in scientific inquiry, with an increasing inclination toward data-centric methodologies within psychological research. This study delineates our endeavor in conducting a comprehensive psychophysiological investigation employing the web-based tool UnnCyberpsy for data acquisition and analysis. Constructed within the PHP programming framework and 'CodeIgniter' microframework (version 4.0), UnnCyberpsy offers pivotal functionalities, including scheduling equipment pick-ups, guiding through test procedures using a branched algorithm, and handling data storage and preprocessing. Each test phase is pre-coded into the system, empowering participants to independently undergo the testing regimen via personal devices. Automatic data collection, preprocessing, and storage streamline the process, ensuring comprehensive participant data validation and preservation upon experiment completion. Embracing UnnCyberpsy resulted in an elimination of data loss risk to 0%, a 30% rise in potential participant engagement, and expedited data gathering constrained solely by participant numbers and equipment availability. Our exploration underscores the pivotal role of online applications like UnnCyberpsy, not only streamlining data collection but also fostering a symbiotic environment benefiting both researchers and participants. This paradigm shift towards digital platforms stands as a beacon for future research endeavors, promising unparalleled convenience and efficiency in the realm of data-driven scientific inquiry.

This paper presents “Caturama VR,” an English-localized VR game that immerses players in Brazilian folklore, portraying the adventures of Caturama, a young indigenous hero. It features a VR-optimized interface, dynamic inventory management, visually captivating special effects, and first-person animated hands for deep narrative interaction within a mystical environment. Employing advanced 3D graphics, the game enriches the educational exploration of folklore, aiming to cultivate an appreciation for indigenous cultural heritage. Rigorous pre-user VR testing, conducted in-house by the development team acting as players, has significantly boosted interaction quality, gameplay balance, immersion, presence, and overall performance, laying a solid foundation for forthcoming advancements and real-user UX evaluations.

With the growth of the digital games market, the occurrence of bugs in games has a significant impact and generates dissatisfaction among users. Therefore, conducting tests is necessary to avoid these events and ensure the quality of the distributed product. Among the tests that are performed in games, one that is particularly effective in identifying bugs from the user's perspective is exploratory testing, but this is little covered in game testing literature, not providing new testers with guides or paths to be used for specific occasions. This paper reports an experience of applying exploratory testing strategies in 2D and 3D platform games. We selected seven well-known strategies of exploratory testing and conducted a study that involved the definition of a game testing procedure and proper adaptations for the games under test. By applying the game testing procedure, several bugs with low, medium and high severity were uncovered. The lessons learned and routines carried out in this study can be used by new testers in games of the same category, in an attempt to obtain better results.

We have long been committed to improving Usability Evaluation and one of the proposals we have been working on the most is the use and improvement of the Heuristic Evaluation (HE) technique. With this in mind, we proposed an improvement which was tested in an experiment. This article describes an experiment carried out in a real business professional context. Fifteen usability experts from a reputable company evaluated eight websites (four supermarket and four bank platforms) using our HE proposal for the first time in real-world scenarios. This experimentation analyzed two main aspects: firstly, whether individual or group evaluations affect the final result, and secondly, whether the heuristic evaluation technique is effective in a real business and professional context. Regarding the Usability Percentage (UP) event, the results indicate that there was little difference between group and individual evaluations. The mean UP for the group was 57.88%, while the mean UP for individuals was 56.66%. The experiment provided sufficient information to suggest a new version of our HE methodology, specifically designed to improve results in real-life contexts. Furthermore, the experiment’s findings support the proposal of this new methodology, which is better suited to the business environment.

Heterogeneous graphs are an essential structure that models real-world data through different types of nodes and relationships between them, including multimodality, which comprises different types of data such as text, image, and audio. Graph Neural Networks (GNNs) are a prominent graph representation learning method that takes advantage of the graph structure and its attributes that, when applied to the multimodal heterogeneous graph, learn a unique semantic space for the different modalities. Consequently, it allows multimodal fusion through simple operators such as sum, average, or multiplication, generating unified representations considering the supplementary and complementarity relationships between the modalities. In multimodal heterogeneous graphs, the labeling process tends to be even more costly due to the multiple modalities analyzed, in addition to the imbalance of classes inherent to some applications. In order to overcome these problems in applications that comprise a class of interest, One-Class Learning (OCL) is used. Given the lack of studies on multimodal early fusion in heterogeneous graphs for OCL tasks, we proposed a method based on unsupervised GNN for heterogeneous graphs and evaluated different early fusion operators. In this paper, we extend another work by evaluating the behavior of the main GNN convolutions in the method. We highlight that using operators such as average, addition, and subtraction were the best early fusion operators. In addition, GNN layers that do not use an attention mechanism performed better. In this way, we argue for heterogeneous graph neural networks in multimodal using early fusion simple operators instead of well-often-used concatenation and less complex convolutions.