Digital Research Research Articles (Page 1)

Digital collaborative education plays a pivotal role in digital education research and significantly contributes to enhancing teaching quality. Furthermore, it provides a new impetus for family-school-community collaboration in talent development. Nevertheless, the key drivers and predictive models of digital collaborative education remain underexplored. To address this gap, this study adopts the perspective of teachers' digital literacy, focusing on primary and secondary school teachers as research subjects. Employing machine learning methods such as gradient boosting regression trees (GBRT) and random forest, we identify the key factors influencing digital collaborative education and develop predictive models. The SHapley Additive exPlanations (SHAP) framework is applied to conduct holistic, heterogeneous, and individual-level explanatory analyses, whereas accumulated local effects (ALE) plots are used for single-feature explanation. The results indicate that random forest outperforms other models in predicting digital collaborative education. Digital academic assessment, digital instructional implementation, digital teaching design, and digital instructional research and innovation are the four most important feature variables in predicting the effectiveness of digital collaborative education, with digital application emerging as the strongest predictor, followed by professional development. These key features exhibit heterogeneity in predicting digital collaborative education across gender, age, educational background, and teaching experience, demonstrating nonlinear relationships. The findings provide empirical support for advancing digital collaborative education and offer valuable insights for enhancing teachers' professional development.

Background: It is currently unknown whether patient engagement in adult congenital heart disease (CHD) care is influenced by social determinants of health (SDOH) such as race/ethnicity, education, and income. Adapting from the chronic care model, patient engagement, defined as the knowledge, skills, ability, and willingness to manage one’s health, can minimize lapses in care and improve CHD care outcomes. Methods: This cross-sectional study analyzes data from patients aged 18 and older with a self-reported diagnosis of CHD enrolled in a digital health research study called Empower My Congenital Heart. Patient engagement was measured using the Gothenburg Empowerment Scale (GES). We compared mean GES scores across demographic categories (race/ethnicity, education, and income), using independent samples t-tests or one-way ANOVA as appropriate. Additionally, we conducted a multivariate linear regression analysis to evaluate the impact of race/ethnicity, education, and income on GES scores, adjusting for age, gender, marital status, parental status, and employment status. Results: Of 323 adult CHD patients, the mean age was 45.6 ±15 years, 32.5% were males, 89.9% were non-Hispanic White, 29.7% had less than a Bachelor's degree, and 43.5% had less than $100,000 annual household income. The mean GES score was 61.8 ± 8, with significantly higher scores observed for non-Hispanic White identifying individuals (p=0.006) and those with a Bachelor's or higher level of education (p=0.005), while no difference across income levels (p = 0.394) (Figure 1). Linear regression analysis demonstrated that race and ethnicity remained significantly associated with GES scores (Table 1). Conclusions: Race/ethnicity and education level (in the bivariate analysis only), but not income, are the SDOH variables significantly associated with how empowered a CHD patient is in managing their health care. Future interventions that target enhancing patient engagement and self-management skills should take into consideration the influence of these demographic and social factors.

Background: Digital health offers a potential solution to support adults with congenital heart disease (CHD), filling the critical gap to help maintain recommended lifelong care with adult CHD specialists. However, its design and implementation require a rigorous scientific approach to make it scalable and usable for the target population. Objective: To pilot test the acceptability and feasibility of a digital health intervention to support lifelong healthcare for adults with CHD. Methods: Using a community-based participatory research and a behavioral theory-based approach, we developed and piloted a digital tool, Empower My Congenital Heart (EmpowerMyCH), within the web—and mobile-based Eureka Digital Research platform at UCSF. Intervention components within EmpowerMyCH include exhibits (expert and peer-developed learning modules on CHD-related topics such as finding specialists or navigating visits, tests, procedures, etc.), peer support, and a digital medical passport (Figure 1). Participants receive an exhibit with study measure surveys every two months, with a two-month completion window. Participant satisfaction with the digital tool is measured at six months using the Client Satisfaction Questionnaire-8 (CSQ-8), a validated survey with scores ranging from 8 to 32; higher scores indicate higher satisfaction with the digital tool. Results: Of the 519 participants recruited, 495 were eligible adults with CHD, 445 consented, and 6 withdrew. The median age was 43 (ranging from 20 to 85) years, and 30.1% were males. Survey completion rate was the highest at 88.7% during the first two months after enrollment and 53.5% at 8 months. Of the five exhibits delivered so far, over 87% of participants rated the exhibits to be helpful (Figure 2). Participants found the exhibits readable, easily accessible, motivational, useful, and educational (Figure 3). The median (25th, 75th) CSQ-8 score was 21 (18, 25) among 144 participants who completed the survey. Conclusions: EmpowerMyCH, the digital health tool, appears to be acceptable to most adults with CHD to support and educate them in navigating their healthcare. The tool and its contents are accessible, user-friendly, and responsive to the needs of the CHD community, resulting in high satisfaction rates. The feasibility rates and satisfaction scores are similar to or higher for EmpowerMyCH than those of prior digital health studies. Future hybrid intervention trials can demonstrate EmpowerMyCH‘s effectiveness.

Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.

The digital transformation has provided an era of opportunity for the high-quality development of China's vocational education. How to seize the opportunities of the times and further promote the innovative development of vocational education digitalization on the basis of existing research has become an urgent problem to be solved. In order to systematically and objectively analyze the global characteristics of vocational education digital transformation research, this study uses CiteSpace software to visualize and analyze 604 relevant papers worldwide. The results show that the number of Chinese articles is generally higher than the international ones, the transformation of higher vocational colleges and digital teaching are the common focus of the world, and China is in a leading position in the research of industry-education integration. In the future, we should promote the sustainable development of digital transformation with resource supply-side reform.

Amidst the increasing datafication of healthcare, deep digital phenotyping is being explored in clinical research to gather comprehensive data that can improve understanding of neurological conditions. However, participants currently do not have access to this data due to researchers' apprehension around whether such data is interpretable or useful. This study focuses on patient perspectives on the potential of deep digital phenotyping data to benefit people with neurodegenerative diseases, such as ataxias, Parkinson's disease, and multiple system atrophy. We present an interview study (n=12) to understand how people with these conditions currently track their symptoms and how they envision interacting with their deep digital phenotyping data. We describe how participants envision the utility of this deep digital phenotyping data in relation to multiple stages of disease and stakeholders, especially its potential to bridge different and sometimes conflicting understandings of their condition. Looking towards a future in which patients have increased agency over their data and can use it to inform their care, we contribute implications for shaping patient-driven clinical research practices and deep digital phenotyping tools that serve a multiplicity of patient needs.

Purpose of the study: This study aims to identify global research trends, thematic evolution, and collaboration patterns in blended learning and learning management systems (LMS) from 2003 to 2025 using a bibliometric approach. Methodology:.A total of 890 peer-reviewed documents indexed in Scopus were analyzed using VOSviewer (v1.6.19) and Biblioshiny (R-based). Bibliometric mapping, keyword co-occurrence, and network visualization techniques were employed following systematic review procedures. Main Findings: The study identified three distinct research phases: initiation (2003–2008), rapid growth (2009–2019), and stabilization (2020–2025). Research focus shifted from technical implementation to learner-centered pedagogy emphasizing engagement, curriculum integration, and learning analytics. Emerging themes include artificial intelligence and adult learning, while Computers & Education remains the most influential source. Novelty/Originality of this study: This study provides a two-decade bibliometric mapping highlighting thematic evolution and collaboration networks in blended learning and LMS research. It advances understanding by identifying emerging areas such as AI and lifelong learning that inform future directions in digital education research.

Digital storytelling is a powerful tool in language learning that combines narrative, multimedia, and technology to improve learner engagement, motivation, and communication skills. This study employs bibliometric analysis to examine the trend of digital storytelling in language learning between 2010 and 2025. By reviewing publications related to digital storytelling indexed in the Elsevier Scopus database, we collected 431 articles. The analysis uses VOSviewer to visualize co-authorship patterns, citation networks, keyword co-occurrence, and thematic clusters. The statistics show that the trend has significantly increased from 2010 until June 2025, aligning with the rise of digital tools in education. Key contributing countries include the United States, China, and Spain, while the most cited articles focus on research projects adapting literary works into cinematic formats. This paper highlights the three most frequently used keywords in the research: "digital storytelling," "students," and "e-learning." Thematic mapping reveals that the United States occupies a central and dominant position in the global digital storytelling research network. This bibliometric research provides a macro-level overview of the field, offering valuable insights for researchers, educators, and policymakers aiming to leverage digital storytelling in language learning.

To offer a student-focused critical evaluation of the content and use of a digital competencies discipline-specific toolkit that was co-designed with students, offering ideas for training and development across several digital skills areas, such as digital creation, research, communication, innovation, and wellbeing. A cross-sectional empirical study. The toolkit was evaluated based on clarity, level of comprehension, accessibility, perceived relevance, and future implementation through a survey, which collected quantitative and qualitative data from 339 undergraduate nursing students in a single school and university in Scotland. Original research data were collected in June 2023. Students evaluated the toolkit positively for its clarity, comprehensive nature, and practical resources, but suggested improvements for neurodivergent learners. Most students recommended implementing the toolkit early in their course and emphasised its benefits in continuous use. The toolkit was found to be relevant for practice placements and career development. Despite study workload concerns, students were positive about upskilling, highlighting the utility of the toolkit. Digital literacy is essential as healthcare increasingly relies on digital tools, behaviours and processes. This study employed co-design strategies, supporting students to act as co-producers, change agents, and partners in learning. The study highlights the need for continuous education in digital skills with suggestions for incorporating advanced skills for future practice, such as data analytics and artificial intelligence, and discusses the value of digital skills development in higher education to enhance student learning and future practice. The research offers insights of international relevance into the development of a digital competencies toolkit that proposes nursing-specific educational digital skills interventions. The work fosters inclusivity, continuous digital skills improvement, and professional readiness. The work followed the Equator Standards for Quality Improvement Reporting Excellence in Education. No patient or public contribution.

Studies of contemporary social movements have explored the role of digital maps and mapmaking in the organisation and visualisation of protest events, yet little is known about the contentious political potential of maps when the political opportunities for street politics fade. This article examines the digital cartography of Hong Kong’s yellow economic circle, a networked system of retailers and consumers linked by political values that support pro-movement stores and boycott pro-establishment businesses, for which citizen activists amassed crowdsourced data to create and update counter-maps that galvanised political consumerism to uphold dissent. Drawing on a renewed conception of the networked movement scene, I contend that counter-data mapping demonstrates a connective structure of self-mobilisation that affords the (trans)formation of (a) dissent spatiality, (b) sociality, and (c) solidarity during the declining stages of movements. Based on digital ethnography and archival research, I show how this nascent cartographic data-as-repertoire not only helped establish and sustain a resistant economy but also allowed people to maintain and refashion their contentious political participation via everyday engagement with data. While the state authorities attempted to expand their territorial control amidst the crisis, counter-data mapping, as a digitally enabled, joint practice of scene-making, (re)invented dissent territory, enabling dispersed citizen activists to continue to connect and mobilise amidst intense urban policing and social distancing protocols. This article casts new light on the utility and capacity of digital cartography during movement latency while illuminating the understudied contours and consequences of counter-data mapping in a non-Western context.

This study systematically traces the development trajectory of ‘digital capability’ within the entrepreneurial field of ‘digital entrepreneurship’ using bibliometric methods, based on 364 articles from the Scopus database spanning 2015 to 2025. Through keyword co-occurrence and collaboration network analysis conducted with VOSviewer, it reveals research hotspots, knowledge structures, and global distribution patterns. Results indicate that digital capability has emerged as a core topic in digital entrepreneurship research. The field exhibits trends toward interdisciplinary convergence, multi-regional collaboration, and progressively deepening theoretical frameworks, providing significant reference for future academic research and practical implementation.

ABSTRACT Introduction Social media has become an effective instrument to enhance adolescents' knowledge‐gaining experiences, even in language acquisition. The mechanisms of interactive feedback in such sites can affect motivation and engagement, especially if looked at through the lens of Self‐Determination Theory, which highlights the role of competence, relatedness, and autonomy in promoting motivation. Aim This research aims to analyse social media's interactive feedback mechanisms' effect on learners' motivation to learn English, specifically focusing on how these mechanisms affect perceived competence, autonomy, and relatedness in consequent learning outcomes. Methods The data were collected from 324 adolescents using a stratified random sampling. Quantitative data were analysed using Statistical Package for the Social Sciences (SPSS) for correlation and mediation, while qualitative data were analysed using NVivo for thematic exploration. Results The findings indicate that the study variables mediate the effect of feedback on intrinsic motivation; consequently, they improve outcomes in learning. Furthermore, there is evidence that extrinsic motivation was influenced positively by the feedback mechanism, which mediated the relationship between this mechanism and learning outcomes in the English language. Novelty The novelty of this study is in exploring the impact of interactive feedback mechanisms on social media in enhancing adolescents' competence, autonomy, relatedness, and motivation in learning English, filling a gap in digital learning research. Conclusions The results reveal the highly significant impact of providing interactive feedback on motivating adolescents and enhancing the latter's outcomes in learning English.

The inaugural Future Directions in Interactive Digital Narratives (FIDN) Student Research Symposium, convened virtually on June 7, 2025 by the Association for Research in Digital Interactive Narratives (ARDIN) Graduate Research Committee, provided a dedicated venue for emerging scholars to articulate near-term trajectories for interactive digital narratives (IDNs). Eight Master’s and doctoral students and early-career researchers presented work and received feedback from established scholars, fostering a collaborative exchange across the global community. Following the event, presenters developed extended abstracts, collected here as a record of the symposium and signals of anticipated directions for the field; these abstracts were not peer-reviewed.

Background - Thrift clothing is becoming increasingly popular these days. Young people and teenagers are increasingly following the rapidly evolving fashion world, fueled by the use of social media for marketing. Competition between thrift fashion and social media has become a hot topic in the digital marketing world. Aim - This study aims to understand the level of influence of Social Media Communication and Lifestyle on Purchase Intention with Brand Trust as an Intervening Variable at the Thrifku.png Store. Design / methodology / approach – This research uses a quantitative approach with the aim of finding more accurate results by interpreting numbers. Data were collected using a questionnaire distributed to the study population, namely young people who have seen, are interested in, or purchased thrift fashion products promoted through social media. The sampling method used a Simple Random Sampling technique with 100 respondents. Data were analyzed using the Structural Equation Model (SEM) method and processed using SmartPLS 4. Findings - The results of this study indicate that Social Media Communication does not have a positive and significant influence on purchasing decisions. However, Social Media Communication has a significant influence on Brand Trust. Lifestyle variables have a significant influence on Brand Trust, but do not significantly influence purchasing decisions. Meanwhile, Brand Trust has a significant influence and mediates Social Media Communication and Lifestyle on purchasing decisions. The implementation of this research is for marketing development, especially in terms of Purchasing Decisions for used clothing products (thrift) through social media. Research implication – This research is useful for digital consumer behavior researchers, particularly in specific product segments, specifically thrift products. Furthermore, it can serve as an evaluation tool for thrift businesses in understanding digital consumer behavior. Limitations – The limitation of this research is that it is part of a digital business marketing strategy with social media communication strategy as the main focus of discussion, which is collaborated with Brand theory.

ABSTRACT This paper examines the contemporary legend surrounding the quotation “Ask Rory Gallagher,” allegedly spoken by Jimi Hendrix when asked how it felt to be the world's greatest guitarist. Long debated within Gallagher’s fan community, the statement's origins and authenticity remain uncertain. Using a netnographic approach combining digital archival research and social media observation, the study traces the legend's sources, circulation, and reception. It also reflects on why the legend remains contentious, particularly in relation to Gallagher’s musical legacy and Irish national identity. Overall, the study advances understanding of digital folklore, myth-making, and the cultural dynamics of fandom.

In-depth investigation for identifying autonomous vehicle crash causations: New insights from system functions, driver behaviors and kinematics.

BackgroundDigital platforms play a vital role in improving the availability and access to health research outputs, enhancing the engagement of policy makers and practitioners in the research processes. Despite their potential, it needs to be explored how digital platforms are used to manage and share health research datasets and publications, and to translate research findings among health networks or institutions in sub-Saharan Africa (SSA).ObjectiveThis study aimed to assess the practices of health research data management, including sharing among researchers and their support staff within 3 large research networks for health innovations in SSA.MethodsA cross-sectional mixed methods survey was conducted across 3 research networks in SSA, showing experiences of sharing research data using digital platforms among researchers of 3 large research and innovation networks in SSA and affiliated institutions in the Global North. A total of 160 respondents completed a self-administered web-based questionnaire, and following data cleansing, the survey data were analyzed using both descriptive and inferential statistics.ResultsMost respondents (91/160, 56.9%) used electronic data collection tools to collect research data. Almost half (79/160, 49.4%) of the respondents have a digital research data management platform. More than half of the respondents shared their research datasets (102/160, 63.8%), and 61.3% (98/160) shared research findings with the research community through different channels. Furthermore, most respondents shared their research datasets and research outputs through institutional data repositories (42/160, 26.1%), scientific conferences (123/160, 76.9%), and journal articles (110/160, 68.8%). This study found that parameters such as sex, professional category (health professional, information and communication technology professional, and data managers), and the role (researcher or student) influence health research data sharing within the community. The results show that the roles of the individual have the strongest association with the sharing of research datasets, followed by years of experience in research, then sex, and profession. Females were less likely to share their research datasets than males. Data managers and information and communication technology professionals exchanged datasets less frequently in the professional group, and the researcher’s role was statistically significant in sharing research datasets.ConclusionsThis study demonstrates that most researchers share research datasets and outputs through various channels. It was further found that digital platforms were essential in managing and sharing research datasets and publications since more than half (85/160, 53.1%) of the respondents have and use digital platforms. In addition, the study identified factors that influenced researchers’ practices of sharing research datasets and publications. Furthermore, key gaps limit the sharing of these research datasets, including inadequate infrastructure, insufficient African dataset sharing platforms, a lack of institutional policy, and limited skills to use available platforms.

BackgroundIn an era marked by a growing reliance on digital platforms for health care consultation, the subreddit r/AskDocs has emerged as a pivotal forum. However, the vast, unstructured nature of forum data presents a formidable challenge; the extraction and meaningful analysis of such data require advanced tools that can navigate the complexities of language and context inherent in user-generated content. The emergence of large language models (LLMs) offers new tools for the extraction of health-related content from unstructured text found in social media platforms such as Reddit.ObjectiveThis methodological study aimed to evaluate the use of LLMs to systematically transform the rich, unstructured textual data from the AskDocs subreddit into a structured dataset, an approach that aligns more closely with human cognitive processes than traditional data extraction methods.MethodsHuman annotators and LLMs were used to extract data from 2800 randomly sampled r/AskDocs subreddit posts. For human annotation, at least 2 medical students extracted demographic information, type of inquiry (diagnosis, symptom, or treatment), proxy relationship, chronic condition, health care consultation status, and primary focus topic. For LLM data extraction, specially engineered prompts were created using JavaScript Object Notation and few-shot prompting. Prompts were used to query several state-of-the-art LLMs (eg, Llama 3, Genna, and GPT). Cohen κ was calculated across all human annotators, with this dataset serving as the gold standard for comparison with LLM data extraction. A high degree of human annotator reliability was observed for the coding of demographic information. Lower reliability was seen in coding the health-related content of the posts.ResultsThe highest performance scores compared with the gold standard were achieved by Llama 3 70B with 7 few-shot prompt examples (average accuracy=87.4) and GPT-4 with 2 few-shot prompt examples (average accuracy=87.4). Llama 3 70B excelled in coding health-related content while GPT-4 performed better coding demographic content from unstructured posts.ConclusionsLLMs performed comparably with human annotators in extracting demographic and health-related information from the AskDocs subreddit unstructured posts. This study validates the use of LLMs for analyzing digital health care communications and highlights their potential as reliable tools for understanding online behaviors and interactions, shifting toward more sophisticated methodologies in digital research and practice.

This study examines the impact between student’s personal learning efforts and institutional support systems in shaping academic performance. It aims to identify how self-regulated learning strategies interact with environmental factors to influence educational outcomes. A quantitative cross-sectional design was employed, and 229 final responses was used from undergraduate levels students. Data were collected using five point Likert-scale questionnaires. Correlation and regression analyses were conducted using SPSS version 20. Results revealed that proactive learning behaviors (goal-setting and time management) and supportive environments (structured curricula and faculty engagement) jointly enhance academic performance whereas motivation showed the strongest predictive power and library access had negligible impact without training. This concludes that academic success requires simultaneous development of student agency and institutional capacity. Findings advocate for integrating self-regulation training into curricula, faculty development programs for student-centered teaching and policy reforms addressing digital research literacy gaps. Institutions could balance resource provision with skill-building initiatives.

Objectives: Determining authorship is crucial in digital humanities, forensics, and research paper attribution. Authorship Verification (AV), an open-set problem, determines whether the same individual authored the provided pair of texts. Methods: This study examines the poetry of the Brontë sisters, which demonstrates considerable thematic and linguistic similarity, therefore presenting a critical problem for AV. The difficulty of the challenge is further exacerbated by the inclusion of Artificial Intelligence (AI)-generated poems emulating the writing style of the Brontë sisters. This study examines the optimal representative feature aspect when trained using both complete poems and excerpts. Findings: The proposed AV framework utilizes a Siamese network that uses Manhattan distance-based similarity assessment with optimal parameters. In the absence of a comprehensive context, the semantic and structural features of poetry significantly enhance AV performance, surpassing quantitative lexical components by 36%. The overall ability of the baseline and deployed models to distinguish between original and AI-generated poems is examined using the sub-task of closed-set Authorship Attribution (AA). This study establishes a basis for future research on AI plagiarism and academic integrity, offering significant insights for further investigation. Also, AV with AI-generated poems highlights that AI can mimic the authors only to some extent, and that is well depicted with the accuracy of the demonstrated method. Novelty: A computationally and literarily challenging AV task, involving poems, has been carried out (including AI-generated poems). To the best of our knowledge, a few works on poem authorship exist, but none systematically on AV on poem excerpts. Keywords: Poetry verification, AI poems, Authorship verification, Siamese networks, Stylistics