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The aboutness (a subject matter of resource) of information has been strongly emphasized when organizing and searching for different types of media resources. For video games, mood is one of the critical subjective elements that supports users in finding games of interest. The current study examines a previously developed video game mood controlled vocabulary (CV) to empirically test its applicability and evaluate the individual terms’ separability and distinctiveness.

The research team collected user reviews from Steam, an online game database. Three different games were selected for triangulation to represent each of the 17 moods identified in the existing CV, resulting in the selection of 51 games. Collected reviews were tokenized and investigated from individual, terminological and categorical levels of text analyses.

Through the application of multiple analysis techniques (frequency, cluster and network), findings confirm the intuitiveness and usefulness of the existing CV. Additionally, opportunities for increased category separability and distinctness are identified for three moods: Aggressive, Quirky and Intense.

The current study adopts a user-centered perspective to evaluate the existing metadata framework created based on literature analysis. This study aims to complement the literature-based framework with users’ perspectives to enhance the metadata for interactive multimedia resources, such as video games.

This study aims to investigate the mental health treatment journeys of stigmatized consumers using user-generated content (UGC) while also examining the role of UGC in the journey.

This study offers valuable insights from 68 distinct, stigmatized consumers through a qualitative content analysis of 73 YouTube product review videos related to ten antidepressants. Data is coded, combining inductive coding with theory to provide a nuanced interpretation. Applying the Common-Sense Model of Self-Regulation to traditional consumer journey concepts, the analysis of UGC is structured by a unique mental health treatment journey.

The findings show that consumers use UGC to destigmatize their mental health treatment by engaging in dynamic reflection throughout their journey, rather than following traditional feedback models. Unlike typical consumption patterns, where search is limited to the initial stage, these consumers search at every journey phase while sharing insights that offer valuable support to others which, sometimes they report, is reciprocated by viewers.

Theoretically, this study introduces an innovative framework blending psychological and marketing theories to address a gap in health-care service marketing literature concerning long-term mental health treatment journeys. By introducing the concept of dynamic reflection, it demonstrates how consumers actively engage in and share insights throughout their treatment process, differing from traditional feedback models, and highlights the impact of UGC on health-care service provision.

Findings could inform potential health-care provider interventions that may improve treatment effectiveness.

Although stigmatized consumers’ experiences have been examined, their treatment experiences have not been framed within a journey framework.

The platformisation of journalism has compelled news media to adapt to network media logic, affecting journalistic practices and norms. Several challenges emerged for media outlets, including competition for attention, news avoidance and the dominance of social media as primary news sources. Among platforms, TikTok represents an opportunity for news media to engage with new audiences, particularly those who tend to avoid traditional news sources. TikTok's algorithm assesses content virality based on factors like sound, hashtags and content itself, prompting media outlets to develop new strategies to compete in this space.

Our study focuses on Portuguese media, which received limited attention despite TikTok's rapid adoption rate growth, rising from 13% in 2020 to 45% in 2023.

We analysed the last 58 TikTok posts of the most popular Portuguese media according to the Digital News report 2023 starting from 1 September 2023, identifying various elements, such as music, hashtags, featured subjects, topic, etc. To uncover newsroom strategies, we applied multiple correspondence analysis and hierarchical clustering on principal components to identify post clusters. These strategies have been then be correlated with the number of views to determine their effectiveness in engaging audiences.

The present study aims to analyse the presumed relationship between VLC use and students’ grades.

The research strategy unfolds as a case study (Yin, 1994), framed by how undergraduate students of pharmaceutical sciences used video lecture capture (VLC) and the impact of VLC on pedagogic differentiation. Looking at the course of Mechanistic Toxicology (MecTox), the objective is to describe this case of pharmaceutical sciences in depth.

The findings reveal that over 90% of students engaged with VLC videos, with the average viewing time exceeding the total available video minutes, indicating strong student engagement. The study particularly highlights VLC’s positive impact on students with lower academic performance (grades D and E), suggesting that VLC can help reduce the performance gap and support a more inclusive educational environment.

The findings may have limited generalisability beyond the specific context and sample used. However, this study allows the research findings to be compared with previous research (Remião et al., 2022), contributing to the debate on how pedagogic research can promote evidence-based decisions regarding innovative strategies. The meaning of educational inclusion processes and diversity is, thus, contingent on the institutionalisation of research as a practice of teaching and learning.

The results of this study thus provide interesting insights for the design of strategic action, considering the diversity of students as seen in parents’ academic qualifications and students’ conditions (e.g. student-workers, living away from home, holding a grant of economic and social support).

The implications of research findings for society bring the issue of equity in education to the fore. By addressing the diverse needs of students, HEIs can contribute to greater educational equity.

Using VLC as a differentiated pedagogic device might give diversity “real” content insofar as institutional and national policies can mitigate the possible negative effects of parents’ low academic qualifications and the students’ conditions of living away from their residence area and holding a grant of economic and social support.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

The purpose of this study is to investigate the capabilities essential to vaccine supply chain (VSC) resilience given a mass vaccination endeavor during a pandemic.

An exploratory qualitative study was used to elicit the required capabilities pertinent to the design of resilient VSC flows. Data were extracted from white papers, reports, academic papers and the presentations of over 100 experts globally who convened at webinars, symposia and workshops to discuss the COVID-19 mass vaccination campaign and the VSC.

The results of this study indicated that 7 primary capabilities, 44 Level 1 sub-factor capabilities and 145 Level 2 sub-factor capabilities are essential to VSC resilience in a mass vaccination situation during a pandemic. Furthermore, through cluster analysis, associations of various degrees were identified between some pairs of resilience capabilities.

To the best of the author’s knowledge, a comprehensive and holistic exploratory research study that identifies systemic resilience capabilities of mass vaccination supply chains and aligns these requirements to the seven critical flows in the VSC has not been previously undertaken. A cluster analysis that depicts the relationships between the resilience capabilities has also not yet been done.

The results have significant consequences as an informative reference for leaders managing herd immunity goals during pandemic situations. Stakeholders in the public sector, private sector and other entities, involved in planning and managing all or part of a mass VSC during a pandemic, should find the results valuable in providing a structured approach for building resilience at systemic and individual flow levels.

This study contributes to the literature on designing resilient mass vaccination supply chains during a pandemic. Using data from a wide spectrum of published and audiovisual sources, this study identifies seven resilience capabilities to reduce disturbances that lead to delays in mass vaccination supply chains. This study develops a structured approach to align these capabilities to the seven critical flows in the VSC. Through cluster analysis, associations between the resilience capabilities are identified, indicating where multiple strategies may be required to reinforce VSC resilience.

The purpose of this research is to evaluate the utility of clickstream data and machine learning algorithms in predicting student performance and enhancing online learning experiences. By leveraging clickstream data and machine learning algorithms, the study aims to predict student performance accurately, enabling timely and personalized interventions. This approach seeks to reduce high failure and dropout rates in online courses, ultimately enhancing educational outcomes and preserving the reputation of educational institutions.

This study utilizes clickstream data from the Open University Learning Analytics Data set (OULAD) to predict student performance in virtual learning environments. The approach involves extracting and organizing data into weekly and monthly interactions. Various machine learning models, including traditional methods (Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, XGBoost) and advanced time-series models (LSTM-XGBoost, GRU), are employed to analyze the data. The GRU model demonstrated the highest accuracy, offering insights into student engagement and learning patterns.

The study reveals that integrating clickstream data with machine learning models provides a robust framework for predicting student performance in virtual learning environments. Among the methods tested, the GRU algorithm outperformed six baseline models, achieving an accuracy of 90.13%. These findings underscore the effectiveness of using advanced time-series models to monitor and improve student engagement and success rates in online education.

This research introduces a novel approach to student performance prediction by combining traditional and advanced time-series machine learning models with clickstream data. The study’s originality lies in its comprehensive analysis of both weekly and monthly student interactions, providing educators with a powerful tool for early intervention. The findings contribute to the growing body of literature on learning analytics, offering practical solutions to enhance online education’s effectiveness and reduce dropout rates.

The number of people with special needs, including citizens and military personnel, has increased as a result of terrorist attacks and challenging conditions in Iraq and other countries. With almost 80% of the world’s amputees having below-the-knee amputations, Iraq has become a global leader in the population of amputees. Important components found in lower limb prostheses include the socket, pylon (shank), prosthetic foot and connections.

There are two types of prosthetic feet: articulated and nonarticulated. The solid ankle cushion heel foot is the nonarticulated foot that is most frequently used. The goal of this study is to use a composite filament to create a revolutionary prosthetic foot that will last longer, have better dorsiflexion and be more stable and comfortable for the user. The current study, in addition to pure polylactic acid (PLA) filament, 3D prints test items using a variety of composite filaments, such as PLA/wood, PLA/carbon fiber and PLA/marble, to accomplish this goal. The experimental step entails mechanical testing of the samples, which includes tensile testing and hardness evaluation, and material characterization by scanning electron microscopy-energy dispersive spectrometer analysis. The study also presents a novel design for the nonarticulated foot that was produced with SOLIDWORKS and put through ANSYS analysis. Three types of feet are produced using PLA, PLA/marble and carbon-covered PLA/marble materials. Furthermore, the manufactured prosthetic foot undergoes testing for dorsiflexion and fatigue.

The findings reveal that the newly designed prosthetic foot using carbon fiber-covered PLA/marble material surpasses the PLA and PLA/marble foot in terms of performance, cost-effectiveness and weight.

To the best of the author’s knowledge, this is the first study to use composite filaments not previously used, such as PLA/wood, PLA/carbon fiber and PLA/marble, to design and produce a new prosthetic foot with a longer lifespan, improved dorsiflexion, greater stability and enhanced comfort for the patient. Beside the experimental work, a numerical technique specifically the finite element method, is used to assess the mechanical behavior of the newly designed foot structure.

The purpose of this study was to investigate the impact of using a jigsaw learning strategy integrated with computer simulation (JLSICS) on the academic achievement and attitudes of students, along with exploring the relationships between them in the process of learning about acids and bases.

The research design used in the study was quasi-experimental, using non-equivalent comparison groups for both pre- and post-tests. A quantitative approach was used to address the research problem, with three groups involved: two experimental and one comparative group. The treatment group, which received the JLSICS intervention, consisted of two intact classes, while the comparison group included one intact class. Data collection involved achievement tests and attitude scale tests on acid and base. Various statistical analyses such as one-way analysis of variance, one-way multivariate analysis of variance, Pearson product-moment correlation, mean and standard deviation were used for data analysis.

The study’s results revealed that the incorporation of the JLSICS had a beneficial influence on the academic achievement and attitudes of grade 10 chemistry students towards acid and base topics. The JLSICS approach proved to be more successful than both conventional methods and the standalone use of the jigsaw learning strategy (JLS) in terms of both achievement and attitudes. The research demonstrated a correlation between positive attitudes towards chemistry among high school students and enhanced achievement in the subject.

The study only focused on one specific aspect of chemistry (acid and base chemistry), which restricts the applicability of the findings to other chemistry topics or subjects. In addition, the study used a quasi-experimental design with a pretest-posttest comparison group, which may introduce variables that could confound the results and restrict causal inferences.

This study addresses the gap in instructional interventions and provides theoretical and practical insights. It emphasizes the importance of incorporating contemporary instructional methods for policymakers, benefiting the government, society and students. By enhancing student achievement, attitudes and critical thinking skills, this approach empowers students to take charge of their learning, fostering deep understanding and analysis. Furthermore, JLSICS aids in grasping abstract chemistry concepts and has the potential to reduce costs associated with purchasing chemicals for schools. This research opens doors for similar studies in different educational settings, offering valuable insights for educators and policymakers.

The originality and value of this study are in its exploration of integrating the jigsaw learning strategy with computer simulations as an instructional approach in chemistry education. This research contributes to the existing literature by showing the effectiveness of JLSICS in improving students’ achievements and attitudes towards acid and base topics. It also emphasizes the importance of fostering positive attitudes towards chemistry to enhance students’ overall achievement in the subject.