Gamification and Augmented Reality (Journal)
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Classcraft: The Impact of Gamification in Higher Education
No full textIn response to the challenges faced by educators regarding the lack of interest among university students, especially in online contexts, a study was conducted to implement gamification using the Classcraft platform. The research compared two groups: a control group (N=118) that used a more traditional method and a study group (N=125) that employed gamification.
The results revealed that gamification led to increased student engagement and improved retention of content and the development of basic competencies related to their degree programs. These findings support the hypothesis that gamification is a motivating approach in higher educatio
Responsive e-learning dynamic assessment structure using intelligent learning design
No full textA previously created e-learning model and learning system research have been conducted based on the \u27one size fits all\u27 idea. This approach ignores the distinctions between learners and pupils, delivering the same educational content to each one. With the advent of a new R&D style, researchers\u27 and students\u27 demands and preferences will shift. These days, quick e-learning courses come with online videos, audios, and desktop recording capabilities that used to need separate software. In contrast to printed textual lectures, students learn more effectively and enhance their abilities using onscreen instructional materials. As a consequence, there is a need for more adaptable learning and knowledge-based e-learning model assessment. This article focusses mostly on the learner modelling module and illustrates an adaptable model for an e-learning system. Students who are modelling are accountable for meeting this criteria in order to assess the degree of performance of learners in an online learning environment and satisfy specific needs
Use of virtual or augmented reality in informal caregivers of stroke survivors: rapid review
No full textIntroduction: The rehabilitation of a stroke survivor has an impact on activities of daily living, physical activity, social interaction and the quality of life. The use of virtual and augmented reality appears as a tool to be explored and incorporated into rehabilitation. Objective: To identify evidence of the use of virtual reality or augmented reality in informal caregivers of stroke survivors. Method: A Rapid Literature Review was carried out using the Cochrane Rapid Review Methods recommendations. Based on the PICo strategy, the following question was formulated: What is known about the use of virtual reality and augmented reality in informal caregivers of stroke survivors? The search was conducted through the EBSCOhost platform, in November 2024, with the 2017-2024 time frame being assumed. Results: 8 articles were identified and 3 were included. The use of virtual reality/augmented reality in informal caregivers of stroke survivors revealed potential for empowerment and increased motivation, in addition to generate feelings of satisfaction and acceptance. Using virtual reality/augmented reality in informal caregivers for stroke survivors, allowed to identify barriers and facilitators of this technology. Conclusions: The use of virtual reality and augmented reality in informal caregivers of stroke survivors is an emerging topic, with great potential for development. Due to the scarce evidence found, this rapid literature review does not allow generalizations, but suggests paths for future investigations, on a rising topic
Virtual reality versus traditional methods in nursing competency development: A Rapid Review
No full textIntroduction: The COVID-19 pandemic has required a readaptation of teaching methods. This readaptation, together with the exponential growth of technology in recent years, has brought about a possible new way of teaching nursing, using virtual reality. By analyzing this new approach, we will be able to understand whether this new methodology has advantages for being adapted to current teaching.Objective: to map the evidence on the contribution of virtual reality to the development of both clinical and instrumental skills in nursing students compared to traditional methods.Methods: A rapid review was carried out with research carried out between March 2023 and May 2023. The Business Source Complete [EBSCO], National Institutes of Health [NIH] and B-ON platforms were used to carry out the research. To assess the quality of the articles, we used the JBI guideline.Results: Six articles of quasi-experimental and systematic review typology were analyzed. The use of virtual reality allows students to develop their nursing skills in a dynamic, interactive and safe way. The results can be enhanced when combined with high-fidelity simulation.Conclusions: This Rapid Review demonstrates how Virtual Reality can be used in nursing education, understanding its benefits in terms of clinical and personal skills. However, it also recognizes the difficulties that may limit the use of Virtual Reality and the need for greater scientific evidence that is less randomized
AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection
No full textIntroduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures and attack patterns, often fail to detect these stealthy threats.Methods: this study proposes a comprehensive framework that combines AI technologies, including machine learning algorithms, natural language processing (NLP), and anomaly detection, to analyze threats in real time. The framework incorporates predictive modeling to anticipate potential attack vectors and automated response mechanisms to enable rapid mitigation.Results: the findings indicate that AI-enhanced threat intelligence significantly improves the detection of zero-day attacks compared to traditional methods. The framework reduces detection time and enhances accuracy by identifying subtle anomalies indicative of zero-day exploits.Conclusion: this research highlights the transformative potential of AI in strengthening threat intelligence against zero-day attacks. By leveraging advanced machine learning and real-time analytics, the proposed framework offers a more robust and adaptive approach to cybersecurity
Comprehensive Evaluation & Improvement of HEMO Routing for Green Smart-City Transport
No full textIntroduction; Smart cities want smart routing to saves fuel, cuts pollution and to handles traffic in real time. This work progresses the existing HEMO algorithm by incorporating eco-friendly parameters. Objective; In this paper, we propose two major enhancements to the HEMO-Routing algorithm. First, we add real-time traffic adjustment and a detailed energy-consumption model as new objectives. Second, we improve optimization by using an Adaptive Genetic Algorithm for broad search and Simultaneous Perturbation Stochastic Approximation for fine-tuning. Method; We test on the Extended Solomon Dataset (25 road segments with realistic distances, congestion, noise, emissions, and speed limits) in MATLAB 2021 on a Windows 11 PC (Intel i5-1135G7, 8 GB RAM). Compared to the original, our enhanced method boosts Pareto hypervolume to +12 %, cuts generational distance from by –18.8 %, lowers CO₂ from 152.4 g/km to 129.8 g/km (–14.8 %), and trims energy use from 8.75 kWh to 7.87 kWh (–10.1 %). It also converges in 200 instead of 250 iterations (–20 %), with only a 5.3 % runtime overhead. Result; These results show that our extensions deliver practical, eco-friendly routes with minimal extra compute, making the approach ideal for real-time smart-city applications.Conclusions; We made HEMO smarter by adding live traffic and energy-saving goals. With AGA and SPSA, it finds better, greener routes faster. Perfect for smart cities, and ready for EVs and bigger setups in future
Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance
No full textIn an era where the military utilization of Unmanned Aerial Vehicles (UAVs) has become essential for surveillance and operational operations, our study tackles the growing demand for real-time, accurate UAV recognition. The rise of UAVs presents numerous safety hazards, requiring systems that distinguish UAVs from non-threatening phenomena, such as birds. This research study conducts a comparative examination of advanced machine learning models, aiming to address the challenge of real-time aerial classification in diverse environmental conditions without model retraining. This research employs extensive datasets to train and validate models such as Neural Networks, Support Vector Machines, ensemble methods, and Gradient Boosting Machines. The fashions are evaluated based on accuracy, forgetfulness, and processing efficiency—criteria determining the viability of real-time operational scenarios. The findings indicate that Neural Networks exhibit enhanced performance, demonstrating exceptional accuracy in distinguishing UAVs from birds. This culminates in our primary assertion: Neural Networks possess vital operational security ramifications and can markedly enhance the allocation of defense resources. The findings significantly improve surveillance systems, highlighting the effectiveness of machine-learning methods in real-time UAV identification. Moreover, incorporating Neural Network systems into military defenses is recommended to enhance decision-making capabilities and security operations. Foresee forthcoming UAV developments and advocate for regular model updates to keep up with increasingly nimble and perhaps stealthier drone designs
The use of social networks and cell phones in the context of the reading and writing workshop subject
No full textEven though the use of social networks is a social phenomenon that has been studied in recent decades, in the situation caused by the lockdown during the Covid-19 pandemic, distance learning emerged and the use of social networks became widespread in the fields of educational planning and teaching strategies at all levels and in all educational institutions in Mexico. The challenge was great and today we can talk about assertive digital communication, that is to say, a before and after in the use and administration of this digital resource as a didactic strategy, to the point that in the specific case of the subject Reading and Writing Workshop, educational innovations were implemented that are still being applied today
The Impact of Gamification in Research and Education: A Communication Review
No full textIntroduction: Traditional educational studies often focus on standard teaching methods and textbook-based learning. However, to enhance the effectiveness of learning and make it more engaging, it is widely recognized that classroom instruction should incorporate interactive activities. These interactive methods can be introduced by integrating playful classroom games, utilizing modern teaching techniques, and engaging students through methods that spark interest and motivationObjective: Gamification offers a simple yet powerful approach to motivate students, encourage learning, and promote the development of essential life skills. By fostering creativity and imagination, gamification helps boost student engagement and makes the learning process more dynamic and enjoyable. Gamification, the incorporation of game-design elements in non-game contexts, has emerged as a potent tool in both research and educatio
Optimizing Antibiotics Prophylaxis in Neurosurgery through Machin Learning: Predicting Infections and Personalizing Treatment Strategies.
No full textIntroduction: Preventing postoperative infections in neurosurgery is crucial to reducing morbidity. Machine learning (ML) models have shown potential in predicting infections and optimizing antibiotic use.
Methods: Patient data from neurosurgical procedures were analyzed to develop and evaluate ML models for predicting postoperative infections. Various algorithms, including logistic regression, Random Forest, Gradient Boosting Machine (GBM), SVM, and neural networks, were compared. Performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) were calculated.
Results: The GBM model achieved the best performance, with an accuracy of 89.1% and an AUC-ROC of 0.91. The most important predictors of infection were surgical duration (27.3%), preoperative CRP levels (21.8%), and blood loss (18.5%). Patients who developed infections had significantly longer surgeries and elevated CRP levels.
Conclusions: ML models demonstrated high accuracy in predicting postoperative infections in neurosurgery. Early identification of high-risk patients may optimize antibiotic prophylaxis and reduce complications. Further validation is required for clinical implementation