Search Results (139)

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings. Full article

CRISPR technology, which is derived from the bacterial adaptive immune system, has transformed traditional genetic engineering techniques, made strain engineering significantly easier, and become a very versatile genome editing system that allows for precise, programmable modifications to a wide range of microbial genomes. The economies of fermentation-based manufacturing are changing because of its quick acceptance in both academic and industry labs. CRISPR processes have been used to modify industrially significant bacteria, including the lactic acid producers, Clostridium spp., Escherichia coli, and Corynebacterium glutamicum, in order to increase the yields of bioethanol, butanol, succinic acid, acetone, and polyhydroxyalkanoate precursors. CRISPR-mediated promoter engineering and single-step multiplex editing have improved inhibitor tolerance, raised ethanol titers, and allowed for the de novo synthesis of terpenoids, flavonoids, and recombinant vaccines in yeasts, especially Saccharomyces cerevisiae and emerging non-conventional species. While enzyme and biopharmaceutical manufacturing use CRISPR for quick strain optimization and glyco-engineering, food and beverage fermentations benefit from starter-culture customization for aroma, texture, and probiotic functionality. Off-target effects, cytotoxicity linked to Cas9, inefficient delivery in specific microorganisms, and regulatory ambiguities in commercial fermentation settings are some of the main challenges. This review provides an industry-specific summary of CRISPR–Cas9 applications in microbial fermentation and highlights technical developments, persisting challenges, and industrial advancements. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

This study investigates the quantitative relationship between the four dimensions of emotional intelligence and the two types of contemporary filial piety, academic achievement in a Chinese university setting. Based on a sample of 240 Chinese undergraduates, the regression analysis was employed to examine how academic achievement and the two types of contemporary filial piety, namely Pragmatic Obligation (PO) and Compassionate Reverence (CR), relate to four dimensions of emotional intelligence—Self-Emotional Monitoring (SEM), Emotional Utilization (EU), Social Competence (SC), and Others’ Emotional Appraisal (OEA). Results revealed that CR, PO, and Grade Point Average (GPA) predicted emotional intelligence positively and significantly. Notably, PO was the strongest predictor of emotional intelligence compared to CR and GPA. These findings advance theoretical understanding in two aspects. Firstly, they challenge the traditional dichotomy of filial piety by demonstrating that both CR and PO serve as cultural resources enhancing emotional competencies. Afterwards, the study bridges collectivistic values by filial piety with emotional intelligence, offering a culturally nuanced framework for interpreting academic success in Confucian societies. Full article

The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human–computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings. Full article

This study investigated the social and behavioural impacts of employing a card game designed to support the teaching of business metrics through active peer-to-peer engagement, contrasting with traditional passive lectures. Grounded in Bandura’s Social Learning Theory (SLT), the study used a multiple-methods approach including student feedback, a focus group, and an interview. A novel card game, Metrics Masters©, was played by 390 students across a range of educational levels and settings. The research found that the game effectively introduced and reinforced their understanding of key business metrics, while simultaneously enhancing social interaction, teamwork, and problem-solving among Millennial and Generation Z students. The findings underline the efficacy of game-based learning and its close alignment with the SLT principles of observation, imitation, and social interaction. The theoretical contribution of this paper lies in its explicit application and extension of SLT within the context of business education, illustrating empirically how social interactions facilitated by game-based activities significantly enhance learning outcomes. Furthermore, this paper contributes to educational practice by providing robust evidence that game-based learning methods can effectively address educational challenges heightened by the COVID-19 pandemic, offering actionable strategies for educators aiming to foster both academic and social development among students. Full article

In the context of carbon trading policy, carbon emissions in the supply chain of new energy vehicles have received much attention in academic research and practice. Consumer preference for environmental friendliness is also growing in new energy vehicle supply chain operations, which has prompted new energy vehicle manufacturers to invest in carbon abatement technologies to improve the environmental friendliness of new energy vehicles. At the same time, the increased demand for new energy vehicles will also increase the green promotion of third-party power battery recycling companies to facilitate the recycling of power batteries. Considering these special features in the new energy vehicle supply chain, we applied a differential game model to examine the carbon emission reduction behaviors and green promotion technologies of the new energy vehicle supply chain members from a long-term and dynamic perspective. Supply chain equilibrium strategies under four different scenarios were analyzed and compared, numerical experiments were conducted to validate the theoretical results, and sensitivity analyses were performed to identify further insights. The results of the study show that a unit carbon trading price reaching a critical threshold is a prerequisite for technical cooperation between the new energy vehicle manufacturer and the third-party power battery recycling company. It provides a theoretical basis for the government to set the carbon price, and it effectively stimulates the cooperation and emission reduction drive of new energy vehicle companies. The study breaks through the traditional cost–benefit framework, internalizes the carbon price as a supply chain cooperation drive, and opens up a new paradigm for new energy vehicle industry research. Full article

Online education provides a wide range of possibilities in order to setup active learning environments. Different dimensions related to the teaching/learning process are available in such environments, although the focus in this paper is set on just three. Those are the space where this process takes place, the time when this process is undertaken, and the language in which this process is carried out. Some combinations of those three dimensions were deployed in a course on computer science in the current academic year, and the results obtained were compared to those achieved in the previous academic year with a traditional environment. Results exhibited an increase of over 17% in academic performance, as well as an increase of over 16% in success rate, which may be due to the motivation of the learners with this innovative approach. A T-test was applied to the results in both years, obtaining a p-value of 0.017, thus showing that the outcome obtained was statistically significant. However, the sample size was calculated considering the effect size attained, namely 0.64, as well as an alpha value of 0.05 and a power of 0.8, leading to the conclusion that the size of the sample involved, namely 30 learners, was not sufficient to detect the effect size desired. Therefore, further research should be carried out with larger groups. Full article

Dyscalculia is a specific learning disorder that affects numerical comprehension, arithmetic reasoning, and problem-solving skills, significantly impacting academic performance and daily life activities. Traditional teaching methods often fail to address the unique cognitive challenges faced by students with dyscalculia, highlighting the need for innovative educational approaches. Recent studies suggest that educational robotics and game-based learning can provide engaging and adaptive learning environments, enhancing numerical cognition and motivation in students with mathematical difficulties. The intervention was designed to improve calculation skills, problem-solving strategies, and overall engagement in mathematics. The study involved 73 secondary students, divided into three classes, among whom only a specific group had been diagnosed with dyscalculia. Data were collected through pre- and post-intervention assessment evaluating improvements in numerical accuracy, processing speed, and support motivation. Preliminary findings indicate that robotics and gamification create an interactive, less anxiety-inducing learning experience, facilitating conceptual understanding and retention of mathematical concepts. The results suggest that these tools hold promise as supplementary interventions for children with dyscalculia. Future research should explore long-term effects, optimal implementation strategies, and their integration within formal educational settings. Full article

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance. Full article

Background: research shows that university students’ academic engagement and performance can be usefully predicted by academic behavioural confidence (ABC), a set of self-beliefs in study-focused behaviours. While demographic and institutional variations in ABC are often reported, less is known about its psychosocial or behavioural correlates. Methods: A total of 328 students in 16 English universities completed an online survey with measures of ABC, self-esteem, ethnic identity, peer pressure, social support, and substance dependence and theirs and their tutor’s demographics. Results: Aspects of ABC differed by student gender (ps < 0.01), university (modern/traditional; ps < 0.01), and degree (nonvocational/vocational; p < 0.01) types and correlated with self-esteem, social support, peer pressure, drug dependence, and, for ethnic minority students, ethnic identity. Hierarchical regression analyses identified gender (β = 0.14–0.25), age (β = −0.16–0.12), self-esteem (β = 0.22–0.46), peer pressure (β = −0.15–−0.17), and drug dependence (β = −0.15–−0.21) as consistent predictors across ABC components. Conclusions: The findings highlight the importance of individual factors and social networks for academic self-efficacy. Recommendations for monitoring ABC and its contributors for targeted study and pastoral support are made. Full article

Influence graphs are essential tools for analyzing interactions and relationships in social networks. However, real-world networks often involve uncertainty due to incomplete, vague, or dynamic information. The structure of influence graphs often exhibits natural symmetries, which play a crucial role in optimizing covering and matching strategies by decreasing redundancy and enhancing efficiency. Traditional influence graph models struggle to address such complexities. To address this gap, we present the novel concepts of covering and matching in intuitionistic fuzzy influence graphs (IFIGs) for modeling academic social networks. These graphs incorporate degrees of membership and non-membership to better reflect uncertainty in influence patterns. Thus, the main aim of this study is to initiate the concepts of covering and matching within the IFIG paradigm and provide its application in social networks. Initially, we establish some basic terms related to covering and matching with illustrative examples. We also investigate complete and complete bipartite IFIGs. To verify the practicality of this study, student interactions across subjects are analyzed using strong paths and strong independent sets. The proposed model is then evaluated using the TOPSIS method to rank participants based on their influence. Moreover, a comparative study is conducted to demonstrate that the proposed model not only handles uncertainty effectively but also performs better than the existing approaches. Full article

Generative Artificial Intelligence (GenAI) tools are transforming academic research by significantly enhancing efficiency, accuracy, and productivity. However, selecting the most appropriate GenAI tool requires careful evaluation of multiple, interdependent criteria. This paper makes two main contributions. First, it introduces IDEMATEL, an improved decision-making method that advances beyond the traditional DEMATEL approach. Unlike DEMATEL, which can encounter technical limitations when analyzing complex relationships, IDEMATEL ensures robust and reliable results by guaranteeing the necessary mathematical conditions for analysis in all cases. This enhancement makes IDEMATEL more broadly applicable and dependable for evaluating interrelated criteria. Second, the paper demonstrates the practical value of IDEMATEL by applying it to the selection of GenAI tools for academic research. Using this method, a comprehensive set of criteria—including functionality, ease of use, cost, data security, and community support—is systematically analyzed. The results provide researchers and decision-makers with clearer insights into how these factors interact and influence the selection process. By leveraging IDEMATEL, stakeholders can make more informed and confident choices, ensuring that the selected GenAI tools best meet the diverse needs of academic research. Full article

Community-Based Education (CBE) is a transformative approach that integrates academic learning with practical problem-solving by engaging students in real-world community challenges. This study, grounded in a comprehensive literature review, first examines the distinctive characteristics of CBE relative to traditional pedagogical frameworks, exploring its application across varied educational contexts and the implementation challenges it faces. In particular, the research investigates the complexities of aligning community projects with established technical standards and overcoming institutional resistance while also assessing the effectiveness of CBE in cultivating socially responsible, career-ready graduates. To ensure that these findings are both relevant and practical, the study then incorporates validation from an expert review panel consisting of educators and administrators. These experts provided firsthand insights into the challenges they have encountered and shared potential strategies for addressing them, which in turn informed the development of a robust set of guidelines and recommendations. The recommendations underscore the importance of forging strong community partnerships, offering targeted faculty development opportunities, and maintaining curricular flexibility to effectively integrate real-world learning experiences. Ultimately, the study reinforces the pivotal role of CBE in preparing future leaders capable of making a positive impact at both local and global levels. Full article

“Martial arts” is a popular phrase in popular and academic discourse but notoriously difficult to define. This article addresses the challenge of defining martial arts, demonstrating the multifarious and sometimes contradictory nature of how the term is conceived in different contexts. Consulting a range of perspectives, the article is critical of essentialist positions in locating a permanent set of features common to all martial arts because definitions under such positions fail to consider these practices’ fluidity, hybridity, and historical evolution. Instead, the article advances a more pragmatic and contextual definition of martial arts, appealing to nominalism and diaeresis to build context-specific definitions appropriate for particular analytical or practical purposes. Acknowledging the diversity and complexity inherent in martial arts, the article suggests that scholars and practitioners can move beyond strict classification and engage in more fruitful discussions regarding these practices’ history, culture, and philosophy. Lastly, the article promotes a more inclusive and dynamic system that recognises both traditional and modern forms of martial arts without being constrained by the strictures of essentialist definitions. Full article