Informatics

22 pages, 2500 KiB

Open AccessArticle

Are We Inclusive? Accessibility Challenges in Philippine E-Government Websites

by Paul Bokingkito, Jr., Jerame Beloy, Jerina Jean Ecleo, Apple Rose Alce, Nenen Borinaga and Adrian Galido

Informatics 2025, 12(2), 41; https://doi.org/10.3390/informatics12020041 - 15 Apr 2025

Abstract

Web accessibility is essential for e-government in the Philippines to ensure that all citizens, including those with disabilities, can access important information and services. This study evaluates government web accessibility using the Web Content Accessibility Guidelines 2.0 from the World Wide Web Consortium [...] Read more.

Web accessibility is essential for e-government in the Philippines to ensure that all citizens, including those with disabilities, can access important information and services. This study evaluates government web accessibility using the Web Content Accessibility Guidelines 2.0 from the World Wide Web Consortium and web presence based on the Government Website Template Design guidelines. A combination of automated testing tools and visual inspections was used for the assessment. Results showed significant discrepancies between web presence and web accessibility. Web presence compliance ranged from 28% to 82.67%, averaging 53.43%. Web accessibility scored higher, with compliance rates ranging from 62.32% to 97.1% and an average of 82.5%. This indicates that while many government agencies have focused on accessibility, there is a need to improve their digital services and visibility. A well-structured and user-friendly website is vital. However, without expanded online services, mobile accessibility, and transactional features, the full potential of digital governance remains untapped. Future studies are directed to aid government agencies with adopting accessible design principles, conducting regular audits, collaborating with disability advocacy groups, and integrating assistive technologies to foster a more inclusive and efficient digital government ecosystem. Full article

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14 pages, 268 KiB

Open AccessArticle

Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women

by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich and Julio Muñoz-Benítez

Informatics 2025, 12(2), 40; https://doi.org/10.3390/informatics12020040 - 11 Apr 2025

Abstract

Intimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using machine learning [...] Read more.

Intimate partner violence (IPV) remains a critical issue that requires data-driven solutions to improve victim profiling and intervention strategies. This study introduces Mujer Segura, an innovative web application designed to collect structured data on IPV cases and predict their severity using machine learning models. The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. The RF model achieved an accuracy of 97%, with a precision of 1.00 for non-severe cases and 0.96 for severe cases, recall values of 0.93 and 1.00 respectively, and an ROC AUC of 0.9534. The GBC model demonstrated an accuracy of 89%, with a precision of 1.00 for non-severe cases and 0.98 for severe cases, recall values of 0.95 and 1.00 respectively, and an ROC AUC of 0.9891. The application also integrates geospatial visualization tools to identify high-risk areas in the State of Mexico, enabling real-time interventions. These findings confirm that machine learning can enhance the timely detection of IPV cases and support evidence-based decision-making for public safety agencies. Full article

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24 pages, 734 KiB

Open AccessArticle

Transparency Unleashed: Privacy Risks in the Age of E-Government

by Cristian Paguay-Chimarro, David Cevallos-Salas, Ana Rodríguez-Hoyos and José Estrada-Jiménez

Informatics 2025, 12(2), 39; https://doi.org/10.3390/informatics12020039 - 11 Apr 2025

Abstract

E-government and transparency are significantly improving public service management by encouraging trust, accountability, and the massive participation of citizens. On the one hand, e-government has facilitated online services to address bureaucratic processes more efficiently. On the other hand, transparency has promoted open access [...] Read more.

E-government and transparency are significantly improving public service management by encouraging trust, accountability, and the massive participation of citizens. On the one hand, e-government has facilitated online services to address bureaucratic processes more efficiently. On the other hand, transparency has promoted open access to public information from the State so that citizens can understand and track aspects of government processes more effectively. However, as both require extensive citizen information management, these initiatives may significantly compromise privacy by exposing personal data. To assess these privacy risks in a concrete scenario, we analyzed 21 public institutions in Ecuador through a proposed taxonomy of 6 categories and 17 subcategories of disclosed personal data on their online portals and websites due to LOTAIP transparency initiative. Moreover, 64 open-access systems from these 21 public institutions that accomplish e-government principles were analyzed through a proposed taxonomy of 8 categories and 77 subcategories of disclosed personal data. Our results suggest that personal data are not handled through suitable protection mechanisms, making them extremely vulnerable to manual and automated exfiltration attacks. The lack of awareness campaigns in Ecuador has also led many citizens to handle their personal data carelessly without being aware of the associated risks. Moreover, Ecuadorian citizens’ privacy is significantly compromised, including personal data from children and teenagers being intentionally exposed through e-government and transparency initiatives. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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Graphical abstract

18 pages, 5048 KiB

Open AccessReview

Clustering with Uncertainty: A Literature Review to Address a Cross-Domain Perspective

by Salvatore Flavio Pileggi

Informatics 2025, 12(2), 38; https://doi.org/10.3390/informatics12020038 - 9 Apr 2025

Abstract

Clustering is a very popular computational technique that, because of imperfect data, is often applied in the presence of some kind of uncertainty. Taking into account such an uncertainty (and model), the computational output accordingly contributes to increasing the accuracy of the computations [...] Read more.

Clustering is a very popular computational technique that, because of imperfect data, is often applied in the presence of some kind of uncertainty. Taking into account such an uncertainty (and model), the computational output accordingly contributes to increasing the accuracy of the computations and their effectiveness in context. However, there are challenges. This paper presents a literature review on the topic. It aims to identify and discuss the associated body of knowledge according to a cross-domain perspective. A semi-systematic methodology has allowed for the selection of 68 papers, prioritizing the most recent contributions and an intrinsic application-oriented approach. The analysis has underscored the relevance of the topic in the last two decades, in which computation has become somewhat pervasive in the context of inherent data complexity. Furthermore, it has identified a trend of domain-specific solutions over generic-purpose approaches. On one side, this trend enables a more specific set of solutions within specific communities; on the other side, the resulting distributed approach is not always well integrated with the mainstream. The latter aspect may generate a further fragmentation of the body of knowledge, mostly because of some lack of abstraction in the definition of specific problems. While in general terms these gaps are largely understandable within the research community, a lack of implementations to provide ready-to-use resources is critical overall. In more technical terms, solutions in the literature present a certain inclination to mixed methods, in addition to the classic application of Fuzzy Logic and other probabilistic approaches. Last but not least, the propagation of the uncertainty in the current technological context, characterised by data and computational intensive solutions, is not fully analysed and critically discussed in the literature. The conducted analysis intrinsically suggests consolidation and enhanced operationalization though Open Software, which is crucial to establish scientifically sound computational frameworks. Full article

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17 pages, 5441 KiB

Open AccessArticle

Enhancing Cultural Heritage Accessibility Through Three-Dimensional Artifact Visualization on Web-Based Open Frameworks

by Sasithorn Rattanarungrot, Martin White and Supaporn Chairungsee

Informatics 2025, 12(2), 37; https://doi.org/10.3390/informatics12020037 - 9 Apr 2025

Abstract

This paper presents an innovative approach to cultural heritage preservation through the development of an open framework that leverages RESTful APIs to make high-fidelity 3D models of cultural artifacts accessible to any application. Focusing on antique kitchenware utensils from the Nakhon Si Thammarat [...] Read more.

This paper presents an innovative approach to cultural heritage preservation through the development of an open framework that leverages RESTful APIs to make high-fidelity 3D models of cultural artifacts accessible to any application. Focusing on antique kitchenware utensils from the Nakhon Si Thammarat National Museum in Thailand, this research utilizes photogrammetry to create detailed 3D models, which are then made available on a web-based platform, accessible globally via standardized HTTP requests. The framework enables real-time access to 3D cultural content, overcoming the geographical and physical barriers that often limit access to cultural heritage. By integrating these 3D models into RESTful APIs, the project not only preserves delicate artifacts but also enhances their educational and cultural value through interactive accessibility. This system demonstrates the practical application of digital preservation technologies and sets a precedent for future initiatives aiming to digitize and disseminate cultural artifacts more broadly. The implications of this study extend beyond preservation to include enhanced global accessibility, enriched educational resources, and a more inclusive approach to cultural engagement. This project illustrates the transformative potential of digital technologies in preserving, accessing, and experiencing cultural heritage worldwide. Full article

(This article belongs to the Section Human-Computer Interaction)

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22 pages, 297 KiB

Open AccessArticle

Exploring the Ethical Implications of Using Generative AI Tools in Higher Education

by Elena Đerić, Domagoj Frank and Dijana Vuković

Informatics 2025, 12(2), 36; https://doi.org/10.3390/informatics12020036 - 7 Apr 2025

Abstract

A significant portion of the academic community, including students, teachers, and researchers, has incorporated generative artificial intelligence (GenAI) tools into their everyday tasks. Alongside increased productivity and numerous benefits, specific challenges that are fundamental to maintaining academic integrity and excellence must be addressed. [...] Read more.

A significant portion of the academic community, including students, teachers, and researchers, has incorporated generative artificial intelligence (GenAI) tools into their everyday tasks. Alongside increased productivity and numerous benefits, specific challenges that are fundamental to maintaining academic integrity and excellence must be addressed. This paper examines whether ethical implications related to copyrights and authorship, transparency, responsibility, and academic integrity influence the usage of GenAI tools in higher education, with emphasis on differences across academic segments. The findings, based on a survey of 883 students, teachers, and researchers at University North in Croatia, reveal significant differences in ethical awareness across academic roles, gender, and experience with GenAI tools. Teachers and researchers demonstrated the highest awareness of ethical principles, personal responsibility, and potential negative consequences, while students—particularly undergraduates—showed lower levels, likely due to limited exposure to structured ethical training. Gender differences were also significant, with females consistently demonstrating higher awareness across all ethical dimensions compared to males. Longer experience with GenAI tools was associated with greater ethical awareness, emphasizing the role of familiarity in fostering understanding. Although strong correlations were observed between ethical dimensions, their connection to future adoption was weaker, highlighting the need to integrate ethical education with practical strategies for responsible GenAI tool use. Full article

17 pages, 5507 KiB

Open AccessArticle

Markov-CVAELabeller: A Deep Learning Approach for the Labelling of Fault Data

by Christian Velasco-Gallego and Nieves Cubo-Mateo

Informatics 2025, 12(2), 35; https://doi.org/10.3390/informatics12020035 - 25 Mar 2025

Abstract

The lack of fault data is still a major concern in the area of smart maintenance, as these data are required to perform an adequate diagnostics and prognostics of the system. In some instances, fault data are adequately collected, even though the fault [...] Read more.

The lack of fault data is still a major concern in the area of smart maintenance, as these data are required to perform an adequate diagnostics and prognostics of the system. In some instances, fault data are adequately collected, even though the fault labels are missing. Accordingly, the development of methodologies that generate these missing fault labels is required. In this study, Markov-CVAELabeller is introduced in an attempt to address the lack of fault label challenge. Markov-CVAELabeller comprises three main phases: (1) image encoding through the application of the first-order Markov chain, (2) latent space representation through the consideration of a convolutional variational autoencoder (CVAE), and (3) clustering analysis through the implementation of k-means. Additionally, to evaluate the accuracy of the method, a convolutional neural network (CNN) is considered as part of the fault classification task. A case study is also presented to highlight the performance of the method. Specifically, a hydraulic test rig is considered to assess its condition as part of the fault diagnosis framework. Results indicate the promising applications that this type of methods can facilitate, as the average accuracy presented in this study was 97%. Full article

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17 pages, 5550 KiB

Open AccessArticle

Offline System for 2D Indoor Navigation Utilizing Advanced Data Structures

by Jorge Luis Veloz, Leo Sebastián Intriago, Jean Carlos Palma, Andrea Katherine Alcívar-Cedeño, Álvaro Antón-Sacho, Pablo Fernández-Arias, Edwan Anderson Ariza and Diego Vergara

Informatics 2025, 12(2), 34; https://doi.org/10.3390/informatics12020034 - 21 Mar 2025

Abstract

This study introduces a robust offline system for 2D indoor navigation, developed to address common challenges such as complex layouts and connectivity constraints in diverse environments. The system leverages advanced spatial modeling techniques to optimize pathfinding and resource efficiency. Utilizing a structured development [...] Read more.

This study introduces a robust offline system for 2D indoor navigation, developed to address common challenges such as complex layouts and connectivity constraints in diverse environments. The system leverages advanced spatial modeling techniques to optimize pathfinding and resource efficiency. Utilizing a structured development process, the proposed solution integrates lightweight data structures and modular components to minimize computational load and enhance scalability. Experimental validation involved a comparative approach: traditional navigation methods were assessed against the proposed system, focusing on usability, search efficiency, and user satisfaction. The results demonstrate that the offline system significantly improves navigation performance and user experience, particularly in environments with limited connectivity. By providing intuitive navigation tools and seamless offline operation, the system enhances accessibility for users in educational and other complex settings. Future work aims to extend this approach to incorporate additional features, such as dynamic adaptability and expanded application in sectors like healthcare and public services. Full article

(This article belongs to the Section Human-Computer Interaction)

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25 pages, 747 KiB

Open AccessArticle

Development of a Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC) Using the eDelphi Method

by Marco Bolpagni and Silvia Gabrielli

Informatics 2025, 12(1), 33; https://doi.org/10.3390/informatics12010033 - 20 Mar 2025

Abstract

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, [...] Read more.

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings. Full article

(This article belongs to the Section Human-Computer Interaction)

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25 pages, 1821 KiB

Open AccessArticle

SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis

by Quang-Hung Le, Brijesh Patel, Donald Adjeroh, Gianfranco Doretto and Ngan Le

Informatics 2025, 12(1), 32; https://doi.org/10.3390/informatics12010032 - 19 Mar 2025

Abstract

Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can [...] Read more.

Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can transpire at any given point, rendering event analysis a continuous concern. Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce SSL-SurvFormer. This entails a continuously monotonic Transformer network, empowered by SSL pre-training, that is designed to address the challenges presented by continuous events and absent features in survival prediction. Our proposed continuously monotonic Transformer model facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. Additionally, our SSL pre-training strategy incorporates data transformation to adeptly manage missing information. The SSL pre-training encompasses two tasks: mask prediction, which identifies positions of absent features, and reconstruction, which endeavors to recover absent elements based on observed ones. Our empirical evaluations conducted across a variety of datasets, including FLCHAIN, METABRIC, and SUPPORT, consistently highlight the superior performance of SSL-SurvFormer in comparison to existing methods. Additionally, SSL-SurvFormer demonstrates effectiveness in handling missing values, a critical aspect often encountered in real-world datasets. Full article

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38 pages, 986 KiB

Open AccessArticle

Determinants of ThaiMOOC Engagement: A Longitudinal Perspective on Adoption to Continuance

by Kanitsorn Suriyapaiboonwattana and Kate Hone

Informatics 2025, 12(1), 31; https://doi.org/10.3390/informatics12010031 - 19 Mar 2025

Abstract

Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them [...] Read more.

Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them remain understudied. This longitudinal study examines the factors influencing learners’ sustained engagement with ThaiMOOC, incorporating demographic characteristics, usage log data, and key predictors of adoption and completion. Our research collected primary data from 841 university students who enrolled in ThaiMOOC as a mandatory curriculum component, using online surveys with open-ended questions and post-course usage log analysis. Logistic regression analysis indicates that adoption intention, course content, and perceived effectiveness significantly predict students’ Actual Continued Usage (ACU). Moreover, gender, prior MOOC experience, and specific usage behaviors emerge as influential factors. Content analysis highlights the importance of local language support and the desire for safety during the COVID-19 pandemic. Key elements driving ACU include video design, course content, assessment, and learner-to-learner interaction. Full article

(This article belongs to the Section Human-Computer Interaction)

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37 pages, 3526 KiB

Open AccessArticle

Human-Centred Design Meets AI-Driven Algorithms: Comparative Analysis of Political Campaign Branding in the Harris–Trump Presidential Campaigns

by Hedda Martina Šola, Fayyaz Hussain Qureshi and Sarwar Khawaja

Informatics 2025, 12(1), 30; https://doi.org/10.3390/informatics12010030 - 18 Mar 2025

Abstract

This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on [...] Read more.

This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on 180,000 screenings) with an AI-LLM neuroscience-based marketing assistant (CoPilot), with 67,429 areas of interest (AOIs). The original flyer, from an Al Jazeera article, served as the baseline. Professional graphic designers created three redesigned versions, and one was done using recommendations from CoPilot. Metrics including total attention, engagement, start attention, end attention, and percentage seen were evaluated across 13–14 areas of interest (AOIs) for each design. Results indicated that human-enhanced Design 1 with AI eye-tracking achieved superior overall performance across multiple metrics. While the AI-enhanced Design 3 demonstrated strengths in optimising specific AOIs, it did not consistently outperform human-touched designs, particularly in text-heavy areas. The study underscores the complex interplay between neuroscience AI algorithms and human-centred design in political campaign branding, offering valuable insights for future research in neuromarketing and design communication strategies. Python, Pandas, Matplotlib, Seaborn, Spearman correlation, and the Kruskal–Wallis H-test were employed for data analysis and visualisation. Full article

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15 pages, 659 KiB

Open AccessArticle

Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT

by Valeria Cioffi, Ottavio Ragozzino, Lucia Luciana Mosca, Enrico Moretto, Enrica Tortora, Annamaria Acocella, Claudia Montanari, Antonio Ferrara, Stefano Crispino, Elena Gigante, Alexander Lommatzsch, Mariano Pizzimenti, Efisio Temporin, Valentina Barlacchi, Claudio Billi, Giovanni Salonia and Raffaele Sperandeo

Informatics 2025, 12(1), 29; https://doi.org/10.3390/informatics12010029 - 17 Mar 2025

Abstract

Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using [...] Read more.

Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their feedback was evaluated by Gestalt psychotherapy trainees using a Likert scale rating of satisfaction. Statistical analysis, using the statistical package SPSS version 25 and applying principal component analysis (PCA) and one-way analysis of variance (ANOVA), demonstrated significant differences in favor of pre-trained AI feedback. PCA highlighted four components of the questionnaire: relational and emotional (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction obtained from the three kinds of supervisory feedback were compared using ANOVA. The feedback generated by the pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3)) in C4; in C1, the superiority of f2 over f1 but not over f3 appears significant. These results suggest that AI, when appropriately calibrated, may be an appreciable tool for complementing the effectiveness of clinical supervision, offering an innovative blended supervision methodology, in particular in the area of career guidance. Full article

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15 pages, 1249 KiB

Open AccessArticle

A Pilot Study Using Natural Language Processing to Explore Textual Electronic Mental Healthcare Data

by Gayathri Delanerolle, Yassine Bouchareb, Suchith Shetty, Heitor Cavalini and Peter Phiri

Informatics 2025, 12(1), 28; https://doi.org/10.3390/informatics12010028 - 13 Mar 2025

Abstract

Mental health illness is the single biggest cause of inability within the UK, contributing up to 22.8% of the whole burden compared to 15.9% for cancer and 16.2% for cardiovascular disease. The more extensive financial costs of mental ailments in Britain have been [...] Read more.

Mental health illness is the single biggest cause of inability within the UK, contributing up to 22.8% of the whole burden compared to 15.9% for cancer and 16.2% for cardiovascular disease. The more extensive financial costs of mental ailments in Britain have been evaluated at British Pound Sterling (GBP) 105.2 billion each year. This burden could be decreased with productive forms and utilization of computerized innovations. Electronical health records (EHRs), for instance, could offer an extraordinary opportunity for research and provide improved and optimized care. Consequently, this technological advance would unburden the mental health system and help provide optimized and efficient care to the patients. Using natural language processing methods to explore unstructured EHR text data from mental health services in the National Health Service (NHS) UK brings opportunities and technical challenges in the use of such data and possible solutions. This descriptive study compared technical methods and approaches to leverage large-scale text data in EHRs of mental health service providers in the NHS. We conclude that the method used is suitable for mental health services. However, broader studies including other hospital sites are still needed to validate the method. Full article

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23 pages, 7992 KiB

Open AccessArticle

Gamification in Virtual Reality Museums: Effects on Hedonic and Eudaimonic Experiences in Cultural Heritage Learning

by Sumalee Sangamuang, Natchaya Wongwan, Kannikar Intawong, Songpon Khanchai and Kitti Puritat

Informatics 2025, 12(1), 27; https://doi.org/10.3390/informatics12010027 - 3 Mar 2025

Abstract

Virtual museums powered by virtual reality (VR) technology serve as innovative platforms for cultural preservation and education, combining accessibility with immersive user experiences. While gamification has been widely explored in educational and entertainment contexts, its impact on user experiences in virtual cultural heritage [...] Read more.

Virtual museums powered by virtual reality (VR) technology serve as innovative platforms for cultural preservation and education, combining accessibility with immersive user experiences. While gamification has been widely explored in educational and entertainment contexts, its impact on user experiences in virtual cultural heritage museums remains underexplored. Prior research has focused primarily on engagement and enjoyment in gamified virtual environments but has not sufficiently distinguished between hedonic (pleasure-driven) and eudaimonic (meaning-driven) experiences or their impact on learning outcomes. This study aims to address this gap by comparing gamified and non-gamified virtual museum designs to evaluate their effects on hedonic and eudaimonic experiences, knowledge acquisition, and behavioral engagement. Using a quasi-experimental approach with 70 participants, the findings indicate that gamification significantly enhances hedonic experiences, including enjoyment, engagement, and satisfaction, while fostering prolonged interaction and deeper exploration. However, eudaimonic outcomes such as personal growth and reflection did not exhibit statistically significant differences. These results underscore the potential of gamified VR environments to balance entertainment and educational value, offering insights into user-centered design strategies for virtual museum systems that bridge technology, culture, and engagement. Full article

(This article belongs to the Section Human-Computer Interaction)

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16 pages, 4536 KiB

Open AccessArticle

Evaluating the Role of Visual Fidelity in Digital Vtubers on Mandarin Chinese Character Learning

by Xiaoxiao Cao, Wei Tong, Kenta Ono and Makoto Watanabe

Informatics 2025, 12(1), 26; https://doi.org/10.3390/informatics12010026 - 25 Feb 2025

Abstract

Despite the growing presence of digital Virtual YouTubers (Vtubers) in educational settings, there is limited empirical evidence on their effectiveness in language acquisition. In this investigation, we delved into the realm of digital education to assess how the visual fidelity of digital Vtuber [...] Read more.

Despite the growing presence of digital Virtual YouTubers (Vtubers) in educational settings, there is limited empirical evidence on their effectiveness in language acquisition. In this investigation, we delved into the realm of digital education to assess how the visual fidelity of digital Vtuber avatars affects the acquisition of Mandarin Chinese characters by beginners. Through incorporating a diverse array of digital Vtubers, ranging from simple two-dimensional figures to complex three-dimensional models, we explored the relationship between digital Vtuber design and learner engagement and efficacy. This study employed a randomized tutorial distribution, immediate post-tutorial quizzing, and a realism scoring rubric, with statistical analysis conducted through Pearson correlation. The analysis, involving 608 participants, illuminated a clear positive correlation: digital Vtubers with higher levels of realism significantly enhanced learning outcomes, underscoring the importance of visual fidelity in educational content. This research substantiates the educational utility of digital Vtubers and underscores their potential in creating more immersive and effective digital learning environments. The findings advocate for leveraging sophisticated digital Vtubers to foster deeper learner engagement, improve educational achievement, and promote sustainable educational practices, offering insights for the future development of digital learning strategies. Full article

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71 pages, 26964 KiB

Open AccessArticle

Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation

by A. Srinivaas, N. R. Sakthivel and Binoy B. Nair

Informatics 2025, 12(1), 25; https://doi.org/10.3390/informatics12010025 - 21 Feb 2025

Abstract

Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. The former uses physical models of engine components to diagnose defects, while the latter [...] Read more.

Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. The former uses physical models of engine components to diagnose defects, while the latter employs statistical analysis of sensor data to identify patterns indicating faults. Various methods for ICE fault identification, such as vibration analysis, thermography, acoustic analysis, and optical approaches, are reviewed. This paper also explores the latest approaches for detecting ICE faults. It highlights the challenges in the diagnostic process and ways to enhance result accuracy and reliability. This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. Additionally, this study incorporates advanced deep learning techniques, including a deep neural network (DNN), a one-dimensional convolutional neural network (1D-CNN), Transformer and a hybrid Transformer and DNN model which demonstrate superior performance in fault detection compared to traditional machine learning methods. Full article

(This article belongs to the Section Machine Learning)

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18 pages, 1054 KiB

Open AccessArticle

Digital Media Victimization Among Older Adults in Upper-Southern Thailand

by Pimpisa Pituk, Nirachon Chutipattana, Pussadee Laor, Thitipong Sukdee, Jiraprapa Kittikun, Witchayaporn Jitwiratnukool, Rohmatul Fajriyah and Wanvisa Saisanan Na Ayudhaya

Informatics 2025, 12(1), 24; https://doi.org/10.3390/informatics12010024 - 21 Feb 2025

Abstract

Online fraud threatens the well-being of older adults, with disparities in digital literacy and socioeconomic conditions amplifying their vulnerability. This study examined digital literacy and fraud victimization behavior among older adults in urban and rural settings, identifying key factors influencing victimization and its [...] Read more.

Online fraud threatens the well-being of older adults, with disparities in digital literacy and socioeconomic conditions amplifying their vulnerability. This study examined digital literacy and fraud victimization behavior among older adults in urban and rural settings, identifying key factors influencing victimization and its consequences. This cross-sectional analytical study, using multi-stage sampling, included 864 participants from Southern Thailand. The findings revealed that 46.3% of participants had adequate digital literacy, while 75.3% experienced fraud victimization, with higher rates of health impacts in rural areas. Higher age (Adjusted Odds Ratios; AOR: 1.83, p = 0.004), income (AOR: 2.28, p = 0.003), and rural residence (AOR: 3.03, p < 0.001) were significantly associated with an increased likelihood of fraudulent victimization. Conversely, being non-Buddhist (AOR: 0.47, p = 0.001) and having an adequate digital literacy (AOR: 0.50, p < 0.001) were protective factors. Fraud victimization significantly affected older adults’ health, with 29.5% reporting the following adverse outcomes: physical (AOR: 5.55), emotional (AOR: 7.80), social (AOR: 4.97), and overall heightened health risks (AOR: 7.71, p < 0.001). This research highlights the importance of improving digital literacy, fostering community awareness, and implementing tailored fraud-prevention strategies to protect older adults. This study provides a foundation for evidence-based policies aimed at mitigating digital risks and enhancing older adults’ well-being in the digital era. Full article

(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)

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19 pages, 274 KiB

Open AccessArticle

Digital Competences of Digital Natives: Measuring Skills in the Modern Technology Environment

by Danijela Pongrac, Marta Alić and Brigitta Cafuta

Informatics 2025, 12(1), 23; https://doi.org/10.3390/informatics12010023 - 21 Feb 2025

Abstract

The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this landscape. By utilising the Youth [...] Read more.

The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this landscape. By utilising the Youth Digital Skills Indicator (yDSI) and integrating it with the Digital Competence Framework for Citizens (DigComp 2.2), this research examines media habits and digital competences among Croatian youth aged 10–24, corresponding to Generations Alpha and Z. A sample of 231 participants across three competence domains—information literacy, security and communication—revealed statistically significant generational differences in the first two areas of digital skills. Furthermore, gender-based analyses, conducted using the Mann–Whitney U-test and Spearman correlations for Likert scale responses, showed no significant differences. These findings deepen our understanding of digital natives, how media habits evolve and influence their digital skills, highlighting the need for more tailored strategies to enhance their competences and bridge generational gaps. Full article

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